[Refactor] Remove legacy --stage-configs-path from the serve CLI (#5647)

Signed-off-by: zwhzzz0821 <2831474076@qq.com>

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

@@ -89,7 +89,7 @@ steps:
       mirror_hardwares: [amdproduction]
       grade: Blocking
       commands:
-        - pytest -s -v tests/diffusion/distributed/test_zimage_parallelism.py
+        - pytest -s -v tests/diffusion/distributed/test_tensor_parallel.py
 
     - label: "Diffusion Model Test"
       agent_pool: mi325_1

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

@@ -35,7 +35,7 @@ steps:
   mirror_hardwares: [amdproduction]
   grade: Blocking
   commands:
-    - timeout 20m pytest -s -v tests/e2e/offline_inference/custom_pipeline/ -m "core_model"
+    - timeout 20m pytest -s -v tests/e2e/features/custom_pipeline/ -m "core_model"
 
 
 - label: "Voxtral TTS CUDA Unit Test"

.buildkite/cuda/test-merge.yml

@@ -8,14 +8,24 @@ steps:
   - group: ":card_index_dividers: Simple Test"
     depends_on: upload-merge-pipeline
     steps:
-      - label: "Simple · Diffusion & Model Executor Test"
+      - label: "Simple · Model Executor Test"
         commands:
-          - "timeout 20m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+          - "timeout 20m pytest -sv tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Diffusion Test"
+        commands:
+          - "timeout 20m pytest -sv tests/diffusion -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Engine&Entrypoints Test"
+        commands:
+          - "timeout 20m pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
         mirror_hardwares: l4_1
 
       - label: "Simple · Other Test"
         commands:
-          - "timeout 20m pytest -sv -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+          - "timeout 20m pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
         mirror_hardwares: l4_1
 
   - group: ":card_index_dividers: Diffusion Test"
@@ -27,12 +37,6 @@ steps:
           - pytest -sv tests/diffusion/lora/ -m 'advanced_model and cuda' --run-level "advanced_model"
         mirror_hardwares: l4_1
 
-      - label: "Diffusion · Offloader Test"
-        timeout_in_minutes: 40
-        commands:
-          - timeout 40m pytest -s -v tests/diffusion/offloader -m "advanced_model and cuda"
-        mirror_hardwares: l4_1
-
   - label: "Distributed Test"
     depends_on: upload-merge-pipeline
     timeout_in_minutes: 60
@@ -49,13 +53,17 @@ steps:
     depends_on: upload-merge-pipeline
     steps:
       - label: "Entrypoint Test with H100"
-        timeout_in_minutes: 60
+        # TEMPORARY 60 -> 90: engine startup/sleep-wake is ~13-15 min slower per
+        # run on the vLLM 0.26 image (model loading ~2x, init ~2x; see builds
+        # 2915 vs 2916), which pushed this suite past 60 min. Revert once the
+        # 0.26 startup regression is understood and fixed.
+        timeout_in_minutes: 90
         commands:
           - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and H100" --run-level "advanced_model"
         mirror_hardwares: h100_2
 
       - label: "Entrypoint Test with L4"
-        timeout_in_minutes: 30
+        timeout_in_minutes: 60
         commands:
           - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and L4" --run-level "advanced_model"
         mirror_hardwares: l4_1
@@ -131,39 +139,32 @@ steps:
         timeout_in_minutes: 50
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
+        mirror_hardwares: h100_2
+
+      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
+        source_file_dependencies:
+          - tests/assets/minicpmo_4_5/response_required_16k.wav
+          - tests/assets/minicpmo_4_5/soft_interrupt_16k.wav
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
+          - tests/e2e/online_serving/minicpmo_realtime_duplex_scenarios.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
+          - tests/helpers/minicpmo_4_5_duplex.py
+          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
+          - vllm_omni/experimental/fullduplex/
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+          - vllm_omni/core/sched/
+          - vllm_omni/outputs/multimodal_accumulation.py
+          - vllm_omni/engine/stage_init_utils.py
+          - vllm_omni/engine/async_omni_engine.py
+          - vllm_omni/entrypoints/cli/serve.py
+        timeout_in_minutes: 50
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+        mirror_hardwares: h100_2
 
       - label: "TTS · VoxCPM2 Test"
         source_file_dependencies:
@@ -181,22 +182,6 @@ steps:
             "
         mirror_hardwares: l4_1
 
-      - label: "TTS · SoulX-Singer Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_soulxsinger.py
-          - vllm_omni/diffusion/models/soulx_singer/
-          - vllm_omni/deploy/soulxsinger_svc.yaml
-          - vllm_omni/deploy/soulxsinger_svs.yaml
-        timeout_in_minutes: 30
-        commands:
-          - |
-            timeout 30m bash -c "
-              export VLLM_LOGGING_LEVEL=DEBUG
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              pytest -s -v tests/e2e/offline_inference/test_soulxsinger.py -k 'svc or precomputed' -m 'advanced_model and cuda' --run-level 'advanced_model'
-            "
-        mirror_hardwares: l4_1
-
       - label: "Diffusion · Qwen Image Edit Test"
         source_file_dependencies:
           - tests/e2e/online_serving/test_qwen_image_edit.py

.buildkite/cuda/test-nightly.yml

@@ -15,7 +15,13 @@ steps:
       - label: ":full_moon: Omni · Function Test with H100"
         timeout_in_minutes: 120
         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
+          - 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
+        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"
         mirror_hardwares: h100_2
 
       - label: ":full_moon: Omni · Doc Test with L4"
@@ -426,7 +432,7 @@ steps:
         commands:
           - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m full_model --run-level full_model
           - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/test_ltx2_3_video_similarity.py -m full_model --run-level full_model
+          - pytest -s -v 'tests/e2e/accuracy/ltx/test_ltx_official_similarity.py::test_ltx_one_stage_matches_official[ltx2_3_i2v]' -m full_model --run-level full_model
           - pytest -s -v tests/e2e/accuracy/test_video_streaming_output_similarity.py -m full_model --run-level full_model
         mirror_hardwares: h100_2
 
@@ -449,36 +455,41 @@ steps:
         mirror_hardwares: h100_2
 
   - group: ":card_index_dividers: Diffusion Test"
+    key: nightly-diffusion-test-group
     depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "diffusion-x2iat-test"
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
     steps:
+      - label: ":full_moon: Diffusion · Distributed Test with L4"
+        timeout_in_minutes: 60
+        commands:
+          - 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"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100' --run-level "full_model"
+        mirror_hardwares: h100_4
 
       - label: ":full_moon: Diffusion · Offloader Test"
-        timeout_in_minutes: 40
+        timeout_in_minutes: 60
         commands:
-          - timeout 40m pytest -s -v tests/diffusion/offloader -m "full_model and cuda"
+          - pytest -sv tests/diffusion/offloader -m "full_model and cuda"
         mirror_hardwares: l4_1
 
       - label: ":full_moon: Diffusion · Quantization Test with H100"
+        timeout_in_minutes: 60
         commands:
-          - timeout 60m pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100' --run-level "full_model"
+          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100' --run-level "full_model"
         mirror_hardwares: h100_1
 
       - label: ":full_moon: Diffusion · Quantization Test with L4"
         timeout_in_minutes: 60
         commands:
+          - pip install "vllm-gguf-plugin>=0.0.3"
           - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and L4' --run-level "full_model"
         mirror_hardwares: l4_1
 
-      - label: ":full_moon: Diffusion · Wan VAE Spatial Shard Correctness Test"
-        timeout_in_minutes: 60
-        commands:
-          - >-
-            pytest -sv tests/diffusion/distributed/test_wan_spatial_shard.py
-            -m "full_model and diffusion and H100 and distributed_cuda"
-            --run-level "full_model"
-        mirror_hardwares: h100_2
-
   - label: ":bar_chart: Testcase Statistics"
     key: nightly-testcase-statistics
     timeout_in_minutes: 120

.buildkite/cuda/test-ready.yml

@@ -8,22 +8,26 @@ steps:
   - group: ":card_index_dividers: Simple Test"
     depends_on: upload-ready-pipeline
     steps:
-      - label: "Simple · Diffusion & Model Executor Test"
+      - label: "Simple · Model Executor Test"
         commands:
-          - "timeout 20m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+          - "timeout 20m pytest -sv tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Diffusion Test"
+        commands:
+          - "timeout 20m pytest -sv tests/diffusion -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Engine&Entrypoints Test"
+        commands:
+          - "timeout 20m pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
         mirror_hardwares: l4_1
 
       - label: "Simple · Other Test"
         commands:
-          - "timeout 20m pytest -sv -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+          - "timeout 20m pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
         mirror_hardwares: l4_1
 
-  - label: "Custom Pipeline Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 20m pytest -s -v tests/e2e/offline_inference/custom_pipeline/ -m "core_model"
-    mirror_hardwares: l4_1
-
   - group: ":card_index_dividers: Diffusion Test"
     depends_on: upload-ready-pipeline
     steps:
@@ -104,11 +108,34 @@ steps:
   - group: ":card_index_dividers: E2E Test"
     depends_on: upload-ready-pipeline
     steps:
+      - label: "Custom Pipeline Test"
+        source_file_dependencies:
+          - tests/e2e/features/custom_pipeline/
+          - tests/e2e/features/helpers/custom_pipeline.py
+          - vllm_omni/diffusion/
+          - vllm_omni/engine/
+          - vllm_omni/entrypoints/
+          - vllm_omni/inputs/
+          - vllm_omni/outputs/
+        commands:
+          - timeout 20m pytest -s -v tests/e2e/features/custom_pipeline/ -m "core_model"
+        agents:
+          queue: "gpu_1_queue"
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
 
       - label: "RLHF · VeRL-Omni E2E Test"
         source_file_dependencies:
-          - tests/e2e/offline_inference/rlhf_test/test_verl_omni_e2e.py
-          - tests/e2e/offline_inference/custom_pipeline/
+          - tests/e2e/features/rlhf_test/test_verl_omni_e2e.py
+          - tests/e2e/features/helpers/verl_omni_server.py
+          - tests/e2e/features/helpers/custom_pipeline.py
           - vllm_omni/diffusion/
           - vllm_omni/engine/
           - vllm_omni/entrypoints/
@@ -117,7 +144,7 @@ steps:
         commands:
           - |
             timeout 20m bash -c "
-              pytest -s -v tests/e2e/offline_inference/rlhf_test/test_verl_omni_e2e.py
+              pytest -s -v tests/e2e/features/rlhf_test/test_verl_omni_e2e.py
             "
         mirror_hardwares: l4_1
 
@@ -143,34 +170,7 @@ steps:
             timeout 20m bash -c "
               pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py -m 'core_model' --run-level 'core_model'
             "
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
+        mirror_hardwares: h100_2
 
       - label: "Omni · MiniCPM-o 4.5 Online Test"
         source_file_dependencies:
@@ -181,34 +181,34 @@ steps:
             timeout 20m bash -c "
               pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5.py -m 'core_model' --run-level 'core_model'
             "
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
+        mirror_hardwares: h100_2
+
+      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
+        source_file_dependencies:
+          - tests/assets/minicpmo_4_5/response_required_16k.wav
+          - tests/assets/minicpmo_4_5/soft_interrupt_16k.wav
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
+          - tests/e2e/online_serving/minicpmo_realtime_duplex_scenarios.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
+          - tests/helpers/minicpmo_4_5_duplex.py
+          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
+          - vllm_omni/experimental/fullduplex/
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+          - vllm_omni/core/sched/
+          - vllm_omni/outputs/multimodal_accumulation.py
+          - vllm_omni/engine/stage_init_utils.py
+          - vllm_omni/engine/async_omni_engine.py
+          - vllm_omni/entrypoints/cli/serve.py
+        commands:
+          - |
+            timeout 20m bash -c "
+              pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'core_model and cuda' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_2
 
       - label: "TTS · VoxCPM2 Test"
         source_file_dependencies:
@@ -344,13 +344,3 @@ steps:
               pytest -s -v tests/e2e/online_serving/test_qwen_image_layered.py -m 'core_model' --run-level 'core_model'
             "
         mirror_hardwares: h100_1
-
-      - label: "Diffusion · GGUF Plugin Test"
-        soft_fail: true
-        source_file_dependencies:
-          - tests/plugin/test_gguf_diffusion.py
-          - vllm_omni/diffusion/model_loader
-        commands:
-          - pip install "vllm-gguf-plugin>=0.0.3"
-          - timeout 30m pytest -sv tests/plugin/test_gguf_diffusion.py
-        mirror_hardwares: l4_1

.buildkite/intel/pipeline-intel.yml

@@ -10,7 +10,7 @@ steps:
           DOCKER_BUILDKIT: "1"
           # Buildkite will automatically replace this with the actual commit hash
           VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
-          VLLM_VERSION: "v0.25.0"
+          VLLM_VERSION: "v0.26.0"
         priority: 100
         timeout_in_minutes: 60
         soft_fail: false

.buildkite/npu/test-npu-nightly.yml

@@ -1,25 +1,59 @@
 steps:
-  - group: ":card_index_dividers: Diffusion X2V Model Test"
-    key: nightly-diffusion-x2v-group
+  - group: ":card_index_dividers: Omni Model Test"
+    key: nightly-omni-test-group
     depends_on: upload-nightly-pipeline
     if: >-
       build.env("NIGHTLY") == "1" ||
       build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2v-test"
+      build.pull_request.labels includes "omni-test"
     steps:
-      - label: ":full_moon: Diffusion X2V · Function Test"
-        mirror_hardwares: a2b3_npu_4
+      - label: ":full_moon: Omni · Perf Test · No Async Chunk"
+        key: nightly-omni-performance-no-async-chunk
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
         env:
           VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
           HF_TOKEN: "${HF_TOKEN}"
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k npu_i2v --run-level "full_model"
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+      - label: ":full_moon: Omni · Perf Test · Async Chunk"
+        key: nightly-omni-performance-async-chunk
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+      - label: ":robot: Omni · MiniCPM-o 4.5 Test"
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5_expansion.py -m 'full_model' --run-level 'full_model'
   - group: ":card_index_dividers: TTS Model Test"
     key: nightly-tts-test-group
     depends_on: upload-nightly-pipeline
     if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
     steps:
       - label: ":full_moon: TTS · Perf Test"
+        timeout_in_minutes: 180
         mirror_hardwares: a3_npu_2
         env:
           VLLM_WORKER_MULTIPROC_METHOD: spawn
@@ -34,25 +68,138 @@ steps:
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
-
-  - group: ":card_index_dividers: Omni Model Test"
-    key: nightly-omni-group
+  - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
+    key: nightly-diffusion-x2iat-group
     depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
     steps:
-      - label: ":robot: Omni · MiniCPM-o 4.5 Test"
-        agents:
-          queue: "ascend-a3"
-          resource_class: "npu-2"
-        image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a3"
-        plugins:
-          - kubernetes:
-              podSpecPatch:
-                imagePullSecrets:
-                  - name: swr-secret
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit-2511"
+        key: nightly-diffusion-x2iat-performance-qwen-image-edit-2511
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_8
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export CACHE_DIT_VERSION=1.3.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+  - group: ":card_index_dividers: Diffusion I2I Model Test"
+    key: nightly-HunyuanImage3-x2i-test-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
+    steps:
+      - label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HUNYUAN_IMAGE3_MODEL: "tencent/HunyuanImage-3.0-Instruct"
+          HUNYUAN_IMAGE3_DEVICES: "0,1,2,3"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m "full_model and A3" --run-level full_model
+  - group: ":card_index_dividers: Diffusion Benchmark Perf Test"
+    key: nightly-diffusion-benchmark-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
+    steps:
+      - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=2 CFGP=2"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m A3 --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp2_cfgp2.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+      - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=2 SP=2"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m A3 --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp2_sp2.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+      - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=4"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m A3 --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp4.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+  - group: ":card_index_dividers: Diffusion X2V Model Test"
+    key: nightly-diffusion-x2v-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2v-test"
+    steps:
+      - label: ":full_moon: Diffusion X2V · Function Test"
+        mirror_hardwares: a2b3_npu_4
         env:
           VLLM_WORKER_MULTIPROC_METHOD: spawn
           VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
           HF_TOKEN: "${HF_TOKEN}"
         commands:
-          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5_expansion.py -m 'full_model' --run-level 'full_model'
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k npu_i2v --run-level "full_model"
+      - label: ":full_moon: Diffusion X2V · Perf Test"
+        key: nightly-diffusion-x2v-performance
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT

.buildkite/npu/test-npu-ready.yml

@@ -31,4 +31,4 @@ steps:
           VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
           HF_TOKEN: "${HF_TOKEN}"
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model' --run-level 'core_model'
+          - pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model and npu' --run-level 'core_model'

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

@@ -1,12 +1,8 @@
 from collections.abc import Callable
 from enum import Enum, auto
-from typing import (
-    Any,
-    Literal,
-    NotRequired,
-    TypeAlias,
-    TypedDict,
-)
+from typing import Any, Literal, TypeAlias
+
+from typing_extensions import NotRequired, TypedDict
 
 AudioFormat: TypeAlias = Literal["mp3", "opus", "aac", "flac", "wav", "pcm"]
 

docker/Dockerfile.ci

@@ -1,7 +1,7 @@
-# Target vllm version: v0.25.0 (releases/v0.25.0 branch).
+# Target vllm version: v0.26.0 (releases/v0.26.0 branch).
 # Keep VLLM_BASE_TAG aligned with the target vLLM release on every rebase.
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.25.0
+ARG VLLM_BASE_TAG=v0.26.0
 
 # We use registry-based layer caching for the dependencies,
 # where the tag is determined based on the hash of the contents

docker/Dockerfile.cuda

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai:v0.25.0
+ARG BASE_IMAGE=vllm/vllm-openai:v0.26.0
 FROM ${BASE_IMAGE}
 
 ARG COMMON_WORKDIR=/app

docker/Dockerfile.npu

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.25.0
+ARG VLLM_ASCEND_TAG=v0.26.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 # WORKDIR /vllm-workspace/vllm
@@ -26,6 +26,10 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
     export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
     python3 -m pip install -v -e /vllm-workspace/vllm-omni/ --no-build-isolation
 
+RUN python3 -m pip install -v --no-deps \
+      "stepaudio2-minicpmo>=0.1.1" \
+      "step-audio2>=1.0.0"
+
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
 
 ENTRYPOINT []

docker/Dockerfile.npu.a3

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.25.0-a3
+ARG VLLM_ASCEND_TAG=v0.26.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 # WORKDIR /vllm-workspace/vllm
@@ -26,6 +26,10 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
     export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
     python3 -m pip install -v -e /vllm-workspace/vllm-omni/ --no-build-isolation
 
+RUN python3 -m pip install -v --no-deps \
+      "stepaudio2-minicpmo>=0.1.1" \
+      "step-audio2>=1.0.0"
+
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
 
 ENTRYPOINT []

docker/Dockerfile.npu.ci

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.25.0
+ARG VLLM_ASCEND_TAG=v0.26.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -35,8 +35,13 @@ RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
       "psutil>=7.2.0" \
       "soundfile>=0.13.1" \
       "pyttsx3>=2.99" \
+      "opencc==1.4.1" \
       "opencv-python>=4.12.0.88" \
       "imageio[ffmpeg]>=0.6.0" \
+      "jiwer>=3.0.0" \
+      "zhon>=2.0.0" \
+      "zhconv>=1.4.2" \
+      "funasr>=1.0.0" \
       "hyperpyyaml>=1.2.0" \
       "onnx>=1.16.0" \
       "librosa>=0.10.0" \

docker/Dockerfile.npu.ci.a3

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.25.0-a3
+ARG VLLM_ASCEND_TAG=v0.26.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -35,11 +35,17 @@ RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
       "psutil>=7.2.0" \
       "soundfile>=0.13.1" \
       "pyttsx3>=2.99" \
+      "opencc==1.4.1" \
       "opencv-python>=4.12.0.88" \
       "imageio[ffmpeg]>=0.6.0" \
+      "jiwer>=3.0.0" \
+      "zhon>=2.0.0" \
+      "zhconv>=1.4.2" \
+      "funasr>=1.0.0" \
       "hyperpyyaml>=1.2.0" \
       "onnx>=1.16.0" \
       "librosa>=0.10.0" \
+      "torchmetrics>=1.9.0" \
       --index-url http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple \
       --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
       --default-timeout=60 && \

docker/Dockerfile.rocm

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.25.0
+ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.26.0
 FROM ${BASE_IMAGE} AS base
 
 # Declare a variable to know if we want to use the nightly build or the stable build.
@@ -10,7 +10,7 @@ FROM ${BASE_IMAGE} AS base
 #    we should swap over to use stable release ASAP.
 #    We should avoid relying on custom commits.
 ARG USE_NIGHTLY_BUILD=0
-ARG VLLM_VERSION_OR_COMMIT_HASH=89138b21cc246ae944c741d5c399c148e2b770ab
+ARG VLLM_VERSION_OR_COMMIT_HASH=v0.26.0
 ARG ARG_PYTORCH_ROCM_ARCH
 ENV PYTORCH_ROCM_ARCH=${ARG_PYTORCH_ROCM_ARCH:-${PYTORCH_ROCM_ARCH}}
 

docker/Dockerfile.xpu

@@ -88,7 +88,7 @@ ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE="copy"
 
-ARG VLLM_VERSION=v0.25.0
+ARG VLLM_VERSION=v0.26.0
 RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
 WORKDIR /workspace/vllm
 

docs/cli/README.md

@@ -18,18 +18,12 @@ Specify the port:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-For a migrated model, load a custom deploy configuration with `--deploy-config`:
+Load a custom deploy configuration with `--deploy-config`:
 
 ```bash
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --deploy-config /path/to/deploy_config.yaml
 ```
 
-The deprecated `--stage-configs-path` flag is retained for models that still use the legacy `stage_args` schema:
-
-```bash
-vllm serve ByteDance-Seed/BAGEL-7B-MoT --omni --stage-configs-path /path/to/legacy_stage_config.yaml
-```
-
 ## bench
 
 Run benchmark tests for online serving throughput.

docs/cli/serve.md

@@ -8,7 +8,6 @@ The stage-based CLI is designed for deployments that require launching each pipe
 - For **migrated models** that utilize the bundled deployment YAML configurations located in
   `vllm_omni/deploy/`, the `--deploy-config` flag is only required to override the default configuration. By default, executing `vllm serve MODEL --omni ...`
   automatically loads the bundled deployment configuration.
-- For custom legacy `stage_args` YAMLs, pass the file with `--stage-configs-path`.
 
 Example: Initializing Stage 0 (Orchestrator and API Server):
 The commands below show a common device mapping where Stage 0 uses GPU 0 and
@@ -32,7 +31,7 @@ CUDA_VISIBLE_DEVICES=1 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
     --omni-master-port 26000
 ```
 
-When utilizing a custom deployment YAML based on the new schema, append `--deploy-config /path/to/override.yaml` to each command execution. Conversely, for legacy models, substitute this parameter with `--stage-configs-path /path/to/stage_configs.yaml`.
+When utilizing a custom deployment YAML, append `--deploy-config /path/to/override.yaml` to each command execution.
 
 In the standard execution paradigm, the `--stage-overrides` argument is utilized to apply stage-specific configurations from a single CLI command.
 However, under the **stage-based CLI** paradigm, where each process strictly encapsulates a single stage, it is recommended to specify tuning parameters directly via discrete command-line flags for the respective stage, rather than constructing a composite `--stage-overrides` JSON string.

docs/configuration/composable_parallel.md

@@ -34,12 +34,8 @@ Two additional `StrategySpec` slots — `layer_hook_specs` and `kernel_specs`
 | Flag | Description |
 |------|-------------|
 | `--strategy-config PATH` | Path to a `strategy.yaml`. Only takes effect on registry-based deploy paths (`--deploy-config` or the bundled default deploy YAML). Its derived sizing is overlaid onto the merged stages *before* CLI overrides. |
-| `--stage-configs-path PATH` | **Legacy.** Loads the old `stage_args` YAML schema for un-migrated models. Passing `--strategy-config` together with this path is silently inapplicable — vLLM-Omni emits a warning and the strategy is ignored (see `vllm_omni/entrypoints/utils.py`, lines 372–380). |
 | `--omni-lb-policy POLICY` | Orchestrator-wide load-balancer policy. Strategy-derived from any `stage_replica` axis; an explicit CLI value wins and must match the derived one or `AsyncOmniEngine` raises `Conflicting load-balancer policy` at construction. |
 
-!!! warning
-    `--strategy-config` cannot be used with `--stage-configs-path` (the legacy deploy path). If your model has not been migrated to the registry-based pipeline yet, the strategy is silently dropped with a warning. Migrate to a registry-based model or use `--deploy-config` to apply a strategy.
-
 ## Sub-schemas
 
 ### `axis`
@@ -216,17 +212,6 @@ Strategy keys MUST be `model_stage` *names* (strings), the same labels stage con
 !!! warning "Mixing CLI overrides with a strategy on the same axis"
     A strategy is meant to be the single writer for the axes it declares. If a CLI override (`--tensor-parallel-size 2`, `--stage-overrides '{"0": {"tensor_parallel_size": 2}}'`) sets the same axis, the **CLI value wins** and a warning is emitted from `_reconcile_strategy_with_cli`. The device-layout guard then re-runs against the effective layout, so a CLI override that breaks `tp * dp * pp * num_replicas` will still fail fast at config time.
 
-!!! warning "Passing `--strategy-config` against the legacy `--stage-configs-path`"
-    Composable parallel only applies on the registry-based deploy path. If a model still resolves through the legacy `stage_args` YAML (loaded via `--stage-configs-path`), `--strategy-config` is silently inapplicable and emits:
-
-    ```text
-    --strategy-config (<path>) was provided but model '<model>' resolves via the
-    legacy stage_configs YAML path, which does not support composable-parallel
-    strategies; the strategy is ignored. Use a registry-based model to apply it.
-    ```
-
-    Migrate the model to the new deploy schema (see `configuration/stage_configs.md`) before applying a strategy.
-
 !!! warning "Conflicting `omni_lb_policy` across stages"
     `omni_lb_policy` is a single pipeline-wide knob. If two `stage_replica` axes in different stages derive different policies, `apply_strategy_specs` raises:
 

docs/configuration/pd_disaggregation.md

@@ -158,7 +158,7 @@ runtime:
 
 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
-    --stage-configs-path /path/to/qwen3_omni_pd.yaml
+    --deploy-config /path/to/qwen3_omni_pd.yaml
 ```
 
 ## Operational Notes

docs/configuration/stage_configs.md

@@ -1,9 +1,9 @@
-# Deploy and legacy stage configurations
+# Pipeline and deploy configurations
 
-In vLLM-Omni, a model's `PipelineConfig` defines its fixed stage topology, while a deploy configuration controls how those stages run. Models that have not migrated to this split still use legacy stage configurations with a `stage_args` schema.
+In vLLM-Omni, a model's `PipelineConfig` defines its fixed stage topology, while a deploy configuration controls how those stages run.
 
 !!! note
-    Default deploy config YAMLs (for example, `vllm_omni/deploy/qwen2_5_omni.yaml`, `vllm_omni/deploy/qwen3_omni_moe.yaml`, and `vllm_omni/deploy/qwen3_tts.yaml`) are bundled and loaded automatically when neither `--stage-configs-path` nor `--deploy-config` is provided — the model registry resolves the right pipeline + deploy YAML by `model_type`. Custom legacy `stage_args` YAMLs are still accepted via `--stage-configs-path`.
+    Default deploy config YAMLs (for example, `vllm_omni/deploy/qwen2_5_omni.yaml`, `vllm_omni/deploy/qwen3_omni_moe.yaml`, and `vllm_omni/deploy/qwen3_tts.yaml`) are bundled and loaded automatically when `--deploy-config` is omitted. The model registry resolves the right pipeline and deploy YAML by `model_type`.
 
 ## Deploy configuration schema
 
@@ -86,10 +86,9 @@ stages:
 
 | Flag | Description |
 |------|-------------|
-| `--deploy-config PATH` | Load a new-schema deploy YAML. It is mutually exclusive with `--stage-configs-path`. **Optional** — when omitted, the bundled `vllm_omni/deploy/<model_type>.yaml` is auto-loaded by the model registry. |
+| `--deploy-config PATH` | Load a deploy YAML. **Optional** — when omitted, the bundled `vllm_omni/deploy/<model_type>.yaml` is auto-loaded by the model registry. |
 | `--stage-overrides JSON` | Per-stage JSON overrides, e.g. `'{"0":{"gpu_memory_utilization":0.5}}'`. Per-stage values always win over global flags. |
 | `--async-chunk` / `--no-async-chunk` | Flip the deploy YAML's `async_chunk:` bool. Unset (default) leaves the YAML value in force. |
-| `--stage-configs-path` | **Deprecated.** Load a legacy `stage_args` YAML. New-schema YAMLs passed to this flag are auto-detected for compatibility, but should use `--deploy-config`. It is mutually exclusive with `--deploy-config` and will be removed in a future release. |
 
 ### Stage-Based CLI Paradigm
 
@@ -119,8 +118,7 @@ CUDA_VISIBLE_DEVICES=1 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
     --omni-master-port 26000
 ```
 
-When instantiating a custom deployment YAML conforming to the updated schema, append the `--deploy-config /path/to/override.yaml` directive
-to all node invocations. For legacy architectures (e.g., BAGEL) configured via deprecated `stage_args:` schemas, continue to specify the relevant configuration via `--stage-configs-path /path/to/config.yaml`.
+When instantiating a custom deployment YAML, append the `--deploy-config /path/to/override.yaml` directive to all node invocations.
 
 In the context of standard initialization architectures, utilizing the `--stage-overrides` parameter operates as the optimal methodology
 for delineating stage-specific tuning from the CLI interface:
@@ -220,8 +218,7 @@ Therefore, as a core part of vLLM-Omni, a model's pipeline and deployment config
 
 To override specific parameters, explicitly inject the customized configuration schema
 in both online and offline instantiation flows. Use the `--deploy-config` flag
-when loading a deploy configuration, reserving the `--stage-configs-path` parameter
-exclusively to maintain compatibility with legacy `stage_args` YAML constructs.
+when loading a deploy configuration.
 
 Examples:
 
@@ -236,11 +233,6 @@ For online serving:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
-Legacy online serving:
-
-```bash
-vllm serve ByteDance-Seed/BAGEL-7B-MoT --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
-```
 !!! important
     We are actively iterating on the definition of deployment configurations, and we welcome feedback from users and developers.
 

docs/contributing/ci/test_system_overview.md

@@ -56,7 +56,11 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <td>/</td>
       <td><code>core_model and cpu</code></td>
       <td rowspan="2">&lt;15min</td>
-      <td>/tests/{component_name}/test_xxx</td>
+      <td>
+        <code>tests/{component}/…</code> mirroring <code>vllm_omni/{component}/</code><br>
+        (e.g. <code>tests/diffusion/</code>, <code>tests/engine/</code>, <code>tests/entrypoints/</code>)<br>
+        Do <strong>not</strong> add new top-level <code>tests/</code> dirs unrelated to a component.
+      </td>
       <td>
         <a href="test_writing_guide.md#l1--l2-level-testing-unit-testing-and-basic-end-to-end-verification">L1 &amp; L2</a><br>
         Section 1 L1&amp;L2: Purpose, Test Content, Directory Location, Example
@@ -68,16 +72,18 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <td><strong>L2</strong><br>(E2E across models & GPU-required UT)</td>
       <td>Online (basic deployment scenarios):<br>dummy, normal inference function (output format, stream), some instance startup UT</td>
       <td>High-priority models + online basic scenarios; request success, non-empty output, format match (no Whisper/accuracy)</td>
-      <td>High-priority features (using random lightweight models)</td>
+      <td>High-priority features and component tests that require launching instances</td>
       <td>High-priority interfaces (using random lightweight models)</td>
       <td><code>core_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
       <td>
-        <strong>Model tests:</strong><br>
-        /tests/e2e/online_serving/test_{model_name}.py<br>
-        <strong>Feature tests:</strong><br>
-        /tests/{component_name}/test_xxx<br>
-        <strong>Interface tests:</strong><br>
-        /tests/entrypoints/test_xxx
+        <strong>Model E2E:</strong><br>
+        <code>tests/e2e/online_serving/test_{model}.py</code><br>
+        <code>tests/e2e/offline_inference/test_{model}.py</code><br>
+        <strong>Feature integration:</strong><br>
+        <code>tests/e2e/features/&lt;feature&gt;/</code><br>
+        (e.g. <code>fullduplex/</code>, <code>custom_pipeline/</code>, <code>rlhf_test/</code>)<br>
+        <strong>Component / interface Test (GPU):</strong><br>
+        <code>tests/{component}/…</code>, <code>tests/entrypoints/…</code>
       </td>
       <td>
         <a href="test_writing_guide.md#l1--l2-level-testing-unit-testing-and-basic-end-to-end-verification">L1 &amp; L2</a><br>
@@ -90,18 +96,19 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <td><strong>L3</strong><br>(Important Perf & Integration & Accuracy)</td>
       <td>Online & Offline (multiple deployment scenarios):<br>real model, normal inference function, normal accuracy</td>
       <td>High/medium-priority models + key online/offline scenarios; real weights, Whisper/similarity, preset voice gender, basic accuracy</td>
-      <td>Medium-priority features (using random lightweight models)</td>
+      <td>Medium-priority features and component tests that require launching instances</td>
       <td>Medium-priority interfaces (using random lightweight models)</td>
       <td><code>advanced_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
       <td>&lt;30min</td>
       <td>
-        <strong>Model tests:</strong><br>
-        /tests/e2e/online_serving/test_{model_name}.py<br>
-        /tests/e2e/offline_inference/test_{model_name}.py<br>
-        <strong>Feature tests:</strong><br>
-        /tests/{component_name}/test_xxx<br>
-        <strong>Interface tests:</strong><br>
-        /tests/entrypoints/test_xxx
+        <strong>Model E2E:</strong><br>
+        <code>tests/e2e/online_serving/test_{model}.py</code><br>
+        <code>tests/e2e/offline_inference/test_{model}.py</code><br>
+        <code>tests/e2e/accuracy/</code><br>
+        <strong>Feature integration:</strong><br>
+        <code>tests/e2e/features/&lt;feature&gt;/</code><br>
+        <strong>Component / interface Test:</strong><br>
+        <code>tests/{component}/…</code>, <code>tests/entrypoints/…</code>
       </td>
       <td>
         <a href="test_writing_guide.md#l3-level-testing-core-integration-performance-and-accuracy-verification">L3</a><br>
@@ -114,26 +121,23 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <td><strong>L4</strong><br>(Perf & Integration & Accuracy)</td>
       <td>Online: full functional scenarios + performance test + doc test + accuracy test</td>
       <td>High-priority models: function, performance, accuracy, and doc testing<br>Medium-priority models: function and doc testing</td>
-      <td>Low-priority features (using real weights)</td>
+      <td>Low-priority features and component tests that require launching instances</td>
       <td>Low-priority interfaces (using real weights)</td>
       <td><code>full_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
       <td>&lt;3 hour</td>
       <td>
-        <strong>Model tests:</strong><br>
-        /tests/e2e/online_serving/test_{model_name}_expansion.py<br>
-        <strong>Feature tests:</strong><br>
-        /tests/{component_name}/test_xxx<br>
-        <strong>Interface tests:</strong><br>
-        /tests/entrypoints/test_xxx<br>
+        <strong>Model E2E:</strong><br>
+        <code>tests/e2e/online_serving/test_{model}_expansion.py</code><br>
+        <code>tests/e2e/offline_inference/test_{model}_expansion.py</code><br>
+        <code>tests/e2e/accuracy/test_{model}.py</code><br>
+        <strong>Feature integration:</strong><br>
+        <code>tests/e2e/features/&lt;feature&gt;/</code><br>
+        <strong>Component / interface Test:</strong><br>
+        <code>tests/{component}/…</code>, <code>tests/entrypoints/…</code><br>
         <strong>Performance:</strong><br>
-        /tests/dfx/perf/tests/test_qwen3_omni_*.json (Omni), test_tts.json (TTS),<br>
-        test_voxcpm2.json, test_higgs_audio_v3.json, and<br>
-        /tests/dfx/perf/tests/test_{diffusion_model}_vllm_omni.json (Diffusion)<br>
-        <strong>Doc Test:</strong><br>
-        tests/examples/online_serving/test_{model_name}.py<br>
-        tests/examples/offline_inference/test_{model_name}.py<br>
-        <strong>Accuracy Test:</strong><br>
-        /tests/e2e/accuracy/test_{model_name}.py
+        <code>tests/dfx/perf/tests/</code><br>
+        <strong>Doc examples:</strong><br>
+        <code>tests/examples/online_serving/</code>, <code>tests/examples/offline_inference/</code>
       </td>
       <td>
         <a href="test_writing_guide.md#l4-level-testing-full-functionality-performance-and-documentation-testing">L4</a><br>
@@ -152,11 +156,9 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <td> Depends on reality </td>
       <td>
         <strong>Stability:</strong><br>
-        /tests/dfx/stability/tests/test_qwen3_omni.json<br>
-        /tests/dfx/stability/tests/test_wan22.json<br>
+        <code>tests/dfx/stability/tests/</code><br>
         <strong>Reliability:</strong><br>
-        tests/dfx/reliability/test_reliability_{model_key}.py<br>
-        (e.g. <code>test_reliability_qwen3_omni.py</code>, <code>test_reliability_wan22.py</code>, <code>test_reliability_hunyuan_image.py</code>, <code>test_reliability_voxcpm2.py</code>)
+        <code>tests/dfx/reliability/test_reliability_{model}.py</code>
       </td>
       <td>
         <a href="test_writing_guide.md#l5-level-testing-stability-and-reliability-testing">L5</a><br>
@@ -168,7 +170,9 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
   </tbody>
 </table>
 
-For per-level test authoring (directories, markers, examples), see [Test Writing Guide](./test_writing_guide.md).
+**Test Dir placement (summary):** component / unit under `tests/{component}/` mirroring `vllm_omni/`; model E2E under `tests/e2e/online_serving/`, `tests/e2e/offline_inference/`, and `tests/e2e/accuracy/`; feature integration under `tests/e2e/features/<feature>/`; doc example tests under `tests/examples/online_serving/` and `tests/examples/offline_inference/`; performance under `tests/dfx/perf/`; stability under `tests/dfx/stability/`; reliability under `tests/dfx/reliability/`. Do **not** add new top-level directories under `tests/` that are unrelated to a `vllm_omni` component (or to the established `e2e` / `dfx` / `helpers` / `examples` / `buildkite` layout).
+
+For per-level test authoring (markers, examples), see [Test Writing Guide](./test_writing_guide.md).
 
 ## Common Specifications
 

docs/contributing/ci/test_writing_guide.md

@@ -4,137 +4,20 @@ This document describes how to author tests for each CI level (L1–L5). For the
 
 ## Test Directory Structure
 
-Legend: `✅` = test exists, `⬜` = suggested to add.
-```
-vllm_omni/                                    tests/
-├── config/                             →     ├── config/
-│   ├── model.py                              │   └── test_model.py                    ⬜
-│   └── lora.py                               │   └── test_lora.py                      ⬜
-│
-├── core/                               →     ├── core/
-│   └── sched/                                 │   └── sched/
-│       ├── omni_ar_scheduler.py               │       ├── test_omni_ar_scheduler.py    ⬜
-│       ├── omni_generation_scheduler.py       │       ├── test_omni_generation_scheduler.py  ⬜
-│       └── output.py                          │       └── test_output.py               ✅ currently in entrypoints/test_omni_new_request_data.py (tests output.OmniNewRequestData)
-│
-├── diffusion/                          →     ├── diffusion/
-│   ├── diffusion_engine.py                    │   ├── test_diffusion_engine.py          ⬜
-│   ├── attention/                             │   ├── attention/
-│   │   ├── layer.py                            │   │   ├── test_attention_sp.py         ✅
-│   │   └── backends/                           │   │   └── test_flash_attn.py           ✅
-│   ├── distributed/                           │   ├── distributed/
-│   │   └── ...                                 │   │   ├── test_comm.py                 ✅
-│   │                                            │   │   ├── test_cfg_parallel.py        ✅
-│   │                                            │   │   └── test_sp_plan_hooks.py       ✅
-│   ├── lora/                                   │   ├── lora/
-│   │   └── ...                                 │   │   ├── test_base_linear.py          ✅
-│   │                                            │   │   └── test_lora_manager.py        ✅
-│   ├── models/                                 │   ├── models/
-│   │   ├── qwen_image/                         │   │   ├── qwen_image/                 (e2e coverage)
-│   │   ├── ovis_image/                         │   │   ├── ovis_image/
-│   │   │   └── ...                             │   │   │   └── test_ovis_image.py     ✅
-│   │   ├── z_image/                            │   │   └── z_image/
-│   │   └── ...                                 │   │       └── test_zimage_tp_constraints.py  ✅
-│   └── worker/                                 │   └── worker/
-│       ├── diffusion_worker.py                 │       └── test_diffusion_worker.py   ✅ file at diffusion/test_diffusion_worker.py
-│       └── diffusion_model_runner.py            │
-│
-├── distributed/                         →     ├── distributed/
-│   └── omni_connectors/                         │   └── omni_connectors/
-│       ├── adapter.py                           │       ├── test_adapter_and_flow.py   ✅
-│       ├── kv_transfer_manager.py               │       ├── test_basic_connectors.py   ✅
-│       ├── connectors/                           │       ├── test_kv_flow.py             ✅
-│       └── utils/                               │       └── test_omni_connector_configs.py  ✅
-│
-├── engine/                             →     ├── engine/
-│   ├── input_processor.py                      │   ├── test_input_processor.py         ⬜  (no processor.py in source)
-│   ├── output_processor.py                     │   └── test_output_processor.py         ⬜
-│   └── arg_utils.py                            │   └── test_arg_utils.py               ⬜
-│
-├── entrypoints/                        →     ├── entrypoints/
-│   ├── stage_utils.py                          │   ├── test_stage_utils.py            ✅
-│   ├── cli/                                     │   ├── cli/                           (benchmarks/test_serve_cli.py covers CLI serve)
-│   │   └── ...                                  │   │   └── test_*.py                  ⬜
-│   └── openai/                                  │   └── openai_api/                    # maps to entrypoints/openai/
-│       ├── api_server.py                        │       ├── test_api_server.py         ⬜  (e2e indirect coverage)
-│       ├── serving_chat.py                       │       ├── test_serving_chat_sampling_params.py  ✅
-│       ├── serving_speech.py                     │       ├── test_serving_speech.py     ✅
-│       └── image_api_utils.py                   │       └── test_image_server.py      ✅
-│
-├── inputs/                             →     ├── inputs/
-│   ├── data.py                                 │   ├── test_data.py                   ⬜
-│   ├── parse.py                                │   ├── test_parse.py                 ⬜
-│   └── preprocess.py                            │   └── test_preprocess.py            ✅ currently in entrypoints/test_omni_input_preprocessor.py
-│
-├── model_executor/                     →     ├── model_executor/
-│   ├── layers/                                  │   ├── layers/
-│   │   └── mrope.py                             │   │   └── test_mrope.py              ⬜
-│   ├── model_loader/                            │   ├── model_loader/
-│   │   └── weight_utils.py                      │   │   └── test_weight_utils.py      ⬜
-│   ├── models/                                  │   ├── models/
-│   │   ├── qwen2_5_omni/                         │   │   ├── qwen2_5_omni/
-│   │   │   ├── qwen2_5_omni_thinker.py           │   │   │   ├── test_audio_length.py  ✅
-│   │   │   ├── qwen2_5_omni_talker.py            │   │   │   ├── test_qwen2_5_omni_thinker.py  ⬜
-│   │   │   └── qwen2_5_omni_token2wav.py         │   │   │   ├── test_qwen2_5_omni_talker.py  ⬜
-│   │   └── qwen3_omni/                          │   │   │   └── test_qwen2_5_omni_token2wav.py  ⬜
-│   │       └── ...                               │   │   └── qwen3_omni/
-│   ├── stage_configs/                           │   │       └── test_*.py              ⬜
-│   │   └── *.yaml                               │   └── stage_configs/                 (used by e2e, test_*.py can be added)  ⬜
-│   └── stage_input_processors/                  │   └── stage_input_processors/
-│       └── ...                                  │       └── test_*.py                 ⬜
-│
-├── sample/                             →     ├── sample/
-│   └── __init__.py                             │   └── test_*.py                      ⬜
-│
-├── utils/                              →     ├── utils/
-│   └── __init__.py                             │   └── test_*.py                       ⬜  (no platform_utils.py currently)
-│
-├── worker/                             →     ├── worker/
-│   ├── gpu_ar_model_runner.py                  │   ├── test_gpu_ar_model_runner.py    ⬜
-│   ├── gpu_ar_worker.py                        │   ├── test_gpu_ar_worker.py           ⬜
-│   ├── gpu_generation_model_runner.py          │   ├── test_gpu_generation_model_runner.py  ✅
-│   ├── gpu_generation_worker.py                │   ├── test_gpu_generation_worker.py  ⬜
-│   ├── gpu_model_runner.py                     │   ├── test_omni_gpu_model_runner.py   ✅
-│   └── mixins.py                               │   └── (npu under platforms/npu/worker/)  # not worker/npu/
-│
-├── platforms/                          →     (no tests/platforms/, e2e and stage_configs provide indirect coverage)
-│   ├── cuda/
-│   ├── npu/worker/                             # NPU worker here, not vllm_omni/worker/npu/
-│   ├── rocm/
-│   └── xpu/worker/
-│
-├── outputs.py                          →     test_outputs.py                         ✅ (at tests root)
-├── (logger, patch, request, version)    →     (no corresponding unit test)
-│
-└── e2e (tests side only)               →     ├── e2e/
-                                               ├── online_serving/                     ✅ non-empty
-                                               │   ├── test_qwen2_5_omni.py
-                                               │   ├── test_async_omni.py
-                                               │   ├── test_qwen3_omni.py
-                                               │   ├── test_qwen3_omni_expansion.py
-                                               │   ├── test_mimo_audio.py
-                                               │   └── test_images_generations_lora.py
-                                               └── offline_inference/                  ✅
-                                                   ├── test_qwen2_5_omni.py
-                                                   ├── test_qwen3_omni.py
-                                                   ├── test_bagel_text2img.py
-                                                   ├── test_z_image.py
-                                                   ├── test_wan22.py
-                                                   ├── test_zimage_tensor_parallel.py
-                                                   ├── test_cache_dit.py
-                                                   ├── test_teacache.py
-                                                   ├── test_stable_audio_expansion.py
-                                                   ├── test_diffusion_cpu_offload.py
-                                                   ├── test_diffusion_layerwise_offload.py
-                                                   ├── test_diffusion_lora.py
-                                                   ├── test_sequence_parallel.py
-                                                   └── stage_configs/                  (legacy schema, still
-                                                       ├── bagel_*.yaml                 present for unmigrated
-                                                       └── npu/, rocm/, etc.            models)
+Canonical placement by CI level is in the **Test Dir** column of the level matrix in [Test System Overview](./test_system_overview.md).
 
-# Migrated models (qwen3_omni_moe, qwen2_5_omni, qwen3_tts) live under
-# vllm_omni/deploy/ instead — see docs/configuration/stage_configs.md.
-```
+Summary:
+
+- **Component / unit:** `tests/{component}/…` mirroring `vllm_omni/{component}/`
+- **Model E2E:** `tests/e2e/online_serving/`, `tests/e2e/offline_inference/`, `tests/e2e/accuracy/`
+- **Feature integration:** `tests/e2e/features/<feature>/`
+- **Doc example tests:** `tests/examples/online_serving/`, `tests/examples/offline_inference/`
+- **Performance:** `tests/dfx/perf/tests/` (JSON configs; runners under `tests/dfx/perf/scripts/`)
+- **Stability:** `tests/dfx/stability/tests/`
+- **Reliability:** `tests/dfx/reliability/`
+- Do **not** add new top-level `tests/` directories unrelated to a component (or to `e2e` / `dfx` / `helpers` / `examples` / `buildkite`)
+
+Deploy YAMLs: `vllm_omni/deploy/` via `tests.helpers.stage_config.get_deploy_config_path` (see [stage configs](../../configuration/stage_configs.md)).
 
 ## Markers for Tests
 
@@ -373,13 +256,13 @@ L2 level testing builds upon L1 by introducing GPU resources and verifying that
 
 #### 1.3 Test Directory and Execution Files
 
-A clear directory structure is key to managing test cases efficiently.
+A clear directory structure is key to managing test cases efficiently. See the **Test Dir** column in [Test System Overview](./test_system_overview.md) for the canonical placement rules.
 
--   ***L1 Test Directory***: `/tests/{component_name}/test_xxx.py`
--   -   Here, `{component_name}` corresponds to modules in the source code, such as `distributed`, `entrypoints`, etc., and `test_xxx.py` is the specific test file.
--   ***L2 Test Directory***:
--   -   Online Serving: `/tests/e2e/online_serving/test_{model_name}.py`
-    -   Offline Inference: `/tests/e2e/offline_inference/test_{model_name}.py`
+-   ***L1 (component / unit)***: `tests/{component_name}/…` mirroring `vllm_omni/{component_name}/` (e.g. `tests/diffusion/`, `tests/entrypoints/`). Do **not** invent new top-level `tests/` folders unrelated to a component.
+-   ***L2 (basic model E2E)***:
+    -   Online serving: `tests/e2e/online_serving/test_{model_name}.py`
+    -   Offline inference: `tests/e2e/offline_inference/test_{model_name}.py`
+-   ***Feature-level integration*** (cross-cutting, not a single-model smoke): `tests/e2e/features/<feature>/` (e.g. `fullduplex/`, `custom_pipeline/`, `rlhf_test/`).
 
 #### 1.4 Execution Method and Example
 
@@ -485,10 +368,12 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 
 #### 2.3 Test Directory and Execution Files
 
--   ***Functional Testing***:
--   -   Online Serving: `/tests/e2e/online_serving/test_{model_name}_expansion.py`
-    -   Offline Inference: `/tests/e2e/offline_inference/test_{model_name}_expansion.py`
-    -   (Note: `_expansion.py` likely means it contains more comprehensive scenario cases compared to L2 tests).
+-   ***Functional / model E2E***:
+    -   Online serving: `tests/e2e/online_serving/test_{model_name}_expansion.py`
+    -   Offline inference: `tests/e2e/offline_inference/test_{model_name}_expansion.py`
+    -   Accuracy / similarity: `tests/e2e/accuracy/`
+    -   (`_expansion.py` holds broader scenario coverage than the L2 smoke file.)
+-   ***Feature-level integration***: `tests/e2e/features/<feature>/` (not under a new top-level `tests/` directory).
 
 #### 2.4 Execution Method and Example
 

docs/contributing/model/adding_diffusion_model.md

@@ -653,7 +653,7 @@ python end2end.py --model your-org/your-model-name --modality text2img --prompts
 Mirror BAGEL’s online serving setup:
 
 - Server launcher: `examples/online_serving/your_model_name/run_server.sh`
-  - Wrap `vllm serve ... --omni --port ...` (and `--stage-configs-path ...` if needed)
+  - Wrap `vllm serve ... --omni --port ...` (and `--deploy-config ...` if needed)
 - Client: `examples/online_serving/your_model_name/openai_chat_client.py`
   - Send requests to `POST /v1/chat/completions`
   - Support multimodal inputs (e.g., base64 image) if your model needs it

docs/contributing/model/adding_omni_model.md

@@ -48,8 +48,8 @@ vllm_omni/deploy/
 ```
 
 New in-tree models should register their `PipelineConfig` in
-`vllm_omni/config/pipeline_registry.py`; use `--stage-configs-path` only for
-custom legacy `stage_args` YAMLs.
+`vllm_omni/config/pipeline_registry.py`; use `--deploy-config` for custom
+deployment YAMLs.
 
 ## Step-by-Step Implementation
 

docs/contributing/model/adding_tts_model.md

@@ -449,9 +449,8 @@ Key configuration fields:
 !!! note
     New in-tree models should define frozen topology in `models/<model>/pipeline.py`,
     register it in `vllm_omni/config/pipeline_registry.py`, and put deployment
-    knobs in `vllm_omni/deploy/<model>.yaml`. Legacy `stage_args` YAMLs are still
-    accepted for custom configs via `--stage-configs-path`, but should not be used
-    for new bundled defaults.
+    knobs in `vllm_omni/deploy/<model>.yaml`. Use `--deploy-config` to load a
+    custom deployment YAML.
 
 ### Batch mode
 

docs/design/feature/async_diffusion_output.md

@@ -0,0 +1,174 @@
+# Async Diffusion Output
+
+## Table of Contents
+
+1. [Overview](#overview)
+2. [Performance](#performance)
+3. [Architecture](#architecture)
+4. [Model Coverage](#model-coverage)
+5. [Configuration](#configuration)
+6. [Related Files](#related-files)
+
+## Overview
+
+The async diffusion output feature moves the D2H (Device→Host) copy and SHM packing from the Worker main thread to a background daemon thread, so the GPU default stream can immediately start the next request's forward pass. This eliminates the GPU bubble caused by synchronous D2H/packing.
+
+**Core benefit**: the previous request's output D2H/packing overlaps with the next request's forward pass.
+
+**Automatically enabled** when `step_execution=False` (default, request-mode). No extra configuration needed. When `step_execution=True` (step-mode), the original synchronous path is used — pump thread and Worker background thread are not started.
+
+## Performance
+
+### HunyuanImage-3.0 (TP4)
+
+| Resolution | Async Off (QPS) | Async On (QPS) | Change |
+|---|---|---|---|
+| 1024×1024 | 0.4773 | 0.4802 | +0.60% |
+| 768×768 | 0.8370 | 0.8533 | +1.95% |
+
+**Analysis**: smaller resolution → larger QPS gain. D2H/packing is relatively fixed overhead; forward is variable (scales with resolution). Smaller images → shorter forward → D2H/packing occupies a larger fraction of total time → more benefit from overlapping it.
+
+## Architecture
+
+### Execution Timeline
+
+```
+Before (synchronous D2H):
+[forward req1] [D2H+SHM pack req1] [forward req2] [D2H+SHM pack req2]
+                                 ^^^^^^^^^^^^^^^^ GPU bubble
+
+After (async D2H):
+[forward req1] [forward req2]
+[D2H+SHM pack req1 (side stream)] [D2H+SHM pack req2 (side stream)]
+                                 ^^^^^^^^^^^^^^^^ bubble eliminated
+```
+
+### Data Flow
+
+```
+                    ┌─────────────────────────────────────────────────────┐
+                    │                 Worker Process                      │
+                    │                                                    │
+  execute_model ──> │ WorkerBusyLoop ──> forward() ──> DiffusionOutput   │
+                    │       │                              │ (GPU tensor)│
+                    │       │                    ┌─────────┴──────────┐  │
+                    │       │                    │  async output path │  │
+                    │       │                    │                    │  │
+                    │       │              compute_done ──> result_mq │  │
+                    │       │                    │                    │  │
+                    │       │                    ▼                    │  │
+                    │       │            AsyncOutputThread            │  │
+                    │       │         (side CUDA stream)             │  │
+                    │       │         wait_event(gpu_event)          │  │
+                    │       │         pack_diffusion_output_shm()    │  │
+                    │       │         D2H + SHM write                │  │
+                    │       │                    │                    │  │
+                    │       │              output_ready ──> result_mq │  │
+                    │       │                    │                    │  │
+                    │       ▼                    ▼                    │  │
+                    │  (dequeue next request)                        │  │
+                    └─────────────────────────────────────────────────────┘
+                                         │
+                                         ▼
+                    ┌─────────────────────────────────────────────────────┐
+                    │              Executor (main process)               │
+                    │                                                    │
+                    │  ResultPumpThread (sole reader of result_mq)       │
+                    │       │                                            │
+                    │       ├── AsyncDiffusionOutput(COMPUTE_DONE)       │
+                    │       │   → resolve _rpc_futures[rpc_id]           │
+                    │       │   → RunnerOutput(async_output_id=...)      │
+                    │       │                                            │
+                    │       ├── AsyncDiffusionOutput(OUTPUT_READY)       │
+                    │       │   → resolve _output_futures[async_output_id]│
+                    │       │   → or batch split via _batch_split_map   │
+                    │       │                                            │
+                    │       └── Non-async message                        │
+                    │           → _sync_result_buffer (for other RPCs)   │
+                    │                                                    │
+                    │  wait_output_ready(async_output_id) → Future       │
+                    └─────────────────────────────────────────────────────┘
+```
+
+### Key Components
+
+1. **AsyncDiffusionOutput** (`data.py`): Protocol envelope for `result_mq`. `kind` field routes messages:
+   - `COMPUTE_DONE` — forward finished, GPU can start next request
+   - `OUTPUT_READY` — D2H/SHM packing finished, final output available
+   - `RPC_RESULT` — ordinary RPC return (including error propagation)
+
+2. **Worker AsyncOutputThread** (`diffusion_worker.py`): Background daemon thread that:
+   - Waits for `gpu_event` (cross-stream sync ensuring forward wrote the tensor)
+   - Runs `pack_diffusion_output_shm()` on a side CUDA stream
+   - Enqueues `OUTPUT_READY` to `result_mq` when packing completes
+
+3. **ResultPumpThread** (`multiproc_executor.py`): Sole reader of `result_mq` in request-mode. Dispatches messages:
+   - `COMPUTE_DONE` / `RPC_RESULT` → resolves `_rpc_futures[rpc_id]`
+   - `OUTPUT_READY` → resolves `_output_futures[async_output_id]` (with batch split via `_batch_split_map`)
+   - Non-async messages → `_sync_result_buffer` for other RPCs
+
+4. **collective_rpc() two-path dispatch** (`multiproc_executor.py`):
+   - **Path 1**: `execute_model` / `execute_model_batch` → generates `rpc_id`, registers Future, waits for pump to deliver `compute_done`
+   - **Path 2**: All other RPCs → `_sync_result_buffer` (pump-fed) or `_result_mq` (step-mode, no pump)
+
+5. **record_device_event** (`platforms/`): Cross-platform GPU event recording for side-stream synchronization. Implemented for CUDA, NPU, ROCm, XPU, MUSA; base class returns `None` (safe no-op).
+
+6. **IPC side-stream D2H** (`ipc.py`): `pack_diffusion_output_shm()` and helpers accept `d2h_stream` parameter; use `pin_memory` + `copy_(non_blocking=True)` on the side stream instead of synchronous `.cpu()`.
+
+### Batch Split
+
+When `execute_model_batch` returns a `COMPUTE_DONE` with a single `async_output_id` for the entire batch, the executor splits it into per-request `async_output_id`s (format `{batch_id}/{request_id}`) via `_batch_split_map`. When `OUTPUT_READY` arrives, the pump extracts per-request results from the batch output and resolves each request's output future independently.
+
+## Model Coverage
+
+### Currently Supported (request-mode, `step_execution=False`)
+
+All models running in request-mode (`step_execution=False`, the default) automatically use async output.
+
+**Verified models**:
+
+| Model | Type | `supports_request_batch` |
+|---|---|---|
+| **HunyuanImage-3.0** | Image | `False` |
+| **Qwen-Image** | Image | `True` |
+
+Other models with `step_execution=False` are also supported but not yet verified.
+
+### Not Yet Supported (step-mode, `step_execution=True`)
+
+When `step_execution=True` (or `streaming_output=True`, which auto-enables step-mode), models use `execute_stepwise` instead of `execute_model`, which is not in the async Path 1 whitelist. Async output is not applicable.
+
+**Verified**: Helios (`step_execution=True`, does not benefit from this feature).
+
+Other models with `step_execution=True` are also not applicable.
+
+Adapting step-mode requires additional design because:
+
+- Each step's `RunnerOutput` must be available synchronously for the scheduler to advance
+- Intermediate latent tensors may not need D2H (only the final step needs D2H + postprocess)
+- Steps are serial (step N+1 depends on step N output), so the overlap benefit is limited to "final step's D2H overlaps with the next request's forward"
+
+**Planned approach**: first cover only the final step's async D2H, keep intermediate steps synchronous. This matches the request-mode benefit pattern.
+
+## Configuration
+
+No configuration needed. Async output is automatically enabled when `step_execution=False` (default).
+
+| `step_execution` | Mode | Async Output | Pump Thread | Worker BG Thread |
+|---|---|---|---|---|
+| `False` (default) | request-mode | ✅ enabled | ✅ started | ✅ started |
+| `True` | step-mode | ❌ disabled | ❌ not started | ❌ not started |
+
+## Related Files
+
+- `vllm_omni/diffusion/data.py`: `AsyncDiffusionOutput`, `AsyncOutputKind`, `DiffusionOutput.async_output_id`
+- `vllm_omni/diffusion/worker/diffusion_worker.py`: `WorkerProc._return_result()`, `_async_output_loop()`, `_generate_async_output_id()`
+- `vllm_omni/diffusion/executor/multiproc_executor.py`: `ResultPumpThread`, `collective_rpc()` two-path dispatch, `wait_output_ready()`, `_batch_split_map`
+- `vllm_omni/diffusion/diffusion_engine.py`: `step()` / `step_streaming()` / `add_req_and_wait_for_response()` async output waiting
+- `vllm_omni/diffusion/sched/request_scheduler.py`: `update_from_output()` — `async_output_id` → `FINISHED_COMPLETED`
+- `vllm_omni/diffusion/ipc.py`: `pack_diffusion_output_shm()` side-stream D2H path
+- `vllm_omni/platforms/interface.py`: `OmniPlatform.record_device_event()` base class
+- `vllm_omni/platforms/cuda/platform.py`: `CudaOmniPlatform.record_device_event()`
+- `vllm_omni/platforms/npu/platform.py`: `NPUOmniPlatform.record_device_event()`
+- `vllm_omni/diffusion/models/hunyuan_image3/pipeline_hunyuan_image3.py`: `get_hunyuan_image3_post_process_func()`, postprocess removal
+- `vllm_omni/diffusion/registry.py`: `_DIFFUSION_POST_PROCESS_FUNCS` postprocess registry

docs/design/feature/tensor_parallel.md

@@ -264,7 +264,7 @@ Complete examples in the codebase:
 | **Z-Image** | `vllm_omni/diffusion/models/z_image/z_image_transformer.py` | Standard TP | Full implementation with validation |
 | **FLUX** | `vllm_omni/diffusion/models/flux/flux_transformer.py` | Dual-stream | Image + text streams |
 | **Qwen-Image** | `vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py` | Standard TP | With RoPE |
-| **TP Tests** | `tests/e2e/offline_inference/test_zimage_parallelism.py` | E2E testing | TP correctness and performance |
+| **TP Tests** | `tests/diffusion/distributed/test_tensor_parallel.py` | E2E testing | TP correctness and performance |
 | **Constraint Tests** | `tests/diffusion/models/z_image/test_zimage_tp_constraints.py` | Unit testing | Validation logic |
 
 ---

docs/design/metrics.md

@@ -148,7 +148,13 @@ Upstream vLLM's `Scheduler.make_stats()` runs on every AR generation step, retur
 
 `OmniSchedulerMixin.make_stats()` (in `vllm_omni/core/sched/omni_scheduler_mixin.py`) throttles stats emission to at most once per second **per scheduler** — i.e. per `(stage, replica)` since each replica owns its own scheduler instance. Between intervals it returns `None`, which the engine core skips serializing. This keeps gauges fresh enough for Prometheus scrapes (typically 15-30s intervals) while eliminating the per-step overhead.
 
-The orchestrator side does not add its own throttle on top: the per-replica recording loop gates only on `raw_outputs.scheduler_stats is not None` (i.e. this replica's scheduler passed its own 1 Hz gate). A previous global `_last_stats_ts` on the orchestrator was removed because it starved every replica other than the first to emit within each second.
+The orchestrator side does not add its own throttle on top. Scheduler gauges are
+recorded when `raw_outputs.scheduler_stats is not None` (i.e. this replica's
+scheduler passed its own 1 Hz gate), while output-batch request/token metrics
+are recorded from `IterationStats` whenever a non-empty output batch is
+processed. A previous global `_last_stats_ts` on the orchestrator was removed
+because it starved every replica other than the first to emit within each
+second.
 
 ## Metric Definitions
 

docs/design/qwen3_omni_tts_performance_optimization.md

@@ -419,8 +419,7 @@ Notes:
 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct \
   --omni \
-  --port 8091 \
-  --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_omni_moe_async_chunk.yaml
+  --port 8091
 ```
 
 #### 3) Key config knobs
@@ -494,7 +493,7 @@ The default config (`qwen3_tts.yaml`) enables the full optimization stack:
 vllm-omni serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
   --omni \
   --port 8000 \
-  --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts_no_async_chunk.yaml
+  --no-async-chunk
 ```
 
 #### 3) Key config knobs

docs/getting_started/installation/gpu.md

@@ -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, and 0.25.0. 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).
+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. 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"
 

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

@@ -20,7 +20,7 @@ Therefore, it is recommended to install vLLM and vLLM-Omni with a **fresh new**
 
 vLLM-Omni is built based on vLLM. Please install it with command below.
 ```bash
-uv pip install vllm==0.25.0 --torch-backend=auto
+uv pip install vllm==0.26.0 --torch-backend=auto
 ```
 
 #### Installation of vLLM-Omni
@@ -39,13 +39,13 @@ uv pip install 'vllm-omni[demo]'
 # --8<-- [start:build-wheel-from-source]
 
 #### Installation of vLLM
-If you do not need to modify source code of vLLM, you can directly install the stable 0.25.0 release version of the library
+If you do not need to modify source code of vLLM, you can directly install the stable 0.26.0 release version of the library
 
 ```bash
-uv pip install vllm==0.25.0 --torch-backend=auto
+uv pip install vllm==0.26.0 --torch-backend=auto
 ```
 
-The 0.25.0 release of vLLM ships CUDA 13.0-compatible binaries by default. If you need a different CUDA variant or want to reuse an existing PyTorch installation, build vLLM from source instead.
+The 0.26.0 release of vLLM ships CUDA 13.0-compatible binaries by default. If you need a different CUDA variant or want to reuse an existing PyTorch installation, build vLLM from source instead.
 
 #### Installation of vLLM-Omni
 Since vllm-omni is rapidly evolving, it's recommended to install it from source
@@ -66,12 +66,12 @@ If you want to check, modify or debug with source code of vLLM, install the libr
 ```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
-git checkout v0.25.0
+git checkout v0.26.0
 ```
 Set up environment variables to get pre-built wheels. If there are internet problems, just download the whl file manually. And set `VLLM_PRECOMPILED_WHEEL_LOCATION` as your local absolute path of whl file.
 ```bash
-#For CUDA 13.0 (the default for v0.25.0; the wheel filename has no `+cu130` suffix)
-export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.25.0/vllm-0.25.0-cp38-abi3-manylinux_2_28_x86_64.whl
+#For CUDA 13.0 (the default for v0.26.0; the wheel filename has no `+cu130` suffix)
+export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.26.0/vllm-0.26.0-cp38-abi3-manylinux_2_28_x86_64.whl
 ```
 Install vllm with command below (If you have no existing PyTorch).
 ```bash
@@ -102,7 +102,7 @@ docker run --runtime nvidia --gpus 2 \
     --env "HF_TOKEN=$HF_TOKEN" \
     -p 8091:8091 \
     --ipc=host \
-    vllm/vllm-omni:v0.25.0 \
+    vllm/vllm-omni:v0.26.0 \
     vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
 

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

@@ -13,7 +13,7 @@ vLLM-Omni current recommends the steps in under setup through Docker Images.
 
 vLLM-Omni is built based on vLLM. Please install it with command below.
 ```bash
-uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723
+uv pip install vllm==0.26.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
 ```
 
 #### Installation of vLLM-Omni
@@ -34,13 +34,13 @@ uv pip install onnxruntime-rocm
 # --8<-- [start:build-wheel-from-source]
 
 #### Installation of vLLM
-If you do not need to modify source code of vLLM, you can directly install the stable 0.25.0 release version of the library
+If you do not need to modify source code of vLLM, you can directly install the stable 0.26.0 release version of the library
 
 ```bash
-uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723
+uv pip install vllm==0.26.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
 ```
 
-The pre-built 0.25.0 vLLM wheel targets ROCm 7.2.3. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.
+The pre-built 0.26.0 vLLM wheel targets ROCm 7.2.3. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.
 
 #### Installation of vLLM-Omni
 Since vllm-omni is rapidly evolving, it's recommended to install it from source
@@ -58,7 +58,7 @@ If you want to check, modify or debug with source code of vLLM, install the libr
 ```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
-git checkout v0.25.0
+git checkout v0.26.0
 python3 -m pip install -r requirements/rocm.txt
 python3 setup.py develop
 ```
@@ -130,7 +130,7 @@ docker run --rm \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HF_TOKEN=$HF_TOKEN" \
   -p 8091:8091 \
-  vllm/vllm-omni-rocm:v0.25.0 \
+  vllm/vllm-omni-rocm:v0.26.0 \
   --model Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
 
@@ -149,7 +149,7 @@ docker run --rm -it \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HF_TOKEN=$HF_TOKEN" \
   --entrypoint bash \
-  vllm/vllm-omni-rocm:v0.25.0
+  vllm/vllm-omni-rocm:v0.26.0
 ```
 
 # --8<-- [end:pre-built-images]

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

@@ -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:v0.25.0
-# Atlas A3: quay.io/atlas-ci/vllm-ascend:v0.25.0-a3
+# Atlas A2: quay.io/atlas-ci/vllm-ascend:v0.26.0
+# Atlas A3: quay.io/atlas-ci/vllm-ascend:v0.26.0-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:0.25.0-a3 bash
+    -it quay.io/atlas-ci/vllm-ascend:v0.26.0-a3 bash
 
 # Inside the container, install vLLM-Omni from source
 cd /vllm-workspace
-git clone -b v0.25.0 https://github.com/vllm-project/vllm-omni.git
+git clone -b v0.26.0 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 .
@@ -84,8 +84,8 @@ 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:v0.25.0
-# Atlas A3: quay.io/ascend/vllm-omni:v0.25.0-a3
+# Atlas A2: quay.io/ascend/vllm-omni:v0.26.0
+# Atlas A3: quay.io/ascend/vllm-omni:v0.26.0-a3
 docker run --rm \
     --name vllm-omni-a3 \
     --shm-size=4g \
@@ -110,7 +110,7 @@ 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:v0.25.0-a3 bash
+    -it quay.io/ascend/vllm-omni:v0.26.0-a3 bash
 ```
 
 !!! tip
@@ -122,9 +122,9 @@ Or build IMAGE from **source code**:
 git clone https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
 # A2
-docker build -t vllm-omni-dev-image:latest -f ./docker/Dockerfile.npu .
+# docker build -t vllm-omni-dev-image:latest -f ./docker/Dockerfile.npu .
 # A3
-# docker build -t vllm-omni-dev-image:latest -f ./docker/Dockerfile.npu.a3 .
+docker build -t vllm-omni-dev-image:latest -f ./docker/Dockerfile.npu.a3 .
 ```
 
 # --8<-- [end:pre-built-images]

docs/getting_started/quickstart.md

@@ -19,10 +19,10 @@ uv venv --python 3.12 --seed
 source .venv/bin/activate
 
 # On CUDA
-uv pip install vllm==0.25.0 --torch-backend=auto
+uv pip install vllm==0.26.0 --torch-backend=auto
 
 # On ROCm
-uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723
+uv pip install vllm==0.26.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
 
 git clone https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
@@ -34,7 +34,7 @@ For additional installation methods — please see the [installation guide](inst
 !!! note
     It is important to install the same major & minor version of vLLM and vLLM Omni, otherwise things may not work as expected. If the versions are misaligned, you will see a warning when you import vLLM Omni.
 
-    If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.25.0` and vLLM Omni `0.25.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.
+    If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.26.0` and vLLM Omni `0.26.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.
 
 ## Offline Inference
 

docs/models/supported_models.md

@@ -41,13 +41,10 @@ th {
 | `Wan22S2VPipeline` | Wan2.2-S2V | `Wan-AI/Wan2.2-S2V-14B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   |   |
-| `LTX2Pipeline` | LTX-2-T2V | `Lightricks/LTX-2` | ✅︎ | ✅︎ | | |
-| `LTX2ImageToVideoPipeline` | LTX-2-I2V | `Lightricks/LTX-2` | ✅︎ | ✅︎ | | |
-| `LTX2TwoStagesPipeline` | LTX-2-T2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | |
-| `LTX2ImageToVideoTwoStagesPipeline` | LTX-2-I2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | |
-| `LTX23Pipeline` | LTX-2.3-T2V | `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | |
-| `LTX23ImageToVideoPipeline` | LTX-2.3-I2V | `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | |
+| `LTX2Pipeline` | LTX-2 / LTX-2.3 one-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | |
+| `LTX2DistilledPipeline` | LTX-2 distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | |
 | `LingBotVideoPipeline` | LingBot-Video dense and MoE T2V | `robbyant/lingbot-video-dense-1.3b`, `robbyant/lingbot-video-moe-30b-a3b` | ✅︎ | | | |
+| `MiniMaxH3Pipeline` | MiniMax H3 T2VA, FL2VA, and Ref2VA | `MiniMaxAI/MiniMax-H3` | ✅︎ | | | |
 | `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `HeliosPipeline`, `HeliosPyramidPipeline` | Helios | `BestWishYsh/Helios-Base`, `BestWishYsh/Helios-Mid`, `BestWishYsh/Helios-Distilled` | ✅︎ | ✅︎ | ✅︎ | |
 | `MagiHumanPipeline` | MagiHuman | `SII-GAIR/daVinci-MagiHuman-Base-1080p` | ✅︎ | ✅︎ | | |

docs/serving/speech_api.md

@@ -276,20 +276,42 @@ Client -> Server:
 
 | Message | Description |
 |---------|-------------|
-| `{"type": "session.config", ...}` | Session configuration (sent once, first message) |
+| `{"type": "session.config", ...}` | Session configuration (first message; may be resent between utterances to change it) |
 | `{"type": "input.text", "text": "..."}` | Text chunk |
-| `{"type": "input.done"}` | End of input, flushes remaining buffer |
+| `{"type": "input.done"}` | End of utterance: flushes the buffer and keeps the connection open |
+| `{"type": "session.close"}` | End of connection |
 
 Server -> Client:
 
 | Message | Description |
 |---------|-------------|
-| `{"type": "audio.start", "sentence_index": 0, "sentence_text": "...", "format": "pcm", "sample_rate": 24000}` | Audio generation starting for the buffered input |
+| `{"type": "audio.start", "utterance_index": 0, "sentence_index": 0, "sentence_text": "...", "format": "pcm", "sample_rate": 24000}` | Audio generation starting for the buffered input |
 | Binary frame | Raw audio bytes (one or more PCM chunks when `stream_audio=true`) |
-| `{"type": "audio.done", "sentence_index": 0, "total_bytes": 96000, "error": false}` | Audio complete for the buffered input |
-| `{"type": "session.done", "total_sentences": N}` | Session complete |
+| `{"type": "audio.done", "utterance_index": 0, "sentence_index": 0, "total_bytes": 96000, "error": false}` | Audio complete for the buffered input |
+| `{"type": "session.done", "utterance_index": 0, "total_sentences": N}` | Flushed utterance complete |
 | `{"type": "error", "message": "..."}` | Non-fatal error |
 
+### Flushing vs. Closing
+
+`input.done` is a flush, not a disconnect. The server synthesizes the buffered
+text, emits `session.done`, and then waits on the same connection for the next
+utterance, so a client that speaks repeatedly (for example one driven by an
+upstream LLM) pays the WebSocket handshake once instead of once per utterance.
+
+* The session config is sticky. Send `input.text` again straight after
+  `session.done` to reuse it, or send another `session.config` first to change
+  voice, format, or reference audio. A `session.config` sent while text is
+  still buffered is rejected so no pending input is silently dropped.
+* An utterance is the flush unit, not a linguistic one: it is whatever text was
+  buffered when `input.done` arrived, of any length, synthesized as one request.
+  `utterance_index` counts those flushes across the connection, so it tells you
+  which `input.done` a frame belongs to. `sentence_index` counts within one
+  flush and so pairs with `total_sentences`, which means every utterance reports
+  `sentence_index: 0` of `total_sentences: 1` (or `0` for an empty buffer).
+* End the connection with `session.close`, or by closing the socket. An idle
+  connection is still closed after the server's idle timeout, which now also
+  applies to the gap between utterances.
+
 ### Session Config Parameters
 
 All REST API parameters are supported, plus:

docs/serving/videos_api.md

@@ -73,7 +73,7 @@ curl -L "http://localhost:8091/v1/videos/${video_id}/content" -o output.mp4
 | `audio_reference` | string | null | JSON-encoded audio reference for speech-to-video: `{"audio_url": "..."}` — supports HTTP(s) URLs or base64 data URLs |
 | `width` | integer | model default | Output video width |
 | `height` | integer | model default | Output video height |
-| `num_frames` | integer | model default | Number of generated frames |
+| `num_frames` | integer | 1 | Number of generated frames |
 | `fps` | integer | model default | Output frames per second |
 | `num_inference_steps` | integer | model default | Number of diffusion steps |
 | `guidance_scale` | number | null | CFG guidance scale for the low-noise stage |

docs/user_guide/diffusion/attention_backends.md

@@ -15,7 +15,7 @@ The full set of backends and their platform defaults is in the **Backend Options
 | Value | Notes |
 |---|---|
 | `TRTLLM_ATTN` | FlashInfer's trtllm-gen FMHA (TensorRT-LLM's generated kernels, vendored by FlashInfer). BF16, GQA native, `head_dim=128`. Datacenter Blackwell only (sm_100 / sm_103). Supports **Skip-Softmax** sparse attention — see below. Requires `flashinfer`. |
-| `FLASH_ATTN` | Wraps FlashAttention 2. Default on Hopper / Ada / Ampere when `flash-attn` is installed. |
+| `FLASH_ATTN` | Wraps FlashAttention 4 on Blackwell when `flash-attn-4` is installed, then falls back to FlashAttention 3/2. Default on Hopper / Ada / Ampere when a compatible FlashAttention package is installed. |
 | `CUDNN_ATTN` | Pins `sdpa_kernel([CUDNN_ATTENTION])`. Default on Blackwell (sm_10x / sm_12x) with cuDNN ≥ 9.5. Wins on mask-heavy DiTs (HunyuanVideo-1.5: 2× e2e vs SDPA). |
 | `FLASHINFER_ATTN` | Calls FlashInfer's dense `single_prefill_with_kv_cache` directly with `custom_mask` for non-causal masked attention. Used as Blackwell fallback when cuDNN is unavailable. Requires `flashinfer`. |
 | `TORCH_SDPA` | PyTorch `scaled_dot_product_attention` with the default backend dispatcher. Most conservative; always available. |
@@ -167,6 +167,48 @@ sequence length, since Skip-Softmax only accelerates attention. Dense BF16 is `T
 Requires datacenter Blackwell with `head_dim == 128`; elsewhere, or without FlashInfer, selecting
 `TRTLLM_ATTN` raises rather than silently degrading.
 
+## TRTLLM_ATTN SAGE Quantization
+
+On top of dense/Skip-Softmax, `TRTLLM_ATTN` can run **SAGE** (SageAttention) — the trtllm-gen
+FMHA kernel with per-block quantized Q/K and per-channel fp8 V, so the two attention matmuls run
+in low precision. Enable it through the typed `quant` block on the attention spec:
+
+| Key | Valid values | Meaning |
+|---|---|---|
+| `dtype_qk` | `int8`, `fp8_e4m3` | Quantization dtype for Q and K. Absent ⇒ dense (SAGE off). |
+| `q_block_size` | `1`, `4`, `16` | Per-token block size for Q scales (default `1`). |
+| `k_block_size` | `1`, `4`, `16` | Per-token block size for K scales (default `16`). |
+
+V is always quantized per-channel to fp8_e4m3 and K-smoothing is applied inside the routine, so
+neither is a user knob. Only block sizes `1`, `4`, `16` have compiled kernels.
+
+The `quant` block is a shared `AttnQuantSpec` consumed by more than one backend; each reads the
+fields that apply and rejects the rest. `TRTLLM_ATTN` (SAGE) reads `dtype_qk` (`int8` / `fp8_e4m3`),
+`q_block_size`, and `k_block_size`; `FLASHINFER_ATTN` reads `dtype_qk` (`float16` / `bfloat16`),
+`dtype_vo`, and `flashinfer_backend` (see its section). A `dtype_qk` from the wrong set raises.
+
+```bash
+vllm-omni serve Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --diffusion-attention-config '{"default": {"backend": "TRTLLM_ATTN",
+      "quant": {"dtype_qk": "fp8_e4m3", "q_block_size": 1, "k_block_size": 16}}}'
+```
+
+```python
+from vllm_omni.diffusion.data import AttentionConfig, AttentionSpec, AttnQuantSpec
+
+AttentionConfig(
+    default=AttentionSpec(
+        backend="TRTLLM_ATTN",
+        quant=AttnQuantSpec(dtype_qk="fp8_e4m3", q_block_size=1, k_block_size=16),
+    ),
+)
+```
+
+
+**Requirements.** Needs FlashInfer ≥ 0.6.16rc1. Kernel
+availability is arch-dependent: `fp8_e4m3` QK has kernels on both **SM100** (B200) and **SM103**
+(B300); `int8` QK kernels are compiled for **SM100 only**.
+
 ## End-to-End Benchmark (BF16, sm_120 RTX Pro 6000 Blackwell)
 
 Same prompt and seed across runs. `Total generation time` from `text_to_video.py` / `text_to_image.py`.
@@ -193,9 +235,17 @@ DIFFUSION_ATTENTION_BACKEND=FLASHINFER_ATTN python examples/offline_inference/te
     --model Lightricks/LTX-2 ...
 ```
 
-### FA4 not yet integrated
+### FlashAttention-4 on Blackwell
 
-FlashAttention-4 (released March 2026) targets Blackwell natively and reportedly beats cuDNN by ~20% on B200. As of this writing the `flash-attn-4 4.0.0b10` wheel crashes with `AttributeError: 'NoneType' object has no attribute '_trait'` during JIT on sm_120. Not yet wired into vLLM-Omni; revisit when stable lands.
+Install the optional CUDA 13 extra to use the CuTe-based FA4 path:
+
+```bash
+pip install 'vllm-omni[fa4]'
+```
+
+`FLASH_ATTN` prefers `flash_attn.cute` on Blackwell and falls back to FA3/FA2
+when it is unavailable. Version `4.0.0b18` is required; earlier beta
+wheels had known JIT failures on Blackwell.
 
 ## Choosing a Backend Manually
 
@@ -353,64 +403,3 @@ DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN_3 python examples/offline_inference/text_t
     --tensor-parallel-size 2 \
     --output outputs/hv15_sage3.mp4
 ```
-
-### Mixed backends across roles
-
-Use `FLASH_ATTN` for self-attention and `TORCH_SDPA` for cross-attention:
-
-```bash
-python examples/offline_inference/text_to_video/text_to_video.py \
-    --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
-    --prompt "A dog running across a field of golden wheat." \
-    --diffusion-attention-config.per_role.self.backend FLASH_ATTN \
-    --diffusion-attention-config.per_role.cross.backend TORCH_SDPA \
-    --tensor-parallel-size 2 \
-    --output outputs/wan22_mixed.mp4
-```
-
-### Compare against FlashAttention
-
-Unset the backend override, or explicitly use `FLASH_ATTN`:
-
-```bash
-python examples/offline_inference/text_to_video/text_to_video.py \
-    --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
-    --prompt "A dog running across a field of golden wheat." \
-    --height 704 --width 1280 --num-frames 49 \
-    --num-inference-steps 30 --seed 42 --guidance-scale 5.0 \
-    --tensor-parallel-size 2 \
-    --output outputs/wan22_fa3.mp4
-```
-
-## Validation Guidance
-
-Don't assume a faster attention backend is numerically interchangeable with `TORCH_SDPA`.
-
-Always compare:
-
-- End-to-end runtime
-- Diffusion-stage runtime (`add_req_and_wait` line in DiffusionEngine.step breakdown)
-- Output quality against a known-good baseline (CLIP similarity, frame-level diff, or visual review)
-
-At minimum, keep the same:
-
-- model
-- prompt
-- seed
-- resolution
-- frame count / step count
-- parallel config (TP / CFG-parallel / Ulysses degrees)
-
-## Reproducing the Benchmark Table
-
-The end-to-end numbers above were collected by running `text_to_video.py` /
-`text_to_image.py` with the same prompt and seed while varying
-`DIFFUSION_ATTENTION_BACKEND`. For a quick kernel-level comparison of the
-backends without loading a model:
-
-```bash
-python benchmarks/diffusion/bench_attention_backends.py --preset hv15
-```
-
-It runs all three BF16 backends on representative DiT attention shapes and
-prints a ranking table at the end.

docs/user_guide/diffusion/cpu_offload_diffusion.md

@@ -179,7 +179,134 @@ class Flux2Transformer2DModel(nn.Module):
 - Support single GPU only for now
 
 
-### Implementation Notes
+## Distributed Layerwise Offloading
+
+### How It Works
+
+Distributed layerwise offloading extends single-GPU layerwise offloading to
+multi-device deployments.  Each DP rank stores only **1/dp_size** of the model
+weights in host memory; full layer weights are reconstructed at runtime via
+**AllGather** on a dedicated communication stream, overlapped with computation
+via a fixed double-buffer scheme.
+
+**Key features:**
+- **Weight sharding + AllGather**: each rank stores 1/dp_size of weights,
+  reconstructed per-layer via `all_gather_into_tensor`
+- **Fixed double-buffer**: exactly 2 transformer blocks on each device at any
+  time, regardless of model size
+- **DP multi-concurrency**: N concurrent requests processed in parallel
+  (AllGather only gathers weight shards, which are request-independent)
+- **mmap weight loading**: weights loaded as mmap views pointing to shared OS
+  page cache, eliminating O(dp_size × model_size) RSS during model creation
+- **Platform-agnostic**: works on NVIDIA GPU (CUDA/NCCL) and Ascend NPU
+  (CANN/HCCL) via vLLM-Omni's platform abstraction
+
+**Execution Flow (per device):**
+
+```
+Compute Stream:  [Layer N]          [Layer N+1]          [Layer N+2]
+H2D Stream:      [H2D Shard N+1]   [H2D Shard N+2]
+AllGather:       [AG N+1]          [AG N+2]
+Slot Usage:      Slot0: Layer N    Slot1: Layer N+1
+```
+
+### Usage
+
+**CLI:**
+```bash
+# 4× GPU/NPU with AllGather (recommended)
+vllm serve /path/to/model --omni \
+  --enable-distributed-layerwise-offload \
+  --data-parallel-size 4
+
+# Without AllGather (each rank loads full weights, no sharding)
+vllm serve /path/to/model --omni \
+  --enable-distributed-layerwise-offload \
+  --data-parallel-size 4 \
+  --dlo-no-use-allgather
+
+# With SP instead of DP (long sequences)
+vllm serve /path/to/model --omni \
+  --enable-distributed-layerwise-offload \
+  --usp 4
+```
+
+**Python API:**
+```python
+from vllm_omni import Omni
+
+m = Omni(
+    model="/path/to/model",
+    enable_distributed_layerwise_offload=True,
+    dlo_use_allgather=True,  # default
+)
+```
+
+### CLI Flags
+
+| Flag | Description | Default |
+|------|-------------|---------|
+| `--enable-distributed-layerwise-offload` | Enable DLO | `false` |
+| `--data-parallel-size N` | Number of DP ranks for weight sharding | `1` |
+| `--dlo-use-allgather` | Shard + AllGather (saves CPU, requires concurrent requests) | `true` |
+| `--dlo-no-use-allgather` | Full weights per rank (no sharding, no AllGather) | `false` |
+
+### How model weights are loaded (mmap path)
+
+When DLO + AllGather is active, the offloader:
+
+1. **Saves non-persistent buffers** (e.g. RoPE `inv_freq`, timestep `freqs`)
+   from the normally-created transformer
+2. **Converts to meta device** via `to_empty(device="meta")`, releasing random
+   initialization weights
+3. **Loads checkpoint weights as mmap views** via `safe_open().get_tensor()`,
+   which return views into the OS page cache (shared across all ranks, 0 RSS)
+4. **Calls `post_load_weights()`** to apply model-specific dtype conversions
+   (e.g. Cosmos3's `time_embedder` → FP32)
+5. **Restores non-persistent buffers** from saved copies
+
+This approach requires **zero model-specific code changes** — no pipeline or
+transformer modifications are needed.
+
+### OffloadPlan (declarative topology metadata)
+
+Models can optionally declare an `OffloadPlan` class variable to provide
+topology metadata (block attribute names, submodules to offload) without
+any offload-specific logic:
+
+```python
+from vllm_omni.diffusion.offloader import OffloadPlan
+
+class MyPipeline(nn.Module):
+    _dit_modules = ["transformer"]
+    _offload_plan = OffloadPlan(
+        block_attrs={"transformer": ("blocks",)},
+        offload_submodules={"context_encoder": "layers"},
+    )
+```
+
+When not declared, the offloader falls back to `_layerwise_offload_blocks_attrs`
+and heuristic attribute search.  This is backward-compatible — existing models
+work without any changes.
+
+### DP Multi-concurrency
+
+When `--data-parallel-size > 1` and AllGather is enabled, the scheduler batches
+up to `dp_size` requests per denoise step.  Each DP rank processes a different
+request while AllGather synchronizes only weight shards (request-independent).
+
+**Requirements for concurrent requests:**
+- `num_inference_steps` must be specified explicitly (None is not allowed)
+- All concurrent requests must have the same `num_inference_steps` value
+- These constraints exist because AllGather is a collective that requires
+  every rank to participate at each denoise step
+
+### Limitations
+- Online quantization (FP8) is incompatible with mmap loading — falls back to
+  regular `load_weights()` automatically
+- Tensor Parallel is not supported (DLO uses DP-based sharding)
+- HSDP + AllGather is rejected (would double-shard weights)
+- `num_inference_steps=None` is not allowed in DP multi-concurrency mode
 
 **Module Discovery**
 
@@ -208,7 +335,10 @@ OffloadBackend (base class)
 ├── ModelLevelOffloadBackend → uses SequentialOffloadHook (.to() swap)
 │                              (delegates to a pipeline's enable_omni_model_cpu_offload
 │                               for split models like Cosmos3)
-└── LayerWiseOffloadBackend → uses LayerwiseOffloadHook
+├── LayerWiseOffloadBackend → uses LayerwiseOffloadHook
+│                          (single-GPU, full weights on host)
+└── DistributedLayerwiseOffloadBackend → uses DistributedLayerwiseOffloadHook
+                                         (multi-GPU, 1/dp_size sharded weights + AllGather)
 ```
 
 Factory function `get_offload_backend()` selects the appropriate backend based on
@@ -221,20 +351,22 @@ reasoner/generator components inside the model forward pass.
 
 ## Supported Models
 
-| Architecture | Example Models | DiT Class | Model-Level Offload | Layerwise Offload | Blocks Attrs (Layerwise specific) |
-|--------------|----------------|-----------|---------------------|-------------------|-----------------------------------|
-| Flux2Pipeline | `black-forest-labs/FLUX.2-dev` | `Flux2Transformer2DModel` | ✓ | ✓ | `"transformer_blocks"`, `"single_transformer_blocks"` |
-| LongCatImagePipeline | `meituan-longcat/LongCat-Image` | `LongCatImageTransformer2DModel` | - | ✓ | `"transformer_blocks"`, `"single_transformer_blocks"` |
-| NextStep11Pipeline | `stepfun-ai/NextStep-1.1` | `NextStepModel` | - | ✓ | `"layers"` |
-| OvisImagePipeline | `AIDC-AI/Ovis-Image-7B` | `OvisImageTransformer2DModel` | - | ✓ | `"transformer"` |
-| QwenImagePipeline | `Qwen/Qwen-Image` | `QwenImageTransformer2DModel` | ✓ | ✓ | `"transformer_blocks"` |
-| StableDiffusionXLPipeline | `stabilityai/stable-diffusion-xl-base-1.0` | `SDXLUNet2DConditionModel` | ✓ | ✓ | `"down_blocks"`, `"up_blocks"` |
-| StableDiffusion3Pipeline | `stabilityai/stable-diffusion-3.5-medium` | `SD3Transformer2DModel` | - | ✓ | `"transformer_blocks"` |
-| Wan22I2VPipeline | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | `WanTransformer3DModel` | ✓ | ✓ | `"blocks"` |
-| Wan22Pipeline | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | `WanTransformer3DModel` | ✓ | ✓ | `"blocks"` |
-| SoulXSingerPipeline / SoulXSingerSVCPipeline | `Soul-AILab/SoulX-Singer` | `DiffLlama` (`cfm_decoder.model.diff_estimator`) | ✓ | ✓ | `"layers"` |
-| BagelPipeline | `ByteDance-Seed/BAGEL-7B-MoT` | `Qwen2MoTModel` | - | ✓ | `"layers"`, `"customized modules"` |
+| Architecture | Example Models | DiT Class | Model-Level Offload | Layerwise Offload | Distributed Layerwise Offload | Blocks Attrs (Layerwise specific) |
+|--------------|----------------|-----------|---------------------|-------------------|-------------------------------|-----------------------------------|
+| Flux2Pipeline | `black-forest-labs/FLUX.2-dev` | `Flux2Transformer2DModel` | ✓ | ✓ | - | `"transformer_blocks"`, `"single_transformer_blocks"` |
+| LongCatImagePipeline | `meituan-longcat/LongCat-Image` | `LongCatImageTransformer2DModel` | - | ✓ | - | `"transformer_blocks"`, `"single_transformer_blocks"` |
+| NextStep11Pipeline | `stepfun-ai/NextStep-1.1` | `NextStepModel` | - | ✓ | - | `"layers"` |
+| OvisImagePipeline | `AIDC-AI/Ovis-Image-7B` | `OvisImageTransformer2DModel` | - | ✓ | - | `"transformer"` |
+| QwenImagePipeline | `Qwen/Qwen-Image` | `QwenImageTransformer2DModel` | ✓ | ✓ | - | `"transformer_blocks"` |
+| StableDiffusionXLPipeline | `stabilityai/stable-diffusion-xl-base-1.0` | `SDXLUNet2DConditionModel` | ✓ | ✓ | - | `"down_blocks"`, `"up_blocks"` |
+| StableDiffusion3Pipeline | `stabilityai/stable-diffusion-3.5-medium` | `SD3Transformer2DModel` | - | ✓ | - | `"transformer_blocks"` |
+| Wan22I2VPipeline | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | `WanTransformer3DModel` | ✓ | ✓ | - | `"blocks"` |
+| Wan22Pipeline | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | `WanTransformer3DModel` | ✓ | ✓ | - | `"blocks"` |
+| SoulXSingerPipeline / SoulXSingerSVCPipeline | `Soul-AILab/SoulX-Singer` | `DiffLlama` (`cfm_decoder.model.diff_estimator`) | ✓ | ✓ | - | `"layers"` |
+| BagelPipeline | `ByteDance-Seed/BAGEL-7B-MoT` | `Qwen2MoTModel` | - | ✓ | - | `"layers"`, `"customized modules"` |
+| Cosmos3OmniDiffusersPipeline | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | `Cosmos3VFMTransformer`, `Cosmos3LanguageModel` | ✓ | ✓ | ✓ | `"layers"`, `"gen_layers"` |
 
 **Notes:**
 - Model-Level Offloading is expected to be supported by all common diffusion models (DiT and encoders) naturally
 - Layerwise Offloading requires DiT class to define `_layerwise_offload_blocks_attrs` pointing to transformer blocks
+- Distributed Layerwise Offloading works with any model that supports Layerwise Offloading — no additional model changes required.  See [Cosmos3 DistOffload recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/cosmos3/Cosmos3-DistOffload.md) for usage examples.

docs/user_guide/examples/offline_inference/image_to_video.md

@@ -4,7 +4,7 @@ Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inf
 
 
 This example demonstrates how to generate videos from images using Wan2.2,
-LTX-2/LTX-2.3, and HunyuanVideo-1.5 Image-to-Video models with vLLM-Omni's
+LTX-2, and HunyuanVideo-1.5 Image-to-Video models with vLLM-Omni's
 offline inference API.
 
 ## Local CLI Usage
@@ -53,34 +53,22 @@ python image_to_video.py \
   --output i2v_output.mp4
 ```
 
-### LTX-2.3
+### LTX-2
 
 ```bash
 python image_to_video.py \
-  --model diffusers/LTX-2.3-Diffusers \
-  --model-class-name LTX23ImageToVideoPipeline \
+  --model Lightricks/LTX-2 \
   --image cherry_blossom.jpg \
   --prompt "Cherry blossoms swaying gently in the breeze with synchronized ambient sound" \
-  --negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
-  --height 384 \
-  --width 512 \
-  --num-frames 25 \
-  --guidance-scale 4.0 \
-  --num-inference-steps 20 \
-  --frame-rate 24 \
-  --fps 24 \
-  --output ltx23_i2v_output.mp4
+  --output ltx2_i2v_output.mp4
 ```
 
-Use the Diffusers-format checkpoint `diffusers/LTX-2.3-Diffusers`; the
-upstream `Lightricks/LTX-2.3` raw safetensors repo is not directly loadable by
-this pipeline. Pass `--model-class-name LTX23ImageToVideoPipeline` to select
-the LTX-2.3 image-to-video pipeline.
+See the [LTX-2 recipe](../../../../recipes/LTX/LTX-2.md) for all checkpoints,
+pipeline selection, T2V, defaults, and advanced options.
 
 Key arguments:
 
-- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V, or
-  LTX-2/LTX-2.3).
+- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V, or LTX-2).
 - `--image`: Path to input image (required).
 - `--prompt`: Text description of desired motion/animation.
 - `--height/--width`: Output resolution (auto-calculated from image if not set).

docs/user_guide/examples/offline_inference/text_to_video.md

@@ -12,7 +12,7 @@ For backend selection and SageAttention usage, see the [Diffusion Attention Back
 | Model | Default Resolution | Default Frames | Default Steps | Guidance | VRAM (BF16) |
 |---|---|---|---|---|---|
 | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 720x1280 | 81 | 40 | 4.0 | ~60 GiB |
-| `diffusers/LTX-2.3-Diffusers` | 384x512 | 25 | 20 | 4.0 | 96GB-class GPU |
+| `Lightricks/LTX-2` | 512x768 | 121 | 40 | video 3.0 / audio 7.0 | Model-dependent |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | 480x832 | 121 | 50 | 6.0 | 1×A100 80GB |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | 720x1280 | 121 | 50 | 6.0 | FP8 + VAE tiling required |
 
@@ -35,45 +35,17 @@ python text_to_video.py \
   --output t2v_out.mp4
 ```
 
-LTX2 example:
+### LTX-2
 
 ```bash
 python text_to_video.py \
-  --model "Lightricks/LTX-2" \
-  --prompt "A cinematic close-up of ocean waves at golden hour." \
-  --negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
-  --height 512 \
-  --width 768 \
-  --num-frames 121 \
-  --num-inference-steps 40 \
-  --guidance-scale 4.0 \
-  --frame-rate 24 \
-  --output ltx2_out.mp4
-```
-
-### LTX-2.3
-
-```bash
-python text_to_video.py \
-  --model diffusers/LTX-2.3-Diffusers \
-  --model-class-name LTX23Pipeline \
+  --model Lightricks/LTX-2 \
   --prompt "Cherry blossoms swaying gently in the breeze with synchronized ambient sound" \
-  --negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
-  --height 384 \
-  --width 512 \
-  --num-frames 25 \
-  --num-inference-steps 20 \
-  --guidance-scale 4.0 \
-  --frame-rate 24 \
-  --fps 24 \
-  --audio-sample-rate 48000 \
-  --output ltx23_t2v_output.mp4
+  --output ltx2_output.mp4
 ```
 
-Use the Diffusers-format checkpoint `diffusers/LTX-2.3-Diffusers`; the
-upstream `Lightricks/LTX-2.3` raw safetensors repo is not directly loadable by
-this pipeline. Pass `--model-class-name LTX23Pipeline` to select the LTX-2.3
-text-to-video pipeline explicitly.
+See the [LTX-2 recipe](../../../../recipes/LTX/LTX-2.md) for all checkpoints,
+pipeline selection, I2V, defaults, and advanced options.
 
 ### HunyuanVideo-1.5 (480p)
 
@@ -135,8 +107,7 @@ python text_to_video.py \
 ### Common
 
 - `--model`: Diffusers model ID or local path.
-- `--model-class-name`: Optional explicit pipeline class. Use `LTX23Pipeline`
-  for LTX-2.3 text-to-video.
+- `--model-class-name`: Optional explicit pipeline override.
 - `--prompt`: text description (string).
 - `--height/--width`: output resolution. Default depends on model.
 - `--num-frames`: number of frames. Default depends on model.
@@ -151,8 +122,7 @@ python text_to_video.py \
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 - `--enable-layerwise-offload`: enable layerwise offloading on DiT modules.
 - `--frame-rate`: generation FPS for pipelines that require it (e.g., LTX2).
-- `--audio-sample-rate`: audio sample rate for embedded audio (when the
-  pipeline returns audio; LTX-2.3 outputs 48kHz audio).
+- `--audio-sample-rate`: fallback audio sample rate when the pipeline returns audio.
 - `--quantization`: quantization method (`fp8` for FP8, `gguf` for GGUF).
 - `--flow-shift`: scheduler flow_shift parameter.
 

docs/user_guide/examples/online_serving/bagel.md

@@ -40,7 +40,7 @@ bash run_server_stage_cli.sh --stage 0
 bash run_server_stage_cli.sh --stage 1
 ```
 
-To use a custom deploy YAML (note: `--stage-configs-path` is deprecated in favor of `--deploy-config`):
+To use a custom deploy YAML:
 
 ```bash
 vllm serve ByteDance-Seed/BAGEL-7B-MoT --omni --port 8091 \

docs/user_guide/examples/online_serving/glm_image.md

@@ -11,8 +11,8 @@ memory, sampling params) live in `vllm_omni/deploy/glm_image.yaml`.
 vllm serve zai-org/GLM-Image --omni --port 8091
 ```
 
-The config system auto-detects the pipeline from the model's `model_index.json` — no
-manual `--stage-configs-path` or `--deploy-config` needed.
+The config system auto-detects the pipeline from the model's `model_index.json`, so
+no manual `--deploy-config` is needed.
 
 By default, stage 0 (AR) runs on GPU 0 and stage 1 (Diffusion) on GPU 1. To colocate
 both stages on a single GPU, override per stage:

docs/user_guide/examples/online_serving/qwen2_5_omni.md

@@ -15,9 +15,9 @@ Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-If you have custom stage configs file, launch the server with command below
+To use a custom deploy config, launch the server with the command below:
 ```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
+vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
 ### Send Multi-modal Request
@@ -182,7 +182,7 @@ The script supports the following arguments:
 - `--model`: Model name/path (default: Qwen/Qwen2.5-Omni-7B)
 - `--server-port`: Port for vLLM server (default: 8091)
 - `--gradio-port`: Port for Gradio demo (default: 7861)
-- `--stage-configs-path`: Path to custom stage configs YAML file (optional)
+- `--deploy-config`: Path to a custom deploy config YAML file (optional)
 - `--server-host`: Host for vLLM server (default: 0.0.0.0)
 - `--gradio-ip`: IP for Gradio demo (default: 127.0.0.1)
 - `--share`: Share Gradio demo publicly (creates a public link)
@@ -195,9 +195,9 @@ The script supports the following arguments:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-If you have custom stage configs file:
+To use a custom deploy config:
 ```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
+vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
 **Step 2: Run the Gradio demo**

docs/user_guide/examples/online_serving/qwen3_omni.md

@@ -306,7 +306,7 @@ The gradio script supports the following arguments:
     ``````
 ??? abstract "qwen3_omni_moe_thinking.yaml"
     ``````yaml
-    --8<-- "examples/online_serving/qwen3_omni/qwen3_omni_moe_thinking.yaml"
+    --8<-- "vllm_omni/deploy/qwen3_omni_moe_thinking.yaml"
     ``````
 ??? abstract "run_curl_multimodal_generation.sh"
     ``````sh

docs/user_guide/examples/online_serving/text_to_speech.md

@@ -422,12 +422,19 @@ curl -X POST http://localhost:8091/v1/audio/speech \
 Raw PCM streaming requires `stream_format="audio"`, `response_format="pcm"`, and `async_chunk: true` on the stage config (default in `qwen3_tts.yaml`). `speed` is not supported when streaming.
 
 ### Streaming WebSocket
-The `/v1/audio/speech/stream` endpoint accepts text incrementally, splits it at sentence boundaries, and emits one PCM stream per sentence:
+The `/v1/audio/speech/stream` endpoint accepts text incrementally and synthesizes the buffered text as one continuous request on `input.done`:
 ```bash
 python qwen3_tts/streaming_speech_client.py --text "Hello world. How are you? I am fine."
 python qwen3_tts/streaming_speech_client.py --text "..." --simulate-stt --stt-delay 0.1
 ```
 
+`input.done` flushes without closing, so repeating `--text` synthesizes several utterances over one connection:
+```bash
+python qwen3_tts/streaming_speech_client.py \
+    --text "First utterance." \
+    --text "Second utterance, same connection."
+```
+
 ### Gradio demos
 ```bash
 ./qwen3_tts/run_gradio_demo.sh                              # CustomVoice (default)

docs/user_guide/examples/online_serving/text_to_video.md

@@ -13,7 +13,6 @@ This example demonstrates how to deploy text-to-video models for online video ge
 | Wan2.1 T2V (14B) | `Wan-AI/Wan2.1-T2V-14B-Diffusers` |
 | Wan2.2 T2V | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` |
 | LTX-2 | `Lightricks/LTX-2` |
-| LTX-2.3 | `diffusers/LTX-2.3-Diffusers` |
 
 ## Wan2.2 T2V
 
@@ -192,7 +191,7 @@ curl -X POST http://localhost:8091/v1/videos \
 | `negative_prompt`     | str    | None    | Negative prompt                                  |
 | `width`               | int    | None    | Video width in pixels                            |
 | `height`              | int    | None    | Video height in pixels                           |
-| `num_frames`          | int    | None    | Number of frames to generate                     |
+| `num_frames`          | int    | 1       | Number of frames to generate; set explicitly for video generation |
 | `fps`                 | int    | None    | Frames per second for output video               |
 | `num_inference_steps` | int    | None    | Number of denoising steps                        |
 | `guidance_scale`      | float  | None    | CFG guidance scale (low-noise stage)             |
@@ -290,101 +289,12 @@ done
 
 ## LTX-2
 
-### Start Server
-
-#### Basic Start
-
 ```bash
-vllm serve Lightricks/LTX-2 --omni --port 8098 \
-    --enforce-eager --boundary-ratio 1.0
+vllm serve Lightricks/LTX-2 --omni --port 8098
 ```
 
-For multi-GPU memory reduction, you can enable HSDP:
-
-```bash
-vllm serve Lightricks/LTX-2 --omni --port 8098 \
-    --enforce-eager --boundary-ratio 1.0 \
-    --use-hsdp --hsdp-shard-size 2
-```
-
-#### Start with Optimization Presets
-
-Use the LTX-2 startup script with built-in optimization presets:
-
-```bash
-# Baseline (1 GPU, eager)
-bash run_server_ltx2.sh baseline
-
-# 4-GPU Ulysses sequence parallelism (lossless)
-bash run_server_ltx2.sh ulysses4
-
-# Cache-DiT lossy acceleration (1 GPU, ~1.4× speedup)
-bash run_server_ltx2.sh cache-dit
-
-# Best combo: 4-GPU Ulysses SP + Cache-DiT (~2.2× speedup)
-bash run_server_ltx2.sh best-combo
-```
-
-#### Optimization Benchmarks
-
-Benchmarked on H800, online serving (480×768, 41 frames, 20 steps, `seed=42`).
-"Inference" is the server-reported inference time; excludes HTTP/poll overhead.
-
-| Preset | Server Command | Inference (s) | Speedup | Type |
-|--------|---------------|---------------|---------|------|
-| `baseline` | `--enforce-eager` | 10.3 | 1.00× | — |
-| `compile` | *(default, no --enforce-eager)* | ~10.3 (warm) | ~1.00× | Lossless |
-| `ulysses4` | `--enforce-eager --usp 4` | ~10.3 | ~1.00× | Lossless |
-| `cache-dit` | `--enforce-eager --cache-backend cache_dit` | 7.4 avg | ~1.4× | Lossy |
-| `best-combo` | `--enforce-eager --usp 4 --cache-backend cache_dit` | 4.7 avg | **~2.2×** | Lossless + Lossy |
-
-**Observations**:
-- **torch.compile**: On H800, warm-request inference time matches the eager baseline (~10.3s).
-  The first request pays ~6s compilation overhead. Benefit depends on model architecture and GPU.
-- **Ulysses SP (4 GPU)**: No measurable speedup alone for 41-frame generation at this resolution.
-  Communication overhead outweighs gains at this sequence length.
-- **Cache-DiT**: Inference varies per request (6–10s) due to dynamic caching decisions.
-  Average is ~7.4s (~1.4× speedup) with slight quality tradeoff.
-- **Best combo**: 4-GPU Ulysses SP + Cache-DiT synergize well — Cache-DiT reduces per-step
-  computation, making the communication overhead of Ulysses SP worthwhile. Average ~4.7s
-  (~2.2× speedup).
-- **FP8 quantization**: Reduces VRAM but does not speed up LTX-2 on H800 (compute-bound).
-
-**Deployment Recommendations**:
-- For **production with quality priority**: use `baseline` with `--enforce-eager`
-- For **maximum throughput** (4 GPUs, quality tradeoff): use `best-combo` (~2.2× speedup)
-- For **single-GPU throughput**: use `cache-dit` (~1.4× speedup)
-- `--enforce-eager` is recommended to avoid torch.compile warmup latency on first request
-
-### Send Requests (curl)
-
-```bash
-# Using the provided script
-bash run_curl_ltx2.sh
-
-# Or directly
-curl -sS -X POST http://localhost:8098/v1/videos \
-  -H "Accept: application/json" \
-  -F "prompt=A serene lakeside sunrise with mist over the water." \
-  -F "width=768" \
-  -F "height=480" \
-  -F "num_frames=41" \
-  -F "fps=24" \
-  -F "num_inference_steps=20" \
-  -F "guidance_scale=3.0" \
-  -F "seed=42"
-```
-
-## LTX-2.3
-
-LTX-2.3 uses a Diffusers-format checkpoint and a dedicated pipeline class:
-
-```bash
-vllm serve diffusers/LTX-2.3-Diffusers --omni --port 8098 \
-    --model-class-name LTX23Pipeline
-```
-
-Send requests through the same `/v1/videos` API shown in the LTX-2 example.
+See the [LTX-2 recipe](../../../../recipes/LTX/LTX-2.md) for all checkpoints,
+pipeline selection, requests, defaults, and advanced options.
 
 ## Example materials
 

docs/user_guide/fault_injection_reliability_matrix.md

@@ -69,19 +69,19 @@ The behaviors and conclusions above are summarized from current fault injection
 
 ### Example: `SIGTERM` fault injection log (Qwen3-Omni)
 
-The following full log is from a real run where a `SIGTERM` signal was sent to the serve root process.
+The following log is based on a real run where a `SIGTERM` signal was sent to the serve root process. Its command line has been normalized to the current `--deploy-config` option.
 
 ```text
-Launching OmniServer with: /workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --stage-configs-path vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
+Launching OmniServer with: /workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --deploy-config vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
 Server ready on 127.0.0.1:60675 (OmniServer startup took 206.023s)
 OmniServer started successfully
-[reliability][process-kill] current_server_proc pid=556855 name=python3 cmdline=/workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --stage-configs-path vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
+[reliability][process-kill] current_server_proc pid=556855 name=python3 cmdline=/workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --deploy-config vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
 [reliability][process-kill] current_server_proc pid=557118 name=python3 cmdline=/workspace/.venv/bin/python3 -c from multiprocessing.resource_tracker import main;main(57)
 [reliability][process-kill] current_server_proc pid=557119 name=VLLM::StageEngineCoreProc_noid_replica0_DP0 cmdline=VLLM::StageEngineCoreProc_noid_replica0_DP0
 [reliability][process-kill] current_server_proc pid=557122 name=VLLM::StageEngineCoreProc_noid_replica0_DP0 cmdline=VLLM::StageEngineCoreProc_noid_replica0_DP0
 [reliability][process-kill] current_server_proc pid=558201 name=VLLM::StageEngineCoreProc_noid_replica0_DP0 cmdline=VLLM::StageEngineCoreProc_noid_replica0_DP0
 
-[reliability][process-kill] root-kill pid=556855 name=python3 signal=SIGTERM cmdline=/workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --stage-configs-path vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
+[reliability][process-kill] root-kill pid=556855 name=python3 signal=SIGTERM cmdline=/workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --deploy-config vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
 [0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [omni_base.py:463] [AsyncOmni] Shutting down
 [0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [async_omni_engine.py:2397] [AsyncOmniEngine] Shutting down Orchestrator
 [0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [orchestrator.py:351] [Orchestrator] Received shutdown signal

examples/model_display_names.yml

@@ -8,5 +8,4 @@ mimo_audio: MiMo-Audio
 ming_flash_omni: Ming-flash-omni 2.0
 qwen2_5_omni: Qwen2.5-Omni
 qwen3_omni: Qwen3-Omni
-sensenova_u1: SenseNova-U1
 step_audio2: Step-Audio2

examples/offline_inference/audex/README.md

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+# Audex (Nemotron-Labs-Audex-2B) offline inference
+
+One checkpoint, four tasks — each script builds its own `Omni` engine with
+the right deploy yaml, so they run as plain `python <script>` with no extra
+flags. Pass the HF repo ROOT as `--model` (default
+`nvidia/Nemotron-Labs-Audex-2B`); per-stage subfolders and download subsets
+resolve automatically.
+
+| script | pipeline (`vllm_omni/deploy/<name>.yaml`) | audio in | text out | speech out | general audio out |
+|---|---|---|---|---|---|
+| `text_to_speech.py` | `audex_tts` | ❌ | ❌ | ✅ | ❌ |
+| `text_to_audio.py` | `audex_tta` | ❌ | ❌ | ❌ | ✅ |
+| `audio_qa.py` | `audex_thinker_only` | ✅ | ✅ | ❌ | ❌ |
+| `speech_to_speech.py` | `audex_s2s` | ✅ | ✅ | ✅ | ❌ |
+
+Shared plumbing (corpus/WAV IO, tokenizer loading, the CFG cond/uncond
+pair contract) lives in `common.py`; each task script keeps only its own
+prompt recipe and flow. Note `text_to_audio.py` here drives the Audex
+two-stage AR pipeline — for diffusion text-to-audio models (Stable Audio
+Open, AudioX) use the generic
+`examples/offline_inference/text_to_audio/text_to_audio.py` instead.
+
+## text_to_speech.py — text → speech
+
+Thinker generates `<speechcodec_N>` tokens; the streaming causal decoder
+turns them into 16 kHz WAVs (one file per prompt, slugified filename).
+
+```bash
+# three built-in demo sentences -> results/audex_wavs/
+python examples/offline_inference/audex/text_to_speech.py
+
+# your own texts, with classifier-free guidance (official quality setting)
+python examples/offline_inference/audex/text_to_speech.py \
+    --texts "Hello there." "Nice to meet you." --cfg-scale 1.5 \
+    --output-dir results/my_tts
+```
+
+Key flags: `--texts` / `--texts-file` (TSV `utt_id<TAB>text`),
+`--cfg-scale` (default 1.0 = off; 1.5 recommended — guided requests run
+one at a time, each paired with a length-matched null prompt),
+`--temperature` (overrides the deploy yaml's stage-0 default).
+
+## text_to_audio.py — caption → general audio (sound effects)
+
+Same thinker over the interleaved 4-codebook `<audiocodec_N>` RVQ block,
+decoded by the external XCodec1 checkpoint. CFG is effectively mandatory
+(default scale 3.0). Clips are capped at `--codec-cap 4000` codec tokens
+(10 s).
+
+```bash
+# three built-in demo captions -> results/audex_tta_wavs/
+python examples/offline_inference/audex/text_to_audio.py
+
+python examples/offline_inference/audex/text_to_audio.py \
+    --captions "Thunder rolling across a valley." --output-dir results/my_tta
+```
+
+XCodec1 resolves from `--xcodec1-path`, the `XCODEC1_PATH` env var, or the
+default `hf-audio/xcodec-hubert-general-balanced` repo (downloaded on
+first use).
+
+## audio_qa.py — audio (+ instruction) → text
+
+Single-stage audio understanding on the full checkpoint (NV-Whisper
+encoder + projector + LM). The default question is ASR
+("Transcribe the input speech."); without `--audio-files`, vLLM's public
+`mary_had_lamb` asset is transcribed.
+
+```bash
+# transcribe the built-in demo asset
+python examples/offline_inference/audex/audio_qa.py
+
+# your own clips / free-form QA
+python examples/offline_inference/audex/audio_qa.py \
+    --audio-files a.wav b.wav --question "What language is being spoken?"
+```
+
+## speech_to_speech.py — spoken question → spoken answer
+
+The official three-pass cascade over ONE `audex_s2s` deployment: ASR
+(audio → transcript, text-final) → chat (transcript → answer, text-final)
+→ TTS (answer → speech through the streaming decoder). Only the TTS pass
+touches the codec path. Without `--audio-file`, the `mary_had_lamb` asset
+is used as the spoken input.
+
+```bash
+# full cascade on the built-in demo asset -> results/audex_s2s_answer.wav
+python examples/offline_inference/audex/speech_to_speech.py
+
+python examples/offline_inference/audex/speech_to_speech.py \
+    --audio-file question.wav --output results/answer.wav --cfg-scale 1.5
+```
+
+`--cfg-scale` (default 1.5) applies to the TTS pass only; 1.0 disables it.
+
+Online-serving counterparts (server + HTTP clients) live in
+`examples/online_serving/audex/`.
+
+## 30B-A3B (nvidia/Nemotron-Labs-Audex-30B-A3B)
+
+The same four scripts serve the 30B MoE checkpoint (hybrid Mamba + MoE
+NemotronH thinker; identical token space and decoder). The 30B REQUIRES an
+explicit 30B deploy yaml — the model root's default resolution lands on the
+2B-tuned configs:
+
+    python examples/offline_inference/audex/text_to_speech.py \
+        --model nvidia/Nemotron-Labs-Audex-30B-A3B \
+        --deploy-config vllm_omni/deploy/audex_tts_30b.yaml
+
+Per-mode yamls: `audex_{tts,tta,thinker_only,s2s}_30b.yaml`. Defaults fit a
+single H100 80 GB (thinker + decoder share the card; prefix caching is off —
+hybrid Mamba); if long sequences OOM, set `tensor_parallel_size: 2` on
+stage 0. First run downloads ~60 GB. Verified on a single H100 80 GB: ~61 GiB
+weights + healthy KV headroom; TP2 was not needed.

examples/offline_inference/audex/audio_qa.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Offline Audex (Nemotron-Labs-Audex-2B) audio understanding example.
+
+Speech (or general audio) + text instruction → text, through the
+single-stage ``audex_thinker_only`` pipeline: NV-Whisper
+encoder + relu2 projector + 2B dense LM on ``checkpoint_folder_full``.
+
+The prompt format mirrors the official audioqa script: a ChatML user turn
+holding the ``<so_embedding>`` placeholder (expanded by the processor to
+750 embedding positions per 30 s clip) plus the instruction, with a closed
+``<think></think>`` priming.
+
+Examples (without --audio-files, vLLM's public ``mary_had_lamb`` asset is
+transcribed):
+
+    # Transcribe WAVs (ASR):
+    python examples/offline_inference/audex/audio_qa.py \\
+        --audio-files a.wav b.wav
+
+    # Free-form audio QA:
+    python examples/offline_inference/audex/audio_qa.py \\
+        --audio-files a.wav --question "What language is being spoken?"
+"""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+from common import default_deploy_config, load_audio_file, request_index
+from vllm.assets.audio import AudioAsset
+
+from vllm_omni import Omni
+
+ASR_QUESTION = "Transcribe the input speech."
+
+
+def build_prompt(question: str) -> str:
+    return f"<|im_start|>user\n<so_embedding>\n{question}<|im_end|>\n<|im_start|>assistant\n<think></think>"
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Offline Audex audio understanding")
+    parser.add_argument("--model", type=str, default="nvidia/Nemotron-Labs-Audex-2B")
+    parser.add_argument(
+        "--audio-files",
+        type=str,
+        nargs="+",
+        default=None,
+        help="Input clips. Defaults to vLLM's mary_had_lamb audio asset.",
+    )
+    parser.add_argument("--question", type=str, default=ASR_QUESTION)
+    parser.add_argument(
+        "--deploy-config",
+        type=str,
+        # The model root's default deploy yaml is the TTS pipeline; audio
+        # understanding needs the single-stage thinker-only pipeline.
+        default=default_deploy_config("audex_thinker_only.yaml"),
+        help="Deploy yaml (defaults to the audex_thinker_only pipeline).",
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    engine = Omni(model=args.model, deploy_config=args.deploy_config, trust_remote_code=True)
+
+    prompt_text = build_prompt(args.question)
+    if args.audio_files:
+        clips = [load_audio_file(path) for path in args.audio_files]
+    else:
+        clips = [AudioAsset("mary_had_lamb").audio_and_sample_rate]
+    prompts = []
+    for audio, sr in clips:
+        prompts.append(
+            {
+                "prompt": prompt_text,
+                "multi_modal_data": {"audio": (audio, sr)},
+            }
+        )
+
+    outputs = engine.generate(prompts)
+
+    ordered = sorted(outputs, key=request_index)
+    names = [Path(p).name for p in args.audio_files] if args.audio_files else ["mary_had_lamb"]
+    for name, req_output in zip(names, ordered):
+        text = req_output.outputs[0].text if req_output.outputs else ""
+        print(f"{name}: {text.strip()}")
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/audex/common.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Shared helpers for the offline Audex examples in this directory.
+
+Import style follows the repo's example convention (same-directory module,
+see ``text_to_speech/qwen3_tts/tts_common.py``): scripts run as files, so
+``from common import ...`` resolves via the script's own directory.
+"""
+
+from __future__ import annotations
+
+import copy
+import re
+from collections.abc import Callable
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import soundfile as sf
+import torch
+
+from vllm_omni import Omni
+from vllm_omni.model_executor.models.audex.prompt import build_null_prompt
+
+SAMPLE_RATE = 16_000
+_DEPLOY_DIR = Path(__file__).resolve().parents[3] / "vllm_omni" / "deploy"
+
+# Guided decoding sharpens the distribution, so the unguided temperature
+# (0.1) adds excess sampling noise under CFG. Measured on the en-24 gate
+# corpus at cfg 1.5: temp 0.1 -> CER 7.31%, temp 0.05 -> CER 6.87% (vs the
+# unguided baseline 7.24%).
+GUIDED_TEMPERATURE = 0.05
+
+
+def default_deploy_config(name: str) -> str:
+    """Absolute path of a deploy yaml bundled with vllm_omni."""
+    return str(_DEPLOY_DIR / name)
+
+
+def slugify(text: str, fallback: str = "prompt") -> str:
+    slug = re.sub(r"\s+", "_", text.strip().lower())
+    slug = re.sub(r"[^a-z0-9_]+", "", slug)
+    return slug[:48] or fallback
+
+
+def load_corpus(
+    corpus_file: str | None,
+    texts: list[str] | None,
+    default_texts: tuple[str, ...],
+) -> list[tuple[str, str]]:
+    """(utt_id, text) pairs from a TSV corpus file, CLI texts, or defaults."""
+    if corpus_file:
+        corpus = []
+        for line in Path(corpus_file).read_text().splitlines():
+            if not line.strip():
+                continue
+            utt, text = line.split("\t", 1)
+            corpus.append((utt, text.strip()))
+        return corpus
+    resolved = texts if texts else list(default_texts)
+    return [(slugify(t), t) for t in resolved]
+
+
+def load_audio_file(path: str) -> tuple[np.ndarray, int]:
+    """Mono float32 samples + native sample rate (processors resample)."""
+    audio, sr = sf.read(path, dtype="float32")
+    if audio.ndim == 2:
+        audio = audio.mean(axis=1)
+    return audio, sr
+
+
+def extract_pcm(multimodal_output: dict) -> torch.Tensor:
+    """Flatten one request's audio payload to a 1-D float32 CPU tensor."""
+    audio = multimodal_output.get("model_outputs")
+    if audio is None:
+        audio = multimodal_output.get("audio")
+    if audio is None:
+        raise ValueError(f"no audio key in multimodal_output: {list(multimodal_output.keys())}")
+    if isinstance(audio, list):
+        valid = [torch.as_tensor(a).float().cpu().reshape(-1) for a in audio if a is not None]
+        if not valid:
+            raise ValueError("audio list is empty")
+        return torch.cat(valid, dim=0) if len(valid) > 1 else valid[0]
+    return torch.as_tensor(audio).float().cpu().reshape(-1)
+
+
+def write_wav(path: Path, pcm: torch.Tensor, sample_rate: int = SAMPLE_RATE) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    arr = (np.clip(pcm.numpy(), -1.0, 1.0) * 32767.0).astype(np.int16)
+    sf.write(str(path), arr, sample_rate, format="WAV", subtype="PCM_16")
+
+
+def load_audex_tokenizer(model: str, profile: str = "tts", folder: str = "checkpoint_folder_audiogen"):
+    """Thinker tokenizer for CFG/TTA prompt building (resolves the snapshot)."""
+    from transformers import AutoTokenizer
+
+    from vllm_omni.model_executor.models.audex.checkpoint import ensure_audex_snapshot
+
+    root = ensure_audex_snapshot(model, profile=profile)
+    return AutoTokenizer.from_pretrained(str(Path(root) / folder), trust_remote_code=True)
+
+
+def request_index(req_output) -> int:
+    """Submission index from the numeric request-id prefix.
+
+    ``Omni.generate`` may return outputs out of submission order, and a
+    lexicographic sort would misplace "10_..." before "2_...".
+    """
+    match = re.search(r"(\d+)", str(req_output.request_id))
+    return int(match.group(1)) if match else 0
+
+
+def guided_stage_params(
+    engine: Omni,
+    cond_prompt: str,
+    tokenizer,
+    cfg_scale: float,
+    pair_id: str,
+    *,
+    temperature: float | None = None,
+    extra_args: dict[str, Any] | None = None,
+    null_prompt_builder: Callable[[str, Any], str] = build_null_prompt,
+) -> list:
+    """Stage sampling params carrying the CFG pair contract for ONE request.
+
+    Clones the engine's resolved defaults (never mutates the shared list),
+    applies ``extra_args`` unconditionally (e.g. the TTA RVQ contract), and
+    attaches the cond/uncond pair contract when ``cfg_scale > 1.0``. Guided
+    requests must go one at a time: each needs its own pair id and a
+    length-matched null prompt built from its own text.
+    """
+    params = copy.deepcopy(engine.resolve_sampling_params_list(None))
+    stage0 = params[0]
+    if temperature is not None:
+        stage0.temperature = temperature
+    if stage0.extra_args is None:
+        stage0.extra_args = {}
+    if extra_args:
+        stage0.extra_args.update(extra_args)
+    if cfg_scale > 1.0:
+        stage0.extra_args.update(
+            {
+                "cfg_scale": float(cfg_scale),
+                "cfg_role": "cond",
+                "cfg_pair_id": pair_id,
+                "cfg_null_prompt": null_prompt_builder(cond_prompt, tokenizer),
+            }
+        )
+    return params

examples/offline_inference/audex/speech_to_speech.py

@@ -0,0 +1,277 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Offline Audex (Nemotron-Labs-Audex-2B) cascaded speech-to-speech example.
+
+The official three-pass cascade over ONE ``audex_s2s``
+deployment:
+
+  1. ASR pass  (audio + "Transcribe the input speech." → transcript, text)
+  2. chat pass (transcript → answer, text)
+  3. TTS pass  (answer → speech, audio via the streaming causal decoder)
+
+Text passes carry ``modalities: ["text"]`` and finish at stage 0; only the
+TTS pass (``modalities: ["audio"]``) streams codec frames into code2wav.
+
+Example (without --audio-file, vLLM's public ``mary_had_lamb`` asset is
+used as the spoken input):
+
+    python examples/offline_inference/audex/speech_to_speech.py \\
+        --audio-file question.wav --output results/answer.wav
+"""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import re
+from pathlib import Path
+
+import torch
+from common import (
+    SAMPLE_RATE,
+    default_deploy_config,
+    guided_stage_params,
+    load_audex_tokenizer,
+    load_audio_file,
+    write_wav,
+)
+from vllm.assets.audio import AudioAsset
+
+from vllm_omni import Omni
+from vllm_omni.model_executor.models.audex.prompt import build_cond_prompt
+
+ASR_QUESTION = "Transcribe the input speech."
+SYSTEM_PROMPT = "You are a helpful and harmless assistant.\n\nYou are not allowed to use any tools."
+# The official chat-pass recipe below (SYSTEM_PROMPT + TEXT_INSTRUCTION
+# prefix, open <think> priming, temp 1.0/top_p 0.95, seed retry) applies to
+# the 30B-A3B ONLY (see is_30b()/_chat_prompt below and
+# tests/e2e/offline_inference/test_audex_s2s.py, which mirrors this same
+# split). It is load-bearing on the 30B: without the formatting-instruction
+# prefix a bare transcript turn is treated as a TTS request and the model
+# emits literal <speechcodec_N> tokens instead of a text answer (the system
+# prompt alone does not prevent this). The 2B must NOT use this recipe: it
+# is long-verified stable on a bare prompt + engine-default sampling, while
+# the official temp-1.0 seed-0 draw hallucinates off-language text (yet
+# closes </think> cleanly, so the seed retry never fires) and fails the
+# round-trip WER gate — measured, reproduced byte-identically.
+TEXT_INSTRUCTION = (
+    "SYSTEM & FORMATTING INSTRUCTION: You are Audex, created by NVIDIA based on the Nemotron-Cascade-2 architecture. "
+    "You may output your reasoning, followed by your final response. "
+    "You may format your reasoning block however you like. "
+    "However, your final response must strictly follow these rules: "
+    "* Write in plain, unformatted prose like a book or newspaper article. "
+    "* Do not use markdown, bullet points, lists, or headers. "
+    "* Standard numbers, abbreviations, and symbols are acceptable. "
+    "* [CRITICAL] You must press enter after every single sentence, placing each sentence on its own separate line."
+)
+
+
+def is_30b(model: str) -> bool:
+    # Users pass arbitrary repo IDs or local snapshot paths, so this is a
+    # heuristic (not an exact match against the official 30B-A3B repo ID)
+    # — good enough for an example script; keep in sync with the same
+    # branch in examples/online_serving/audex/client.py and
+    # tests/e2e/offline_inference/test_audex_s2s.py.
+    return "30b" in model.lower()
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Offline Audex cascaded S2S")
+    parser.add_argument("--model", type=str, default="nvidia/Nemotron-Labs-Audex-2B")
+    parser.add_argument(
+        "--audio-file",
+        type=str,
+        default=None,
+        help="Spoken question (WAV). Defaults to vLLM's mary_had_lamb audio asset.",
+    )
+    parser.add_argument("--output", type=str, default="results/audex_s2s_answer.wav")
+    parser.add_argument(
+        "--deploy-config",
+        type=str,
+        # The model root's default deploy yaml is the TTS-only pipeline; the
+        # cascade needs the audio-capable 2-stage full pipeline.
+        default=default_deploy_config("audex_s2s.yaml"),
+        help="Deploy yaml (defaults to the audex_s2s pipeline).",
+    )
+    parser.add_argument(
+        "--cfg-scale",
+        type=float,
+        default=1.5,
+        help="CFG strength for the TTS pass (1.0 disables; official setting 1.5).",
+    )
+    return parser.parse_args()
+
+
+def _chat_prompt(user_text: str) -> str:
+    # Official chat-pass recipe: system prompt + formatting instruction
+    # prefixed to the user text, thinking ENABLED via an open <think> block
+    # (the official demo's web UI ships "Enable reasoning" checked, i.e.
+    # build_chat_prompt(..., enable_thinking=True); chat_template.jinja then
+    # renders "<|im_start|>assistant\n<think>\n"). The instruction invites
+    # the model to reason before answering — priming the block open keeps
+    # that reasoning inside <think>...</think> so _extract_final_response
+    # below can drop it; with a closed block the 30B leaks the reasoning as
+    # plain text in front of the answer.
+    return (
+        f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
+        f"<|im_start|>user\n{TEXT_INSTRUCTION}\n\n{user_text}<|im_end|>\n"
+        "<|im_start|>assistant\n<think>\n"
+    )
+
+
+def _asr_prompt() -> str:
+    return f"<|im_start|>user\n<so_embedding>\n{ASR_QUESTION}<|im_end|>\n<|im_start|>assistant\n<think></think>"
+
+
+def _extract_final_response(text: str) -> str:
+    # Mirror the official demo's extract_spoken_answer
+    # (cascaded_s2s_web_server.py): keep only what follows the LAST closing
+    # think tag, falling back to the full text when the model skipped
+    # reasoning or never closed the block. Benign no-op for the 2B, whose
+    # answers carry no think block.
+    if "</think>" in text:
+        answer = text.rsplit("</think>", 1)[-1].strip()
+        if answer:
+            return answer
+    return text.strip()
+
+
+def _clean(text: str) -> str:
+    # The full checkpoint prefixes transcripts with a language tag sentence
+    # and quotes the content; extract the quoted payload when present. Span
+    # first-to-last quote (not split on every apostrophe) so contractions
+    # inside the transcript (e.g. "don't") survive intact.
+    text = text.strip()
+    match = re.search(r"'(.*)'", text, flags=re.S)
+    if match and match.group(1).strip():
+        return match.group(1).strip()
+    return text
+
+
+def _text_of(outputs) -> str:
+    (req_output,) = outputs
+    return (req_output.outputs[0].text or "").strip()
+
+
+CHAT_MAX_TOKENS = 2048  # official --text-max-tokens default
+# The official demo samples this pass unseeded; some trajectories deliberate
+# past the token budget without ever closing the think block, so retry a few
+# deterministic seeds until one closes (see the chat pass below).
+CHAT_SEEDS = (0, 1, 2)
+
+
+def _chat_params(engine: Omni, seed: int):
+    # Official demo text-pass sampling (cascaded_s2s_web_server.py defaults:
+    # --text-temperature 1.0, --text-top-p 0.95, --text-max-tokens 2048).
+    # The yaml stage-0 defaults (temperature 0.1, top_k 80) are TTS-thinker
+    # settings; under near-greedy decoding the 30B loops inside its
+    # reasoning block until the token budget runs out and never emits
+    # </think>, so the extracted "answer" would be truncated reasoning.
+    params = copy.deepcopy(engine.resolve_sampling_params_list(None))
+    stage0 = params[0]
+    stage0.temperature = 1.0
+    stage0.top_p = 0.95
+    stage0.top_k = -1
+    stage0.max_tokens = CHAT_MAX_TOKENS
+    stage0.seed = seed
+    return params
+
+
+def _chat_once(engine: Omni, prompt: str, seed: int) -> tuple[str, bool]:
+    (req_output,) = engine.generate([{"prompt": prompt, "modalities": ["text"]}], _chat_params(engine, seed))
+    out = req_output.outputs[0]
+    text = (out.text or "").strip()
+    truncated = getattr(out, "finish_reason", None) == "length" or len(out.token_ids or []) >= CHAT_MAX_TOKENS
+    return text, truncated
+
+
+def main():
+    args = parse_args()
+    engine = Omni(model=args.model, deploy_config=args.deploy_config, trust_remote_code=True)
+
+    tokenizer = None
+    if args.cfg_scale > 1.0:
+        tokenizer = load_audex_tokenizer(args.model, profile="full", folder="checkpoint_folder_full")
+
+    if args.audio_file:
+        audio, sr = load_audio_file(args.audio_file)
+    else:
+        audio, sr = AudioAsset("mary_had_lamb").audio_and_sample_rate
+
+    # Pass 1 — ASR (text-final; never touches the speech decoder).
+    transcript = _clean(
+        _text_of(
+            engine.generate(
+                [
+                    {
+                        "prompt": _asr_prompt(),
+                        "multi_modal_data": {"audio": (audio, sr)},
+                        "modalities": ["text"],
+                    }
+                ]
+            )
+        )
+    )
+    print(f"[1/3] transcript : {transcript}")
+    if not transcript:
+        raise SystemExit("ASR pass produced an empty transcript; aborting cascade")
+
+    # Pass 2 — chat answer (text-final). The recipe is size-conditional (see
+    # the TEXT_INSTRUCTION comment above): the 30B requires the official
+    # recipe, the 2B regresses under it.
+    if is_30b(args.model):
+        # Official recipe. The raw completion is
+        # <reasoning...></think><answer>; speak only the final response.
+        # Retry with the next seed only when the budget ran out before the
+        # reasoning block closed (a natural stop without </think> is a
+        # complete answer); if every seed spins, fall back to the official
+        # demo's behavior of keeping the full text.
+        raw = ""
+        for seed in CHAT_SEEDS:
+            raw, truncated = _chat_once(engine, _chat_prompt(transcript), seed)
+            if "</think>" in raw or not truncated:
+                break
+            print(f"[2/3] chat seed {seed} hit the token budget mid-reasoning; retrying")
+        answer = _extract_final_response(raw)
+    else:
+        # 2B: bare prompt with a closed think block, engine-default sampling
+        # (yaml near-greedy), no retry; the answer is used as-is.
+        chat_prompt = f"<|im_start|>user\n{transcript}<|im_end|>\n<|im_start|>assistant\n<think></think>"
+        answer = _text_of(engine.generate([{"prompt": chat_prompt, "modalities": ["text"]}]))
+    print(f"[2/3] answer     : {answer}")
+    if not answer:
+        raise SystemExit("Chat pass produced an empty answer; aborting cascade")
+
+    # Pass 3 — TTS (audio-final; streams through code2wav). Both final
+    # stages of the pipeline may emit an output record for the request;
+    # collect the audio-bearing one.
+    # The formatting instruction puts every sentence on its own line;
+    # flatten to plain prose for the TTS thinker.
+    tts_prompt = build_cond_prompt(re.sub(r"\s+", " ", answer).strip())
+    outputs = engine.generate(
+        [{"prompt": tts_prompt, "modalities": ["audio"]}],
+        guided_stage_params(engine, tts_prompt, tokenizer, args.cfg_scale, "s2s-tts-pass"),
+    )
+    chunks: list[torch.Tensor] = []
+    for req_output in outputs:
+        mm = getattr(req_output, "multimodal_output", None) or {}
+        if not mm and req_output.outputs:
+            mm = getattr(req_output.outputs[0], "multimodal_output", None) or {}
+        audio_val = mm.get("model_outputs", mm.get("audio"))
+        if audio_val is None:
+            continue
+        if isinstance(audio_val, list):
+            chunks.extend(torch.as_tensor(a).float().cpu().reshape(-1) for a in audio_val if a is not None)
+        else:
+            chunks.append(torch.as_tensor(audio_val).float().cpu().reshape(-1))
+    pcm = torch.cat(chunks) if chunks else torch.empty(0)
+    if pcm.numel() == 0:
+        raise SystemExit("TTS pass produced empty audio")
+
+    out_path = Path(args.output)
+    write_wav(out_path, pcm)
+    print(f"[3/3] speech     : {pcm.numel() / SAMPLE_RATE:.2f}s -> {out_path}")
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/audex/text_to_audio.py

@@ -0,0 +1,157 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Offline Audex (Nemotron-Labs-Audex-2B) text-to-audio inference example.
+
+Caption → general audio through the vLLM-Omni TTA pipeline: the audiogen
+thinker generates interleaved 4-codebook <audiocodec_N> RVQ tokens under an
+RVQ phase mask, and the external XCodec1 checkpoint decodes them to a 16 kHz
+mono WAV.
+
+(For DIFFUSION text-to-audio models — Stable Audio Open, AudioX — use
+``examples/offline_inference/text_to_audio/text_to_audio.py`` instead; Audex
+TTA is a two-stage autoregressive pipeline with a different interface.)
+
+Classifier-free guidance is effectively mandatory for TTA quality (official
+default scale 3.0), so every request submits a cond/uncond pair; requests
+run one at a time.
+
+Pass the HF repo ROOT as --model. XCodec1 resolves from --xcodec1-path /
+XCODEC1_PATH / the default hf-audio repo.
+
+Example:
+
+    python examples/offline_inference/audex/text_to_audio.py \\
+        --captions "Heavy rain falling on a tin roof." \\
+        --output-dir results/audex_tta_wavs
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import time
+from pathlib import Path
+
+from common import (
+    SAMPLE_RATE,
+    default_deploy_config,
+    extract_pcm,
+    guided_stage_params,
+    load_audex_tokenizer,
+    load_corpus,
+    write_wav,
+)
+
+from vllm_omni import Omni
+from vllm_omni.model_executor.models.audex.prompt import build_tta_cond_prompt, build_tta_null_prompt
+from vllm_omni.model_executor.models.audex.tta import build_tta_phase_token_ids
+
+DEFAULT_CAPTIONS = (
+    "Heavy rain falling on a tin roof.",
+    "A dog barking in the distance while birds chirp.",
+    "Ocean waves crashing on a rocky shore.",
+)
+DEFAULT_CODEC_CAP = 4000
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Offline Audex TTA inference")
+    parser.add_argument("--model", type=str, default="nvidia/Nemotron-Labs-Audex-2B")
+    parser.add_argument("--captions", type=str, nargs="+", default=None, help="Audio captions.")
+    parser.add_argument(
+        "--captions-file",
+        type=str,
+        default=None,
+        help="TSV corpus: one 'utt_id<TAB>caption' per line (overrides --captions).",
+    )
+    parser.add_argument("--output-dir", type=str, default="results/audex_tta_wavs")
+    parser.add_argument(
+        "--deploy-config",
+        type=str,
+        # The model root's default deploy yaml is the TTS pipeline; TTA
+        # prompts and RVQ sampling params require the dedicated TTA pipeline.
+        default=default_deploy_config("audex_tta.yaml"),
+        help="Deploy yaml (defaults to the audex_tta pipeline).",
+    )
+    parser.add_argument(
+        "--xcodec1-path",
+        type=str,
+        default=None,
+        help="Local XCodec1 checkpoint (defaults to $XCODEC1_PATH or the hf-audio repo).",
+    )
+    parser.add_argument(
+        "--cfg-scale",
+        type=float,
+        default=3.0,
+        help="Classifier-free guidance strength (official TTA default 3.0; 1.0 disables).",
+    )
+    parser.add_argument(
+        "--codec-cap",
+        type=int,
+        default=DEFAULT_CODEC_CAP,
+        help="Max generated codec tokens before the phase mask forces <audiogen_end>.",
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    if args.xcodec1_path:
+        os.environ["XCODEC1_PATH"] = args.xcodec1_path
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    corpus = load_corpus(args.captions_file, args.captions, DEFAULT_CAPTIONS)
+
+    tokenizer = load_audex_tokenizer(args.model, profile="tta")
+    phase_token_ids, start_tid, end_tid = build_tta_phase_token_ids(tokenizer)
+    tta_rvq = {
+        "phase_token_ids": phase_token_ids,
+        "start_tid": start_tid,
+        "end_tid": end_tid,
+        "codec_cap": args.codec_cap,
+        # The prompt ends with <audiogen_start>, so generation begins at
+        # RVQ phase 0 immediately.
+        "start_in_prompt": True,
+    }
+
+    engine = Omni(model=args.model, deploy_config=args.deploy_config, trust_remote_code=True)
+
+    print(f"Model       : {args.model}")
+    print(f"Captions    : {len(corpus)}")
+    print(f"CFG scale   : {args.cfg_scale}")
+    print(f"Codec cap   : {args.codec_cap}")
+    print(f"Output dir  : {output_dir}")
+
+    total_elapsed = 0.0
+    total_dur = 0.0
+    for utt, caption in corpus:
+        cond_prompt = build_tta_cond_prompt(caption)
+        sampling = guided_stage_params(
+            engine,
+            cond_prompt,
+            tokenizer,
+            args.cfg_scale,
+            f"tta-{utt}",
+            extra_args={"tta_rvq": tta_rvq},
+            null_prompt_builder=build_tta_null_prompt,
+        )
+        t_start = time.perf_counter()
+        outputs = engine.generate([cond_prompt], sampling)
+        total_elapsed += time.perf_counter() - t_start
+
+        (req_output,) = outputs
+        pcm = extract_pcm(req_output.outputs[0].multimodal_output)
+        dur = pcm.numel() / SAMPLE_RATE
+        if dur <= 0:
+            raise RuntimeError(f"empty audio for {utt}")
+        out_path = output_dir / f"{utt}.wav"
+        write_wav(out_path, pcm)
+        total_dur += dur
+        print(f"  {utt:<48} dur={dur:6.2f}s  -> {out_path}")
+
+    rtf = total_elapsed / total_dur if total_dur > 0 else float("inf")
+    print(f"Total infer : {total_elapsed:.2f}s  total audio: {total_dur:.2f}s  RTF: {rtf:.3f}")
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/audex/text_to_speech.py

@@ -0,0 +1,162 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Offline Audex (Nemotron-Labs-Audex-2B) TTS inference example.
+
+Runs Stage 0 (thinker) + Stage 1 (streaming causal speech decoder) end-to-end
+through the vLLM-Omni engine and saves a 16 kHz mono WAV per prompt.
+
+Pass the HF repo ROOT as --model; per-stage subfolders resolve automatically.
+
+Examples:
+
+    python examples/offline_inference/audex/text_to_speech.py \\
+        --texts "The weather is so good today." \\
+        --output-dir results/audex_wavs
+
+    # TSV corpus (utt_id<TAB>text per line), all requests in flight at once:
+    python examples/offline_inference/audex/text_to_speech.py \\
+        --texts-file corpus.tsv --output-dir results/audex_wavs
+
+    # One request at a time:
+    python examples/offline_inference/audex/text_to_speech.py \\
+        --texts-file corpus.tsv --sequential --output-dir results/audex_wavs
+"""
+
+from __future__ import annotations
+
+import argparse
+import time
+from pathlib import Path
+
+from common import (
+    GUIDED_TEMPERATURE,
+    SAMPLE_RATE,
+    extract_pcm,
+    guided_stage_params,
+    load_audex_tokenizer,
+    load_corpus,
+    request_index,
+    write_wav,
+)
+
+from vllm_omni import Omni
+from vllm_omni.model_executor.models.audex.prompt import build_cond_prompt
+
+DEFAULT_TEXTS = (
+    "Hello world.",
+    "The quick brown fox jumps over the lazy dog.",
+    "Today is a beautiful day for a walk in the park.",
+)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Offline Audex TTS inference")
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="nvidia/Nemotron-Labs-Audex-2B",
+        help="HF repo root (or local snapshot root).",
+    )
+    parser.add_argument("--texts", type=str, nargs="+", default=None, help="Plain-text prompts.")
+    parser.add_argument(
+        "--texts-file",
+        type=str,
+        default=None,
+        help="TSV corpus: one 'utt_id<TAB>text' per line (overrides --texts).",
+    )
+    parser.add_argument("--output-dir", type=str, default="results/audex_wavs")
+    parser.add_argument("--deploy-config", type=str, default=None, help="Override the deploy config path.")
+    parser.add_argument(
+        "--sequential",
+        action="store_true",
+        help="Submit one request at a time instead of all at once.",
+    )
+    parser.add_argument(
+        "--cfg-scale",
+        type=float,
+        default=1.0,
+        help=(
+            "Classifier-free guidance strength. 1.0 disables guidance (default, "
+            "matches the v1 baseline); 1.5 is the official quality setting. "
+            "Guided runs submit one request at a time (each carries its own "
+            "length-matched null prompt and pair id)."
+        ),
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=None,
+        help="Override the deploy yaml's stage-0 sampling temperature.",
+    )
+    return parser.parse_args()
+
+
+def _write_outputs(outputs, batch: list[tuple[str, str]], output_dir: Path) -> float:
+    ordered = sorted(outputs, key=request_index)
+    if len(ordered) != len(batch):
+        raise RuntimeError(f"expected {len(batch)} outputs, got {len(ordered)}")
+
+    total_dur = 0.0
+    for (utt, _text), req_output in zip(batch, ordered):
+        pcm = extract_pcm(req_output.outputs[0].multimodal_output)
+        dur = pcm.numel() / SAMPLE_RATE
+        if dur <= 0:
+            raise RuntimeError(f"empty audio for {utt}")
+        out_path = output_dir / f"{utt}.wav"
+        write_wav(out_path, pcm)
+        total_dur += dur
+        print(f"  {utt:<40} dur={dur:6.2f}s  -> {out_path}")
+    return total_dur
+
+
+def main():
+    args = parse_args()
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    corpus = load_corpus(args.texts_file, args.texts, DEFAULT_TEXTS)
+
+    engine = Omni(model=args.model, deploy_config=args.deploy_config, trust_remote_code=True)
+
+    cfg_enabled = args.cfg_scale > 1.0
+    tokenizer = load_audex_tokenizer(args.model) if cfg_enabled else None
+
+    print(f"Model       : {args.model}")
+    print(f"Prompts     : {len(corpus)}")
+    print(f"Mode        : {'sequential' if args.sequential or cfg_enabled else 'batched'}")
+    print(f"CFG scale   : {args.cfg_scale}" + (" (guidance off)" if not cfg_enabled else ""))
+    print(f"Output dir  : {output_dir}")
+
+    total_elapsed = 0.0
+    total_dur = 0.0
+    # CFG runs one request at a time: each request needs its own pair id and
+    # text-specific null prompt in the stage-0 sampling params.
+    batches = [[item] for item in corpus] if (args.sequential or cfg_enabled) else [corpus]
+    for batch in batches:
+        prompts = [build_cond_prompt(text) for _utt, text in batch]
+        sampling_params_list = None
+        if cfg_enabled:
+            sampling_params_list = guided_stage_params(
+                engine,
+                prompts[0],
+                tokenizer,
+                args.cfg_scale,
+                f"cfg-{batch[0][0]}",
+                temperature=GUIDED_TEMPERATURE,
+            )
+        if args.temperature is not None:
+            import copy
+
+            if sampling_params_list is None:
+                sampling_params_list = copy.deepcopy(engine.resolve_sampling_params_list(None))
+            sampling_params_list[0].temperature = args.temperature
+        t_start = time.perf_counter()
+        outputs = engine.generate(prompts, sampling_params_list)
+        total_elapsed += time.perf_counter() - t_start
+        total_dur += _write_outputs(outputs, batch, output_dir)
+
+    rtf = total_elapsed / total_dur if total_dur > 0 else float("inf")
+    print(f"Total infer : {total_elapsed:.2f}s  total audio: {total_dur:.2f}s  RTF: {rtf:.3f}")
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/image_to_video/README.md

@@ -1,7 +1,7 @@
 # Image-To-Video
 
-This shared example generates videos from images with VACE, Wan2.2,
-LTX-2/LTX-2.3, HunyuanVideo-1.5, Cosmos3, and other compatible pipelines.
+This shared example generates videos from images with VACE, Wan2.2, LTX-2,
+HunyuanVideo-1.5, Cosmos3, and other compatible pipelines.
 
 - `image_to_video.py`: command-line script for single video generation with advanced options.
 
@@ -26,7 +26,7 @@ This folder provides a unified CLI script for image-to-video generation using vL
 | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480 x 832 | 81 | 50 | 5.0 | Around 60 GiB BF16 for basic single-card usage |
 | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 480 x 832 | 81 | 50 | 4.0 | Around 20–25 GiB BF16, smallest I2V model |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v` | 480 x 832 | 121 | 50 | 6.0 | Around 100 GiB at default settings; the example enables `--enable-cpu-offload` + VAE tiling/slicing to fit an 80 GiB card |
-| LTX2 (local path + `--model-class-name LTX2ImageToVideoPipeline`) | 512 x 768 | 121 | 40 | 4.0 | Memory use depends on frame count and tensor parallelism |
+| `Lightricks/LTX-2` | 512 x 768 | 121 | 40 | video 3.0 / audio 7.0 | Memory use depends on frame count and tensor parallelism |
 
 !!! info
     Peak VRAM: based on basic single-card usage, batch size = 1, without any acceleration/optimization features. Some model weights cannot fit into one card with 80 GiB VRAM, which may need to use CPU offloading.
@@ -105,7 +105,7 @@ python image_to_video.py \
 | Argument | Type | Default | Description |
 | -------- | ---- | ------- | ----------- |
 | `--model` | str | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | Diffusers I2V model ID or local path |
-| `--model-class-name` | str | `None` | Override model class name (e.g., `LTX2ImageToVideoPipeline`) |
+| `--model-class-name` | str | `None` | Optional pipeline override |
 | `--image` | str | (required) | Path to input image |
 | `--prompt` | str | `""` | Text description of desired motion/animation |
 | `--negative-prompt` | str | `""` | Optional list of artifacts to suppress |
@@ -195,24 +195,19 @@ python image_to_video.py \
   --output hunyuan_i2v.mp4
 ```
 
-### LTX2 Image-to-Video
+### LTX-2
 
 ```bash
 python image_to_video.py \
-  --model /path/to/LTX-2 \
-  --model-class-name LTX2ImageToVideoPipeline \
+  --model Lightricks/LTX-2 \
   --image cherry_blossom.jpg \
   --prompt "A cinematic dolly shot of cherry blossoms" \
-  --height 512 \
-  --width 768 \
-  --num-frames 121 \
-  --num-inference-steps 40 \
-  --guidance-scale 4.0 \
-  --frame-rate 24 \
-  --fps 24 \
   --output ltx2_i2v.mp4
 ```
 
+See the [LTX-2 recipe](../../../recipes/LTX/LTX-2.md) for all checkpoints,
+pipeline selection, T2V, defaults, and advanced options.
+
 ## Advanced Features
 
 ### CFG Parallel
@@ -233,30 +228,6 @@ python image_to_video.py \
   --output i2v_cached.mp4
 ```
 
-### LTX-2.3
-
-```bash
-python image_to_video.py \
-  --model diffusers/LTX-2.3-Diffusers \
-  --model-class-name LTX23ImageToVideoPipeline \
-  --image cherry_blossom.jpg \
-  --prompt "Cherry blossoms swaying gently in the breeze with synchronized ambient sound" \
-  --negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
-  --height 384 \
-  --width 512 \
-  --num-frames 25 \
-  --guidance-scale 4.0 \
-  --num-inference-steps 20 \
-  --frame-rate 24 \
-  --fps 24 \
-  --output ltx23_i2v_output.mp4
-```
-
-Use the Diffusers-format checkpoint `diffusers/LTX-2.3-Diffusers`; the
-upstream `Lightricks/LTX-2.3` raw safetensors repo is not directly loadable by
-this pipeline. Pass `--model-class-name LTX23ImageToVideoPipeline` to select
-the LTX-2.3 image-to-video pipeline.
-
 ### Cosmos3
 
 ```bash
@@ -274,8 +245,8 @@ python image_to_video.py \
 
 Key arguments:
 
-- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V,
-  LTX-2/LTX-2.3, Cosmos3, or VACE).
+- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V, LTX-2,
+  Cosmos3, or VACE).
 - `--image`: Path to the first-frame or source image.
 - `--last-image`: Optional last-frame condition for models such as VACE.
 - `--mask-image`: Optional inpainting mask. White pixels are regenerated and black pixels are preserved.
@@ -290,7 +261,7 @@ Key arguments:
 - `--guidance-scale` and `--guidance-scale-high`: CFG scale (applied to low/high-noise stages for MoE).
 - `--negative-prompt`: Optional list of artifacts to suppress.
 - `--boundary-ratio`: Boundary split ratio for two-stage MoE models.
-- `--flow-shift`: Scheduler flow shift (default: model-specific — Wan/LTX2 5.0, Cosmos3 10.0).
+- `--flow-shift`: Scheduler flow shift (default: Wan 5.0, Cosmos3 10.0). LTX does not consume this option.
 - `--sample-solver`: Wan2.2 sampling solver. Use `unipc` for the default multistep solver, or `euler` for Lightning/Distill checkpoints.
 - `--num-inference-steps`: Number of denoising steps (default: model-specific — Wan 50, LTX2 40, Cosmos3 35).
 - `--fps`: Frames per second for the saved MP4 (requires `diffusers` export_to_video).

examples/offline_inference/image_to_video/image_to_video.py

@@ -23,16 +23,14 @@ Usage:
 
     # LTX2 image-to-video
     python image_to_video.py --model /path/to/LTX-2 \
-        --model-class-name LTX2ImageToVideoPipeline \
         --image input.jpg --prompt "A cinematic dolly shot of a boat" \
-        --num-frames 121 --num-inference-steps 40 --guidance-scale 4.0 \
+        --num-frames 121 --num-inference-steps 40 \
         --frame-rate 24 --fps 24 --output ltx2_i2v.mp4
 
     # LTX-2.3 image-to-video
     python image_to_video.py --model diffusers/LTX-2.3-Diffusers \
-        --model-class-name LTX23ImageToVideoPipeline \
         --image input.jpg --prompt "A cinematic dolly shot of a boat" \
-        --height 384 --width 512 --num-frames 25 --num-inference-steps 20 \
+        --height 512 --width 768 --num-frames 121 --num-inference-steps 30 \
         --frame-rate 24 --fps 24 --output ltx23_i2v.mp4
 
     # HunyuanVideo-1.5 I2V (480p)
@@ -100,7 +98,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--model-class-name",
         default=None,
-        help="Override model class name (e.g., LTX2ImageToVideoPipeline or LTX23ImageToVideoPipeline).",
+        help="Override model class name (LTX checkpoints default to LTX2Pipeline).",
     )
     parser.add_argument(
         "--deploy-config",
@@ -117,7 +115,7 @@ def parse_args() -> argparse.Namespace:
         help="Path to a reference image. Repeat to provide multiple references.",
     )
     parser.add_argument("--prompt", default="", help="Text prompt describing the desired motion.")
-    parser.add_argument("--negative-prompt", default="", help="Negative prompt.")
+    parser.add_argument("--negative-prompt", default=None, help="Negative prompt. Default: model-specific.")
     parser.add_argument("--seed", type=int, default=42, help="Random seed.")
     parser.add_argument("--guidance-scale", type=float, default=None, help="CFG scale. Default: model-specific.")
     parser.add_argument(
@@ -331,8 +329,12 @@ def main():
     generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
     model_name = str(args.model).lower() if args.model is not None else ""
     model_class_name = args.model_class_name
-    is_ltx2 = model_class_name in {"LTX2ImageToVideoPipeline", "LTX23ImageToVideoPipeline"}
+    model_class_name_lower = (model_class_name or "").lower()
+    is_ltx2_distilled = "distilled" in model_class_name_lower or "distilled" in model_name
+    is_ltx23 = "ltx23" in model_class_name_lower or "ltx-2.3" in model_name
+    is_ltx2 = is_ltx2_distilled or is_ltx23 or "ltx2" in model_class_name_lower or "ltx-2" in model_name
     is_cosmos = "cosmos" in model_name or (model_class_name is not None and "cosmos" in model_class_name.lower())
+    is_cosmos_edge = is_cosmos and ("edge" in model_name or "edge" in model_class_name_lower)
 
     image = PIL.Image.open(args.image).convert("RGB") if args.image else None
     last_image = PIL.Image.open(args.last_image).convert("RGB") if args.last_image else None
@@ -346,7 +348,19 @@ def main():
 
     # Per-model generation defaults, applied only when the matching flag is omitted.
     # Cosmos3 would otherwise silently inherit the Wan2.2 defaults (wrong size/steps/shift).
-    if is_cosmos:
+    if is_cosmos_edge:
+        # Cosmos3-Edge native defaults: 480x832, gs 5.0, flow_shift 3.0 — NOT the Nano/Super
+        # 720p / gs 6.0 / flow_shift 10.0 below, which produce degenerate output on Edge.
+        d_fps, d_guidance, d_num_frames, d_steps, d_flow_shift, d_max_area, d_mod = (
+            24,
+            5.0,
+            189,
+            35,
+            3.0,
+            480 * 832,
+            16,
+        )
+    elif is_cosmos:
         d_fps, d_guidance, d_num_frames, d_steps, d_flow_shift, d_max_area, d_mod = (
             24,
             6.0,
@@ -356,13 +370,23 @@ def main():
             1280 * 720,
             16,
         )
+    elif is_ltx2_distilled:
+        d_fps, d_guidance, d_num_frames, d_steps, d_flow_shift, d_max_area, d_mod = (
+            24,
+            None,
+            121,
+            8,
+            None,
+            1024 * 1536,
+            64,
+        )
     elif is_ltx2:
         d_fps, d_guidance, d_num_frames, d_steps, d_flow_shift, d_max_area, d_mod = (
             24,
-            4.0,
+            None,
             121,
-            40,
-            5.0,
+            30 if is_ltx23 else 40,
+            None,
             512 * 768,
             32,
         )
@@ -445,7 +469,6 @@ def main():
         vae_use_slicing=args.vae_use_slicing,
         vae_use_tiling=args.vae_use_tiling,
         boundary_ratio=args.boundary_ratio,
-        flow_shift=flow_shift,
         diffusion_kv_cache_dtype=args.diffusion_kv_cache_dtype,
         diffusion_kv_cache_skip_steps=args.diffusion_kv_cache_skip_steps,
         diffusion_kv_cache_skip_layers=args.diffusion_kv_cache_skip_layers,
@@ -460,6 +483,8 @@ def main():
     )
     if args.deploy_config:
         omni_kwargs["deploy_config"] = args.deploy_config
+    if flow_shift is not None:
+        omni_kwargs["flow_shift"] = flow_shift
     if args.quantization is not None:
         omni_kwargs["quantization"] = args.quantization
     # Cosmos3 loads its (gated) guardrail models at build time, so the guardrails
@@ -492,10 +517,14 @@ def main():
     print(f"  Video size: {width}x{height}")
     print(f"{'=' * 60}\n")
 
+    negative_prompt = args.negative_prompt
+    if negative_prompt is None and not is_ltx2:
+        # Preserve the historical empty-prompt behavior for non-LTX examples.
+        negative_prompt = ""
     prompt_dict = build_image_to_video_prompt(
         model_class_name=model_class_name,
         prompt=args.prompt,
-        negative_prompt=args.negative_prompt,
+        negative_prompt=negative_prompt,
         media_inputs=media_inputs,
         height=height,
         width=width,
@@ -511,11 +540,10 @@ def main():
         num_inference_steps=num_inference_steps,
         num_frames=num_frames,
         frame_rate=frame_rate,
-        extra_args={
-            "sample_solver": args.sample_solver,
-            "flow_shift": flow_shift,
-        },
+        extra_args={"sample_solver": args.sample_solver},
     )
+    if flow_shift is not None:
+        sampling_params.extra_args["flow_shift"] = flow_shift
 
     # Route model-specific knobs through extra_body, filtered against the model's
     # declared extra_body_params. Models without a declaration only forward explicit

examples/offline_inference/text_to_speech/cosyvoice3/end2end.py

@@ -182,10 +182,13 @@ def run_e2e():
                 print(f"Multimodal output keys: {mm.keys()}")
                 if "audio" in mm:
                     audio_out = mm["audio"]
+                    # The model emits its own rate (24 kHz for CosyVoice3); take
+                    # it from the payload so the file is not mislabeled.
+                    sample_rate = int(mm["sr"]) if "sr" in mm.keys() else 24000
                     print(f"Generated Audio Shape: {audio_out.shape}")
                     out_path = f"output_{i}.wav"
-                    sf.write(out_path, audio_out.cpu().numpy().squeeze(), 22050)
-                    print(f"Saved audio to {out_path}")
+                    sf.write(out_path, audio_out.cpu().numpy().squeeze(), sample_rate)
+                    print(f"Saved audio to {out_path} (sr={sample_rate})")
             else:
                 print("No multimodal output found.")
         except Exception as e:

examples/offline_inference/text_to_video/text_to_video.md

@@ -8,7 +8,7 @@ A unified script for text-to-video generation. Supports multiple models with mod
 |---|---|---|---|---|---|
 | `Wan-AI/Wan2.1-VACE-1.3B-diffusers` | 480x832 | 81 | 30 | 5.0 | ~20 GiB (RTX 5090, VAE tiling) |
 | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 720x1280 | 81 | 40 | 4.0 | ~60 GiB |
-| `diffusers/LTX-2.3-Diffusers` | 384x512 | 25 | 20 | 4.0 | 96GB-class GPU |
+| `Lightricks/LTX-2` | 512x768 | 121 | 40 | video 3.0 / audio 7.0 | Model-dependent |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | 480x832 | 121 | 50 | 6.0 | 1×A100 80GB |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | 720x1280 | 121 | 50 | 6.0 | FP8 + VAE tiling required |
 | `nvidia/Cosmos3-Nano` | 720x1280 | 189 | 35 | 6.0 | ~46 GiB (peak, 720p) |
@@ -55,45 +55,17 @@ python text_to_video.py \
   --output vace_t2v_output.mp4
 ```
 
-LTX2 example:
+### LTX-2
 
 ```bash
 python text_to_video.py \
-  --model "Lightricks/LTX-2" \
-  --prompt "A cinematic close-up of ocean waves at golden hour." \
-  --negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
-  --height 512 \
-  --width 768 \
-  --num-frames 121 \
-  --num-inference-steps 40 \
-  --guidance-scale 4.0 \
-  --frame-rate 24 \
-  --output ltx2_out.mp4
-```
-
-### LTX-2.3
-
-```bash
-python text_to_video.py \
-  --model diffusers/LTX-2.3-Diffusers \
-  --model-class-name LTX23Pipeline \
+  --model Lightricks/LTX-2 \
   --prompt "Cherry blossoms swaying gently in the breeze with synchronized ambient sound" \
-  --negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
-  --height 384 \
-  --width 512 \
-  --num-frames 25 \
-  --num-inference-steps 20 \
-  --guidance-scale 4.0 \
-  --frame-rate 24 \
-  --fps 24 \
-  --audio-sample-rate 48000 \
-  --output ltx23_t2v_output.mp4
+  --output ltx2_output.mp4
 ```
 
-Use the Diffusers-format checkpoint `diffusers/LTX-2.3-Diffusers`; the
-upstream `Lightricks/LTX-2.3` raw safetensors repo is not directly loadable by
-this pipeline. Pass `--model-class-name LTX23Pipeline` to select the LTX-2.3
-text-to-video pipeline explicitly.
+See the [LTX-2 recipe](../../../recipes/LTX/LTX-2.md) for all checkpoints,
+pipeline selection, I2V, defaults, and advanced options.
 
 ### HunyuanVideo-1.5 (480p)
 
@@ -212,8 +184,7 @@ python text_to_video.py \
 ### Common
 
 - `--model`: Diffusers model ID or local path.
-- `--model-class-name`: Optional explicit pipeline class. Use `LTX23Pipeline`
-  for LTX-2.3 text-to-video.
+- `--model-class-name`: Optional explicit pipeline override.
 - `--prompt`: text description (string).
 - `--height/--width`: output resolution. Default depends on model.
 - `--num-frames`: number of frames. Default depends on model.
@@ -229,8 +200,7 @@ python text_to_video.py \
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 - `--enable-layerwise-offload`: enable layerwise offloading on DiT modules.
 - `--frame-rate`: generation FPS for pipelines that require it (e.g., LTX2).
-- `--audio-sample-rate`: audio sample rate for embedded audio (when the
-  pipeline returns audio; LTX-2.3 outputs 48kHz audio).
+- `--audio-sample-rate`: fallback audio sample rate when the pipeline returns audio.
 - `--quantization`: quantization method (such as `fp8` for FP8).
 - `--flow-shift`: scheduler flow_shift parameter.
 - `--extra-body`: JSON object of model-specific generation params, filtered against the model's declared `extra_body_params` (see [`vllm_omni/model_extras`](../../../vllm_omni/model_extras)). Used by Cosmos3 (see above).

examples/offline_inference/text_to_video/text_to_video.py

@@ -56,6 +56,19 @@ _MODEL_PRESETS = {
         "flow_shift": 10.0,
         "output": "cosmos3_t2v_output.mp4",
     },
+    # Cosmos3-Edge is a compact checkpoint with its own native resolution / guidance /
+    # flow_shift. It must NOT inherit the Nano/Super "cosmos" numbers above (720p / gs 6.0 /
+    # flow_shift 10.0), which produce degenerate output on Edge.
+    "cosmos3_edge": {
+        "height": 480,
+        "width": 832,
+        "num_frames": 189,
+        "num_inference_steps": 35,
+        "guidance_scale": 5.0,
+        "fps": 24,
+        "flow_shift": 3.0,
+        "output": "cosmos3_edge_t2v_output.mp4",
+    },
     "helios": {
         "height": 384,
         "width": 640,
@@ -65,13 +78,48 @@ _MODEL_PRESETS = {
         "fps": 16,
         "output": "helios_output.mp4",
     },
+    "ltx2": {
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "num_inference_steps": 40,
+        "fps": 24,
+        "output": "ltx2_output.mp4",
+    },
+    "ltx2_distilled": {
+        "height": 1024,
+        "width": 1536,
+        "num_frames": 121,
+        "num_inference_steps": 8,
+        "fps": 24,
+        "output": "ltx2_distilled_output.mp4",
+    },
+    "ltx23": {
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "num_inference_steps": 30,
+        "fps": 24,
+        "output": "ltx23_output.mp4",
+    },
 }
 
 
-def _detect_preset(model: str) -> dict:
+def _detect_preset(model: str, model_class_name: str | None = None) -> dict:
     model_lower = model.lower()
+    class_lower = (model_class_name or "").lower()
+    if "distilled" in class_lower or "distilled" in model_lower:
+        return _MODEL_PRESETS["ltx2_distilled"]
+    if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower:
+        return _MODEL_PRESETS["ltx23"]
+    if "ltx2" in class_lower or "ltx-2" in model_lower or "ltx_2" in model_lower:
+        return _MODEL_PRESETS["ltx2"]
     if "vace" in model_lower:
         return _MODEL_PRESETS["vace"]
+    # Edge must be matched before the generic cosmos branch (its "cosmos" substring would
+    # otherwise pick up the Nano/Super 720p / gs 6.0 / flow_shift 10.0 preset).
+    if ("cosmos" in model_lower or "cosmos" in class_lower) and ("edge" in model_lower or "edge" in class_lower):
+        return _MODEL_PRESETS["cosmos3_edge"]
     if "cosmos" in model_lower:
         return _MODEL_PRESETS["cosmos"]
     if "hunyuan" in model_lower:
@@ -122,7 +170,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--model-class-name",
         default=None,
-        help="Override model class name (e.g., LTX2TwoStagesVideoPipeline).",
+        help="Override model class name (e.g., LTX2Pipeline).",
     )
     parser.add_argument(
         "--deploy-config",
@@ -130,7 +178,7 @@ def parse_args() -> argparse.Namespace:
         help="Optional deploy config YAML to use for pipeline-backed runs.",
     )
     parser.add_argument("--prompt", default="A serene lakeside sunrise with mist over the water.", help="Text prompt.")
-    parser.add_argument("--negative-prompt", default="", help="Negative prompt.")
+    parser.add_argument("--negative-prompt", default=None, help="Negative prompt. Default: model-specific.")
     parser.add_argument(
         "--extra-body",
         type=parse_extra_body,
@@ -331,7 +379,7 @@ def main():
     args = parse_args()
     model_class_name = args.model_class_name
 
-    preset = _detect_preset(args.model)
+    preset = _detect_preset(args.model, model_class_name)
     for key, default_val in preset.items():
         if getattr(args, key.replace("-", "_"), None) is None:
             setattr(args, key.replace("-", "_"), default_val)
@@ -422,8 +470,11 @@ def main():
     print(f"{'=' * 60}\n")
 
     prompt_dict = {"prompt": args.prompt}
-    if args.negative_prompt:
+    if args.negative_prompt is not None:
         prompt_dict["negative_prompt"] = args.negative_prompt
+    elif preset not in (_MODEL_PRESETS["ltx2"], _MODEL_PRESETS["ltx23"]):
+        # Preserve the historical empty-prompt behavior for non-LTX examples.
+        prompt_dict["negative_prompt"] = ""
 
     sampling_kwargs = dict(
         height=args.height,

examples/online_serving/audex/README.md

@@ -0,0 +1,89 @@
+# Audex (Nemotron-Labs-Audex-2B) online serving
+
+One checkpoint, four deployment modes. `run_server.sh` starts the server
+for a given `MODE`; `client.py --mode <mode>` tests it. The capability
+matrix:
+
+| mode (`vllm_omni/deploy/audex_<mode>.yaml`) | audio in | text out | speech out | general audio out | endpoint |
+|---|---|---|---|---|---|
+| `tts` | ❌ | ❌ | ✅ | ❌ | `/v1/audio/speech` |
+| `tta` | ❌ | ❌ | ❌ | ✅ | `/v1/audio/speech` |
+| `thinker_only` | ✅ | ✅ | ❌ | ❌ | `/v1/chat/completions` |
+| `s2s` | ✅ | ✅ | ✅ | ❌ | both |
+
+Audio-input modes of `client.py` fall back to vLLM's public
+`mary_had_lamb` asset when `--audio-file` is omitted; CFG defaults follow
+the official settings per mode (tts/s2s 1.5, tta 3.0; `--cfg-scale 1.0`
+disables).
+
+## tts — text → speech
+
+Text-only thinker (`checkpoint_folder_audiogen`) emits `<speechcodec_N>`
+tokens; the streaming causal decoder produces 16 kHz WAVs. This is also
+the DEFAULT pipeline when the repo root is served without a deploy config.
+
+```bash
+./run_server.sh                       # MODE=tts, port 8097
+
+python client.py --mode tts --text "Hello there." --output hello.wav
+# or raw curl:
+curl -s http://localhost:8097/v1/audio/speech \
+    -H 'Content-Type: application/json' \
+    -d '{"model": "nvidia/Nemotron-Labs-Audex-2B", "input": "Hello there.", "response_format": "wav", "extra_params": {"cfg_scale": 1.5}}' \
+    -o hello.wav
+```
+
+## tta — caption → general audio
+
+Same thinker over the 4-codebook `<audiocodec_N>` RVQ block, decoded by
+the external XCodec1 checkpoint (auto-downloaded; override with
+`XCODEC1_PATH`). Clips are capped at 10 s.
+
+```bash
+MODE=tta ./run_server.sh
+
+python client.py --mode tta --caption "Heavy rain falling on a tin roof." \
+    --output rain.wav
+```
+
+## thinker_only — audio (+ instruction) → text
+
+Single-stage audio understanding on the full checkpoint (NV-Whisper
+encoder + projector + LM). Send the clip as `input_audio` chat content.
+
+```bash
+MODE=thinker_only ./run_server.sh
+
+python client.py --mode thinker_only                      # transcribe the demo asset
+python client.py --mode thinker_only --audio-file a.wav \
+    --question "What language is being spoken?"
+```
+
+## s2s — spoken question → spoken answer
+
+The audio-capable thinker plus the speech decoder in one deployment.
+`client.py --mode s2s` runs the official three passes: ASR (chat with
+`input_audio`) → chat on the transcript → TTS on the answer. Text-final
+passes carry `"modalities": ["text"]`; only the TTS pass streams through
+the decoder.
+
+```bash
+MODE=s2s PORT=8098 ./run_server.sh
+
+python client.py --mode s2s --port 8098 \
+    --audio-file question.wav --output answer.wav
+```
+
+Offline counterparts (no HTTP, one `Omni` engine per script) live in
+`examples/offline_inference/audex/`.
+
+## 30B-A3B (nvidia/Nemotron-Labs-Audex-30B-A3B)
+
+`SIZE=30b` switches run_server.sh to the 30B yamls and model id:
+
+    SIZE=30b ./run_server.sh                 # 30B tts on port 8097
+    SIZE=30b MODE=s2s PORT=8098 ./run_server.sh
+
+Pass `--model nvidia/Nemotron-Labs-Audex-30B-A3B` to the client — the server
+validates the model id and 404s on a mismatch. Single-H100 defaults; see the
+yaml comments for the TP2 fallback. First launch downloads ~60 GB.

examples/online_serving/audex/client.py

@@ -0,0 +1,319 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Audex online client for all four deployment modes.
+
+Pick ``--mode`` to match the server's deploy yaml (see run_server.sh):
+
+  tts           /v1/audio/speech: text -> speech WAV
+  tta           /v1/audio/speech: caption -> general-audio WAV
+  thinker_only  /v1/chat/completions: audio (+ question) -> text
+  s2s           the official three-pass cascade against one audex_s2s
+                server: ASR (chat, input_audio) -> chat -> TTS
+
+Audio-input modes fall back to vLLM's public ``mary_had_lamb`` asset when
+``--audio-file`` is omitted.
+
+Examples:
+
+    python examples/online_serving/audex/client.py --mode tts \\
+        --text "Hello there." --output hello.wav
+    python examples/online_serving/audex/client.py --mode tta \\
+        --caption "Heavy rain falling on a tin roof." --output rain.wav
+    python examples/online_serving/audex/client.py --mode thinker_only
+    python examples/online_serving/audex/client.py --mode s2s \\
+        --audio-file question.wav --output answer.wav
+"""
+
+from __future__ import annotations
+
+import argparse
+import base64
+import re
+from pathlib import Path
+
+import requests
+
+ASR_QUESTION = "Transcribe the input speech."
+SYSTEM_PROMPT = "You are a helpful and harmless assistant.\n\nYou are not allowed to use any tools."
+# The official chat-pass recipe below (SYSTEM_PROMPT + TEXT_INSTRUCTION
+# prefix, reasoning enabled, temp 1.0/top_p 0.95, seed retry) applies to the
+# 30B-A3B ONLY (see is_30b() below and
+# tests/e2e/offline_inference/test_audex_s2s.py, which mirrors this same
+# split). It is load-bearing on the 30B: without the formatting-instruction
+# prefix a bare transcript turn is treated as a TTS request and the model
+# emits literal <speechcodec_N> tokens instead of a text answer (the system
+# prompt alone does not prevent this). The 2B must NOT use this recipe: the
+# official temp-1.0 seed-0 draw measurably hallucinates off-language text on
+# the 2B (yet closes </think> cleanly, so the seed retry never fires) and
+# fails the round-trip WER gate — measured, reproduced byte-identically. The
+# 2B instead keeps its original bare-message / enable_thinking=False /
+# temperature=0.0 recipe.
+TEXT_INSTRUCTION = (
+    "SYSTEM & FORMATTING INSTRUCTION: You are Audex, created by NVIDIA based on the Nemotron-Cascade-2 architecture. "
+    "You may output your reasoning, followed by your final response. "
+    "You may format your reasoning block however you like. "
+    "However, your final response must strictly follow these rules: "
+    "* Write in plain, unformatted prose like a book or newspaper article. "
+    "* Do not use markdown, bullet points, lists, or headers. "
+    "* Standard numbers, abbreviations, and symbols are acceptable. "
+    "* [CRITICAL] You must press enter after every single sentence, placing each sentence on its own separate line."
+)
+DEFAULT_TEXT = "Hello there! This is the Audex text to speech pipeline."
+DEFAULT_CAPTION = "Heavy rain falling on a tin roof."
+# Official quality settings; 1.0 disables guidance.
+DEFAULT_CFG = {"tts": 1.5, "tta": 3.0, "s2s": 1.5}
+
+
+def is_30b(model: str) -> bool:
+    # Users pass arbitrary repo IDs or local snapshot paths, so this is a
+    # heuristic (not an exact match against the official 30B-A3B repo ID)
+    # — good enough for an example script; keep in sync with the same
+    # branch in examples/offline_inference/audex/speech_to_speech.py and
+    # tests/e2e/offline_inference/test_audex_s2s.py.
+    return "30b" in model.lower()
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Audex online client (tts / tta / thinker_only / s2s)")
+    parser.add_argument("--mode", choices=("tts", "tta", "thinker_only", "s2s"), default="s2s")
+    parser.add_argument("--host", type=str, default="127.0.0.1")
+    parser.add_argument("--port", type=int, default=8097)
+    parser.add_argument("--model", type=str, default="nvidia/Nemotron-Labs-Audex-2B")
+    parser.add_argument("--text", type=str, default=DEFAULT_TEXT, help="TTS input text (mode=tts).")
+    parser.add_argument("--caption", type=str, default=DEFAULT_CAPTION, help="TTA caption (mode=tta).")
+    parser.add_argument(
+        "--audio-file",
+        type=str,
+        default=None,
+        help="Input WAV for thinker_only/s2s. Defaults to vLLM's mary_had_lamb asset.",
+    )
+    parser.add_argument("--question", type=str, default=ASR_QUESTION, help="Instruction (mode=thinker_only).")
+    parser.add_argument("--output", type=str, default="results/audex_online.wav")
+    parser.add_argument(
+        "--cfg-scale",
+        type=float,
+        default=None,
+        help="CFG strength; defaults per mode (tts/s2s 1.5, tta 3.0). 1.0 disables.",
+    )
+    parser.add_argument("--max-tokens", type=int, default=512)
+    return parser.parse_args()
+
+
+def _audio_b64(audio_file: str | None) -> str:
+    if audio_file:
+        return base64.b64encode(Path(audio_file).read_bytes()).decode()
+    # Fall back to vLLM's public asset, re-encoded as WAV for the chat API.
+    import io
+
+    import numpy as np
+    import soundfile as sf
+    from vllm.assets.audio import AudioAsset
+
+    audio, sr = AudioAsset("mary_had_lamb").audio_and_sample_rate
+    buf = io.BytesIO()
+    sf.write(buf, np.asarray(audio, dtype="float32"), int(sr), format="WAV")
+    return base64.b64encode(buf.getvalue()).decode()
+
+
+def _chat(
+    base_url: str,
+    model: str,
+    messages: list[dict],
+    max_tokens: int,
+    enable_thinking: bool = False,
+    temperature: float = 0.0,
+    top_p: float = 1.0,
+    seed: int = 0,
+) -> tuple[str, str]:
+    resp = requests.post(
+        f"{base_url}/v1/chat/completions",
+        json={
+            "model": model,
+            "messages": messages,
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+            "top_p": top_p,
+            "seed": seed,
+            # Text-final passes must say so explicitly: without "modalities"
+            # the server falls back to the deployment's configured output
+            # modalities, which for the s2s pipeline include the audio final
+            # stage — routing ASR/chat through code2wav instead of stopping
+            # at stage 0.
+            "modalities": ["text"],
+            # The checkpoint's chat template thinking toggle. The official
+            # cascaded demo transcribes with enable_thinking=False but runs
+            # the chat pass with reasoning ENABLED (its web UI ships the
+            # "Enable reasoning" checkbox checked), priming the generation
+            # prompt with an open "<think>\n" so the model's reasoning stays
+            # inside the think block; _extract_final_response then drops it.
+            # With thinking disabled the 30B leaks its reasoning as plain
+            # text in front of the final response.
+            "chat_template_kwargs": {"enable_thinking": enable_thinking},
+        },
+        timeout=300,
+    )
+    resp.raise_for_status()
+    choice = resp.json()["choices"][0]
+    return choice["message"]["content"].strip(), choice.get("finish_reason") or ""
+
+
+def _speech(base_url: str, model: str, text: str, cfg_scale: float, out_path: Path) -> None:
+    payload: dict = {"model": model, "input": text, "response_format": "wav"}
+    if cfg_scale > 1.0:
+        payload["extra_params"] = {"cfg_scale": cfg_scale}
+    resp = requests.post(f"{base_url}/v1/audio/speech", json=payload, timeout=600)
+    resp.raise_for_status()
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_bytes(resp.content)
+
+
+def _audio_question(audio_b64: str, question: str) -> list[dict]:
+    return [
+        {
+            "role": "user",
+            "content": [
+                {"type": "input_audio", "input_audio": {"data": audio_b64, "format": "wav"}},
+                {"type": "text", "text": question},
+            ],
+        }
+    ]
+
+
+def _tts_text(answer: str) -> str:
+    # The formatting instruction's CRITICAL rule puts each final-response
+    # sentence on its own line, and the official demo TTS-es those sentence
+    # segments (split_sentence_segments). Mirror that per-line segmentation
+    # but keep a single /v1/audio/speech request: speak the leading whole
+    # sentences of the CLEANED final response up to ~200 chars, never
+    # cutting mid-sentence. Markdown markup is stripped as before (it makes
+    # the TTS thinker stop almost immediately).
+    sentences = []
+    for line in answer.splitlines():
+        line = re.sub(r"[*#_`]+", "", line)
+        line = re.sub(r"\s+", " ", line).strip()
+        if line:
+            sentences.append(line)
+    picked: list[str] = []
+    total = 0
+    for sentence in sentences:
+        if picked and total + len(sentence) > 200:
+            break
+        picked.append(sentence)
+        total += len(sentence) + 1
+    return " ".join(picked)
+
+
+def _extract_final_response(text: str) -> str:
+    # Mirror the official demo's extract_spoken_answer
+    # (cascaded_s2s_web_server.py): the chat pass reasons inside the primed
+    # <think> block, so keep only what follows the LAST closing tag; fall
+    # back to the full text when the model skipped reasoning or never
+    # closed the block. Benign no-op for the 2B's clean answers.
+    if "</think>" in text:
+        answer = text.rsplit("</think>", 1)[-1].strip()
+        if answer:
+            return answer
+    return text.strip()
+
+
+def _clean_transcript(text: str) -> str:
+    # The full checkpoint prefixes transcripts with a language-tag sentence
+    # and quotes the content; extract the quoted payload when present. Span
+    # first-to-last quote (not split on every apostrophe) so contractions
+    # inside the transcript (e.g. "don't") survive intact.
+    text = text.strip()
+    match = re.search(r"'(.*)'", text, flags=re.S)
+    if match and match.group(1).strip():
+        return match.group(1).strip()
+    return text
+
+
+def main():
+    args = parse_args()
+    base_url = f"http://{args.host}:{args.port}"
+    cfg_scale = args.cfg_scale if args.cfg_scale is not None else DEFAULT_CFG.get(args.mode, 1.0)
+    out_path = Path(args.output)
+
+    if args.mode == "tts":
+        _speech(base_url, args.model, args.text, cfg_scale, out_path)
+        print(f"speech: {out_path.stat().st_size} bytes -> {out_path}")
+        return
+
+    if args.mode == "tta":
+        _speech(base_url, args.model, args.caption, cfg_scale, out_path)
+        print(f"audio : {out_path.stat().st_size} bytes -> {out_path}")
+        return
+
+    audio_b64 = _audio_b64(args.audio_file)
+
+    if args.mode == "thinker_only":
+        content, _ = _chat(base_url, args.model, _audio_question(audio_b64, args.question), args.max_tokens)
+        print(f"answer: {_extract_final_response(content)}")
+        return
+
+    # s2s — the official three-pass cascade.
+    asr_content, _ = _chat(base_url, args.model, _audio_question(audio_b64, ASR_QUESTION), args.max_tokens)
+    transcript = _clean_transcript(asr_content)
+    print(f"[1/3] transcript : {transcript}")
+    if not transcript:
+        raise SystemExit("ASR pass produced an empty transcript; aborting cascade")
+
+    # The chat-pass recipe is size-conditional (see the TEXT_INSTRUCTION
+    # comment above): the 30B requires the official recipe, the 2B keeps its
+    # original bare-message recipe.
+    if is_30b(args.model):
+        # Official recipe: system prompt + formatting instruction prefixed
+        # to the transcript, reasoning enabled, and the official demo's
+        # text-pass sampling (its defaults: --text-temperature 1.0,
+        # --text-top-p 0.95, --text-max-tokens 2048). Near-greedy decoding
+        # makes the 30B loop inside its reasoning block until the token
+        # budget runs out without ever closing </think>. Even at the
+        # official temperature some trajectories spin past the budget, so
+        # retry a few deterministic seeds; a natural stop without </think>
+        # is a complete answer, and if every seed spins we keep the full
+        # text like the official demo does.
+        chat_messages = [
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": f"{TEXT_INSTRUCTION}\n\n{transcript}"},
+        ]
+        raw = ""
+        for seed in (0, 1, 2):
+            raw, finish_reason = _chat(
+                base_url,
+                args.model,
+                chat_messages,
+                max(args.max_tokens, 2048),
+                enable_thinking=True,
+                temperature=1.0,
+                top_p=0.95,
+                seed=seed,
+            )
+            if "</think>" in raw or finish_reason != "length":
+                break
+            print(f"[2/3] chat seed {seed} hit the token budget mid-reasoning; retrying")
+        answer = _extract_final_response(raw)
+    else:
+        # 2B: bare user message (no system/instruction prefix), reasoning
+        # disabled, temperature 0.0, no retry — its original recipe. The
+        # official temp-1.0 seed-0 recipe measurably makes the 2B
+        # hallucinate off-language answers (see the module-level comment).
+        chat_messages = [{"role": "user", "content": transcript}]
+        raw, _ = _chat(
+            base_url,
+            args.model,
+            chat_messages,
+            args.max_tokens,
+            enable_thinking=False,
+            temperature=0.0,
+        )
+        answer = _extract_final_response(raw)
+    print(f"[2/3] answer     : {answer}")
+    if not answer:
+        raise SystemExit("Chat pass produced an empty answer; aborting cascade")
+
+    _speech(base_url, args.model, _tts_text(answer), cfg_scale, out_path)
+    print(f"[3/3] speech     : {out_path.stat().st_size} bytes -> {out_path}")
+
+
+if __name__ == "__main__":
+    main()

examples/online_serving/audex/run_server.sh

@@ -0,0 +1,57 @@
+#!/bin/bash
+# Launch the vLLM-Omni server for Audex (Nemotron-Labs-Audex-2B).
+#
+# MODE picks the deployment (see README.md for the capability matrix):
+#   tts           text -> speech            /v1/audio/speech   (default)
+#   tta           caption -> general audio  /v1/audio/speech
+#   thinker_only  audio -> text             /v1/chat/completions
+#   s2s           cascaded speech-to-speech (both endpoints)
+#
+# Pass the HF repo ROOT as MODEL — per-stage subfolders resolve
+# automatically.
+#
+# Usage:
+#   ./run_server.sh                       # 2B tts on port 8097, GPU 0
+#   SIZE=30b ./run_server.sh              # 30B-A3B tts (explicit 30B yaml)
+#   SIZE=30b MODE=s2s PORT=8098 ./run_server.sh
+#   MODE=tta ./run_server.sh              # needs XCodec1 (auto-downloaded)
+#   MODEL=/path/to/local/snapshot ./run_server.sh
+
+set -e
+
+MODE="${MODE:-${1:-tts}}"
+SIZE="${SIZE:-2b}"
+if [ "$SIZE" = "30b" ]; then
+    MODEL="${MODEL:-nvidia/Nemotron-Labs-Audex-30B-A3B}"
+    YAML_SUFFIX="_30b"
+elif [ "$SIZE" = "2b" ]; then
+    MODEL="${MODEL:-nvidia/Nemotron-Labs-Audex-2B}"
+    YAML_SUFFIX=""
+else
+    echo "Unknown SIZE '$SIZE'; expected 2b|30b" >&2
+    exit 1
+fi
+PORT="${PORT:-8097}"
+GPUS="${GPUS:-0}"
+
+REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../.." && pwd)"
+DEPLOY_YAML="$REPO_ROOT/vllm_omni/deploy/audex_${MODE}${YAML_SUFFIX}.yaml"
+if [ ! -f "$DEPLOY_YAML" ]; then
+    echo "Unknown MODE '$MODE' / SIZE '$SIZE' (no $DEPLOY_YAML); expected MODE=tts|tta|thinker_only|s2s, SIZE=2b|30b" >&2
+    exit 1
+fi
+
+echo "Starting Audex server"
+echo "  MODE=$MODE ($DEPLOY_YAML)"
+echo "  SIZE=$SIZE"
+echo "  MODEL=$MODEL"
+echo "  PORT=$PORT"
+echo "  CUDA_VISIBLE_DEVICES=$GPUS"
+
+CUDA_VISIBLE_DEVICES="$GPUS" \
+vllm-omni serve "$MODEL" \
+    --host 0.0.0.0 \
+    --port "$PORT" \
+    --trust-remote-code \
+    --deploy-config "$DEPLOY_YAML" \
+    --omni

examples/online_serving/chart-helm/templates/_helpers.tpl

@@ -28,8 +28,11 @@ If image.command is set, uses that as a full override.
 - {{ .Values.omniArgs.numGpus | quote }}
 {{- end }}
 {{- if .Values.omniArgs.stageConfigsPath }}
-- "--stage-configs-path"
-- {{ .Values.omniArgs.stageConfigsPath | quote }}
+{{- fail "omniArgs.stageConfigsPath has been removed; use omniArgs.deployConfigPath" }}
+{{- end }}
+{{- if .Values.omniArgs.deployConfigPath }}
+- "--deploy-config"
+- {{ .Values.omniArgs.deployConfigPath | quote }}
 {{- end }}
 {{- if and .Values.omniArgs.cacheBackend (ne .Values.omniArgs.cacheBackend "none") }}
 - "--cache-backend"

examples/online_serving/chart-helm/tests/deployment_test.yaml

@@ -78,6 +78,26 @@ tests:
           path: spec.template.spec.containers[0].command
           content: "--enable-layerwise-offload"
 
+  - it: should include deploy config in command
+    set:
+      omniArgs:
+        deployConfigPath: "/configs/deploy.yaml"
+    asserts:
+      - equal:
+          path: spec.template.spec.containers[0].command[8]
+          value: "--deploy-config"
+      - equal:
+          path: spec.template.spec.containers[0].command[9]
+          value: "/configs/deploy.yaml"
+
+  - it: should reject removed stage config value
+    set:
+      omniArgs:
+        stageConfigsPath: "/configs/legacy.yaml"
+    asserts:
+      - failedTemplate:
+          errorMessage: "omniArgs.stageConfigsPath has been removed; use omniArgs.deployConfigPath"
+
   - it: should use full command override when image.command is set
     set:
       image:

examples/online_serving/chart-helm/values.yaml

@@ -67,8 +67,8 @@ omniArgs:
   enableCpuOffload: false
   # -- Number of GPUs for diffusion inference (empty = auto)
   numGpus:
-  # -- Path to stage configs file (empty = auto-detected from model)
-  stageConfigsPath:
+  # -- Path to deploy config file (empty = auto-detected from model)
+  deployConfigPath:
   # -- Cache backend: none, tea_cache, or cache_dit
   cacheBackend: "none"
   # -- Default sampling params as a JSON string

examples/online_serving/minicpmo/README.md

@@ -23,17 +23,18 @@ including `librosa`.
 
 The deploy config auto-loads via `--omni`.
 The default `vllm_omni/deploy/minicpmo_4_5.yaml` keeps all three stages on
-logical device 0 with memory budgets of 55%, 22%, and 22%. The profile admits
-at most four sequences per stage and bounds startup video profiling to 32
-frames per video. For throughput,`minicpmo_4_5_batching.yaml` gives the Thinker
+logical device 0 with memory budgets of 55%, 15%, and 15%, leaving headroom
+for runtime kernels such as the HiFi-GAN vocoder's cuDNN workspace. The
+profile admits at most four sequences per stage and bounds startup video
+profiling to 32 frames per video. For throughput,
+`minicpmo_4_5_2gpu.yaml` gives the Thinker
 GPU 0 (90%) and colocates the Talker (55%) and Code2Wav (35%) on GPU 1. That
 profile admits at most four concurrent sequences per stage.
 
 | deploy config | GPUs | Notes |
 |---|---|---|
 | `minicpmo_4_5.yaml` (default) | 1 | Memory-constrained compatibility layout. |
-| `minicpmo_4_5_batching.yaml` | 2 | Recommended continuous-batching layout; Talker and Code2Wav share GPU 1. |
-| `minicpmo_4_5_2gpu.yaml` | 2 | Backward-compatible alias for `minicpmo_4_5_batching.yaml`. |
+| `minicpmo_4_5_2gpu.yaml` | 2 | Recommended continuous-batching layout; Talker and Code2Wav share GPU 1. |
 | `minicpmo_4_5_3gpu.yaml` | 3 | One GPU per stage. |
 | `minicpmo_4_5_8x4090.yaml` | 8 | Full 8x4090 layout. |
 | `minicpmo_4_5_duplex.yaml` | 1 | Experimental native full-duplex overlay. |
@@ -89,17 +90,18 @@ python openai_chat_completion_client_for_multimodal_generation.py \
     --prompt "Briefly introduce yourself."
 ```
 
-Streaming text + audio:
+Streaming text + audio (use `--stream`):
 
 ```bash
-python streaming_chat_completion.py \
-    --base-url http://localhost:8099/v1 \
-    --output minicpmo_stream.wav
+python openai_chat_completion_client_for_multimodal_generation.py \
+    --query-type text \
+    --prompt "Briefly introduce yourself." \
+    --port 8099 \
+    --stream
 ```
 
-The example prints text deltas immediately and joins the independently encoded
-24 kHz WAV audio deltas into one valid WAV file. Add `--text-only` to skip
-Talker and Code2Wav.
+The client prints text deltas as they arrive and saves streamed audio chunks
+to WAV files.
 
 Shared helpers also work if you pass MiniCPM defaults yourself:
 

examples/online_serving/minicpmo/realtime_web/app/static/playback_worklet.js

@@ -6,6 +6,7 @@ class FullDuplexPcmPlayback extends AudioWorkletProcessor {
     this.playedFrames = 0;
     this.underrunFrames = 0;
     this.drain = null;
+    this.terminalFadeFrames = 0;
     this.started = false;
     this.activeResponseId = null;
     this.initialBufferFrames = Math.round(sampleRate * 0.2);
@@ -31,6 +32,7 @@ class FullDuplexPcmPlayback extends AudioWorkletProcessor {
       }
     } else if (message.type === 'drain') {
       this.drain = { responseId: message.responseId || null };
+      this.terminalFadeFrames = Math.min(this.fadeFrames, this.bufferedFrames());
       if (!this.started && this.bufferedFrames() > 0) {
         this.bufferWaitFrames = 0;
         this.startPlayback();
@@ -42,6 +44,7 @@ class FullDuplexPcmPlayback extends AudioWorkletProcessor {
       this.playedFrames = 0;
       this.underrunFrames = 0;
       this.drain = null;
+      this.terminalFadeFrames = 0;
       this.started = false;
       this.activeResponseId = null;
       this.bufferWaitFrames = this.initialBufferFrames;
@@ -76,6 +79,7 @@ class FullDuplexPcmPlayback extends AudioWorkletProcessor {
     this.playedFrames = 0;
     this.underrunFrames = 0;
     this.drain = null;
+    this.terminalFadeFrames = 0;
     this.started = false;
     this.activeResponseId = null;
     this.bufferWaitFrames = this.initialBufferFrames;
@@ -116,6 +120,7 @@ class FullDuplexPcmPlayback extends AudioWorkletProcessor {
     while (target < output.length && this.queue.length > 0) {
       const pcm = this.queue[0];
       const count = Math.min(output.length - target, pcm.length - this.offset);
+      const remainingBeforeChunk = this.bufferedFrames();
       for (let index = 0; index < count; index += 1) {
         let sample = pcm[this.offset + index] / 32768;
         if (this.fadeInFrames > 0) {
@@ -123,6 +128,16 @@ class FullDuplexPcmPlayback extends AudioWorkletProcessor {
           sample *= elapsed / this.fadeFrames;
           this.fadeInFrames -= 1;
         }
+        const remainingFrames = remainingBeforeChunk - index;
+        if (
+          this.drain
+          && this.terminalFadeFrames > 0
+          && remainingFrames <= this.terminalFadeFrames
+        ) {
+          sample *= this.terminalFadeFrames === 1
+            ? 0
+            : (remainingFrames - 1) / (this.terminalFadeFrames - 1);
+        }
         output[target + index] = sample;
       }
       target += count;

examples/online_serving/minicpmo/run_gradio_demo.sh

@@ -3,7 +3,7 @@
 #
 # Prereq:
 #   Start a vllm-omni OpenAI server for MiniCPM-o 4.5 on :8099 (see the
-#   8x4090 stage config under vllm_omni/model_executor/stage_configs).
+#   8x4090 deploy config at vllm_omni/deploy/minicpmo_4_5_8x4090.yaml).
 set -e
 
 HERE="$(cd "$(dirname "$0")" && pwd)"

examples/online_serving/minicpmo/streaming_chat_completion.py

@@ -1,71 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Stream MiniCPM-o 4.5 text and audio from /v1/chat/completions."""
-
-from __future__ import annotations
-
-import argparse
-import base64
-import io
-import wave
-from pathlib import Path
-
-from openai import OpenAI
-
-
-def append_wav_chunk(encoded: str, pcm_parts: list[bytes]) -> tuple[int, int, int]:
-    """Decode one base64 WAV delta and append its PCM frames."""
-    with wave.open(io.BytesIO(base64.b64decode(encoded)), "rb") as wav:
-        channels = wav.getnchannels()
-        sample_width = wav.getsampwidth()
-        sample_rate = wav.getframerate()
-        pcm_parts.append(wav.readframes(wav.getnframes()))
-    return channels, sample_width, sample_rate
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--base-url", default="http://localhost:8099/v1")
-    parser.add_argument("--model", default="openbmb/MiniCPM-o-4_5")
-    parser.add_argument("--prompt", default="Say hello, then introduce vLLM in one sentence.")
-    parser.add_argument("--output", type=Path, default=Path("minicpmo_stream.wav"))
-    parser.add_argument("--text-only", action="store_true")
-    args = parser.parse_args()
-
-    modalities = ["text"] if args.text_only else ["text", "audio"]
-    stream = OpenAI(base_url=args.base_url, api_key="EMPTY").chat.completions.create(
-        model=args.model,
-        messages=[{"role": "user", "content": args.prompt}],
-        modalities=modalities,
-        stream=True,
-        extra_body={"chat_template_kwargs": {"use_tts_template": not args.text_only}},
-    )
-
-    pcm_parts: list[bytes] = []
-    audio_format: tuple[int, int, int] | None = None
-    for chunk in stream:
-        for choice in chunk.choices:
-            content = getattr(choice.delta, "content", None)
-            if not content:
-                continue
-            if getattr(chunk, "modality", None) == "audio":
-                current_format = append_wav_chunk(content, pcm_parts)
-                if audio_format is not None and current_format != audio_format:
-                    raise ValueError(f"Audio format changed: {audio_format} -> {current_format}")
-                audio_format = current_format
-            elif getattr(chunk, "modality", None) == "text":
-                print(content, end="", flush=True)
-
-    print()
-    if pcm_parts and audio_format is not None:
-        channels, sample_width, sample_rate = audio_format
-        with wave.open(str(args.output), "wb") as wav:
-            wav.setnchannels(channels)
-            wav.setsampwidth(sample_width)
-            wav.setframerate(sample_rate)
-            wav.writeframes(b"".join(pcm_parts))
-        print(f"Audio saved to {args.output} ({sample_rate} Hz)")
-
-
-if __name__ == "__main__":
-    main()

examples/online_serving/qwen2_5_omni/README.md

@@ -12,9 +12,9 @@ Please refer to [README.md](../../../README.md)
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-If you have custom stage configs file, launch the server with command below
+To use a custom deploy config, launch the server with the command below:
 ```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
+vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
 ### Send Multi-modal Request
@@ -173,7 +173,7 @@ The script supports the following arguments:
 - `--model`: Model name/path (default: Qwen/Qwen2.5-Omni-7B)
 - `--server-port`: Port for vLLM server (default: 8091)
 - `--gradio-port`: Port for Gradio demo (default: 7861)
-- `--stage-configs-path`: Path to custom stage configs YAML file (optional)
+- `--deploy-config`: Path to a custom deploy config YAML file (optional)
 - `--server-host`: Host for vLLM server (default: 0.0.0.0)
 - `--gradio-ip`: IP for Gradio demo (default: 127.0.0.1)
 - `--share`: Share Gradio demo publicly (creates a public link)
@@ -186,9 +186,9 @@ The script supports the following arguments:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-If you have custom stage configs file:
+To use a custom deploy config:
 ```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
+vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
 **Step 2: Run the Gradio demo**

examples/online_serving/qwen2_5_omni/run_gradio_demo.sh

@@ -13,7 +13,7 @@ set -e
 MODEL="Qwen/Qwen2.5-Omni-7B"
 SERVER_PORT=8091
 GRADIO_PORT=7861
-STAGE_CONFIGS_PATH=""
+DEPLOY_CONFIG_PATH=""
 SERVER_HOST="0.0.0.0"
 GRADIO_IP="127.0.0.1"
 GRADIO_SHARE=false
@@ -33,8 +33,8 @@ while [[ $# -gt 0 ]]; do
             GRADIO_PORT="$2"
             shift 2
             ;;
-        --stage-configs-path)
-            STAGE_CONFIGS_PATH="$2"
+        --deploy-config)
+            DEPLOY_CONFIG_PATH="$2"
             shift 2
             ;;
         --server-host)
@@ -56,7 +56,7 @@ while [[ $# -gt 0 ]]; do
             echo "  --model MODEL                 Model name/path (default: Qwen/Qwen2.5-Omni-7B)"
             echo "  --server-port PORT            Port for vLLM server (default: 8091)"
             echo "  --gradio-port PORT            Port for Gradio demo (default: 7861)"
-            echo "  --stage-configs-path PATH     Path to custom stage configs YAML file (optional)"
+            echo "  --deploy-config PATH          Path to custom deploy config YAML file (optional)"
             echo "  --server-host HOST            Host for vLLM server (default: 0.0.0.0)"
             echo "  --gradio-ip IP                IP for Gradio demo (default: 127.0.0.1)"
             echo "  --share                       Share Gradio demo publicly"
@@ -87,8 +87,8 @@ echo "=========================================="
 
 # Build vLLM server command
 SERVER_CMD=("vllm" "serve" "$MODEL" "--omni" "--port" "$SERVER_PORT" "--host" "$SERVER_HOST")
-if [ -n "$STAGE_CONFIGS_PATH" ]; then
-    SERVER_CMD+=("--stage-configs-path" "$STAGE_CONFIGS_PATH")
+if [ -n "$DEPLOY_CONFIG_PATH" ]; then
+    SERVER_CMD+=("--deploy-config" "$DEPLOY_CONFIG_PATH")
 fi
 
 # Function to cleanup on exit

examples/online_serving/qwen3_omni/qwen3_omni_moe_thinking.yaml

@@ -1,36 +0,0 @@
-# Stage config for running Qwen3-Omni-MoE-Thinking (text-only output)
-# This config is for models like Qwen3-Omni-30B-A3B-Thinking that only have the
-# thinker component and do not support audio output.
-#
-# Single stage: Thinker (multimodal understanding + text generation)
-
-# The following config has been verified on 2x H100-80G GPUs.
-stage_args:
-  - stage_id: 0
-    runtime:
-      devices: "0,1"
-    engine_args:
-      model_stage: thinker
-      max_num_seqs: 1
-      model_arch: Qwen3OmniMoeForConditionalGeneration
-      worker_type: ar
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-      gpu_memory_utilization: 0.9
-      enforce_eager: true
-      trust_remote_code: true
-      engine_output_type: text
-      distributed_executor_backend: "mp"
-      enable_prefix_caching: true
-      hf_config_name: thinker_config
-      tensor_parallel_size: 2
-    final_output: true
-    final_output_type: text
-    is_comprehension: true
-    default_sampling_params:
-      temperature: 0.4
-      top_p: 0.9
-      top_k: 1
-      max_tokens: 2048
-      seed: 42
-      detokenize: True
-      repetition_penalty: 1.05

examples/online_serving/qwen3_omni/run_gradio_demo.sh

@@ -13,7 +13,7 @@ set -e
 MODEL="Qwen/Qwen3-Omni-30B-A3B-Instruct"
 SERVER_PORT=8091
 GRADIO_PORT=7861
-STAGE_CONFIGS_PATH=""
+DEPLOY_CONFIG=""
 SERVER_HOST="0.0.0.0"
 GRADIO_IP="127.0.0.1"
 GRADIO_SHARE=false
@@ -33,8 +33,8 @@ while [[ $# -gt 0 ]]; do
             GRADIO_PORT="$2"
             shift 2
             ;;
-        --stage-configs-path)
-            STAGE_CONFIGS_PATH="$2"
+        --deploy-config)
+            DEPLOY_CONFIG="$2"
             shift 2
             ;;
         --server-host)
@@ -56,7 +56,7 @@ while [[ $# -gt 0 ]]; do
             echo "  --model MODEL                 Model name/path (default: Qwen/Qwen3-Omni-30B-A3B-Instruct)"
             echo "  --server-port PORT            Port for vLLM server (default: 8091)"
             echo "  --gradio-port PORT            Port for Gradio demo (default: 7861)"
-            echo "  --stage-configs-path PATH     Path to custom stage configs YAML file (optional)"
+            echo "  --deploy-config PATH          Path to custom deploy config YAML file (optional)"
             echo "  --server-host HOST            Host for vLLM server (default: 0.0.0.0)"
             echo "  --gradio-ip IP                IP for Gradio demo (default: 127.0.0.1)"
             echo "  --share                       Share Gradio demo publicly"
@@ -87,8 +87,8 @@ echo "=========================================="
 
 # Build vLLM server command
 SERVER_CMD=("vllm" "serve" "$MODEL" "--omni" "--port" "$SERVER_PORT" "--host" "$SERVER_HOST")
-if [ -n "$STAGE_CONFIGS_PATH" ]; then
-    SERVER_CMD+=("--stage-configs-path" "$STAGE_CONFIGS_PATH")
+if [ -n "$DEPLOY_CONFIG" ]; then
+    SERVER_CMD+=("--deploy-config" "$DEPLOY_CONFIG")
 fi
 
 # Function to cleanup on exit

examples/online_serving/text_to_speech/README.md

@@ -336,7 +336,7 @@ vllm serve OpenMOSS-Team/MOSS-TTS-Nano --omni --port 8091
 # or:
 ./moss_tts_nano/run_server.sh
 ```
-The deploy config at `vllm_omni/deploy/moss_tts_nano.yaml` auto-loads; no `--stage-configs-path`, `--trust-remote-code`, or `--enforce-eager` flags are needed.
+The deploy config at `vllm_omni/deploy/moss_tts_nano.yaml` auto-loads; no `--deploy-config`, `--trust-remote-code`, or `--enforce-eager` flags are needed.
 
 ### Sending requests
 ```bash
@@ -537,12 +537,19 @@ curl -X POST http://localhost:8091/v1/audio/speech \
 Raw PCM streaming requires `stream_format="audio"`, `response_format="pcm"`, and `async_chunk: true` on the stage config (default in `qwen3_tts.yaml`). `speed` is not supported when streaming.
 
 ### Streaming WebSocket
-The `/v1/audio/speech/stream` endpoint accepts text incrementally, splits it at sentence boundaries, and emits one PCM stream per sentence:
+The `/v1/audio/speech/stream` endpoint accepts text incrementally and synthesizes the buffered text as one continuous request on `input.done`:
 ```bash
 python qwen3_tts/streaming_speech_client.py --text "Hello world. How are you? I am fine."
 python qwen3_tts/streaming_speech_client.py --text "..." --simulate-stt --stt-delay 0.1
 ```
 
+`input.done` flushes without closing, so repeating `--text` synthesizes several utterances over one connection:
+```bash
+python qwen3_tts/streaming_speech_client.py \
+    --text "First utterance." \
+    --text "Second utterance, same connection."
+```
+
 To receive word-level timestamps, launch the server with a forced aligner:
 ```bash
 vllm-omni serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \

examples/online_serving/text_to_speech/qwen3_tts/streaming_speech_client.py

@@ -7,6 +7,12 @@ Usage:
     # Send full text at once
     python streaming_speech_client.py --text "Hello world. How are you? I am fine."
 
+    # Several utterances over one connection: input.done flushes each of them
+    # and the connection stays open, so only the first pays the handshake
+    python streaming_speech_client.py \
+        --text "First utterance." \
+        --text "Second utterance, same connection."
+
     # Pick a built-in speaker for CustomVoice models
     python streaming_speech_client.py \
         --text "打开电子枪，关闭扫描，切换到低倍模式。" \
@@ -64,18 +70,27 @@ except ImportError:
     raise SystemExit(1)
 
 
-def save_audio_file(output_dir: str, sentence_index: int, response_format: str, chunks: list[bytes]) -> str:
+def frame_basename(msg: dict) -> str:
+    """Name a file after the utterance and sentence a frame belongs to.
+
+    Every utterance is sentence 0, so the utterance index is what keeps the
+    files of one connection from overwriting each other.
+    """
+    return f"utterance_{msg['utterance_index']:03d}_sentence_{msg['sentence_index']:03d}"
+
+
+def save_audio_file(output_dir: str, basename: str, response_format: str, chunks: list[bytes]) -> str:
     audio_bytes = b"".join(chunks)
     if response_format == "pcm":
-        raw_path = os.path.join(output_dir, f"sentence_{sentence_index:03d}.pcm")
+        raw_path = os.path.join(output_dir, f"{basename}.pcm")
         with open(raw_path, "wb") as f:
             f.write(audio_bytes)
 
-        wav_path = os.path.join(output_dir, f"sentence_{sentence_index:03d}.wav")
+        wav_path = os.path.join(output_dir, f"{basename}.wav")
         save_pcm_wav(wav_path, audio_bytes)
         return wav_path
 
-    filename = os.path.join(output_dir, f"sentence_{sentence_index:03d}.{response_format}")
+    filename = os.path.join(output_dir, f"{basename}.{response_format}")
     with open(filename, "wb") as f:
         f.write(audio_bytes)
     return filename
@@ -95,183 +110,207 @@ def timestamp_words_text(timestamps: list[dict] | None) -> str:
     return " ".join(str(item.get("word", "")) for item in timestamps if item.get("word"))
 
 
+async def send_utterance(ws, text: str, simulate_stt: bool, stt_delay: float) -> None:
+    """Send one utterance's text, then input.done to flush it."""
+    if simulate_stt:
+        words = text.split(" ")
+        for i, word in enumerate(words):
+            chunk = word + (" " if i < len(words) - 1 else "")
+            await ws.send(
+                json.dumps(
+                    {
+                        "type": "input.text",
+                        "text": chunk,
+                    }
+                )
+            )
+            print(f"  Sent: {chunk!r}")
+            await asyncio.sleep(stt_delay)
+    else:
+        await ws.send(
+            json.dumps(
+                {
+                    "type": "input.text",
+                    "text": text,
+                }
+            )
+        )
+        print(f"Sent full text: {text!r}")
+
+    await ws.send(json.dumps({"type": "input.done"}))
+    print("Sent input.done")
+
+
+async def receive_utterance(
+    ws,
+    output_dir: str,
+    response_format: str,
+    word_timestamps: bool,
+    save_chunks: bool,
+) -> None:
+    """Consume frames until session.done marks the flushed utterance complete."""
+    current_label = "utterance 0"
+    current_sentence_text = ""
+    current_chunks: list[bytes] = []
+    current_timestamps: list[dict] = []
+    # Distinguish `null` (aligner failed) from `[]` (silence / no tokens):
+    # the sentence is a failure only if no frame ever carried timestamps.
+    alignment_frame_seen = False
+
+    while True:
+        message = await ws.recv()
+
+        if isinstance(message, bytes):
+            current_chunks.append(message)
+            print(f"  Received audio chunk for {current_label}: {len(message)} bytes")
+        else:
+            # JSON frame
+            msg = json.loads(message)
+            msg_type = msg.get("type")
+
+            if msg_type == "audio.start":
+                current_label = f"utterance {msg['utterance_index']}"
+                current_sentence_text = msg.get("sentence_text", "")
+                current_chunks = []
+                current_timestamps = []
+                alignment_frame_seen = False
+                print(f"  [{current_label}] Generating: {current_sentence_text!r}")
+            elif msg_type == "audio.chunk":
+                audio = base64.b64decode(msg["audio_b64"])
+                current_chunks.append(audio)
+                chunk_timestamps = msg.get("timestamps")
+                # Sentence-level alignment sends a trailing timestamp-only
+                # frame with empty audio; don't write an empty chunk file.
+                if save_chunks and audio:
+                    chunk_base = os.path.join(
+                        output_dir,
+                        f"{frame_basename(msg)}_chunk_{msg['chunk_id']:03d}",
+                    )
+                    chunk_pcm = f"{chunk_base}.pcm"
+                    chunk_wav = f"{chunk_base}.wav"
+                    chunk_json = f"{chunk_base}_timestamps.json"
+                    with open(chunk_pcm, "wb") as f:
+                        f.write(audio)
+                    save_pcm_wav(chunk_wav, audio, int(msg.get("sample_rate", 24000)))
+                    with open(chunk_json, "w", encoding="utf-8") as f:
+                        json.dump(
+                            {
+                                "utterance_index": msg["utterance_index"],
+                                "sentence_index": msg["sentence_index"],
+                                "chunk_id": msg["chunk_id"],
+                                "chunk_start_ms": msg.get("chunk_start_ms"),
+                                "chunk_end_ms": msg.get("chunk_end_ms"),
+                                "sentence_text": current_sentence_text,
+                                "covered_text": timestamp_words_text(chunk_timestamps),
+                                "timestamps": chunk_timestamps,
+                            },
+                            f,
+                            ensure_ascii=False,
+                            indent=2,
+                        )
+                if chunk_timestamps is None:
+                    print(f"  [{current_label}] chunk {msg['chunk_id']}: {len(audio)} bytes, timestamps unavailable")
+                else:
+                    alignment_frame_seen = True
+                    current_timestamps.extend(chunk_timestamps)
+                    print(
+                        f"  [{current_label}] chunk {msg['chunk_id']}: "
+                        f"{len(audio)} bytes, {len(chunk_timestamps)} timestamp(s)"
+                    )
+                if save_chunks and audio:
+                    print(f"    saved chunk -> {chunk_wav}")
+            elif msg_type == "audio.done":
+                filename = save_audio_file(
+                    output_dir,
+                    frame_basename(msg),
+                    response_format,
+                    current_chunks,
+                )
+                if word_timestamps:
+                    # `null` on failure, else the accumulated list (`[]` for silence).
+                    sentence_timestamps = current_timestamps if alignment_frame_seen else None
+                    ts_filename = os.path.join(
+                        output_dir,
+                        f"{frame_basename(msg)}_timestamps.json",
+                    )
+                    with open(ts_filename, "w", encoding="utf-8") as f:
+                        json.dump(
+                            {
+                                "utterance_index": msg["utterance_index"],
+                                "sentence_index": msg["sentence_index"],
+                                "sentence_text": current_sentence_text,
+                                "covered_text": timestamp_words_text(sentence_timestamps),
+                                "timestamps": sentence_timestamps,
+                            },
+                            f,
+                            ensure_ascii=False,
+                            indent=2,
+                        )
+                print(
+                    f"  [{current_label}] Done"
+                    f" bytes={msg.get('total_bytes', len(b''.join(current_chunks)))}"
+                    f" error={msg.get('error', False)}"
+                    f" -> {filename}"
+                )
+                if word_timestamps:
+                    print(f"  [{current_label}] Timestamps -> {ts_filename}")
+                current_chunks = []
+                current_timestamps = []
+                alignment_frame_seen = False
+            elif msg_type == "session.done":
+                print(f"\nUtterance {msg['utterance_index']} complete: {msg['total_sentences']} sentence(s) generated")
+                return
+            elif msg_type == "error":
+                print(f"  ERROR: {msg['message']}")
+            else:
+                print(f"  Unknown message: {msg}")
+
+
 async def stream_tts(
     url: str,
-    text: str,
+    texts: list[str],
     config: dict,
     output_dir: str,
     simulate_stt: bool = False,
     stt_delay: float = 0.1,
     save_chunks: bool = False,
 ) -> None:
-    """Connect to the streaming TTS endpoint and process audio responses."""
+    """Connect to the streaming TTS endpoint and process audio responses.
+
+    Every text is synthesized over the same connection: input.done flushes an
+    utterance without closing, so only the first one pays the handshake.
+    """
     os.makedirs(output_dir, exist_ok=True)
+    response_format = config.get("response_format", "wav")
+    word_timestamps = bool(config.get("word_timestamps", False))
 
     async with websockets.connect(url) as ws:
-        # 1. Send session config
+        # 1. Send session config; it stays in effect for every utterance below.
         config_msg = {"type": "session.config", **config}
         await ws.send(json.dumps(config_msg))
         print(f"Sent session config: {config}")
 
-        # 2. Send text (either all at once or word-by-word)
-        async def send_text():
-            if simulate_stt:
-                words = text.split(" ")
-                for i, word in enumerate(words):
-                    chunk = word + (" " if i < len(words) - 1 else "")
-                    await ws.send(
-                        json.dumps(
-                            {
-                                "type": "input.text",
-                                "text": chunk,
-                            }
-                        )
-                    )
-                    print(f"  Sent: {chunk!r}")
-                    await asyncio.sleep(stt_delay)
-            else:
-                await ws.send(
-                    json.dumps(
-                        {
-                            "type": "input.text",
-                            "text": text,
-                        }
-                    )
-                )
-                print(f"Sent full text: {text!r}")
-
-            # 3. Signal end of input
-            await ws.send(json.dumps({"type": "input.done"}))
-            print("Sent input.done")
-
-        # Run sender and receiver concurrently
-        sender_task = asyncio.create_task(send_text())
-
-        response_format = config.get("response_format", "wav")
-        word_timestamps = bool(config.get("word_timestamps", False))
-        current_sentence_index = 0
-        current_sentence_text = ""
-        current_chunks: list[bytes] = []
-        current_timestamps: list[dict] = []
-        # Distinguish `null` (aligner failed) from `[]` (silence / no tokens):
-        # the sentence is a failure only if no frame ever carried timestamps.
-        alignment_frame_seen = False
-
-        try:
-            while True:
-                message = await ws.recv()
-
-                if isinstance(message, bytes):
-                    current_chunks.append(message)
-                    print(f"  Received audio chunk for sentence {current_sentence_index}: {len(message)} bytes")
-                else:
-                    # JSON frame
-                    msg = json.loads(message)
-                    msg_type = msg.get("type")
-
-                    if msg_type == "audio.start":
-                        current_sentence_index = msg["sentence_index"]
-                        current_sentence_text = msg.get("sentence_text", "")
-                        current_chunks = []
-                        current_timestamps = []
-                        alignment_frame_seen = False
-                        print(f"  [sentence {msg['sentence_index']}] Generating: {current_sentence_text!r}")
-                    elif msg_type == "audio.chunk":
-                        audio = base64.b64decode(msg["audio_b64"])
-                        current_chunks.append(audio)
-                        chunk_timestamps = msg.get("timestamps")
-                        # Sentence-level alignment sends a trailing timestamp-only
-                        # frame with empty audio; don't write an empty chunk file.
-                        if save_chunks and audio:
-                            chunk_base = os.path.join(
-                                output_dir,
-                                f"sentence_{msg['sentence_index']:03d}_chunk_{msg['chunk_id']:03d}",
-                            )
-                            chunk_pcm = f"{chunk_base}.pcm"
-                            chunk_wav = f"{chunk_base}.wav"
-                            chunk_json = f"{chunk_base}_timestamps.json"
-                            with open(chunk_pcm, "wb") as f:
-                                f.write(audio)
-                            save_pcm_wav(chunk_wav, audio, int(msg.get("sample_rate", 24000)))
-                            with open(chunk_json, "w", encoding="utf-8") as f:
-                                json.dump(
-                                    {
-                                        "sentence_index": msg["sentence_index"],
-                                        "chunk_id": msg["chunk_id"],
-                                        "chunk_start_ms": msg.get("chunk_start_ms"),
-                                        "chunk_end_ms": msg.get("chunk_end_ms"),
-                                        "sentence_text": current_sentence_text,
-                                        "covered_text": timestamp_words_text(chunk_timestamps),
-                                        "timestamps": chunk_timestamps,
-                                    },
-                                    f,
-                                    ensure_ascii=False,
-                                    indent=2,
-                                )
-                        if chunk_timestamps is None:
-                            print(
-                                f"  [sentence {msg['sentence_index']}] chunk {msg['chunk_id']}: "
-                                f"{len(audio)} bytes, timestamps unavailable"
-                            )
-                        else:
-                            alignment_frame_seen = True
-                            current_timestamps.extend(chunk_timestamps)
-                            print(
-                                f"  [sentence {msg['sentence_index']}] chunk {msg['chunk_id']}: "
-                                f"{len(audio)} bytes, {len(chunk_timestamps)} timestamp(s)"
-                            )
-                        if save_chunks and audio:
-                            print(f"    saved chunk -> {chunk_wav}")
-                    elif msg_type == "audio.done":
-                        filename = save_audio_file(
-                            output_dir,
-                            msg["sentence_index"],
-                            response_format,
-                            current_chunks,
-                        )
-                        if word_timestamps:
-                            # `null` on failure, else the accumulated list (`[]` for silence).
-                            sentence_timestamps = current_timestamps if alignment_frame_seen else None
-                            ts_filename = os.path.join(
-                                output_dir,
-                                f"sentence_{msg['sentence_index']:03d}_timestamps.json",
-                            )
-                            with open(ts_filename, "w", encoding="utf-8") as f:
-                                json.dump(
-                                    {
-                                        "sentence_index": msg["sentence_index"],
-                                        "sentence_text": current_sentence_text,
-                                        "covered_text": timestamp_words_text(sentence_timestamps),
-                                        "timestamps": sentence_timestamps,
-                                    },
-                                    f,
-                                    ensure_ascii=False,
-                                    indent=2,
-                                )
-                        print(
-                            f"  [sentence {msg['sentence_index']}] Done"
-                            f" bytes={msg.get('total_bytes', len(b''.join(current_chunks)))}"
-                            f" error={msg.get('error', False)}"
-                            f" -> {filename}"
-                        )
-                        if word_timestamps:
-                            print(f"  [sentence {msg['sentence_index']}] Timestamps -> {ts_filename}")
-                        current_chunks = []
-                        current_timestamps = []
-                        alignment_frame_seen = False
-                    elif msg_type == "session.done":
-                        print(f"\nSession complete: {msg['total_sentences']} sentence(s) generated")
-                        break
-                    elif msg_type == "error":
-                        print(f"  ERROR: {msg['message']}")
-                    else:
-                        print(f"  Unknown message: {msg}")
-        finally:
-            sender_task.cancel()
+        for text in texts:
+            # 2. Send text (either all at once or word-by-word), then flush.
+            sender_task = asyncio.create_task(send_utterance(ws, text, simulate_stt, stt_delay))
             try:
-                await sender_task
-            except asyncio.CancelledError:
-                pass  # Task cancellation is expected during shutdown
+                await receive_utterance(
+                    ws,
+                    output_dir=output_dir,
+                    response_format=response_format,
+                    word_timestamps=word_timestamps,
+                    save_chunks=save_chunks,
+                )
+            finally:
+                sender_task.cancel()
+                try:
+                    await sender_task
+                except asyncio.CancelledError:
+                    pass  # Task cancellation is expected during shutdown
+
+        # 3. Release the connection once there is nothing left to say.
+        await ws.send(json.dumps({"type": "session.close"}))
+        print("Sent session.close")
 
     print(f"\nAudio files saved to: {output_dir}/")
 
@@ -286,7 +325,8 @@ def main():
     parser.add_argument(
         "--text",
         required=True,
-        help="Text to synthesize",
+        action="append",
+        help="Text to synthesize; repeat to send several utterances over one connection",
     )
     parser.add_argument(
         "--output-dir",
@@ -385,7 +425,7 @@ def main():
     asyncio.run(
         stream_tts(
             url=args.url,
-            text=args.text,
+            texts=args.text,
             config=config,
             output_dir=args.output_dir,
             simulate_stt=args.simulate_stt,

examples/online_serving/text_to_video/README.md

@@ -248,82 +248,12 @@ done
 
 ## LTX-2
 
-### Start Server
-
-#### Basic Start
-
 ```bash
-vllm serve Lightricks/LTX-2 --omni --port 8098 \
-    --enforce-eager --boundary-ratio 1.0
+vllm serve Lightricks/LTX-2 --omni --port 8098
 ```
 
-#### Start with Optimization Presets
-
-Use the LTX-2 startup script with built-in optimization presets:
-
-```bash
-# Baseline (1 GPU, eager)
-bash run_server_ltx2.sh baseline
-
-# 4-GPU Ulysses sequence parallelism (lossless)
-bash run_server_ltx2.sh ulysses4
-
-# Cache-DiT lossy acceleration (1 GPU, ~1.4× speedup)
-bash run_server_ltx2.sh cache-dit
-
-# Best combo: 4-GPU Ulysses SP + Cache-DiT (~2.2× speedup)
-bash run_server_ltx2.sh best-combo
-```
-
-#### Optimization Benchmarks
-
-Benchmarked on H800, online serving (480×768, 41 frames, 20 steps, `seed=42`).
-"Inference" is the server-reported inference time; excludes HTTP/poll overhead.
-
-| Preset | Server Command | Inference (s) | Speedup | Type |
-|--------|---------------|---------------|---------|------|
-| `baseline` | `--enforce-eager` | 10.3 | 1.00× | — |
-| `compile` | *(default, no --enforce-eager)* | ~10.3 (warm) | ~1.00× | Lossless |
-| `ulysses4` | `--enforce-eager --usp 4` | ~10.3 | ~1.00× | Lossless |
-| `cache-dit` | `--enforce-eager --cache-backend cache_dit` | 7.4 avg | ~1.4× | Lossy |
-| `best-combo` | `--enforce-eager --usp 4 --cache-backend cache_dit` | 4.7 avg | **~2.2×** | Lossless + Lossy |
-
-**Observations**:
-- **torch.compile**: On H800, warm-request inference time matches the eager baseline (~10.3s).
-  The first request pays ~6s compilation overhead. Benefit depends on model architecture and GPU.
-- **Ulysses SP (4 GPU)**: No measurable speedup alone for 41-frame generation at this resolution.
-  Communication overhead outweighs gains at this sequence length.
-- **Cache-DiT**: Inference varies per request (6–10s) due to dynamic caching decisions.
-  Average is ~7.4s (~1.4× speedup) with slight quality tradeoff.
-- **Best combo**: 4-GPU Ulysses SP + Cache-DiT synergize well — Cache-DiT reduces per-step
-  computation, making the communication overhead of Ulysses SP worthwhile. Average ~4.7s
-  (~2.2× speedup).
-- **FP8 quantization**: Reduces VRAM but does not speed up LTX-2 on H800 (compute-bound).
-
-**Deployment Recommendations**:
-- For **production with quality priority**: use `baseline` with `--enforce-eager`
-- For **maximum throughput** (4 GPUs, quality tradeoff): use `best-combo` (~2.2× speedup)
-- For **single-GPU throughput**: use `cache-dit` (~1.4× speedup)
-- `--enforce-eager` is recommended to avoid torch.compile warmup latency on first request
-
-### Send Requests (curl)
-
-```bash
-# Using the provided script
-bash run_curl_ltx2.sh
-
-# Or directly
-curl -sS -X POST http://localhost:8098/v1/videos \
-  -H "Accept: application/json" \
-  -F "prompt=A serene lakeside sunrise with mist over the water." \
-  -F "width=768" \
-  -F "height=480" \
-  -F "num_frames=41" \
-  -F "fps=24" \
-  -F "num_inference_steps=20" \
-  -F "guidance_scale=3.0" \
-  -F "seed=42"
-```
+See the [LTX-2 recipe](../../../recipes/LTX/LTX-2.md) for all checkpoints,
+pipeline selection, requests, defaults, and advanced options.
 
 ## Helios
 

examples/online_serving/text_to_video/run_curl_ltx2.sh

@@ -22,7 +22,6 @@ create_response=$(
     -F "num_frames=41" \
     -F "fps=24" \
     -F "num_inference_steps=20" \
-    -F "guidance_scale=3.0" \
     -F "seed=42"
 )
 

examples/online_serving/text_to_video/run_server_ltx2.sh

@@ -21,8 +21,6 @@ set -euo pipefail
 
 MODEL="${MODEL:-Lightricks/LTX-2}"
 PORT="${PORT:-8098}"
-FLOW_SHIFT="${FLOW_SHIFT:-1.0}"
-BOUNDARY_RATIO="${BOUNDARY_RATIO:-1.0}"
 
 PRESET="${1:-baseline}"
 
@@ -66,19 +64,13 @@ case "$PRESET" in
         echo "Environment variables:"
         echo "  MODEL           - Model path (default: Lightricks/LTX-2)"
         echo "  PORT            - Server port (default: 8098)"
-        echo "  FLOW_SHIFT      - Scheduler flow shift (default: 1.0)"
-        echo "  BOUNDARY_RATIO  - Boundary ratio (default: 1.0)"
         exit 1
         ;;
 esac
 
 echo "Model: $MODEL"
 echo "Port: $PORT"
-echo "Flow shift: $FLOW_SHIFT"
-echo "Boundary ratio: $BOUNDARY_RATIO"
 
 vllm serve "$MODEL" --omni \
     --port "$PORT" \
-    --flow-shift "$FLOW_SHIFT" \
-    --boundary-ratio "$BOUNDARY_RATIO" \
     "${EXTRA_ARGS[@]}"

pyproject.toml

@@ -75,6 +75,7 @@ dev = [
 ]
 
 demo = [
+    "gradio>=6.7.0",
     "opencv-python>=4.12.0.88",  # cli/stream_client frame reading
     "requests>=2.28.0",  # cli/stream_client HTTP
     "cn2an>=0.5.22",  # Chinese number normalization for TTS (e.g. "1次" -> "一次")
@@ -95,6 +96,12 @@ quack = [
     "quack-kernels>=0.3.11",
 ]
 
+# Optional Blackwell-native FlashAttention-4 backend. Keep this opt-in because
+# the CuTe/CUTLASS dependency is CUDA 13 and Blackwell specific.
+fa4 = [
+    "flash-attn-4[cu13]==4.0.0b18",
+]
+
 docs = [
     "mkdocs>=1.5.0",
     "mkdocs-api-autonav",

realtime_video_prompt_interaction_protocol.md

@@ -1,138 +0,0 @@
-# Realtime Video Prompt Interaction Protocol
-
-This document specifies the prompt-update subset of the realtime video
-interaction protocol exposed by:
-
-```text
-WebSocket /v1/realtime/video
-```
-
-A WebSocket connection contains exactly one video-generation request. Clients
-therefore do not send a request or session ID with an interaction. The server
-assigns a `request_id` in `video.start` and includes it in subsequent
-server-to-client messages.
-
-## Interaction Request
-
-After `video.start`, a client may submit a prompt update:
-
-```json
-{
-  "type": "session.interaction",
-  "interaction": {
-    "event_id": "ui-update-42",
-    "event": {
-      "prompt": "A snowy street illuminated by warm lanterns"
-    },
-    "transition_chunks": 3
-  }
-}
-```
-
-`interaction` has the following fields:
-
-- `event_id` (optional string): session-unique idempotency key. The
-  server preserves a client-provided value or generates a UUID when omitted.
-- `event.prompt` (required string): non-empty target prompt.
-  over which the prompt reaches its target.
-- `transition_chunks` (optional non-negative integer): model-generation chunks
-  over which the prompt reaches its target. If omitted, the model's default transition is used.
-
-Note: Although `["interaction"]["event"]` only contains `prompt`, still consider using `OmniTextPrompt` for this part (with total=false) so that other fields will be supported in the future. Currently, `vllm_omni/diffusion/worker/diffusion_model_runner.py` already takes care of enforcing subset format, routing to meaningful input handing routines, and raising `NotImplementedError`. Only need to adapt this design to the input data structure above.
-
-### Transition Semantics
-
-Prompt transitions are target updates and begin at the next eligible model
-generation boundary after the interaction is processed.
-
-- `transition_chunks = 0` is a hard update: the first affected generation
-  chunk uses the target prompt.
-- `transition_chunks = N`, where `N > 0`, reaches the target in the `N`th
-  affected generation chunk. For example, a three-chunk linear transition uses
-  progress values `1/3`, `2/3`, and `1`.
-
-### Idempotency and Replacement
-
-Within a session:
-
-- repeating an `event_id` with the same payload is a retry and must not
-  apply the update twice;
-- repeating an `event_id` with a different payload is rejected with
-  `event_id_conflict`;
-- a newer prompt target supersedes any older pending prompt target;
-- when a new target interrupts an active transition, its transition starts
-  from the prompt state resolved at its effective boundary.
-
-## Queue Acknowledgement
-
-The server quickly acknowledges admission to the interaction queue:
-
-```json
-{
-  "type": "session.interaction.queued",
-  "request_id": "video_stream-...",
-  "event_id": "ui-update-42"
-}
-```
-
-`queued` confirms syntax validation, idempotency handling, and queue admission.
-It does not claim that the model has scheduled or applied the update. A later
-failure is reported as a correlated error:
-
-```json
-{
-  "type": "error",
-  "request_id": "video_stream-...",
-  "event_id": "ui-update-42",
-  "code": "interaction_failed",
-  "message": "The active model does not support prompt updates"
-}
-```
-
-## Video Chunk Metadata
-
-Each emitted binary payload is preceded by a JSON metadata message. The
-metadata message and its binary payload form one ordered pair and must not be
-interleaved with another outbound message.
-
-```json
-{
-  "type": "video.chunk_metadata",
-  "request_id": "video_stream-...",
-  "kind": "media",
-  "transport_chunk_index": 7,
-  "generation_chunk_index": 6,
-  "num_frames": 9,
-  "byte_length": 184320,
-  "started_event_ids": ["ui-update-42"],
-  "active_event_ids": ["ui-update-42"],
-  "completed_event_ids": []
-}
-```
-
-- `transport_chunk_index` identifies the binary WebSocket payload.
-- `generation_chunk_index` identifies the model-generation chunk represented
-  by the payload.
-- `started_event_ids` lists interactions that first affect this
-  generation chunk.
-- `active_event_ids` lists interactions contributing to this generation
-  chunk, including an ongoing transition.
-- `completed_event_ids` lists interactions that reach their target in
-  this generation chunk.
-
-Encoder trailer bytes are emitted as:
-
-```json
-{
-  "type": "video.chunk_metadata",
-  "request_id": "video_stream-...",
-  "kind": "trailer",
-  "transport_chunk_index": 8,
-  "generation_chunk_index": null,
-  "num_frames": 0,
-  "byte_length": 824,
-  "started_event_ids": [],
-  "active_event_ids": [],
-  "completed_event_ids": []
-}
-```

recipes/JD/JoyAI-VL-Interaction.md

@@ -168,7 +168,7 @@ the model + orchestrator + WebUI together — point `WEBUI_DIR` at your clone.
 python examples/online_serving/joyvl_interaction/cli/run_cli_demo.py \
   path/to/video.mp4 --query "Alert me if a fire breaks out"
 
-pytest tests/fullduplex   # framework + JoyVL unit tests
+pytest tests/e2e/features/fullduplex   # framework + JoyVL unit tests
 ```
 
 ## Testing with an RTSP stream (optional)

recipes/LTX/LTX-2.3.md

@@ -1,154 +0,0 @@
-# LTX-2.3
-
-> 22B parameter text-to-video + audio generation model served via vLLM-Omni
-
-## Summary
-
-- Vendor: Lightricks
-- Model: `diffusers/LTX-2.3-Diffusers`
-- Task: Text-to-video and image-to-video with synchronized audio generation
-- Mode: Online serving (pure diffusion)
-- Maintainer: @oglok
-
-## When to use this recipe
-
-Use this recipe when you want to serve LTX-2.3 for text-to-video or
-image-to-video generation with audio. The model generates videos up to 20+
-seconds at 768x512 resolution with 48kHz audio from text prompts, and can
-condition video generation on an initial image. Start with a 96GB-class GPU for
-validation because the model combines a 22B parameter transformer with text
-encoder, VAE, and vocoder components.
-
-## References
-
-- Upstream raw checkpoints: <https://huggingface.co/Lightricks/LTX-2.3>
-- Diffusers-format checkpoint: <https://huggingface.co/diffusers/LTX-2.3-Diffusers>
-- Requires `diffusers >= 0.38.0` (install from git: `pip install git+https://github.com/huggingface/diffusers.git`)
-
-## Serving
-
-### Command
-
-```bash
-vllm serve diffusers/LTX-2.3-Diffusers \
-  --omni \
-  --model-class-name LTX23Pipeline \
-  --stage-init-timeout 600
-```
-
-For image-to-video:
-
-```bash
-vllm serve diffusers/LTX-2.3-Diffusers \
-  --omni \
-  --model-class-name LTX23ImageToVideoPipeline \
-  --stage-init-timeout 600
-```
-
-### T2V Verification
-
-```bash
-# Health check
-curl http://localhost:8000/health
-
-# Generate a 3-second video (81 frames at 24fps)
-curl -X POST http://localhost:8000/v1/videos \
-  -F "prompt=A majestic bald eagle soaring over a misty mountain valley at dawn, golden sunlight breaking through clouds" \
-  -F "negative_prompt=blurry, low quality, distorted, watermark" \
-  -F "model=diffusers/LTX-2.3-Diffusers" \
-  -F "num_frames=81" \
-  -F "fps=24" \
-  -F "size=768x512" \
-  -F "num_inference_steps=30" \
-  -F "guidance_scale=4.0" \
-  -F "seed=42"
-
-# Generate a 10-second video (241 frames)
-curl -X POST http://localhost:8000/v1/videos \
-  -F "prompt=A cozy Japanese ramen shop at night in the rain, steam rising from bowls, neon signs reflecting on wet cobblestone streets" \
-  -F "model=diffusers/LTX-2.3-Diffusers" \
-  -F "num_frames=241" \
-  -F "fps=24" \
-  -F "size=768x512" \
-  -F "num_inference_steps=30" \
-  -F "guidance_scale=4.0"
-
-# Generate a 20-second video (481 frames)
-curl -X POST http://localhost:8000/v1/videos \
-  -F "prompt=An underwater coral reef teeming with tropical fish, sea turtles gliding gracefully, National Geographic documentary style" \
-  -F "model=diffusers/LTX-2.3-Diffusers" \
-  -F "num_frames=481" \
-  -F "fps=24" \
-  -F "size=768x512" \
-  -F "num_inference_steps=30" \
-  -F "guidance_scale=4.0"
-```
-
-### I2V Verification
-
-Start the server with `--model-class-name LTX23ImageToVideoPipeline`, then pass
-an image reference. The I2V pipeline requires `input_reference` or
-`image_reference` because the first frame is the conditioning input.
-
-```bash
-curl http://localhost:8000/health
-
-curl -X POST http://localhost:8000/v1/videos/sync \
-  -F "prompt=A plush toy astronaut gently waving while the camera slowly pushes in." \
-  -F "input_reference=@/absolute/path/to/reference.png" \
-  -F "model=diffusers/LTX-2.3-Diffusers" \
-  -F "num_frames=81" \
-  -F "fps=24" \
-  -F "size=768x512" \
-  -F "num_inference_steps=30" \
-  -F "guidance_scale=4.0" \
-  -F "seed=42" \
-  -o ltx23_i2v.mp4
-```
-
-### Notes
-
-- Memory usage: run a latest-head serving smoke or DFX benchmark on the target
-  GPU before publishing load/peak VRAM numbers.
-- Checkpoint format: use a Diffusers-format checkpoint such as
-  `diffusers/LTX-2.3-Diffusers`; the upstream `Lightricks/LTX-2.3` repository ships
-  raw safetensors and does not contain the subfolder configs required by this
-  pipeline loader.
-- Key flags:
-  - `--stage-init-timeout 600`: Gives the initial `torch.compile` warmup enough time on large checkpoints
-  - `--model-class-name LTX23Pipeline`: Selects the LTX-2.3 text-to-video pipeline (not LTX-2)
-  - `--model-class-name LTX23ImageToVideoPipeline`: Selects the LTX-2.3 image-to-video pipeline
-  - `input_reference=@...`: Uploads the I2V reference image directly
-- Audio: 48kHz AAC via BWE vocoder, automatically synced with video
-- CPU offloading: Text encoder (Gemma-3-12B), connectors, VAE, audio VAE, and vocoder stay on CPU and are moved to GPU only when needed
-- Supported resolutions: 768x512, 512x384 (must be divisible by 32)
-- Frame rate: 24 fps
-- Duration: Controlled by `num_frames` (frames = duration_seconds * 24 + 1)
-- Known limitations:
-  - LTX-2.3 I2V currently supports first-frame image conditioning, matching the LTX image-conditioning contract.
-  - Requires `diffusers >= 0.38.0` (not yet on PyPI, install from git)
-
-## Hardware Support
-
-## GPU
-
-### 1x NVIDIA RTX PRO 6000 Blackwell (96GB)
-
-#### Environment
-
-- OS: Ubuntu 22.04
-- Python: 3.10+
-- Driver / runtime: CUDA 13.0, Driver 580.126.09
-- vLLM version: Match the PR or release checkout being validated
-- vLLM-Omni version or commit: Use the commit you are deploying from
-
-### Benchmarking
-
-Do not copy latency or VRAM numbers between commits or machines. Record them
-only after running a latest-head sweep on the target hardware.
-
-For formal PR benchmarking, reuse
-`tests/dfx/perf/tests/test_ltx2_vllm_omni.json` with
-`tests/dfx/perf/scripts/run_diffusion_benchmark.py`. Each case in that file includes a JSON **`mark`** (`hardware_marks` + `full_model` + `diffusion`) for local `-m` filtering. That config captures the
-single-device eager baseline and CFG-parallel=2 case for a small 384x512,
-25-frame, 20-step workload.

recipes/LTX/LTX-2.md

@@ -1,121 +1,337 @@
-# LTX-2
+# LTX-2 Family
 
-> Text-to-video and image-to-video serving
+> LTX-2 and LTX-2.3 text-to-video and image-to-video generation with synchronized audio
 
-## Summary
+## Pipelines
 
-- Vendor: Lightricks
-- Model: `Lightricks/LTX-2`
-- Task: Text-to-video generation and image-to-video generation
-- Mode: Online serving with the OpenAI-compatible video API
-- Maintainer: Community
+| Model | Checkpoint | Task | `--model-class-name` | Batching |
+|---|---|---|---|---|
+| LTX-2 | `Lightricks/LTX-2` | One-stage T2V/I2V | `LTX2Pipeline` | Yes |
+| LTX-2 distilled | `rootonchair/LTX-2-19b-distilled` | Two-stage T2V/I2V | `LTX2DistilledPipeline` | No |
+| LTX-2.3 | `diffusers/LTX-2.3-Diffusers` | One-stage T2V/I2V | `LTX2Pipeline` | Yes |
 
-## When to use this recipe
+`LTX2Pipeline` is the unified one-stage entry. Checkpoint metadata selects the
+LTX-2 or LTX-2.3 profile; omitting an image selects T2V, while one initial
+image selects I2V. Both one-stage repositories declare this class, so
+`--model-class-name` is optional. LTX-2.3 requires the Diffusers checkpoint;
+the raw `Lightricks/LTX-2.3` safetensors repository is not directly loadable.
 
-Use this recipe when you want to deploy the base `Lightricks/LTX-2` model
-for both text-to-video and image-to-video generation. The same model ID is used for
-both tasks; switch the pipeline class with `--model-class-name` depending on
-whether you want T2V or I2V.
+`LTX2DistilledPipeline` is the unified distilled entry: omit `image` for T2V
+or provide one initial image for I2V. As in the official LTX pipelines,
+distilled mode is selected explicitly rather than inferred from the checkpoint
+name or metadata; always pass `--model-class-name LTX2DistilledPipeline`.
+
+## API Migration
+
+Only `req` may be passed positionally to `LTX2Pipeline`; every optional
+`forward` argument is keyword-only:
+
+```python
+# No longer supported
+pipe(req, prompt)
+pipe(req, image, prompt)
+
+# Supported
+pipe(req, prompt=prompt)
+pipe(req, image=image, prompt=prompt)
+```
+
+The consolidation also removes these registry names without aliases:
+
+| Removed name | Replacement |
+|---|---|
+| `LTX23Pipeline` | `LTX2Pipeline`; checkpoint metadata selects LTX-2.3 |
+| `LTX2ImageToVideoPipeline` | `LTX2Pipeline` with `image=` |
+| `LTX23ImageToVideoPipeline` | `LTX2Pipeline` with `image=`; checkpoint metadata selects LTX-2.3 |
+| `LTX2TwoStagesPipeline` | `LTX2DistilledPipeline` |
+| `LTX2ImageToVideoTwoStagesPipeline` | `LTX2DistilledPipeline` with `image=` |
+
+Passing any second positional argument now raises `TypeError`. These changes
+affect direct Python callers and explicit `--model-class-name` overrides;
+offline and serving entrypoints already use named fields and are unaffected.
+
+## One-Stage Defaults
+
+| Parameter | LTX-2 | LTX-2.3 |
+|---|---:|---:|
+| Width × height | 768 × 512 | 768 × 512 |
+| Frames / frame rate | 121 / 24 | 121 / 24 |
+| Denoise steps | 40 | 30 |
+| Video/audio CFG | 3.0 / 7.0 | 3.0 / 7.0 |
+| Video/audio STG | 1.0 / 1.0 | 1.0 / 1.0 |
+| Video/audio modality guidance | 3.0 / 3.0 | 3.0 / 3.0 |
+| Video/audio rescale | 0.7 / 0.7 | 0.7 / 0.7 |
+| Video/audio STG blocks | `[29]` / `[29]` | `[28]` / `[28]` |
+
+The model recipe also supplies the default negative prompt. Top-level
+`guidance_scale` overrides both video and audio CFG values. The online video
+API defaults `num_frames` to `1`, so set it explicitly; the offline LTX scripts
+default to `121`.
+
+## Distilled Two-Stage Defaults
+
+| Parameter | Value |
+|---|---:|
+| Final width × height | 1536 × 1024 |
+| Stage 1 width × height | 768 × 512 |
+| Frames / frame rate | 121 / 24 |
+| Stage 1 / Stage 2 denoise steps | 8 / 3 |
+| Guidance | Fixed positive-only |
+
+The dimensions passed to the API are the final output dimensions and must be
+divisible by 64. Both one- and two-stage requests require `num_frames = 8k+1`.
+The distilled recipe fixes both sigma schedules; `num_inference_steps` must be
+`8` when supplied.
+
+## Serving
+
+Start either checkpoint:
+
+```bash
+vllm serve Lightricks/LTX-2 --omni --stage-init-timeout 600
+```
+
+```bash
+vllm serve diffusers/LTX-2.3-Diffusers --omni --stage-init-timeout 600
+```
+
+Start the distilled checkpoint with its two-stage entry:
+
+```bash
+vllm serve rootonchair/LTX-2-19b-distilled --omni \
+  --model-class-name LTX2DistilledPipeline --stage-init-timeout 600
+```
+
+The same server handles T2V and I2V. A T2V request using the selected recipe's
+default guidance is:
+
+```bash
+curl -X POST http://localhost:8000/v1/videos/sync \
+  -F "prompt=A cinematic close-up of ocean waves at golden hour." \
+  -F "negative_prompt=worst quality, inconsistent motion, blurry, jittery, distorted" \
+  -F "size=768x512" \
+  -F "num_frames=121" \
+  -F "fps=24" \
+  -F "seed=42" \
+  -o ltx_t2v.mp4
+```
+
+For I2V, provide exactly one initial image:
+
+```bash
+curl -X POST http://localhost:8000/v1/videos/sync \
+  -F "prompt=A plush toy astronaut gently waving while the camera slowly pushes in." \
+  -F "negative_prompt=worst quality, inconsistent motion, blurry, jittery, distorted" \
+  -F "input_reference=@/absolute/path/to/reference.png" \
+  -F "size=768x512" \
+  -F "num_frames=121" \
+  -F "fps=24" \
+  -F "seed=42" \
+  -o ltx_i2v.mp4
+```
+
+Use `image_reference` for a URL or JSON-safe image reference. Do not provide it
+together with `input_reference`.
+
+## Guidance
+
+LTX supports independent video/audio CFG, spatio-temporal guidance (STG),
+cross-modality guidance, and rescaling:
+
+| Parameter | Default | Effect | Alias |
+|---|---:|---|---|
+| `video_cfg_scale` | 3.0 | Video text CFG; `1.0` disables it | `video_cfg_guidance_scale` |
+| `audio_cfg_scale` | 7.0 | Audio text CFG; `1.0` disables it | `audio_cfg_guidance_scale` |
+| `video_stg_scale` | 1.0 | Video STG; `0.0` disables it | `video_stg_guidance_scale` |
+| `audio_stg_scale` | 1.0 | Audio STG; `0.0` disables it | `audio_stg_guidance_scale` |
+| `video_modality_scale` | 3.0 | Audio-to-video guidance; `1.0` disables it | `a2v_guidance_scale` |
+| `audio_modality_scale` | 3.0 | Video-to-audio guidance; `1.0` disables it | `v2a_guidance_scale` |
+| `video_rescale_scale` | 0.7 | Video guidance rescale; `0.0` disables it | — |
+| `audio_rescale_scale` | 0.7 | Audio guidance rescale; `0.0` disables it | — |
+| `video_stg_blocks` | `[29]` / `[28]` | Perturbed video transformer blocks | — |
+| `audio_stg_blocks` | `[29]` / `[28]` | Perturbed audio transformer blocks | — |
+
+The STG block defaults are shown as LTX-2 / LTX-2.3. A canonical name wins
+over its alias. Explicit top-level `guidance_scale` wins over both CFG fields;
+omit it for independent video/audio CFG.
+
+Pass these fields in online `extra_params` or offline `--extra-body`:
+
+```bash
+# Add to the curl request above
+-F 'extra_params={"video_cfg_scale":3.0,"audio_cfg_scale":7.0}'
+
+# Add to text_to_video.py or image_to_video.py
+--extra-body '{"video_cfg_scale":3.0,"audio_cfg_scale":7.0}'
+```
+
+### Guidance Parallelism
+
+For LTX, `cfg_parallel_size` is the number of ranks used to execute the
+complete guidance plan in parallel. Despite the legacy `cfg` name, it covers
+text CFG, STG, and cross-modality guidance passes; guidance rescaling is
+applied after all pass predictions are gathered.
+
+The default one-stage recipe has four Transformer passes per denoise step:
+`cond`, `uncond`, `ptb` (STG), and `mod` (cross-modality). The useful balanced
+configurations are therefore:
+
+| `--cfg-parallel-size` | Passes per rank | Guidance-slot utilization | Notes |
+|---:|---:|---:|---|
+| `1` | 4 | 100% | Single-rank fused guidance batch |
+| `2` | 2 | 100% | Recommended two-rank configuration |
+| `4` | 1 | 100% | One guidance pass per rank |
+
+Other positive sizes are accepted. When the pass count is not divisible by
+`cfg_parallel_size`, ranks are padded to an equal number of execution slots
+and LTX emits a warning with the expected utilization. For example, four
+passes on three ranks use six slots and have 66.7% guidance-slot utilization.
+
+Start an LTX-2.3 server with two-way guidance parallelism:
+
+```bash
+vllm serve diffusers/LTX-2.3-Diffusers --omni \
+  --cfg-parallel-size 2 --stage-init-timeout 600
+```
+
+The total device count is the product of `cfg_parallel_size` and the other
+configured parallel dimensions. Distilled pipelines use positive-only
+guidance, so `cfg_parallel_size > 1` does not provide guidance speedup for
+them.
+
+### Python API
+
+Normal per-request values belong in `OmniDiffusionSamplingParams`. Pass the
+resulting `DiffusionRequestBatch` as the only positional argument:
+
+```python
+from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+params = OmniDiffusionSamplingParams(
+    width=768,
+    height=512,
+    num_frames=121,
+    frame_rate=24.0,
+    num_inference_steps=30,
+    seed=42,
+    extra_args={"video_cfg_scale": 3.0, "audio_cfg_scale": 7.0},
+)
+req = DiffusionRequestBatch([
+    OmniDiffusionRequest(
+        prompt={"prompt": "Cherry blossoms moving in a light breeze"},
+        sampling_params=params,
+        request_id="ltx-example",
+    )
+])
+
+t2v_output = pipe(req)
+i2v_output = pipe(req, image=image)
+```
+
+Direct keyword arguments are low-level fallbacks, for example
+`pipe(req, prompt=prompt, width=768)`. Request fields take precedence unless
+noted below.
+
+### Complete `forward` Surface
+
+| Argument | Type/default | Meaning and constraints |
+|---|---|---|
+| `req` | `DiffusionRequestBatch`, required | Only positional argument; contains prompts and per-request sampling parameters. |
+| `image` | image or batch, `None` | Direct value wins over request images; no image selects T2V. I2V accepts one image per prompt, and a batch cannot mix T2V/I2V. |
+| `prompt` | string or list, `None` | Positive-text fallback; request prompts win. Mutually exclusive with `prompt_embeds`. |
+| `negative_prompt` | string or list, `None` | Fallback after request values and before the recipe default; mutually exclusive with negative embeddings. |
+| `height` | `int`, `None` | Request → direct value → recipe default; divisible by 32 one-stage or 64 distilled. |
+| `width` | `int`, `None` | Same precedence and alignment as `height`. |
+| `num_frames` | `int`, `None` | Request → direct value → recipe default; must be `8k+1` and also determines audio duration with `frame_rate`. |
+| `frame_rate` | `float`, `None` | Request `frame_rate` → request `fps` → direct value → recipe default. |
+| `num_inference_steps` | `int`, `None` | Request → direct value → recipe default; minimum 2 one-stage, fixed at 8 for distilled Stage 1. Custom one-stage `sigmas` determine actual steps. |
+| `sigmas` | list of float, `None` | One-stage only. Request values win; every request in a fused batch must use the same schedule. |
+| `timesteps` | list of int, `None` | Compatibility slot; LTX accepts only `None`. Use `sigmas`. |
+| `guidance_scale` | `float`, `None` | One-stage common video/audio CFG fallback; an explicit request value wins. Distilled two-stage uses fixed positive-only guidance. |
+| `guidance_rescale` | `float`, `None` | Accepts only `None` or `0.0`; use the modality rescale fields. |
+| `noise_scale` | `float`, `0.0` | Compatibility slot; LTX accepts only `0.0`. |
+| `num_videos_per_prompt` | `int`, `1` | Output-count fallback; positive request `num_outputs_per_prompt` wins. |
+| `generator` | generator or list, `None` | Explicit RNG; otherwise request generators/seeds are collated. Lists must match the effective output batch. |
+| `latents` | tensor, `None` | Request tensors win. One-stage accepts packed `[B, S, C]` and validated unpacked video layouts. |
+| `audio_latents` | tensor, `None` | One-stage initial audio latents; request tensors win and are collated. |
+| `prompt_embeds` | tensor, `None` | Precomputed positive conditioning; requires `prompt_attention_mask` and cannot accompany `prompt`. |
+| `negative_prompt_embeds` | tensor, `None` | One-stage precomputed negative conditioning; requires its mask and cannot accompany `negative_prompt`. |
+| `prompt_attention_mask` | tensor, `None` | Mask for positive embeddings; request `prompt_attention_mask` or `attention_mask` wins. |
+| `negative_prompt_attention_mask` | tensor, `None` | Mask for negative embeddings; request negative mask fields win. |
+| `decode_timestep` | float or list, `0.0` | Video-VAE decode timestep; request value wins. Lists may match 1, prompt batch, or output batch. |
+| `decode_noise_scale` | float or list, `None` | Same list rules; request value wins. Defaults to `decode_timestep`. |
+| `output_type` | string, `"np"` | Request value wins. `"np"` decodes output; `"latent"` skips VAE/vocoder decode. |
+| `return_dict` | `bool`, `True` | Compatibility slot; only `True` is accepted. |
+| `attention_kwargs` | dict, `None` | Per-call values are unsupported; configure attention at engine startup. |
+| `max_sequence_length` | `int`, `None` | Request → direct value → tokenizer limit. |
+
+Request-object naming differs only in a few places: `num_videos_per_prompt`
+maps to `num_outputs_per_prompt`; images and prompt text/embeddings live in the
+request prompt payload; LTX guidance fields live in sampling `extra_args`.
+
+### Recipe-Specific Request Capabilities
+
+| Override | One-stage | Distilled two-stage |
+|---|---|---|
+| Guidance | Supported | Fixed positive-only |
+| Negative prompt/embeddings | Supported | Rejected |
+| `num_inference_steps` | Supported | Fixed at 8 for Stage 1; Stage 2 uses 3 |
+| Custom `sigmas` | Supported | Rejected; both phases use fixed schedules |
+| Video/audio latents | Supported | Rejected |
+
+These capability checks apply equally to direct `forward` keywords and
+values in `OmniDiffusionSamplingParams`; unsupported values fail instead of
+being silently ignored.
+
+### Custom Sigma Schedules
+
+One-stage Python requests may set final scheduler boundaries directly:
+
+```python
+params = OmniDiffusionSamplingParams(sigmas=[1.0, 0.75, 0.5, 0.25])
+```
+
+Each nonterminal value produces one denoise step, and a terminal `0.0` is
+appended when omitted. This schedule overrides `num_inference_steps`. All
+requests in a fused batch must use the same list. The video form API and
+bundled offline CLI do not currently expose `sigmas`.
+
+### Constraints
+
+- LTX rejects non-default `timesteps`, `flow_shift`, `guidance_rescale`,
+  `noise_scale`, `attention_kwargs`, and `return_dict=False`. Use final
+  `sigmas`, modality rescale fields, startup attention configuration, and the
+  standard `DiffusionOutput` respectively.
+- Fused one-stage requests must resolve to identical LTX guidance and sigma
+  schedules. Keep concurrent requests guidance-homogeneous or use
+  `--max-num-seqs 1`.
+- `--cfg-parallel-size` shards the complete LTX guidance plan, including STG,
+  modality guidance, and rescale-compatible prediction gathering.
+
+## Operational Notes
+
+- LTX-2 one-stage previously loaded and peaked at about 73.5 GiB on one H200
+  141GB; remeasure on the deployed commit and hardware.
+- LTX-2.3 includes a 22B transformer, Gemma encoder, two VAEs, and a vocoder.
+  Start on a 96GB-class GPU or use CPU/layerwise offload on smaller devices.
+- The distilled upsampler uses the configured pipeline dtype and participates
+  in component discovery/device placement; it remains resident during denoise
+  offload because it is used only at the phase boundary.
+- The output audio sample rate comes from the loaded components and is not a
+  request parameter.
+- For benchmarks, use `tests/dfx/perf/tests/test_ltx2_vllm_omni.json` with
+  `tests/dfx/perf/scripts/run_diffusion_benchmark.py`.
+- Current two-stage support is limited to the distilled checkpoint; official
+  LTX two-stage/HQ execution remains out of scope.
 
 ## References
 
-- Model: <https://huggingface.co/Lightricks/LTX-2>
-- User guide:
-  [`docs/user_guide/examples/online_serving/text_to_video.md`](../../docs/user_guide/examples/online_serving/text_to_video.md)
-- Example guide:
-  [`examples/online_serving/text_to_video/README.md`](../../examples/online_serving/text_to_video/README.md)
-  [`examples/online_serving/image_to_video/README.md`](../../examples/online_serving/image_to_video/README.md)
-
-## Hardware Support
-
-## GPU
-
-### 1x H200 141GB (Text-to-Video)
-
-#### Environment
-
-- OS: Ubuntu 22.04.5 LTS
-- Python: 3.12+
-- Driver / runtime: NVIDIA CUDA environment with H200 141GB GPU
-- vLLM version: Match the repository requirements from your current checkout
-- vLLM-Omni version or commit: Use the commit you are deploying from
-
-#### Command
-
-```bash
-vllm serve Lightricks/LTX-2 \
-  --omni \
-  --model-class-name LTX2Pipeline
-```
-
-#### Verification
-
-```bash
-curl http://localhost:8000/health
-
-curl -X POST http://localhost:8000/v1/videos \
-  -F "prompt=A cinematic close-up of ocean waves at golden hour." \
-  -F "negative_prompt=worst quality, inconsistent motion, blurry, jittery, distorted" \
-  -F "width=512" \
-  -F "height=768" \
-  -F "num_frames=121" \
-  -F "fps=24" \
-  -F "num_inference_steps=40" \
-  -F "guidance_scale=4.0" \
-  -o ltx2_t2v_h200.mp4
-```
-
-#### Notes
-
-- Memory usage: Model loads at ~73.5GB, peaks at ~73.5 GiB during inference.
-- Key flags:
-  - `--model-class-name LTX2Pipeline` selects the text-to-video pipeline explicitly.
-- Known limitations:
-  - This recipe is tested on 1x H200 validation; add measured latency and VRAM after running it on your target machine.
-
-### 1x H200 141GB (Image-to-Video)
-
-#### Environment
-
-- OS: Ubuntu 22.04.5 LTS
-- Python: 3.12+
-- Driver / runtime: NVIDIA CUDA environment with H200 141GB GPU
-- vLLM version: Match the repository requirements from your current checkout
-- vLLM-Omni version or commit: Use the commit you are deploying from
-
-#### Command
-
-```bash
-vllm serve Lightricks/LTX-2 \
-  --omni \
-  --model-class-name LTX2ImageToVideoPipeline
-```
-
-#### Verification
-
-```bash
-curl http://localhost:8000/health
-
-curl -X POST http://localhost:8000/v1/videos/sync \
-  -F "prompt=A plush toy astronaut gently waving while the camera slowly pushes in." \
-  -F "input_reference=@/absolute/path/to/reference.png" \
-  -F "width=512" \
-  -F "height=768" \
-  -F "num_frames=121" \
-  -F "fps=24" \
-  -F "num_inference_steps=40" \
-  -F "guidance_scale=4.0" \
-  -o ltx2_i2v_h200.mp4
-```
-
-#### Notes
-
-- Memory usage: Model loads at ~73.5GB, peaks at ~73.5 GiB during inference.
-- Key flags:
-  - `--model-class-name LTX2ImageToVideoPipeline` switches the same `Lightricks/LTX-2` weights into image-to-video mode.
-  - `input_reference=@...` uploads the reference image directly
-- Known limitations:
-  - Use `image_reference` when you want to pass a URL or JSON-safe image reference
-instead of uploading a file.
-  - Do not send `input_reference` and `image_reference` in the same request.
-  - This recipe is tested on 1x H200 validation; add measured latency and VRAM after running it on your target machine.
+- <https://huggingface.co/Lightricks/LTX-2>
+- <https://huggingface.co/Lightricks/LTX-2.3>
+- <https://huggingface.co/diffusers/LTX-2.3-Diffusers>
+- [Online video generation](../../docs/user_guide/examples/online_serving/text_to_video.md)
+- [Request batching](../../docs/user_guide/diffusion/request_batching.md)
+- [T2V offline example](../../examples/offline_inference/text_to_video/text_to_video.md)
+- [I2V offline example](../../examples/offline_inference/image_to_video/README.md)

recipes/MiniMaxAI/MiniMax-H3-NPU.md

@@ -0,0 +1,162 @@
+# MiniMax H3 on Ascend NPU
+
+> Joint video and audio generation with text, first-frame, image/audio, or
+> multi-video conditions — Ascend NPU deployment guide
+
+## Summary
+
+- Vendor: MiniMaxAI
+- Model: [`MiniMaxAI/MiniMax-H3`](https://huggingface.co/MiniMaxAI/MiniMax-H3)
+- Tasks: T2VA, FL2VA, and Ref2VA
+- Mode: OpenAI-compatible `/v1/videos` HTTP serving
+- Hardware: Atlas 800I A3
+- Maintainer: Community
+
+This recipe adapts [MiniMax-H3.md](MiniMax-H3.md) for Ascend NPU
+environments. Differences from the GPU path:
+
+- Runs on a CPU-only (aarch64) PyTorch build with `torch_npu`; no CUDA
+  runtime is present.
+- Audio loading does **not** require TorchCodec (whose aarch64 wheels are
+  built against CUDA torch and fail to load on CPU-only builds). vLLM-Omni
+  automatically falls back to soundfile / ffmpeg for wav/mp3/m4a/mp4 audio
+  inputs.
+
+## Prerequisites
+
+### Checkpoint
+
+Same as the GPU recipe — Hugging Face access approval is required:
+
+```bash
+hf auth login
+export MODEL_ROOT=/path/to/MiniMax-H3
+hf download MiniMaxAI/MiniMax-H3 --local-dir "${MODEL_ROOT}"
+```
+
+### Environment
+
+- Ascend driver & firmware: 25.5.1
+- CANN toolkit: 9.0.1
+- Python: 3.12
+- PyTorch: 2.10.0+cpu
+- torch_npu: 2.10.0.post2
+- Install vLLM-Omni from a checkout with MiniMax H3 support:
+
+```bash
+uv venv
+source .venv/bin/activate
+uv pip install -e .
+```
+
+Install the **mindie-sd** operator library to enable Ascend-optimized fused
+operators (`adalayernorm`, etc.):
+
+```bash
+git clone https://gitcode.com/Ascend/MindIE-SD.git && cd MindIE-SD
+
+# Comment out the tik_ops build step (not needed for this use case)
+sed -i 's|^\(\s*\)source ${current_script_dir}/build_tik_ops.sh|\1# source ${current_script_dir}/build_tik_ops.sh|' build/build_ops.sh
+
+python setup.py bdist_wheel
+cd dist
+pip install mindiesd-*.whl
+```
+
+- `ffmpeg` and `ffprobe` must be available on `PATH`. They are used for
+  reference-video preparation and MP4 output.
+- Reference-video decoding uses `decord` when available and falls back to
+  PyAV otherwise.
+- Audio inputs do not require TorchCodec: wav/mp3/m4a/mp4 files are loaded
+  at native sample rate through the soundfile / ffmpeg fallback.
+
+## Start a server
+
+One server loads one checkpoint partition. Set `MODEL` to `FL2VA` for T2VA
+and FL2VA requests, or to `Ref2VA` for Ref2VA requests.
+
+### Multi-NPU: 768P validated configuration
+
+Validated on eight NPUs of an Atlas 800I A3 server with Ulysses sequence
+parallelism degree 8, text-encoder tensor parallelism degree 8, native tiled
+VAE patch parallelism degree 8, and layerwise offload:
+
+```bash
+export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export PORT=9098
+export MODEL="${MODEL_ROOT}/FL2VA"
+export VLLM_WORKER_MULTIPROC_METHOD=spawn
+export VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800
+export PYTHONDONTWRITEBYTECODE=1
+export HF_HUB_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+
+vllm serve "${MODEL}" \
+  --omni \
+  --host 0.0.0.0 \
+  --port "${PORT}" \
+  --trust-remote-code \
+  --num-gpus 8 \
+  --usp 8 \
+  --ring 1 \
+  --text-encoder-tp-size 8 \
+  --enable-layerwise-offload \
+  --vae-parallel-mode tile \
+  --vae-use-tiling \
+  --vae-patch-parallel-size 8 \
+  --diffusion-attention-backend FLASH_ATTN
+```
+
+Do not add `--enforce-eager`. The first request includes regional
+compilation; warm the server once before measuring steady-state latency.
+H3 is CFG-distilled, so `--cfg-parallel-size` must remain 1.
+
+To serve Ref2VA, stop the FL2VA server and restart with:
+
+```bash
+export MODEL="${MODEL_ROOT}/Ref2VA"
+```
+
+## HTTP API examples
+
+The request format is identical to the GPU recipe; see
+[MiniMax-H3.md § HTTP API examples](MiniMax-H3.md#http-api-examples).
+Use the validated 768P shapes (e.g. `width=1344 height=768`) on NPU.
+
+## Key parameters
+
+Same as the GPU recipe; see
+[MiniMax-H3.md § Key parameters](MiniMax-H3.md#key-parameters).
+The validated resolution on NPU is 768P (e.g. 1344x768).
+
+## Validated NPU evidence
+
+Measured on an Atlas 800I A3 server (8x NPU) with CANN 9.0.1,
+PyTorch 2.10.0+cpu, and torch_npu 2.10.0.post2, using the multi-NPU
+configuration above:
+
+| Workload | Configuration |
+|----------|---------------|
+| T2VA, 209 frames, 1344x768 | TE TP8, layerwise offload, Ulysses 8, VPP8 tile, regional compile |
+| Ref2VA (prompt + video), 124 frames, 1344x768 | TE TP8, layerwise offload, Ulysses 8, VPP8 tile, regional compile |
+
+These measurements describe the validated shapes rather than a general
+throughput guarantee.
+
+## Known limitations
+
+- Each server process loads only one checkpoint partition.
+- H3 currently executes one generation request per diffusion batch.
+- The first regional-compile request is a warmup and should not be included
+  in steady-state performance measurements.
+- Image+audio Ref2VA accepts exactly one image and one audio reference.
+- Video Ref2VA accepts one or more video files, but not an additional
+  standalone audio reference.
+- VAE patch parallelism requires size 1 or the full DiT group size and
+  supports the H3 native `tile` mode only.
+
+## Additional resources
+
+- [MiniMax-H3.md](MiniMax-H3.md) — full GPU guide
+- [Supported models](../../docs/models/supported_models.md)
+- [Video API](../../docs/serving/videos_api.md)

recipes/MiniMaxAI/MiniMax-H3.md

@@ -0,0 +1,329 @@
+# MiniMax H3
+
+> Joint video and audio generation with text, first-frame, image/audio, or
+> multi-video conditions
+
+## Summary
+
+- Vendor: MiniMaxAI
+- Model: [`MiniMaxAI/MiniMax-H3`](https://huggingface.co/MiniMaxAI/MiniMax-H3)
+- Tasks: T2VA, FL2VA, and Ref2VA
+- Mode: OpenAI-compatible `/v1/videos` HTTP serving
+- Maintainer: Community
+
+MiniMax H3 is a CFG-distilled joint video/audio diffusion transformer. Its
+checkpoint has two independently served partitions:
+
+- `FL2VA`: text-to-video+audio (`t2va`) and first-frame-to-video+audio
+  (`fl2va`)
+- `Ref2VA`: image+audio or one-or-more-video reference-to-video+audio
+  (`ref2va`)
+
+The generated MP4 contains H.264 video and synchronized stereo audio.
+
+## Prerequisites
+
+The checkpoint requires Hugging Face access approval. Download it and point
+`MODEL_ROOT` at the directory containing the `FL2VA` and `Ref2VA`
+subdirectories:
+
+```bash
+hf auth login
+export MODEL_ROOT=/path/to/MiniMax-H3
+hf download MiniMaxAI/MiniMax-H3 --local-dir "${MODEL_ROOT}"
+```
+
+Install vLLM-Omni from the checkout containing MiniMax H3 support. On
+Blackwell, install the optional FlashAttention-4 dependency:
+
+```bash
+uv venv
+source .venv/bin/activate
+uv pip install -e '.[fa4]'
+```
+
+`ffmpeg` and `ffprobe` must be available on `PATH`. They are used for
+reference-video preparation and MP4 output.
+
+## Start a server
+
+One server loads one checkpoint partition. Set `MODEL` to `FL2VA` for T2VA
+and FL2VA requests, or to `Ref2VA` for either Ref2VA request.
+
+### Single GPU: accuracy and memory first
+
+The single-GPU configuration uses model-level CPU offload.
+This matches the accuracy-qualified reference path and prevents the Qwen3-VL
+encoder and DiT from being resident on the GPU at the same time.
+
+```bash
+export MODEL="${MODEL_ROOT}/FL2VA"
+export PORT=8091
+
+CUDA_VISIBLE_DEVICES=0 \
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
+vllm serve "${MODEL}" \
+  --omni \
+  --host 0.0.0.0 \
+  --port "${PORT}" \
+  --trust-remote-code \
+  --num-gpus 1 \
+  --enable-cpu-offload \
+  --diffusion-attention-backend FLASH_ATTN
+```
+
+Use a GPU with enough memory for the active H3 component and enough system RAM
+for the offloaded components. CPU offload reduces GPU memory pressure but adds
+PCIe/NVLink transfer latency.
+
+### Four GPUs: measured best-practice throughput
+
+The best validated four-GPU configuration on four NVIDIA B300 GPUs is:
+
+- no CPU or layerwise offload;
+- Ulysses sequence parallelism degree 4;
+- native tiled VAE patch parallelism degree 4;
+- regional `torch.compile` for the repeated DiT blocks;
+- FlashAttention, with Ring and TP left at 1.
+
+```bash
+export MODEL="${MODEL_ROOT}/FL2VA"
+export PORT=8091
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+FLASHINFER_DISABLE_VERSION_CHECK=1 \
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
+vllm serve "${MODEL}" \
+  --omni \
+  --host 0.0.0.0 \
+  --port "${PORT}" \
+  --trust-remote-code \
+  --num-gpus 4 \
+  --usp 4 \
+  --ring 1 \
+  --vae-patch-parallel-size 4 \
+  --vae-parallel-mode tile \
+  --vae-use-tiling \
+  --diffusion-attention-backend FLASH_ATTN
+```
+
+Do not add `--enforce-eager` to this performance configuration. The first
+request includes regional compilation; warm the server once before measuring
+steady-state latency. H3 is CFG-distilled, so `--cfg-parallel-size` must remain
+1. The H3 VAE supports its native `tile` mode, not
+`spatial_shard_height` or `spatial_shard_width`.
+
+### Text encoder tensor parallelism
+
+The Qwen3-VL text encoder (~51.5 GB in BF16 for the retained 50 layers) is by
+default fully resident on the DiT main rank.  On multi-GPU no-offload runs that
+rank becomes the peak-memory hotspot.  Add `--text-encoder-tp-size N` to shard
+the encoder across the first `N` DiT ranks (the encoder is implemented with
+vLLM-style tensor-parallel layers and runs with distributed collectives over
+its own encoder process group):
+
+```bash
+export MODEL="${MODEL_ROOT}/FL2VA"
+export PORT=8091
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+FLASHINFER_DISABLE_VERSION_CHECK=1 \
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
+vllm serve "${MODEL}" \
+  --omni \
+  --host 0.0.0.0 \
+  --port "${PORT}" \
+  --trust-remote-code \
+  --num-gpus 4 \
+  --usp 4 \
+  --ring 1 \
+  --text-encoder-tp-size 4 \
+  --vae-patch-parallel-size 4 \
+  --vae-parallel-mode tile \
+  --vae-use-tiling \
+  --diffusion-attention-backend FLASH_ATTN
+```
+
+`N` must divide the Qwen3-VL head counts (64 attention heads / 8 KV heads), so
+valid values on a 4-GPU server are 1, 2, and 4 (1, 2, 4, 8 on 8 GPUs).  The
+encoder TP rank set is the first `N` DiT ranks; on 4 GPUs
+`--text-encoder-tp-size 4` shards the encoder 4-way, dropping the DiT main
+rank's no-offload peak by roughly `(N-1)/N` of the ~51.5 GB encoder while the
+other ranks each gain `~51.5/N` GB.  The encoder output remains identical to
+the reference path within bf16 rounding: every encoder rank all-reduces the
+row-parallel projections, so the full `[seq, 5120]` layer-50 hidden state is
+replicated on every rank.
+
+To serve Ref2VA, stop the FL2VA server and restart the same one- or four-GPU
+command with:
+
+```bash
+export MODEL="${MODEL_ROOT}/Ref2VA"
+```
+
+## HTTP API examples
+
+The following requests use the synchronous endpoint so the returned body can
+be saved directly as an MP4. The asynchronous `POST /v1/videos` endpoint can
+also be used when job polling is preferred.
+
+All four tasks use 24 FPS, 50 sigma points, seed values from the validated
+workloads, and the checkpoint-reference video/audio flow shifts of 12 and 3.
+Decimal durations are passed through `extra_params`.
+
+Set the endpoint once:
+
+```bash
+export API_URL="http://127.0.0.1:${PORT}/v1/videos/sync"
+```
+
+### 1. T2VA: text to video and audio
+
+Run this request against the `FL2VA` partition:
+
+```bash
+curl -sS -X POST "${API_URL}" \
+  -F 'prompt=In a snowy blue-purple forest, Ori carefully walks past a sleeping giant; footsteps crunch in the snow while the creature breathes and softly snorts.' \
+  -F 'width=1344' \
+  -F 'height=768' \
+  -F 'fps=24' \
+  -F 'num_inference_steps=50' \
+  -F 'flow_shift=12' \
+  -F 'seed=1101' \
+  -F 'extra_params={"task":"t2va","duration":8.7,"audio_flow_shift":3.0}' \
+  -o t2va.mp4
+```
+
+### 2. FL2VA: first frame to video and audio
+
+Run this request against the `FL2VA` partition. When width and height are
+omitted, H3 preserves the first-frame aspect ratio and uses a 768-pixel short
+edge.
+
+```bash
+export FIRST_FRAME=/path/to/fl2va_first_frame.png
+
+curl -sS -X POST "${API_URL}" \
+  -F 'prompt=A man stands beside a yellow car at night. The car drives away; he follows it with his eyes and begins singing sadly, with synchronized voice and city ambience.' \
+  -F 'fps=24' \
+  -F 'num_inference_steps=50' \
+  -F 'flow_shift=12' \
+  -F 'seed=2101' \
+  -F 'extra_params={"task":"fl2va","duration":8.7,"audio_flow_shift":3.0}' \
+  -F "input_reference=@${FIRST_FRAME};type=image/png" \
+  -o fl2va.mp4
+```
+
+### 3. Ref2VA: image and audio reference
+
+Run this request against the `Ref2VA` partition. `audio_reference` accepts an
+HTTP(S) URL or a `data:` URL. In one terminal, expose the local reference
+assets to the serving host:
+
+```bash
+python -m http.server 8092 \
+  --bind 127.0.0.1 \
+  --directory /path/to/reference_assets
+```
+
+Then submit the image and audio request from another terminal:
+
+```bash
+export REF_IMAGE=/path/to/reference_assets/ref2va_image.png
+export AUDIO_URL=http://127.0.0.1:8092/ref2va_audio.mp3
+
+curl -sS -X POST "${API_URL}" \
+  -F 'prompt=A white cat with black mustache and eyebrow markings sits on a beige couch, lip-syncing precisely to the complete reference audio before shifting from confusion to deadpan speechlessness.' \
+  -F 'width=1344' \
+  -F 'height=768' \
+  -F 'fps=24' \
+  -F 'num_inference_steps=50' \
+  -F 'flow_shift=12' \
+  -F 'seed=3101' \
+  -F 'extra_params={"task":"ref2va","duration":15.0,"audio_flow_shift":3.0}' \
+  -F "input_reference=@${REF_IMAGE};type=image/png" \
+  -F "audio_reference={\"audio_url\":\"${AUDIO_URL}\"}" \
+  -o ref2va_image_audio.mp4
+```
+
+The requested duration should cover the complete audio. If `duration` is
+shorter, the reference soundtrack is truncated to the generated clip.
+
+### 4. Ref2VA: two video references
+
+Run this request against the `Ref2VA` partition. Repeat the
+`input_references` multipart field once per source video. H3 consumes the
+videos in form order and preserves their original soundtracks during
+conditioning.
+
+```bash
+export SUBJECT_VIDEO=/path/to/green_screen_subject.mp4
+export BACKGROUND_VIDEO=/path/to/fairytale_background.mov
+
+curl -sS -X POST "${API_URL}" \
+  -F 'prompt=Remove the green screen background of Video 1 and replace it with the fairytale environment from Video 2. Match the background motion to the character actions and relight the character to fit the scene.' \
+  -F 'width=1344' \
+  -F 'height=768' \
+  -F 'fps=24' \
+  -F 'num_inference_steps=50' \
+  -F 'flow_shift=12' \
+  -F 'seed=3101' \
+  -F 'extra_params={"task":"ref2va","duration":15.0,"audio_flow_shift":3.0}' \
+  -F "input_references=@${SUBJECT_VIDEO};type=video/mp4" \
+  -F "input_references=@${BACKGROUND_VIDEO};type=video/quicktime" \
+  -o ref2va_video_video.mp4
+```
+
+The server stores uploaded multi-video references only for the lifetime of the
+request and deletes the temporary files after generation. Video Ref2VA uses
+the source-video soundtracks and does not accept a separate
+`audio_reference`.
+
+## Key parameters
+
+| Parameter | Recommended value | Notes |
+|-----------|-------------------|-------|
+| `task` | `t2va`, `fl2va`, or `ref2va` | Passed in `extra_params`; must match the served partition |
+| `duration` | Workload-specific | Decimal seconds in `extra_params`; converted to H3-compatible frame count |
+| `fps` | `24` | H3 output FPS is fixed |
+| `num_inference_steps` | `50` | Matches the reference accuracy workloads |
+| `flow_shift` | `12` | Video sigma shift |
+| `audio_flow_shift` | `3` | Audio sigma shift, passed in `extra_params` |
+| `seed` | Task-specific | Use a fixed value for reproducibility |
+| `width`, `height` | Multiples of 32 | Aspect ratio must be between 1:4 and 4:1 |
+
+## Validated four-GPU evidence
+
+The four-GPU recommendation was measured on four NVIDIA B300 GPUs with one
+excluded warmup followed by three requests.
+
+| Workload | Configuration | Observed result |
+|----------|---------------|-----------------|
+| FL2VA, 209 frames, 1248x768 | no offload, U4, VPP4 tile, regional compile | 86.964 s mean HTTP client latency |
+| Two-video Ref2VA, 362 frames, 1344x768 | no offload, U4, VPP4 tile, regional compile | 784.394 s accounted model-stage mean |
+
+These measurements describe the validated shapes rather than a general
+throughput guarantee. Multi-video Ref2VA is much slower because the two
+reference videos expand both the Qwen3-VL vision sequence and the packed DiT
+attention sequence.
+
+## Known limitations
+
+- Each server process loads only one checkpoint partition.
+- H3 currently executes one generation request per diffusion batch.
+- The first regional-compile request is a warmup and should not be included in
+  steady-state performance measurements.
+- Image+audio Ref2VA accepts exactly one image and one audio reference.
+- Video Ref2VA accepts one or more video files, but not an additional standalone
+  audio reference.
+- VAE patch parallelism requires size 1 or the full DiT group size and supports
+  the H3 native `tile` mode only.
+
+## Additional resources
+
+- [Supported models](../../docs/models/supported_models.md)
+- [Video API](../../docs/serving/videos_api.md)
+- [Diffusion parallelism](../../docs/user_guide/diffusion/parallelism/overview.md)

recipes/NVIDIA/GR00T-N1.7.md

@@ -69,8 +69,13 @@ Notes:
 ## Verification
 
 ```python
-from tests.gr00t.openpi_client_helper import run_policy_session, validate_session_result
-validate_session_result(run_policy_session(host="127.0.0.1", port=8000))
+from tests.helpers.runtime import OpenAIClientHandler
+
+handler = OpenAIClientHandler(host="127.0.0.1", port=8000, log_stats=False)
+response = handler.send_robot_openpi_ws_request(
+    {"run_default_policy_session": True, "session_id": "gr00t-smoke"}
+)[0]
+assert response.operation_responses[-1]["status"] == "reset successful"
 ```
 
 Or run the e2e test suite:

recipes/NVIDIA/Nemotron-Labs-Audex.md

@@ -0,0 +1,142 @@
+# Nemotron-Labs Audex (2B / 30B-A3B)
+
+> Speech suite on 1×H100: TTS, text-to-audio, audio understanding, and cascaded speech-to-speech
+
+## Summary
+
+- Vendor: NVIDIA
+- Model: `nvidia/Nemotron-Labs-Audex-2B`, `nvidia/Nemotron-Labs-Audex-30B-A3B`
+- Task: Text-to-speech (English), text-to-audio (sound effects), audio understanding (ASR / audio QA), cascaded speech-to-speech
+- Mode: Online serving (`/v1/audio/speech`, `/v1/chat/completions`) and offline batch inference
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe to serve either Audex checkpoint through one of its four
+deployment pipelines. Both sizes share the same repo layout, codec token
+space, and streaming causal speech decoder (16 kHz output); they differ in
+the thinker: the 2B is a dense LM, the 30B-A3B is a hybrid Mamba + MoE
+NemotronH (~3B active parameters). Pick the pipeline by task:
+
+| pipeline (`vllm_omni/deploy/<name>[_30b].yaml`) | audio in | text out | speech out | general audio out | endpoint |
+|---|---|---|---|---|---|
+| `audex_tts` | ❌ | ❌ | ✅ | ❌ | `/v1/audio/speech` |
+| `audex_tta` | ❌ | ❌ | ❌ | ✅ | `/v1/audio/speech` |
+| `audex_thinker_only` | ✅ | ✅ | ❌ | ❌ | `/v1/chat/completions` |
+| `audex_s2s` | ✅ | ✅ | ✅ | ❌ | both |
+
+## References
+
+- Model cards: [Nemotron-Labs-Audex-2B](https://huggingface.co/nvidia/Nemotron-Labs-Audex-2B), [Nemotron-Labs-Audex-30B-A3B](https://huggingface.co/nvidia/Nemotron-Labs-Audex-30B-A3B)
+- Offline examples: [`examples/offline_inference/audex/`](../../examples/offline_inference/audex/)
+- Online examples: [`examples/online_serving/audex/`](../../examples/online_serving/audex/)
+
+## Hardware Support
+
+## GPU
+
+### 1×H100 80GB — Audex-2B
+
+#### Environment
+
+- OS: Linux
+- Python: 3.12+
+- CUDA: 12.x
+- vLLM version: 0.24.0
+- vLLM-Omni version or commit: PR #4976 (branch `audex`)
+
+#### Command
+
+**Online serving** (TTS is the default pipeline; the repo root's
+`model_type` auto-resolves `audex_tts.yaml`):
+
+```bash
+vllm-omni serve nvidia/Nemotron-Labs-Audex-2B \
+    --host 0.0.0.0 --port 8097 \
+    --trust-remote-code --omni
+# other modes: add --stage-configs-path vllm_omni/deploy/audex_{tta,thinker_only,s2s}.yaml
+# or use the launcher: MODE=s2s PORT=8098 examples/online_serving/audex/run_server.sh
+```
+
+**Offline batch inference** (one script per task, each defaults to its
+correct deploy yaml):
+
+```bash
+python examples/offline_inference/audex/text_to_speech.py \
+    --texts "Hello world." --cfg-scale 1.5 --output-dir results/audex_wavs
+python examples/offline_inference/audex/text_to_audio.py     # sound effects
+python examples/offline_inference/audex/audio_qa.py          # ASR / audio QA
+python examples/offline_inference/audex/speech_to_speech.py  # 3-pass cascade
+```
+
+#### Verification
+
+```bash
+curl -X POST http://localhost:8097/v1/audio/speech \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "nvidia/Nemotron-Labs-Audex-2B",
+        "input": "Hello, how are you?",
+        "response_format": "wav",
+        "extra_params": {"cfg_scale": 1.5}
+    }' --output hello.wav
+# or the universal client (all four modes):
+python examples/online_serving/audex/client.py --mode tts --output hello.wav
+```
+
+#### Notes
+
+- Memory usage: TTS deploy — stage 0 (thinker) 0.4, stage 1 (decoder) 0.25
+  GPU-memory fraction; both stages share GPU 0.
+- Key flags: `--trust-remote-code` and `--omni` are required.
+- Classifier-free guidance: `extra_params.cfg_scale` (1.0 = off; 1.5 is the
+  official quality setting; TTA effectively requires its default 3.0).
+  Measured en-24 self-transcribed CER: 6.87% guided vs 7.24% unguided.
+- Output: 16 kHz mono WAV. English, single built-in voice (no cloning).
+- TTA decodes through the external XCodec1 checkpoint
+  (`hf-audio/xcodec-hubert-general-balanced`, auto-downloaded; override
+  with `XCODEC1_PATH`).
+
+### 1×H100 80GB — Audex-30B-A3B
+
+#### Environment
+
+Same as the 2B row above.
+
+#### Command
+
+The 30B REQUIRES its explicit deploy yaml (the shared HF `model_type`
+auto-resolves to the 2B-tuned config otherwise):
+
+```bash
+vllm-omni serve nvidia/Nemotron-Labs-Audex-30B-A3B \
+    --host 0.0.0.0 --port 8097 \
+    --trust-remote-code --omni \
+    --stage-configs-path vllm_omni/deploy/audex_tts_30b.yaml
+# or: SIZE=30b MODE=tts examples/online_serving/audex/run_server.sh
+```
+
+Offline: same four scripts with
+`--model nvidia/Nemotron-Labs-Audex-30B-A3B --deploy-config vllm_omni/deploy/audex_<mode>_30b.yaml`.
+
+#### Verification
+
+Same curl / `client.py` as the 2B with
+`"model": "nvidia/Nemotron-Labs-Audex-30B-A3B"` (the server validates the
+model id). First launch downloads ~60 GB.
+
+#### Notes
+
+- Memory usage: ~61 GiB weights on one H100 80 GB (stage 0 at 0.85 GPU
+  fraction + decoder at 0.08 on the same card); verified with healthy KV
+  headroom. If long sequences OOM, set `tensor_parallel_size: 2` on
+  stage 0 (documented in the yaml headers).
+- `enable_prefix_caching: false` on stage 0 — hybrid Mamba constraint.
+- CUDA graphs stay ON for the thinker: the NemotronH stage captures
+  cleanly and eager mode costs ~7× single-stream TTS RTF (review-measured
+  0.30 vs 2.08).
+- Quality (en-24 self-transcribed CER): 0.72% unguided baseline; 0.72% at
+  cfg 1.5 with guided temperature 0.1.
+- Known limitation: the S2S chat pass needs the official
+  formatting-instruction prompt (the bundled examples/client send it);
+  a bare transcript turn makes the 30B answer in speech-codec tokens.

recipes/OpenBMB/MiniCPM-o-4_5.md

@@ -30,8 +30,6 @@ also provided.
   `hf_config.version="4.5"`):
   - Default single-GPU compatibility layout (auto-loaded):
     [`vllm_omni/deploy/minicpmo_4_5.yaml`](../../vllm_omni/deploy/minicpmo_4_5.yaml)
-  - Recommended 2-GPU continuous-batching layout:
-    [`vllm_omni/deploy/minicpmo_4_5_batching.yaml`](../../vllm_omni/deploy/minicpmo_4_5_batching.yaml)
   - 2-GPU and 3-GPU layouts:
     [`vllm_omni/deploy/minicpmo_4_5_2gpu.yaml`](../../vllm_omni/deploy/minicpmo_4_5_2gpu.yaml),
     [`vllm_omni/deploy/minicpmo_4_5_3gpu.yaml`](../../vllm_omni/deploy/minicpmo_4_5_3gpu.yaml)
@@ -68,7 +66,7 @@ Code2Wav consumes them through a shared-memory async connector.
 MiniCPM-o 4.5 now requires the three-stage topology: the Talker owns
 request-local codec generation and Code2Wav owns waveform state and
 reference-voice prompt features. `minicpmo_4_5.yaml` remains the stable
-single-GPU entry point; `minicpmo_4_5_batching.yaml` is the recommended
+single-GPU entry point; `minicpmo_4_5_2gpu.yaml` is the recommended
 two-GPU profile. The removed fused two-stage implementation is not retained as
 a fallback because it would duplicate state machines and correctness paths.
 
@@ -79,7 +77,9 @@ a fallback because it would duplicate state machines and correctness paths.
 The default
 [`vllm_omni/deploy/minicpmo_4_5.yaml`](../../vllm_omni/deploy/minicpmo_4_5.yaml)
 co-locates Thinker, codec-only Talker, and Code2Wav on GPU 0. Their
-`gpu_memory_utilization` budgets are 0.55, 0.22, and 0.22. All stages admit
+`gpu_memory_utilization` budgets are 0.55, 0.15, and 0.15. The remaining
+device memory is left available for runtime workspaces such as the HiFi-GAN
+vocoder's cuDNN kernels. All stages admit
 up to four sequences. Startup video profiling is bounded to 32 frames per
 video. Use the two-GPU profile for production throughput.
 
@@ -105,7 +105,7 @@ The deploy config is auto-loaded by the model registry — no
 For the recommended two-GPU layout, add:
 
 ```bash
---deploy-config vllm_omni/deploy/minicpmo_4_5_batching.yaml
+--deploy-config vllm_omni/deploy/minicpmo_4_5_2gpu.yaml
 ```
 
 #### Performance comparison
@@ -175,16 +175,18 @@ in another choice's `message.audio.data` (24 kHz mono, see Notes). With
 `modalities: ["text", "audio"]` you typically get two `choices` entries
 (one text, one audio).
 
-**Streaming text + speech**:
+**Streaming text + speech** (use `--stream`):
 
 ```bash
-python examples/online_serving/minicpmo/streaming_chat_completion.py \
-    --base-url http://localhost:8099/v1 \
-    --output minicpmo_stream.wav
+python examples/online_serving/minicpmo/openai_chat_completion_client_for_multimodal_generation.py \
+    --query-type text \
+    --prompt "Say hello, then introduce vLLM in one sentence." \
+    --port 8099 \
+    --stream
 ```
 
-The client prints text deltas as they arrive and reconstructs one valid WAV
-from the independently encoded audio deltas.
+The client prints text deltas as they arrive and saves streamed audio chunks
+to WAV files.
 
 **Gradio demo (text + image + audio + video UI)**:
 
@@ -202,9 +204,10 @@ speech output (TTS)"** checkbox on / off.
 
 #### Notes
 
-- Memory budget: Thinker, Talker, and Code2Wav reserve 0.55, 0.22, and
-  0.22 of GPU 0. The larger Thinker share protects its multimodal KV cache;
-  all three model processes still share one CUDA device.
+- Memory budget: Thinker, Talker, and Code2Wav reserve 0.55, 0.15, and
+  0.15 of GPU 0. The larger Thinker share protects its multimodal KV cache,
+  while the unreserved memory remains available to runtime kernels; all three
+  model processes still share one CUDA device.
 - `--trust-remote-code` is required — the HF repo ships a custom
   `MiniCPMO` config / model class.
 - Stage 0 Thinker and Stage 1 Talker enable vLLM CUDA Graphs. Stage 2 remains

recipes/README.md

@@ -45,9 +45,9 @@ recipes/
 | [`inclusionAI/Ming-flash-omni-2.0.md`](./inclusionAI/Ming-flash-omni-2.0.md) | Online serving for multimodal chat + standalone TTS | 4x H100 / 1x H100 80GB |
 | [`inclusionAI/Ming-omni-tts.md`](./inclusionAI/Ming-omni-tts.md) | Offline + online dense Ming TTS/audio generation | 1x H100 80GB / 1x AMD MI300X (ROCm 7.2) |
 | [`IndexTeam/IndexTTS-2.md`](./IndexTeam/IndexTTS-2.md) | Online serving for voice-cloned TTS with optional emotion control | 1x L4 24GB or larger CUDA GPU |
+| [`MiniMaxAI/MiniMax-H3.md`](./MiniMaxAI/MiniMax-H3.md) | T2VA, FL2VA, image+audio Ref2VA, and multi-video Ref2VA serving | 1x GPU with CPU offload / 4x B300 |
 | [`krea/Krea-2.md`](./krea/Krea-2.md) | Text-to-image (Turbo + Raw), offline + online, with LoRA | 1x H100 80GB |
-| [`LTX/LTX-2.md`](./LTX/LTX-2.md) | Text-to-video and image-to-video serving | 1x H200 141GB |
-| [`LTX/LTX-2.3.md`](./LTX/LTX-2.3.md) | Text-to-video with audio generation (22B) | 1x GPU (96GB VRAM) |
+| [`LTX/LTX-2.md`](./LTX/LTX-2.md) | LTX-2/LTX-2.3 text-to-video and image-to-video with synchronized audio | H200 141GB / 96GB-class GPU |
 | [`MammothModa2/MammothModa2.md`](./MammothModa2/MammothModa2.md) | Preview and Dev text-to-image (AR → DiT); Dev text/image understanding | Preview: 1x L40S 48GB / 1x ≥40GB GPU; Dev: 1x NVIDIA GPU with sufficient cache headroom |
 | [`mistralai/Voxtral-TTS.md`](./mistralai/Voxtral-TTS.md) | Online serving for TTS | 1x RTX 4090 24GB |
 | [`Robbyant/LingBot-Video.md`](./Robbyant/LingBot-Video.md) | Native dense and MoE text-to-video serving | Dense: 1x L20X; MoE: 1x L20X (~67.7 GiB peak) |
@@ -57,6 +57,7 @@ recipes/
 | [`OpenBMB/MiniCPM-o-4_5.md`](./OpenBMB/MiniCPM-o-4_5.md) | Online serving for omni multimodal chat (text / image / audio / video → text + 24 kHz speech) | 2x A100/H100 80GB / 3x mid-tier GPU / 8x RTX 4090 24GB |
 | [`OpenBMB/VoxCPM2.md`](./OpenBMB/VoxCPM2.md) | Online + offline TTS with native AR pipeline (48 kHz, 30+ languages) | 1x RTX 4090 24GB |
 | [`OpenMOSS/MOSS-TTS.md`](./OpenMOSS/MOSS-TTS.md) | Online + offline multilingual TTS (MOSS-TTS family, 8B) | 1x H100 80GB |
+| [`NVIDIA/Nemotron-Labs-Audex.md`](./NVIDIA/Nemotron-Labs-Audex.md) | TTS / text-to-audio / audio understanding / cascaded S2S (Audex 2B + 30B-A3B) | 1x H100 80GB |
 | [`Qwen/Qwen-Image.md`](./Qwen/Qwen-Image.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt mixed FP8/NVFP4 | 1x A100 80GB / 2x B200 |
 | [`Qwen/Qwen-Image-2512.md`](./Qwen/Qwen-Image-2512.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt FP8 / mixed FP8/NVFP4 | 1x A800 80GB / 2x B200 |
 | [`Qwen/Qwen-Image-Edit.md`](./Qwen/Qwen-Image-Edit.md) | Text-guided single-image editing | 1x or 2x H200 141GB |

recipes/cosmos3/Cosmos3-DistOffload.md

@@ -0,0 +1,191 @@
+# Cosmos3 — Distributed Layerwise Offload
+
+> Sharding weights across DP ranks with H2D + AllGather overlap for multi-NPU/GPU deployment
+
+## When to use this recipe
+
+Use this recipe when deploying Cosmos3-Super (64B / 124 GB) or Cosmos3-Nano (17B / 33 GB) across multiple devices where the model does not fit on a single device's HBM, or when you need higher throughput via DP multi-concurrency.
+
+**Key benefits:**
+- Each rank stores only 1/dp_size of the model weights on host memory
+- Only 2 transformer blocks reside on each device at any time (fixed double-buffer)
+- DP multi-concurrency: N concurrent requests with near-linear throughput scaling
+- mmap weight loading: shared page cache eliminates O(dp_size × model_size) RSS
+
+## Prerequisites
+
+- 2+ NPU or GPU devices (Ascend 910B3, NVIDIA B300, etc.)
+- vLLM-Omni with distributed layerwise offload support
+- Cosmos3-Nano or Cosmos3-Super checkpoint (local path or HuggingFace repo ID)
+
+## Serve commands
+
+### Cosmos3-Nano with DP=4 + AllGather (recommended for max throughput)
+
+```bash
+vllm serve /path/to/Cosmos3-Nano \
+  --omni \
+  --host 0.0.0.0 --port 8000 \
+  --no-guardrails --init-timeout 3600 \
+  --vae-use-tiling --vae-patch-parallel-size 1 \
+  --enable-distributed-layerwise-offload \
+  --data-parallel-size 4
+```
+
+### Cosmos3-Super with DP=2 + AllGather (for models > single-card HBM)
+
+```bash
+vllm serve /path/to/Cosmos3-Super \
+  --omni \
+  --host 0.0.0.0 --port 8000 \
+  --no-guardrails --init-timeout 3600 \
+  --vae-use-tiling --vae-patch-parallel-size 1 \
+  --enable-distributed-layerwise-offload \
+  --data-parallel-size 2
+```
+
+### Cosmos3-Super with DP=4 + AllGather
+
+```bash
+vllm serve /path/to/Cosmos3-Super \
+  --omni \
+  --host 0.0.0.0 --port 8000 \
+  --no-guardrails --init-timeout 3600 \
+  --vae-use-tiling --vae-patch-parallel-size 1 \
+  --enable-distributed-layerwise-offload \
+  --data-parallel-size 4
+```
+
+### Without AllGather (full weights per rank, no sharding)
+
+Use `--dlo-no-use-allgather` when you want each rank to load full weights independently —
+no AllGather synchronization, no concurrent request requirement, but N× host memory.
+
+```bash
+vllm serve /path/to/Cosmos3-Nano \
+  --omni \
+  --host 0.0.0.0 --port 8000 \
+  --no-guardrails --init-timeout 3600 \
+  --vae-use-tiling --vae-patch-parallel-size 1 \
+  --enable-distributed-layerwise-offload \
+  --data-parallel-size 4 \
+  --dlo-no-use-allgather
+```
+
+### With SP (sequence parallel) instead of DP
+
+For long-sequence workloads where SP parallelism is preferred:
+
+```bash
+vllm serve /path/to/Cosmos3-Super \
+  --omni \
+  --host 0.0.0.0 --port 8000 \
+  --no-guardrails --init-timeout 3600 \
+  --vae-use-tiling --vae-patch-parallel-size 1 \
+  --enable-distributed-layerwise-offload \
+  --usp 4
+```
+
+## Request examples
+
+### Text-to-Image (T2I)
+
+```bash
+curl -s -o output.png -X POST "http://localhost:8000/v1/images/generations" \
+  -H "Content-Type: multipart/form-data" \
+  -F "model=/path/to/Cosmos3-Nano" \
+  -F "prompt=A robot arm cleaning a plate, cinematic" \
+  -F "size=1024x1024" \
+  -F "num_inference_steps=50" \
+  -F "guidance_scale=6.0" \
+  -F "seed=42"
+```
+
+### Text-to-Video (T2V)
+
+```bash
+curl -s -o output.mp4 -X POST "http://localhost:8000/v1/videos/sync" \
+  -H "Accept: video/mp4" \
+  -F "model=/path/to/Cosmos3-Nano" \
+  -F "prompt=A robot arm cleaning a plate, cinematic shot" \
+  -F "negative_prompt=blurry" \
+  -F "size=832x480" \
+  -F "num_frames=29" \
+  -F "fps=24" \
+  -F "num_inference_steps=35" \
+  -F "guidance_scale=6.0" \
+  -F "max_sequence_length=4096" \
+  -F "flow_shift=10.0" \
+  -F 'extra_params={"use_resolution_template":false,"use_duration_template":false,"guardrails":false}' \
+  -F "seed=17"
+```
+
+> **Important:** When using DP multi-concurrency (AllGather mode), `num_inference_steps`
+> must be specified explicitly and must be the same for all concurrent requests.
+> `num_inference_steps=None` is not allowed because it may resolve differently
+> per request mode (e.g., T2V=35 vs action_mode=30), which would deadlock AllGather.
+
+### Multiple concurrent requests (DP multi-concurrency)
+
+With `--data-parallel-size 4`, send 4 concurrent requests to maximize throughput:
+
+```bash
+for i in 1 2 3 4; do
+  curl -s -o output_${i}.mp4 -X POST "http://localhost:8000/v1/videos/sync" \
+    -H "Accept: video/mp4" \
+    -F "model=/path/to/Cosmos3-Nano" \
+    -F "prompt=A robot arm cleaning a plate, shot ${i}" \
+    -F "negative_prompt=blurry" \
+    -F "size=832x480" \
+    -F "num_frames=29" \
+    -F "fps=24" \
+    -F "num_inference_steps=35" \
+    -F "guidance_scale=6.0" \
+    -F "max_sequence_length=4096" \
+    -F "flow_shift=10.0" \
+    -F 'extra_params={"use_resolution_template":false,"use_duration_template":false,"guardrails":false}' \
+    -F "seed=17" &
+done
+wait
+```
+
+## CLI flags
+
+| Flag | Description | Default |
+|------|-------------|---------|
+| `--enable-distributed-layerwise-offload` | Enable DLO with H2D + AllGather overlap | `false` |
+| `--data-parallel-size N` | Number of DP ranks (weight sharding + concurrent requests) | `1` |
+| `--dlo-use-allgather` | Use shard + AllGather for weight reconstruction (recommended) | `true` |
+| `--dlo-no-use-allgather` | Each rank loads full weights independently (no sharding, no AllGather) | `false` |
+| `--usp N` | Use SP instead of DP for weight sharding (long sequences) | `1` |
+
+## Mode comparison
+
+| Mode | CLI flags | CPU/rank | HBM/card | Throughput | Use case |
+|------|----------|---------|---------|-----------|----------|
+| DLO + AllGather (DP4) | `--enable-distributed-layerwise-offload --data-parallel-size 4` | 1/4 model | 2 blocks | **3.3× HSDP** | Max throughput, short sequences |
+| DLO + AllGather (DP2) | `--enable-distributed-layerwise-offload --data-parallel-size 2` | 1/2 model | 2 blocks | 2× HSDP | Balanced throughput + memory |
+| DLO + AllGather (SP4) | `--enable-distributed-layerwise-offload --usp 4` | 1/4 model | 2 blocks | 1× (single req) | Long sequences (720p+) |
+| DLO no-AllGather | `--enable-distributed-layerwise-offload --dlo-no-use-allgather` | Full model | 2 blocks | 0.4× HSDP | No AllGather overhead, high CPU |
+| HSDP | `--use-hsdp --hsdp-shard-size 4` | 0 | 1/4 model | 1× (baseline) | Weights fit in HBM |
+
+## Memory expectations
+
+| Model | Config | Host RAM (cgroup) | HBM/card |
+|-------|--------|-------------------|----------|
+| Nano (33 GB) | DP2 + AllGather | ~38 GB | ~10 GB |
+| Nano (33 GB) | DP4 + AllGather | ~47 GB | ~10 GB |
+| Super (124 GB) | DP2 + AllGather | ~157 GB | ~15 GB |
+| Super (124 GB) | DP4 + AllGather | ~172 GB | ~10 GB |
+
+> Host RAM is dominated by page cache (1× model_size, shared across ranks).
+> HBM only holds 2 transformer blocks + framework + activations, independent of model size.
+
+## Limitations
+
+- `--dlo-use-allgather` (default) requires all concurrent requests to have the same
+  `num_inference_steps` (AllGather is a collective that needs all ranks synchronized)
+- Online quantization (FP8) is incompatible with mmap loading — falls back to regular
+  `load_weights()` automatically
+- Tensor Parallel is not supported (DLO uses DP-based sharding, a different dimension)
+- HSDP + AllGather is rejected (would double-shard weights)

recipes/cosmos3/Cosmos3-Edge.md

@@ -17,7 +17,9 @@ The small Cosmos3 checkpoint, for constrained deployments. It runs on the shared
 
 - **Transformer**: `Cosmos3EdgeVFMTransformer` (Nemotron-based), auto-selected from
   `backbone_type: cosmos3_edge_nemotron_dense` — nothing to pass.
-- **Defaults** differ; omit `--height` / `--width` to get them:
+- **Defaults** differ. The offline examples auto-detect Edge (from `edge` in the model id /
+  transformer class) and apply the Edge column below, so you can omit `--height` / `--width` /
+  `--guidance-scale` / `--flow-shift` and still get native Edge output:
 
 | | Edge | Nano / Super |
 |---|---|---|
@@ -26,6 +28,10 @@ The small Cosmos3 checkpoint, for constrained deployments. It runs on the shared
 | Video `guidance_scale` | 5.0 | 6.0 |
 | Video `flow_shift` | 3.0 | 10.0 |
 
+> **Important:** these auto-defaults live in the example CLIs
+> (`text_to_video.py` / `image_to_video.py`). Do **not** pass the Nano/Super numbers
+> (720p / `guidance_scale` 6.0 / `flow_shift` 10.0) to Edge — they produce degenerate output.
+
 For the larger checkpoints see [Cosmos3-Nano](./Cosmos3-Nano.md) and [Cosmos3-Super](./Cosmos3-Super.md).
 
 ## Support notes

recipes/cosmos3/Cosmos3-Super.md

@@ -125,3 +125,134 @@ curl -sS -X POST http://localhost:8000/v1/videos/sync -H "Accept: video/mp4" \
   under the top-level `action` field. Verified on the 64B Super under
   `--cfg-parallel-size 2`: async `policy` returns the predicted action (`[16, 10]`)
   and the rollout video reliably.
+
+## NPU
+
+### 8× Ascend910 (A2, A3)
+
+#### Environment
+
+- OS: Linux
+- Python: 3.10+
+- Driver / runtime: Ascend NPU driver with CANN toolkit
+- Recommended operator library: **mindie-sd** (Ascend high-performance fused
+  operators — enables `adalayernorm` and other fused kernels automatically upon
+  installation)
+- vLLM version: Match the repository requirements for your checkout
+- vLLM-Omni version or commit: Use the commit you are deploying from
+
+A pre-built Docker image is available on
+[Docker Hub](https://hub.docker.com/r/vllm/vllm-omni) and
+[Quay.io](https://quay.io/ascend/vllm-omni). Ensure the image tag matches your
+vLLM-Omni checkout so that NPU-specific code is in sync with the container.
+
+#### Prerequisites
+
+Install the **mindie-sd** operator library to enable Ascend-optimized fused
+operators (`adalayernorm`, etc.):
+
+```bash
+git clone https://gitcode.com/Ascend/MindIE-SD.git && cd MindIE-SD
+
+# Comment out the tik_ops build step (not needed for this use case)
+sed -i 's|^\(\s*\)source ${current_script_dir}/build_tik_ops.sh|\1# source ${current_script_dir}/build_tik_ops.sh|' build/build_ops.sh
+
+python setup.py bdist_wheel
+cd dist
+pip install mindiesd-*.whl
+```
+
+After installation, enable the Laser Attention kernel for significant
+long-sequence speedups:
+
+```bash
+export MINDIE_SD_FA_TYPE=ascend_laser_attention
+```
+
+#### Command
+
+```bash
+export MINDIE_SD_FA_TYPE=ascend_laser_attention
+
+vllm serve nvidia/Cosmos3-Super \
+  --omni \
+  --host 0.0.0.0 --port 8000 \
+  --tensor-parallel-size 8 \
+  --model-class-name Cosmos3OmniDiffusersPipeline \
+  --no-guardrails \
+  --init-timeout 1800
+```
+
+#### Verification
+
+Same requests as the GPU section above — all modes (T2I, T2V, I2V, V2V,
+T2VS, I2VS) work identically on NPU. Quick reference with
+`--no-guardrails`:
+
+```bash
+curl http://localhost:8000/v1/models
+
+# T2I (1024x1024, 50 steps)
+curl -sS -X POST http://localhost:8000/v1/images/generations \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "nvidia/Cosmos3-Super",
+    "prompt": "A photorealistic red sports car on a city street at golden hour, cinematic lighting.",
+    "size": "1024x1024", "n": 1, "response_format": "b64_json",
+    "num_inference_steps": 50, "guidance_scale": 7.0, "seed": 42
+  }' | python3 -c "import sys,json,base64; open('cosmos3_super_t2i.png','wb').write(base64.b64decode(json.load(sys.stdin)['data'][0]['b64_json']))"
+
+# T2V (1280×720, 189 frames, 35 steps — official params)
+curl -sS -X POST http://localhost:8000/v1/videos/sync -H "Accept: video/mp4" \
+  -F "model=nvidia/Cosmos3-Super" -F "prompt=A robot arm is cleaning a plate in the kitchen" \
+  -F "size=1280x720" -F "num_frames=189" -F "fps=24" -F "num_inference_steps=35" \
+  -F "guidance_scale=6.0" -F "max_sequence_length=4096" -F "flow_shift=10.0" \
+  -F 'extra_params={"use_resolution_template":false,"use_duration_template":false,"guardrails":false}' \
+  -F "seed=17" -o cosmos3_super_t2v.mp4
+
+# I2V — add an uploaded reference image
+curl -sS -X POST http://localhost:8000/v1/videos/sync -H "Accept: video/mp4" \
+  -F "model=nvidia/Cosmos3-Super" -F "prompt=The scene comes to life with smooth, natural motion." \
+  -F "size=1280x720" -F "num_frames=189" -F "fps=24" -F "num_inference_steps=35" \
+  -F "guidance_scale=6.0" -F "max_sequence_length=4096" -F "flow_shift=10.0" \
+  -F 'extra_params={"use_resolution_template":false,"use_duration_template":false,"guardrails":false}' \
+  -F "seed=1111" -F "input_reference=@/path/to/reference.jpg;type=image/jpeg" \
+  -o cosmos3_super_i2v.mp4
+
+# V2V — add an uploaded reference video. condition_video_keep can be "first" or "last".
+curl -sS -X POST http://localhost:8000/v1/videos/sync -H "Accept: video/mp4" \
+  -F "model=nvidia/Cosmos3-Super" -F "prompt=Continue the same scene with smooth natural motion." \
+  -F "size=1280x720" -F "num_frames=189" -F "fps=24" -F "num_inference_steps=35" \
+  -F "guidance_scale=6.0" -F "max_sequence_length=4096" -F "flow_shift=10.0" \
+  -F 'extra_params={"condition_frame_indexes_vision":[0,1],"condition_video_keep":"first","guardrails":false}' \
+  -F "seed=2222" -F "input_reference=@/path/to/reference.mp4;type=video/mp4" \
+  -o cosmos3_super_v2v.mp4
+
+# T2V + sound — add generate_sound/sound_duration (output muxes AAC 48 kHz stereo)
+curl -sS -X POST http://localhost:8000/v1/videos/sync -H "Accept: video/mp4" \
+  -F "model=nvidia/Cosmos3-Super" -F "prompt=A robot arm is cleaning a plate in the kitchen" \
+  -F "size=1280x720" -F "num_frames=189" -F "fps=24" -F "num_inference_steps=35" \
+  -F "guidance_scale=6.0" -F "max_sequence_length=4096" -F "flow_shift=10.0" \
+  -F "generate_sound=true" -F "sound_duration=7.875" \
+  -F 'extra_params={"use_resolution_template":false,"use_duration_template":false,"guardrails":false}' \
+  -F "seed=17" -o cosmos3_super_t2vs.mp4
+
+# I2V + sound — reference image with synchronized audio
+curl -sS -X POST http://localhost:8000/v1/videos/sync -H "Accept: video/mp4" \
+  -F "model=nvidia/Cosmos3-Super" -F "prompt=The scene comes to life with smooth, natural motion and ambient sound." \
+  -F "size=1280x720" -F "num_frames=189" -F "fps=24" -F "num_inference_steps=35" \
+  -F "guidance_scale=6.0" -F "max_sequence_length=4096" -F "flow_shift=10.0" \
+  -F "generate_sound=true" -F "sound_duration=7.875" \
+  -F 'extra_params={"use_resolution_template":false,"use_duration_template":false,"guardrails":false}' \
+  -F "seed=1111" -F "input_reference=@/path/to/reference.jpg;type=image/jpeg" \
+  -o cosmos3_super_i2vs.mp4
+```
+
+#### Notes
+
+- **Parallelism:** `--tensor-parallel-size 8` matches the model's 8 KV heads (`num_key_value_heads: 8`, `num_attention_heads: 64` → GQA). This uses 8 davinci devices (NPU 0–3, both cores each). The remaining NPU 4–7 stay idle.
+- **Model config:** 64 layers × 5120 hidden size × 64 attention heads, ~120 GB transformer on disk (27 shards). Each NPU device loads ~15 GB of model weights.
+- **Peak HBM:** ~22.7 GB per device at startup (bf16 weights). Additional memory is allocated per-generation for KV cache and activations — resolution and frame count drive the peak.
+- **Performance (verified on 8× Ascend910):** T2I 256² / 2 steps / guidance 1.0 → ~1.5 s.
+- **Guardrails** are disabled with `--no-guardrails` (guards are on by default). The gated `nvidia/Cosmos-1.0-Guardrail` model and `cosmos-guardrail` package are not shipped. Add `guardrails: false` in `extra_params` for per-request overrides when the server has guardrails enabled.
+- **Known limitations:** FP8 quantization not yet validated on Ascend NPU. `--enable-layerwise-offload` is available but untested on NPU.