OmniRequestOutput inherits RequestOutput

Signed-off-by: Boao Shi <aoibosh@connect.hku.hk>

.buildkite/pipeline-intel.yaml

@@ -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: "v0.23.0"
+          VLLM_VERSION: "v0.25.0"
         priority: 100
         timeout_in_minutes: 60
         soft_fail: false

.buildkite/scripts/hardware_ci/run-xpu-test.sh

@@ -13,12 +13,33 @@ container_name="xpu_${BUILDKITE_COMMIT}_$(
 )"
 
 cd "${omni_source_dir}"
+
+# The XPU base image is ~37GB; the default gzip layer exporter is single-threaded
+# and takes ~16min to compress it. zstd is multi-threaded (uses all cores) at a
+# similar ratio; set EXPORT_COMPRESSION=uncompressed to skip compression entirely
+# for local-only images. This requires the containerd image store (buildx docker
+# driver), which is the default here.
+EXPORT_COMPRESSION="${EXPORT_COMPRESSION:-zstd}"
+if [ "${EXPORT_COMPRESSION}" = "uncompressed" ]; then
+    export_args=(--output "type=image,name={{IMAGE}},compression=uncompressed")
+else
+    export_args=(--output "type=image,name={{IMAGE}},compression=${EXPORT_COMPRESSION},compression-level=3,force-compression=true")
+fi
+
+docker_build() {
+    # $1 = image name; remaining args passed through to docker build.
+    local image="$1"
+    shift
+    local out=("${export_args[@]/'{{IMAGE}}'/${image}}")
+    docker build "${out[@]}" "$@" -f docker/Dockerfile.xpu .
+}
+
 if [ -z "$(docker images -q "${base_image_name}")" ]; then
-    docker build --target vllm-base -t "${base_image_name}" --build-arg "VLLM_VERSION=${VLLM_VERSION}" -f docker/Dockerfile.xpu .
+    docker_build "${base_image_name}" --target vllm-base --build-arg "VLLM_VERSION=${VLLM_VERSION}"
 fi
 
 # Try building the docker image
-docker build --build-arg "VLLM_BASE=${base_image_name}" --build-arg "VLLM_VERSION=${VLLM_VERSION}" -t "${image_name}" -f docker/Dockerfile.xpu .
+docker_build "${image_name}" --build-arg "VLLM_BASE=${base_image_name}" --build-arg "VLLM_VERSION=${VLLM_VERSION}"
 
 # Setup cleanup
 remove_docker_container() {

.buildkite/test-amd-merge.yml

@@ -7,17 +7,29 @@ steps:
 
 - group: ":card_index_dividers: Simple Test"
   steps:
-    - label: "Simple · Diffusion & Model Executor Test"
+    - label: "Simple · Diffusion & Model Executor Test (1/2)"
       agent_pool: mi325_1
       depends_on: amd-build
-      timeout_in_minutes: 50
+      timeout_in_minutes: 70
       mirror_hardwares: [amdproduction]
       grade: Blocking
       commands:
         - export VLLM_ROCM_USE_AITER=0
         # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
         # that is not supported on CPU
-        - "timeout 40m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        # CPU tests on ROCm gpu machines are very slow, so we give them more time.
+        - "timeout 60m pytest -sv tests/diffusion -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+
+    - label: "Simple · Diffusion & Model Executor Test (2/2)"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      timeout_in_minutes: 70
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # CPU tests on ROCm gpu machines are very slow, so we give them more time.
+        - "timeout 60m pytest -sv tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
 
     - label: "Simple · Other Test"
       agent_pool: mi325_1
@@ -158,7 +170,7 @@ steps:
       grade: Blocking
       commands:
         - |
-          timeout 20m bash -c '
+          timeout 50m bash -c '
             export VLLM_LOGGING_LEVEL=DEBUG
             export VLLM_WORKER_MULTIPROC_METHOD=spawn
             export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
@@ -170,39 +182,14 @@ steps:
       depends_on: amd-build
       mirror_hardwares: [amdproduction]
       grade: Blocking
-      commands:
-        - |
-          timeout 30m bash -c '
-            export VLLM_LOGGING_LEVEL=DEBUG
-            export VLLM_WORKER_MULTIPROC_METHOD=spawn
-            export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-            pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m "advanced_model" --run-level "advanced_model"
-          '
-
-    # It is the whisper ASR validation step that is slow, not the Voxtral TTS generation.
-    # whisper ASR runs on CPU.
-    - label: "Voxtral-TTS E2E Test (Online Serving)"
-      agent_pool: mi325_1
-      depends_on: amd-build
-      mirror_hardwares: [amdproduction]
-      grade: Blocking
+      timeout_in_minutes: 50
       commands:
         - |
           timeout 40m bash -c '
+            export VLLM_LOGGING_LEVEL=DEBUG
             export VLLM_WORKER_MULTIPROC_METHOD=spawn
-            pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-          '
-
-    - label: "Voxtral-TTS E2E Test (Offline Inference)"
-      agent_pool: mi325_1
-      depends_on: amd-build
-      mirror_hardwares: [amdproduction]
-      grade: Blocking
-      commands:
-        - |
-          timeout 30m bash -c '
-            export VLLM_WORKER_MULTIPROC_METHOD=spawn
-            pytest -s -v tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
+            export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+            pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m 'advanced_model and cuda' --run-level 'advanced_model'
           '
 
 # TODO: Bagel test on ROCm is very unstable. @tjtanaa

.buildkite/test-amd-ready.yaml

@@ -161,19 +161,6 @@ steps:
 
 - group: ":card_index_dividers: E2E Test"
   steps:
-    - label: "MiMo-Audio E2E Test with H100"
-      agent_pool: mi325_1
-      depends_on: amd-build
-      mirror_hardwares: [amdproduction]
-      grade: Blocking
-      commands:
-        - |
-          timeout 30m bash -c '
-            export VLLM_LOGGING_LEVEL=DEBUG
-            export VLLM_WORKER_MULTIPROC_METHOD=spawn
-            pytest -s -v tests/e2e/online_serving/test_mimo_audio.py -m "core_model" --run-level "core_model"
-          '
-
     - label: "Qwen3-TTS E2E Test"
       agent_pool: mi325_1
       depends_on: amd-build
@@ -193,7 +180,7 @@ steps:
       commands:
         - |
           timeout 20m bash -c '
-            pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "full_model" --run-level "core_model"
+            pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "slow" --run-level "core_model"
           '
 
 # TODO: Bagel test on ROCm is very unstable. @tjtanaa

.buildkite/test-merge.yml

@@ -61,13 +61,14 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
   - label: "Distributed Test"
+    depends_on: upload-merge-pipeline
     timeout_in_minutes: 60
     commands:
       - |
         timeout 55m bash -c "
           set -e
           export VLLM_IMAGE_FETCH_TIMEOUT=60
-          pytest -s -v tests/distributed -m 'advanced_model and cuda' --run-level 'advanced_model'
+          pytest -s -v tests/distributed -m 'advanced_model and cuda and H100' --run-level 'advanced_model'
         "
     agents:
       queue: "mithril-h100-pool"
@@ -164,28 +165,6 @@ steps:
   - group: ":card_index_dividers: E2E Test"
     depends_on: upload-merge-pipeline
     steps:
-      - label: "TTS · Step-Audio2 E2E Test"
-        timeout_in_minutes: 20
-        commands:
-          - |
-            timeout 20m bash -c "
-              export VLLM_LOGGING_LEVEL=DEBUG
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              pytest -s -v tests/e2e/offline_inference/test_step_audio2.py -m 'advanced_model' --run-level 'advanced_model'
-            "
-        agents:
-          queue: "gpu_4_queue"
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
       - label: "TTS · Qwen3-TTS CustomVoice Test"
         source_file_dependencies:
           - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
@@ -195,7 +174,7 @@ steps:
           - vllm_omni/deploy/qwen3_tts.yaml
         commands:
           - |
-            timeout 20m bash -c "
+            timeout 40m bash -c "
               export VLLM_LOGGING_LEVEL=DEBUG
               export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
               pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m 'advanced_model and cuda' --run-level 'advanced_model'
@@ -222,7 +201,7 @@ steps:
           - vllm_omni/deploy/qwen3_tts.yaml
         commands:
           - |
-            timeout 20m bash -c "
+            timeout 40m bash -c "
               export VLLM_LOGGING_LEVEL=DEBUG
               export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
               pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m 'advanced_model and cuda' --run-level 'advanced_model'
@@ -240,19 +219,19 @@ steps:
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
-      - label: "TTS · Ming-Omni-TTS Test"
+      - label: "TTS · Higgs-Audio-V3 Test"
         source_file_dependencies:
-          - tests/e2e/online_serving/test_ming_tts.py
-          - tests/e2e/offline_inference/test_ming_tts.py
-          - vllm_omni/model_executor/models/ming_tts/
-          - vllm_omni/model_executor/stage_input_processors/ming_tts.py
-          - vllm_omni/deploy/ming_tts.yaml
-        timeout_in_minutes: 20
+          - tests/e2e/online_serving/test_higgs_audio_v3.py
+          - vllm_omni/model_executor/models/higgs_audio_v3/
+          - vllm_omni/model_executor/stage_input_processors/higgs_audio_v3.py
+          - vllm_omni/deploy/higgs_multimodal_qwen3.yaml
         commands:
           - |
             timeout 20m bash -c "
-              export VLLM_LOGGING_LEVEL=DEBUG
-              pytest -s -v tests/e2e/online_serving/test_ming_tts.py tests/e2e/offline_inference/test_ming_tts.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+              export VLLM_WORKER_MULTIPROC_METHOD=spawn
+              export VLLM_USE_DEEP_GEMM=0
+              export VLLM_MOE_USE_DEEP_GEMM=0
+              pytest -s -v tests/e2e/online_serving/test_higgs_audio_v3.py -m 'advanced_model' --run-level 'advanced_model'
             "
         agents:
           queue: "gpu_1_queue"
@@ -264,105 +243,10 @@ steps:
               shm-size: "8gb"
               environment:
                 - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
-      - label: "TTS · Voxtral-TTS Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_voxtral_tts.py
-          - tests/e2e/online_serving/test_voxtral_tts.py
-          - vllm_omni/model_executor/models/voxtral_tts/
-          - vllm_omni/model_executor/stage_input_processors/voxtral_tts.py
-          - vllm_omni/deploy/voxtral_tts.yaml
-        timeout_in_minutes: 20
-        commands:
-          - |
-            timeout 20m bash -c "
-              export VLLM_LOGGING_LEVEL=DEBUG
-              pytest -s -v tests/e2e/offline_inference/test_voxtral_tts.py tests/e2e/online_serving/test_voxtral_tts.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: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: "TTS · GLM-TTS Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_glm_tts.py
-          - tests/e2e/offline_inference/test_glm_tts.py
-          - vllm_omni/model_executor/models/glm_tts/
-          - vllm_omni/model_executor/stage_input_processors/glm_tts.py
-          - vllm_omni/deploy/glm_tts.yaml
-        timeout_in_minutes: 20
-        commands:
-          - |
-            timeout 20m bash -c "
-              export VLLM_LOGGING_LEVEL=DEBUG
-              pytest -s -v tests/e2e/online_serving/test_glm_tts.py tests/e2e/offline_inference/test_glm_tts.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: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
       - label: "TTS · IndexTTS2 Test"
         source_file_dependencies:
           - tests/e2e/online_serving/test_indextts2.py
@@ -378,10 +262,10 @@ steps:
           - vllm_omni/entrypoints/openai/tts_adapters/indextts2.py
           - vllm_omni/config/pipeline_registry.py
           - vllm_omni/model_executor/models/registry.py
-        timeout_in_minutes: 20
+        timeout_in_minutes: 60
         commands:
           - |
-            timeout 20m bash -c "
+            timeout 45m bash -c "
               export VLLM_LOGGING_LEVEL=DEBUG
               pytest -s -v tests/e2e/online_serving/test_indextts2.py tests/e2e/offline_inference/test_indextts2.py -m 'advanced_model and cuda' --run-level 'advanced_model'
             "
@@ -406,7 +290,7 @@ steps:
           - vllm_omni/model_executor/models/qwen3_omni/
           - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
           - vllm_omni/deploy/qwen3_omni_moe.yaml
-        timeout_in_minutes: 30
+        timeout_in_minutes: 50
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
         agents:
@@ -443,49 +327,6 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: "Omni · Mimo Audio Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_mimo_audio.py
-          - vllm_omni/model_executor/models/mimo_audio/
-          - vllm_omni/model_executor/stage_input_processors/mimo_audio.py
-          - vllm_omni/deploy/mimo_audio.yaml
-        timeout_in_minutes: 30
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_mimo_audio.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
-
       - label: "TTS · VoxCPM2 Test"
         source_file_dependencies:
           - tests/e2e/offline_inference/test_voxcpm2_tts.py
@@ -514,37 +355,6 @@ steps:
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
-      - label: "Omni · Higgs-Audio V2 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_higgs_audio_v2.py
-          - tests/e2e/offline_inference/test_higgs_audio_v2.py
-          - vllm_omni/model_executor/models/higgs_audio_v2/
-          - vllm_omni/model_executor/stage_input_processors/higgs_audio_v2.py
-          - vllm_omni/deploy/higgs_audio_v2.yaml
-        timeout_in_minutes: 30
-        commands:
-          - |
-            timeout 30m bash -c "
-              export VLLM_LOGGING_LEVEL=DEBUG
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              export VLLM_USE_DEEP_GEMM=0
-              export VLLM_MOE_USE_DEEP_GEMM=0
-              pytest -s -v tests/e2e/online_serving/test_higgs_audio_v2.py tests/e2e/offline_inference/test_higgs_audio_v2.py -m 'advanced_model and cuda' --run-level 'advanced_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
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
       - label: "TTS · SoulX-Singer Test"
         source_file_dependencies:
           - tests/e2e/offline_inference/test_soulxsinger.py
@@ -614,88 +424,6 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: "Diffusion · Longcat Image Edit Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_longcat_image_edit.py
-          - vllm_omni/diffusion/models/longcat_image/
-        timeout_in_minutes: 20
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_longcat_image_edit.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:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: "Diffusion · Longcat Image Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_longcat_image.py
-          - vllm_omni/diffusion/models/longcat_image/
-        timeout_in_minutes: 20
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_longcat_image.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
       - label: "Diffusion · Bagel Test"
         source_file_dependencies:
           - tests/e2e/offline_inference/test_bagel.py
@@ -875,50 +603,6 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: "Diffusion · HunyuanVideo 1.5 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_hunyuan_video_15.py
-          - vllm_omni/diffusion/models/hunyuan_video/
-        timeout_in_minutes: 40
-        commands:
-          - |
-            timeout 40m bash -c "
-              pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15.py -m 'advanced_model and cuda and diffusion' --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: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
       - label: "Diffusion · Z Image Test"
         source_file_dependencies:
           - tests/e2e/offline_inference/test_z_image.py
@@ -943,11 +627,10 @@ steps:
       - label: "Diffusion · FLUX.2 Klein Test"
         source_file_dependencies:
           - tests/e2e/offline_inference/test_flux2_klein.py
-          - tests/e2e/online_serving/test_flux2_klein.py
           - vllm_omni/diffusion/models/flux2_klein/
         timeout_in_minutes: 30
         commands:
-          - pytest -s -v tests/e2e/offline_inference/test_flux2_klein.py tests/e2e/online_serving/test_flux2_klein.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+          - pytest -s -v tests/e2e/offline_inference/test_flux2_klein.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
         agents:
           queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
         plugins:
@@ -960,48 +643,3 @@ steps:
                 - "HF_TOKEN"
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
-
-      - label: "Diffusion · SD3 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_sd3.py
-          - vllm_omni/diffusion/models/sd3/
-        timeout_in_minutes: 45
-        commands:
-          - |
-            timeout 45m bash -c "
-              export VLLM_IMAGE_FETCH_TIMEOUT=60
-              pytest -s -v tests/e2e/online_serving/test_sd3.py -m 'advanced_model and cuda and diffusion' --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: 4
-                    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

.buildkite/test-nightly.yml

@@ -12,7 +12,7 @@ steps:
     if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
     steps:
       - label: ":full_moon: Omni · Function Test with H100"
-        timeout_in_minutes: 90
+        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
         agents:
@@ -49,25 +49,6 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: ":full_moon: Omni · Function Test with L4"
-        timeout_in_minutes: 90
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
-        agents:
-          queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
       - label: ":full_moon: Omni · Doc Test with L4"
         timeout_in_minutes: 90
         commands:
@@ -172,7 +153,7 @@ steps:
 
       - label: ":full_moon: Omni · Perf Test · No Async Chunk"
         key: nightly-omni-performance-no-async-chunk
-        timeout_in_minutes: 180
+        timeout_in_minutes: 300
         commands:
           - export BENCHMARK_DIR=tests/dfx/perf/results
           - |
@@ -395,9 +376,11 @@ steps:
     if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
     steps:
       - label: ":full_moon: TTS · Function Test with L4"
-        timeout_in_minutes: 90
+        timeout_in_minutes: 120
         commands:
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - export VLLM_USE_DEEP_GEMM="0"
+          - export VLLM_MOE_USE_DEEP_GEMM="0"
           - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_voxcpm2_tts_expansion.py
         agents:
           queue: "gpu_1_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
@@ -568,54 +551,84 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-  - label: ":full_moon: TTS · Higgs-Audio-V3 · Perf Test"
-    key: nightly-tts-performance-higgs-audio-v3
-    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
-    timeout_in_minutes: 180
-    commands:
-      - export BENCHMARK_DIR=tests/dfx/perf/results
-      - export VLLM_USE_DEEP_GEMM="0"
-      - export VLLM_MOE_USE_DEEP_GEMM="0"
-      - |
-        set +e
-        pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_higgs_audio_v3.json
-        EXIT=$$?
-        buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-        exit $$EXIT
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
+      - label: ":full_moon: TTS · Higgs-Audio-V3 · Perf Test"
+        key: nightly-tts-performance-higgs-audio-v3
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - export VLLM_USE_DEEP_GEMM="0"
+          - export VLLM_MOE_USE_DEEP_GEMM="0"
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_higgs_audio_v3.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 1
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
                   - name: devshm
-                    mountPath: /dev/shm
+                    emptyDir:
+                      medium: Memory
                   - 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
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+  - group: ":card_index_dividers: Tiny Model Tests [Multi-GPU]"
+    key: nightly-tiny-model-test-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test"
+    steps:
+      - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations)"
+        timeout_in_minutes: 30
+        # NOTE: Single-GPU (core_model) tiny model tests run on the ready label,
+        # so nightly only adds the multi-GPU parallelism configurations.
+        #
+        # This is a bit confusing, but `full_model` is used to mark the multigpu tests, and
+        # --run-level `core_model` is used to indicate that we should use tiny model weights
+        # for the CI, since these tests should pass irrespective of whether or not we use
+        # the full weights or not.
+        commands:
+          - timeout 25m pytest -sv tests/model_tests/diffusion -m 'full_model and cuda' --run-level "core_model"
+        agents:
+          queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
 
   # Diffusion X2I suite: x2i / x2a / x2t and related non-video paths; x2v is only in "Diffusion X2V Model Test" below.
   - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
@@ -636,11 +649,9 @@ steps:
             tests/e2e/online_serving/test_qwen_image_expansion.py
             tests/e2e/online_serving/test_qwen_image_edit_expansion.py
             tests/e2e/online_serving/test_qwen_image_layered_expansion.py
-            tests/e2e/online_serving/test_longcat_image_expansion.py
-            tests/e2e/online_serving/test_longcat_image_edit_expansion.py
-            tests/e2e/online_serving/test_flux_2_dev_expansion.py
             tests/e2e/online_serving/test_bagel_expansion.py
-            tests/e2e/offline_inference/test_sensenova_u1_expansion.py
+            tests/e2e/online_serving/test_krea2_expansion.py
+            tests/e2e/offline_inference/test_krea2_expansion.py
             -m "full_model and diffusion and H100 and not distributed_cuda"
             --run-level "full_model"
         agents:
@@ -684,6 +695,7 @@ steps:
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_expansion.py
             tests/e2e/online_serving/test_qwen_image_edit_expansion.py
+            tests/e2e/offline_inference/test_krea2_expansion.py
             -m "full_model and diffusion and H100 and distributed_cuda"
             --run-level "full_model"
         agents:
@@ -726,10 +738,6 @@ steps:
           - >-
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_layered_expansion.py
-            tests/e2e/online_serving/test_longcat_image_expansion.py
-            tests/e2e/online_serving/test_longcat_image_edit_expansion.py
-            tests/e2e/online_serving/test_flux_2_dev_expansion.py
-            tests/e2e/offline_inference/test_glm_image_autoround_w4a16_expansion.py
             -m "full_model and diffusion and H100 and distributed_cuda"
             --run-level "full_model"
         agents:
@@ -808,13 +816,14 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU DreamZero"
+      - label: ":full_moon: Diffusion X2I(&A&T) · GLM-Image Function Test with H100"
         timeout_in_minutes: 120
         commands:
           - >-
             pytest -sv
-            tests/e2e/online_serving/test_dreamzero_expansion.py
-            -m "full_model and diffusion and H100 and distributed_cuda"
+            tests/e2e/online_serving/test_glm_image_expansion.py
+            tests/e2e/offline_inference/test_glm_image_autoround_w4a16_expansion.py
+            -m "full_model and diffusion and H100"
             --run-level "full_model"
         agents:
           queue: "mithril-h100-pool"
@@ -851,47 +860,8 @@ steps:
                       type: DirectoryOrCreate
 
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · GLM-Image Function Test with H100"
-        timeout_in_minutes: 120
-        commands:
-          - pytest -sv tests/e2e/online_serving/test_glm_image_expansion.py -m "full_model and diffusion and H100" --run-level "full_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
-
-
       - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
-        timeout_in_minutes: 60
+        timeout_in_minutes: 120
         commands:
           - pytest -sv tests/e2e/ -k "not test_wan and not test_bagel_expansion and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
         agents:
@@ -1276,10 +1246,10 @@ steps:
       build.pull_request.labels includes "nightly-test" ||
       build.pull_request.labels includes "diffusion-x2v-test"
     steps:
-      - label: ":full_moon: Diffusion X2V · Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test"
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_hunyuan_video_15_expansion.py tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py tests/e2e/online_serving/test_ltx2_expansion.py -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda" --run-level "full_model"
         agents:
           queue: "mithril-h100-pool"
         plugins:
@@ -1314,14 +1284,166 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: ":full_moon: Diffusion X2V · Accuracy Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda" --run-level "full_model"
+        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:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 2
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: ":full_moon: Diffusion X2V · Doc Test"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -s -v tests/examples/offline_inference/test_image_to_video.py -m "full_model and example and H100" --run-level "full_model"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 1
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: ":full_moon: Diffusion X2V · Other Function Test"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_ltx2_expansion.py -m "full_model and cuda" --run-level "full_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:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 2
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
         timeout_in_minutes: 180
         commands:
           - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m full_model --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m full_model --run-level full_model
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$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
+
+      - label: ":full_moon: Diffusion X2V · Other Accuracy Test"
+        timeout_in_minutes: 180
+        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/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -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
         agents:
           queue: "mithril-h100-pool"
@@ -1367,9 +1489,12 @@ steps:
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
             EXIT1=$$?
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
+            EXIT2=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
             buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$EXIT1
+            if [ $$EXIT1 -ne 0 ]; then exit $$EXIT1; fi
+            exit $$EXIT2
         agents:
           queue: "mithril-h100-pool"
         plugins:
@@ -1466,7 +1591,7 @@ steps:
     key: nightly-testcase-statistics
     timeout_in_minutes: 120
     depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+    if: build.env("NIGHTLY") == "1"
     commands:
       - python tools/nightly/buildkite_testcase_statistics.py -o tests/dfx/perf/results/buildkite_testcase_statistics.html
       - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"

.buildkite/test-ready.yml

@@ -157,6 +157,27 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
 
+  - group: ":card_index_dividers: Tiny Model Tests [Single GPU]"
+    depends_on: upload-ready-pipeline
+    steps:
+      - label: "Tiny Model Tests · Diffusion (base)"
+        commands:
+          - timeout 15m pytest -sv tests/model_tests/diffusion -m 'core_model and cuda' --run-level "core_model"
+        agents:
+          # Ready label only runs the cases that don't need parallelism for tiny
+          # model tests, which is usually just a smoke test for TTI and I2I
+          queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
   - label: "Engine&Model Executor Test"
     depends_on: upload-ready-pipeline
     commands:
@@ -184,96 +205,43 @@ steps:
     commands:
       - |
         timeout 20m bash -c "
-                  pytest -sv tests/distributed/ -m 'core_model and cuda' --run-level 'core_model'
+          pytest -sv tests/distributed/ -m 'core_model and cuda and L4' --run-level 'core_model'
         "
     agents:
-      queue: "mithril-h100-pool"
+      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
     plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-            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
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          shm-size: "8gb"
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+            - "HF_TOKEN"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
 
-  - group: ":card_index_dividers: Entrypoints Test"
+  - label: "Entrypoints Test"
     depends_on: upload-ready-pipeline
-    steps:
-      - label: "Entrypoints Test with H100"
-        commands:
-          - |
-            timeout 30m bash -c "
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              pytest -sv tests/entrypoints/ -m 'core_model and cuda and H100' --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:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: "Entrypoints Test with L4"
-        commands:
-          - |
-            timeout 30m bash -c "
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              pytest -sv tests/entrypoints/ -m 'core_model and cuda and L4' --run-level 'core_model'
-            "
-        agents:
-          queue: "gpu_1_queue"
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
+    commands:
+      - |
+        timeout 30m bash -c "
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          pytest -sv tests/entrypoints/ -m 'core_model and cuda' --run-level '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
+          shm-size: "8gb"
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+            - "HF_TOKEN"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
 
   - group: ":card_index_dividers: E2E Test"
     depends_on: upload-ready-pipeline
@@ -584,6 +552,50 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
+      - label: "Diffusion · Cosmos3 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_cosmos3.py
+          - vllm_omni/diffusion/models/cosmos3/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_cosmos3.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: 1
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
 
       - label: "Diffusion · Qwen Image Test"
         source_file_dependencies:
@@ -672,3 +684,24 @@ steps:
                     hostPath:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
+
+      - 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
+        agents:
+          queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+        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"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"

.buildkite/test-weekly.yml

@@ -206,3 +206,246 @@ steps:
                 - "HF_TOKEN"
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
+
+  - group: ":card_index_dividers: E2E Tests"
+    depends_on: upload-weekly-pipeline
+    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
+    steps:
+      - label: "Omni · L4"
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
+          - >-
+            pytest -s -v tests/e2e/
+            --ignore=tests/e2e/online_serving/test_qwen2_5_omni_expansion.py
+            --ignore=tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py
+            -m "slow and L4 and omni"
+            --run-level "full_model"
+        agents:
+          queue: "gpu_4_queue"
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
+      - label: "Omni · H100"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and H100 and omni"
+            --run-level "full_model"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$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
+
+      - label: "TTS · L4"
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - export VLLM_USE_DEEP_GEMM="0"
+          - export VLLM_MOE_USE_DEEP_GEMM="0"
+          - |
+            failed=0
+            for f in $(pytest --collect-only -q tests/e2e/ -m "slow and L4 and tts" --run-level "full_model" | grep '\.py::' | sed -E 's|::.*||' | sort -u); do
+              pytest -s -v "$$f" -m "slow and L4 and tts" --run-level "full_model" || failed=1
+            done
+            exit $$failed
+        agents:
+          queue: "gpu_1_queue"
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
+      - label: "TTS · H100"
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - export VLLM_USE_DEEP_GEMM="0"
+          - export VLLM_MOE_USE_DEEP_GEMM="0"
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and H100 and tts"
+            --run-level "full_model"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 1
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: "Diffusion · L4"
+        timeout_in_minutes: 60
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and L4"
+            --run-level "full_model"
+        agents:
+          queue: "gpu_4_queue"
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
+      - label: "Diffusion · H100 · Single-GPU"
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and H100 and not distributed_cuda"
+            --run-level "full_model"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 1
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: "Diffusion · H100 · Multi-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
+            --run-level "full_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

.github/CODEOWNERS

@@ -22,30 +22,30 @@
 /.buildkite/ @yenuo26 @congw729
 
 # Engine and model_executor
-/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan
-/vllm_omni/engine/ @tzhouam @yinpeiqi @fake0fan
-/vllm_omni/entrypoints/ @tzhouam @yinpeiqi @fake0fan
+/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan @Sy0307
+/vllm_omni/engine/ @tzhouam @yinpeiqi @fake0fan @Sy0307
+/vllm_omni/entrypoints/ @tzhouam @yinpeiqi @fake0fan @alex-jw-brooks
 /vllm_omni/model_executor/ @tzhouam @gcanlin
 /vllm_omni/worker/ @tzhouam @gcanlin
 
 # Diffusion
-/vllm_omni/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @david6666666
+/vllm_omni/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @david6666666 @xuechendi
 /vllm_omni/lora/ @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao
 
 # Diffusion CPU offloading
-/vllm_omni/diffusion/offloader/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao
+/vllm_omni/diffusion/offloader/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao @xuechendi
 
 # Omni and TTS support
 /vllm_omni/transformers_utils/ @tzhouam @yuanheng-zhao
-/vllm_omni/model_executor/models/ @linyueqian @ZeldaHuang @princepride @yuanheng-zhao @amy-why-3459 @Bounty-hunter
+/vllm_omni/model_executor/models/ @linyueqian @ZeldaHuang @princepride @yuanheng-zhao @amy-why-3459 @Bounty-hunter @Sy0307 @gcanlin
 
 # Benchmarks
 /benchmarks/accuracy/ @david6666666
 /benchmarks/build_dataset/ @linyueqian @yuanheng-zhao
-/benchmarks/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa
+/benchmarks/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @xuechendi
 /benchmarks/distributed/ @princepride
 /benchmarks/glm_image/ @princepride @JaredforReal
-/benchmarks/tts/ @linyueqian @ZeldaHuang @princepride
+/benchmarks/tts/ @linyueqian @ZeldaHuang @princepride @Sy0307 @gcanlin
 
 # Usability and Observability
 /vllm_omni/deploy/ @tzhouam @lishunyang12
@@ -53,13 +53,13 @@
 /vllm_omni/profiler/ @gcanlin
 
 # ComfyUI
-/apps/ @fhfuih
+/apps/ @fhfuih @alex-jw-brooks
 
 # Large Scale Serving
 /vllm_omni/distributed/ @princepride @yuanheng-zhao @xuechendi @natureofnature @fake0fan
 
 # Quantization and configuration
-/vllm_omni/config/ @lishunyang12
+/vllm_omni/config/ @lishunyang12 @alex-jw-brooks
 /vllm_omni/quantization/ @david6666666 @Isotr0py @lishunyang12
 
 # Hardware plugins and accelerators

.gitignore

@@ -208,6 +208,7 @@ cache/
 .cache/
 diffusion_cache/
 kv_cache/
+!vllm_omni/experimental/ar_diffusion/kv_cache/
 
 # Logs
 logs/
@@ -247,6 +248,7 @@ datasets/
 
 # Output files
 outputs/
+!vllm_omni/outputs/
 results/
 generated/
 output_*/

README.md

@@ -16,6 +16,7 @@ Easy, fast, and cheap omni-modality model serving for everyone
 ---
 
 *Latest News* 🔥
+- [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support.
 - [2026/06] We released [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) - an **omnimodal world-model** release aligned with vLLM 0.22, featuring [Nvidia Cosmos3](recipes/cosmos3/Cosmos3-Nano.md)/DreamZero world model support, expanded quantization coverage across Blackwell/NPU/XPU, TTS production improvements, new models including MiniCPM-o 4.5, MOSS-TTS, and Lance, plus RL integration with [VeRL-Omni](https://github.com/verl-project/verl-omni).
 - [2026/05] We released [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0) - refreshes the serving/runtime stack for large-scale omni workloads, and improves diffusion model performance, quantization, and hardware readiness across CUDA, ROCm, MUSA, NPU, and XPU backends.
 - [2026/03] We released [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0) - strengthens the core runtime through a large entrypoint refactor and scheduler/runtime cleanups, expands unified quantization and diffusion execution, broadens multimodal model coverage, and improves production readiness across audio, omni, image, video, RL, and multi-platform deployments.
@@ -31,9 +32,9 @@ Easy, fast, and cheap omni-modality model serving for everyone
 
 [vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving:
 
-- **Omni-modality**: Text, image, video, and audio data processing
+- **Omni-modality**: Text, image, audio, video, and action data processing
 - **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
-- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
+- **Heterogeneous outputs**: from traditional text generation to multimodal and action outputs
 
 <p align="center">
   <picture>
@@ -57,9 +58,10 @@ vLLM-Omni is flexible and easy to use with:
 
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
-- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos, HunyuanImage, BAGEL)
+- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
 - **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
 - **Diffusion models** — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
+- **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
 
 ## Getting Started
 

benchmarks/diffusion/backends.py

@@ -28,6 +28,7 @@ class RequestFuncInput:
     slo_ms: float | None = None
     extra_body: dict[str, Any] = field(default_factory=dict)
     image_paths: list[str] | None = None
+    video_paths: list[str] | None = None
     request_id: str = field(default_factory=lambda: str(uuid.uuid4()))
     default_bot_task: str | None = DEFAULT_EDITS_BOT_TASK
 
@@ -338,6 +339,11 @@ async def async_request_v1_videos(
         form.add_field(k, str(v))
 
     image_file = None
+    if input.image_paths and input.video_paths:
+        output.error = "Only one of image_paths or video_paths can be provided"
+        output.success = False
+        return output
+
     if input.image_paths and len(input.image_paths) > 0:
         image_path = input.image_paths[0]
         image_file = open(image_path, "rb")
@@ -345,7 +351,16 @@ async def async_request_v1_videos(
             "input_reference",
             image_file,
             filename=os.path.basename(image_path),
-            content_type="application/octet-stream",
+            content_type=_guess_mime_type(image_path),
+        )
+    elif input.video_paths and len(input.video_paths) > 0:
+        video_path = input.video_paths[0]
+        image_file = open(video_path, "rb")
+        form.add_field(
+            "input_reference",
+            image_file,
+            filename=os.path.basename(video_path),
+            content_type=_guess_mime_type(video_path),
         )
 
     job_id = None

benchmarks/diffusion/diffusion_benchmark_serving.py

@@ -73,6 +73,10 @@ Usage:
             {"width":854,"height":480,"num_inference_steps":18,"num_frames":120,"fps":24,"weight":1}
         ]'
 
+    v2v:
+    python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
+        --endpoint /v1/videos --dataset random --task v2v --num-prompts 1 \
+        --height 720 --width 1280 --fps 24 --num-frames 189 --num-inference-steps 35
 
 """
 
@@ -542,11 +546,17 @@ class TraceDataset(BaseDataset):
         if not image_paths:
             single = row.get("image_path")
             image_paths = [single] if single else None
+        video_paths = row.get("video_paths")
+        if not video_paths:
+            single_video = row.get("video_path")
+            video_paths = [single_video] if single_video else None
 
         if not image_paths and self.args.task in ["i2v", "i2i", "ti2v", "ti2i", "it2i"]:
             raise ValueError(
                 f"Task {self.args.task} requires image input, but no image_path or image_paths found in trace row."
             )
+        if not video_paths and self.args.task == "v2v":
+            raise ValueError("Task v2v requires video input, but no video_path or video_paths found in trace row.")
 
         override_w = self.args.width
         override_h = self.args.height
@@ -570,6 +580,7 @@ class TraceDataset(BaseDataset):
             timestamp=timestamp,
             slo_ms=slo_ms,
             image_paths=image_paths,
+            video_paths=video_paths,
             request_id=str(row.get("request_id")) if row.get("request_id") is not None else str(uuid.uuid4()),
         )
 
@@ -589,6 +600,8 @@ class CustomDataset(BaseDataset):
     - seed (optional): Random seed
     - image_paths (optional): List of input image paths for i2i/i2v/ti2i tasks
     - image_urls (optional): List of input image URLs (alternative to image_paths)
+    - video_paths (optional): List of input video paths for v2v tasks
+    - video_urls (optional): List of input video URLs (alternative to video_paths)
 
     Example JSONL for ti2i:
     {"prompt": "Add sunset lighting", "width": 1024, "height": 1024, "image_urls": ["https://example.com/image.jpg"]}
@@ -660,6 +673,42 @@ class CustomDataset(BaseDataset):
 
         return None
 
+    def _resolve_video_paths(self, item: dict) -> list[str] | None:
+        if "video_paths" in item and item["video_paths"]:
+            paths = item["video_paths"]
+            if isinstance(paths, str):
+                paths = [paths]
+
+            valid_paths = []
+            for path in paths:
+                if os.path.exists(path):
+                    valid_paths.append(path)
+                else:
+                    raise ValueError(f"Video file not found: {path}")
+
+            return valid_paths if valid_paths else None
+
+        if "video_urls" in item and item["video_urls"]:
+            urls = item["video_urls"]
+            if isinstance(urls, str):
+                urls = [urls]
+
+            downloaded_paths = []
+            for url in urls:
+                try:
+                    response = requests.get(url, timeout=30)
+                    response.raise_for_status()
+                    suffix = os.path.splitext(url)[1] or ".mp4"
+                    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+                        tmp.write(response.content)
+                        downloaded_paths.append(tmp.name)
+                except Exception as e:
+                    raise ValueError(f"Failed to download video from {url}: {e}")
+
+            return downloaded_paths if downloaded_paths else None
+
+        return None
+
     def __len__(self) -> int:
         return len(self.data)
 
@@ -672,11 +721,22 @@ class CustomDataset(BaseDataset):
         height = item.get("height", self.args.height)
         num_inference_steps = item.get("num_inference_steps", self.args.num_inference_steps)
         seed = item.get("seed", self.args.seed)
-        reserved_keys = {"prompt", "width", "height", "num_inference_steps", "seed", "image_paths", "image_urls"}
+        reserved_keys = {
+            "prompt",
+            "width",
+            "height",
+            "num_inference_steps",
+            "seed",
+            "image_paths",
+            "image_urls",
+            "video_paths",
+            "video_urls",
+        }
         extra_body = {k: v for k, v in item.items() if k not in reserved_keys}
 
         # Handle image paths/URLs
         image_paths = self._resolve_image_paths(item)
+        video_paths = self._resolve_video_paths(item)
 
         return RequestFuncInput(
             prompt=prompt,
@@ -684,6 +744,7 @@ class CustomDataset(BaseDataset):
             model=self.model,
             seed=seed,
             image_paths=image_paths,
+            video_paths=video_paths,
             extra_body=extra_body,
             width=width,
             height=height,
@@ -737,6 +798,10 @@ class RandomDataset(BaseDataset):
             self._random_image_path = self._generate_random_image_paths()
         else:
             self._random_image_path = None
+        if self.args.task == "v2v":
+            self._random_video_path = self._generate_random_video_paths()
+        else:
+            self._random_video_path = None
 
     def __len__(self) -> int:
         return self.num_prompts
@@ -756,13 +821,15 @@ class RandomDataset(BaseDataset):
         if self._sampled_requests:
             profile = self._sampled_requests[idx]
             params.update(profile)
+        prompt = str(params.pop("prompt", f"Random prompt {idx} for benchmarking diffusion models"))
         return RequestFuncInput(
-            prompt=f"Random prompt {idx} for benchmarking diffusion models",
+            prompt=prompt,
             api_url=self.api_url,
             model=self.model,
             seed=self.args.seed,
             extra_body=extra_body,
             image_paths=self._random_image_path,
+            video_paths=self._random_video_path,
             **params,
         )
 
@@ -781,6 +848,35 @@ class RandomDataset(BaseDataset):
             image_paths.append(image_path)
         return image_paths
 
+    def _generate_random_video_paths(self) -> list[str]:
+        video_path = os.path.join(tempfile.gettempdir(), "diffusion_benchmark_random_video.mp4")
+        try:
+            import cv2
+
+            profile = self.random_request_config[0] if self.random_request_config else {}
+            width = int(self.args.width or profile.get("width") or 1280)
+            height = int(self.args.height or profile.get("height") or 720)
+            num_frames = int(self.args.num_frames or profile.get("num_frames") or 16)
+            fps = float(self.args.fps or profile.get("fps") or 8)
+            writer = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
+            if not writer.isOpened():
+                raise RuntimeError("cv2.VideoWriter failed to open")
+            for frame_idx in range(num_frames):
+                frame = np.zeros((height, width, 3), dtype=np.uint8)
+                x = int((frame_idx / max(num_frames - 1, 1)) * max(width - 96, 1))
+                y = height // 2
+                frame[:, :, 0] = 24
+                frame[:, :, 1] = 32
+                frame[:, :, 2] = 48
+                frame[max(y - 36, 0) : min(y + 36, height), x : min(x + 96, width), :] = (64, 128, 220)
+                writer.write(frame)
+            writer.release()
+            return [video_path]
+        except Exception as e:
+            raise RuntimeError(
+                "Failed to generate synthetic v2v input video. Install opencv-python or provide a custom dataset."
+            ) from e
+
 
 def _compute_expected_latency_ms_from_base(req: RequestFuncInput, args, base_time_ms: float | None) -> float | None:
     """Compute expected execution time (ms) based on a base per-step-per-frame unit time.
@@ -1112,7 +1208,7 @@ def wait_for_service(base_url: str, timeout: int = 120) -> None:
 
 
 def _default_endpoint_for_task(task: str) -> str:
-    if task in {"t2v", "i2v", "ti2v"}:
+    if task in {"t2v", "i2v", "ti2v", "v2v"}:
         return "/v1/videos"
     if task in {"i2i", "ti2i", "it2i"}:
         return "/v1/images/edits"
@@ -1126,7 +1222,7 @@ async def benchmark(args):
     if args.base_url is None:
         args.base_url = f"http://{args.host}:{args.port}"
 
-    VIDEO_TASKS = {"t2v", "i2v", "ti2v"}
+    VIDEO_TASKS = {"t2v", "i2v", "ti2v", "v2v"}
     IMAGE_TASKS = {"t2i", "i2i", "ti2i", "it2i"}
 
     if args.task in VIDEO_TASKS:
@@ -1177,6 +1273,10 @@ async def benchmark(args):
     if args.return_stage_metrics and args.endpoint in _STAGE_METRICS_ENDPOINTS:
         for req in requests_list:
             req.extra_body.setdefault(_RETURN_STAGE_METRICS_FIELD, True)
+    if args.extra_body:
+        extra_body = json.loads(args.extra_body)
+        for req in requests_list:
+            req.extra_body.update(extra_body)
 
     if args.endpoint == "/v1/images/edits":
         for req in requests_list:
@@ -1328,7 +1428,7 @@ if __name__ == "__main__":
         "--task",
         type=str,
         default="t2v",
-        choices=["t2v", "i2v", "ti2v", "ti2i", "i2i", "it2i", "t2i"],
+        choices=["t2v", "i2v", "ti2v", "v2v", "ti2i", "i2i", "it2i", "t2i"],
         help="Task type.",
     )
     parser.add_argument(
@@ -1443,6 +1543,12 @@ if __name__ == "__main__":
             '{"width":768,"height":768,"num_inference_steps":20,"weight":0.85}]'
         ),
     )
+    parser.add_argument(
+        "--extra-body",
+        type=str,
+        default=None,
+        help="JSON object merged into every generated request body/form.",
+    )
     parser.add_argument(
         "--num-input-images",
         type=int,

benchmarks/fish-speech/bench_speaker_cache.py

@@ -201,6 +201,7 @@ async def main():
             "input": prompt,
             "voice": "default",
             "stream": True,
+            "stream_format": "audio",
             "response_format": "pcm",
             "ref_audio": ref_audio_b64,
             "ref_text": args.ref_text,
@@ -240,6 +241,7 @@ async def main():
             "input": prompt,
             "voice": args.voice_name,
             "stream": True,
+            "stream_format": "audio",
             "response_format": "pcm",
             "ref_text": args.ref_text,
             "max_new_tokens": 2048,

docker/Dockerfile.ci

@@ -1,8 +1,8 @@
-# Target vllm version: v0.21.0 (releases/v0.21.0 branch).
+# Target vllm version: v0.24.0 (releases/v0.24.0 branch).
 # The pinned VLLM_PRECOMPILED_WHEEL_COMMIT and VLLM_BASE_TAG below must
 # be updated on every rebase to match the new upstream vllm commit/tag.
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.23.0
+ARG VLLM_BASE_TAG=v0.25.0
 FROM ${VLLM_BASE_IMAGE}:${VLLM_BASE_TAG}
 ARG APP_DIR=/workspace/vllm-omni
 WORKDIR ${APP_DIR}

docker/Dockerfile.cuda

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai:v0.23.0
+ARG BASE_IMAGE=vllm/vllm-openai:v0.25.0
 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=v0.23.0-4885d8e
+ARG VLLM_ASCEND_TAG=v0.25.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 # WORKDIR /vllm-workspace/vllm

docker/Dockerfile.npu.a3

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.23.0-4885d8e-a3
+ARG VLLM_ASCEND_TAG=v0.25.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 # WORKDIR /vllm-workspace/vllm

docker/Dockerfile.npu.ci

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.23.0-4885d8e
+ARG VLLM_ASCEND_TAG=v0.25.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni

docker/Dockerfile.npu.ci.a3

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.23.0-4885d8e-a3
+ARG VLLM_ASCEND_TAG=v0.25.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni

docker/Dockerfile.rocm

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.23.0
+ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.25.0
 FROM ${BASE_IMAGE} AS base
 
 # Declare a variable to know if we want to use the nightly build or the stable build.

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=v0.23.0
+ARG VLLM_VERSION=v0.25.0
 RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
 WORKDIR /workspace/vllm
 
@@ -107,7 +107,9 @@ ENV VLLM_TARGET_DEVICE=xpu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
 
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --no-build-isolation .
+    uv pip uninstall triton triton-xpu && \
+    uv pip install triton-xpu==3.7.1 && \
+    uv pip install --no-build-isolation --no-deps .
 
 CMD ["/bin/bash"]
 
@@ -143,12 +145,11 @@ COPY . .
 
 ENV VLLM_OMNI_TARGET_DEVICE=xpu
 RUN --mount=type=cache,target=/root/.cache/uv \
-    echo "triton @ https://example.com/blocked" > /tmp/vllm_omni_constraints.txt && \
-    PIP_CONSTRAINT=/tmp/vllm_omni_constraints.txt \
     uv pip install --no-build-isolation \
     --extra-index-url https://mirrors.ustc.edu.cn/pypi/simple/ \
     ".[dev]" && \
-    uv pip uninstall oneccl oneccl-devel
+    uv pip uninstall triton oneccl oneccl-devel && \
+    uv pip install triton-xpu==3.7.1 --reinstall
 
 FROM vllm-omni AS vllm-omni-openai
 

docs/.nav.yml

@@ -23,7 +23,7 @@ nav:
       - Image-To-Video: user_guide/examples/offline_inference/image_to_video.md
       - InternVLA-A1: user_guide/examples/offline_inference/internvla_a1.md
       - MammothModa2-Preview: user_guide/examples/offline_inference/mammothmodal2_preview.md
-      - MiMo-Audio Offline Inference: user_guide/examples/offline_inference/mimo_audio.md
+      - MiMo-Audio: user_guide/examples/offline_inference/mimo_audio.md
       - Qwen2.5-Omni: user_guide/examples/offline_inference/qwen2_5_omni.md
       - Qwen3-Omni: user_guide/examples/offline_inference/qwen3_omni.md
       - Text-To-Audio: user_guide/examples/offline_inference/text_to_audio.md
@@ -38,7 +38,7 @@ nav:
       - GLM-Image Online Serving: user_guide/examples/online_serving/glm_image.md
       - Image-To-Image: user_guide/examples/online_serving/image_to_image.md
       - Image-To-Video: user_guide/examples/online_serving/image_to_video.md
-      - Online serving Example of vLLM-Omni for MiMo-Audio: user_guide/examples/online_serving/mimo_audio.md
+      - MiMo-Audio: user_guide/examples/online_serving/mimo_audio.md
       - Qwen2.5-Omni: user_guide/examples/online_serving/qwen2_5_omni.md
       - Qwen3-Omni: user_guide/examples/online_serving/qwen3_omni.md
       - Text-To-Speech: user_guide/examples/online_serving/text_to_speech.md
@@ -68,10 +68,11 @@ nav:
         - Hybrid Sharded Data Parallel: user_guide/diffusion/parallelism/hsdp.md
         - Sequence Parallel: user_guide/diffusion/parallelism/sequence_parallel.md
         - Tensor Parallel: user_guide/diffusion/parallelism/tensor_parallel.md
-        - VAE Patch Parallel: user_guide/diffusion/parallelism/vae_patch_parallel.md
+        - VAE Parallelism: user_guide/diffusion/parallelism/vae_parallelism.md
       - CPU Offloading: user_guide/diffusion/cpu_offload_diffusion.md
       - LoRA: user_guide/diffusion/lora.md
       - Custom Pipeline: features/custom_pipeline.md
+      - Request-Level Batching: user_guide/diffusion/request_batching.md
       - Step Execution: user_guide/diffusion/step_execution.md
     - Quantization:
       - Overview: user_guide/quantization/overview.md
@@ -79,6 +80,7 @@ nav:
       - Quantized KV Cache: user_guide/quantization/quantized_kvcache.md
       - FP8 W8A8: user_guide/quantization/fp8.md
       - Int8 W8A8: user_guide/quantization/int8.md
+      - BitsAndBytes W4: user_guide/quantization/bitsandbytes.md
       - MXFP8 W8A8: user_guide/quantization/mxfp8.md
       - MXFP4 W4A4: user_guide/quantization/mxfp4.md
       - ModelOpt: user_guide/quantization/modelopt.md
@@ -118,6 +120,7 @@ nav:
       - design/feature/async_chunk.md
       - design/feature/vae_parallel.md
       - design/feature/diffusion_step_execution.md
+      - design/feature/diffusion_request_level_batching.md
       - design/feature/diffusion_continuous_batching.md
     - Module Design:
       - design/module/ar_module.md

docs/README.md

@@ -28,9 +28,9 @@ Easy, fast, and cheap omni-modality model serving for everyone
 
 [vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving:
 
-- **Omni-modality**: Text, image, video, and audio data processing
+- **Omni-modality**: Text, image, audio, video, and action data processing
 - **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
-- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
+- **Heterogeneous outputs**: from traditional text generation to multimodal and action outputs
 
 <p align="center">
   <picture>
@@ -55,8 +55,10 @@ vLLM-Omni is flexible and easy to use with:
 
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
-- Omni-modality models (e.g. Qwen2.5-Omni, Qwen3-Omni)
-- Multi-modality generation models (e.g. Qwen-Image)
+- Omni-modality models (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
+- TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
+- Diffusion models — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
+- Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
 
 For more information, checkout the following:
 

docs/api/README.md

@@ -46,17 +46,17 @@ Engine classes for offline and online inference.
 - [vllm_omni.engine.PromptEmbedsPayload][]
 - [vllm_omni.engine.arg_utils.OmniEngineArgs][]
 - [vllm_omni.engine.async_omni_engine.AsyncOmniEngine][]
-- [vllm_omni.engine.mm_outputs.MultimodalCompletionOutput][]
-- [vllm_omni.engine.mm_outputs.MultimodalPayload][]
 - [vllm_omni.engine.orchestrator.Orchestrator][]
 - [vllm_omni.engine.orchestrator.OrchestratorRequestState][]
-- [vllm_omni.engine.output_modality.OutputModality][]
-- [vllm_omni.engine.output_modality.TensorAccumulationStrategy][]
-- [vllm_omni.engine.output_processor.MultimodalOutputProcessor][]
-- [vllm_omni.engine.output_processor.OmniRequestState][]
 - [vllm_omni.engine.stage_engine_core_client.StageEngineCoreClient][]
 - [vllm_omni.engine.stage_init_utils.StageMetadata][]
 - [vllm_omni.engine.stage_init_utils.StartedLlmStage][]
+- [vllm_omni.outputs.mm_outputs.MultimodalCompletionOutput][]
+- [vllm_omni.outputs.mm_outputs.MultimodalPayload][]
+- [vllm_omni.outputs.output_modality.OutputModality][]
+- [vllm_omni.outputs.output_modality.TensorAccumulationStrategy][]
+- [vllm_omni.outputs.output_processor.MultimodalOutputProcessor][]
+- [vllm_omni.outputs.output_processor.OmniRequestState][]
 
 ## Core
 

docs/cli/README.md

@@ -18,11 +18,17 @@ Specify the port:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
 
-If you have custom stage configs file, launch the server with command below
+For a migrated model, load a custom deploy configuration with `--deploy-config`:
+
 ```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni --stage-configs-path /path/to/stage_configs_file
+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
 

docs/cli/bench/serve.md

@@ -2,7 +2,7 @@
 The vllm bench command launches the vLLM-Omni benchmark to evaluate the performance of multimodal models.
 
 ## Notes
-We currently only support using the "openai-chat-omni" and "openai-image-edits-omni" backend.
+vLLM-Omni registers the `openai-chat-omni`, `openai-audio-speech`, `openai-image-edits-omni`, and `daily-omni` serving benchmark backends.
 
 ## Basic Parameter Description
 You can use `vllm bench serve --omni --help=all` to get descriptions of all parameters. The commonly used parameters are described below:
@@ -10,7 +10,7 @@ You can use `vllm bench serve --omni --help=all` to get descriptions of all para
   Enable Omni (multimodal) mode, supporting multimodal inputs and outputs such as images, videos, and audio.
 
 - `--backend`
-  Specify the backend adapter as openai-chat-omni, using OpenAI Chat compatible API behavior as the protocol. Currently only openai-chat-omni is supported.
+  Specify the backend adapter. vLLM-Omni adds `openai-chat-omni`, `openai-audio-speech`, `openai-image-edits-omni`, and `daily-omni` to the upstream vLLM backend choices.
 
 - `--model`
   The model identifier to load, filled according to the models supported by vLLM-Omni.
@@ -55,7 +55,7 @@ You can use `vllm bench serve --omni --help=all` to get descriptions of all para
                     'Allowed metric names are "ttft", "tpot", "itl", "ttfc", "tpoc", "icl", '
                     '"tpop", "e2el", "audio_ttfp", "audio_rtf", "audio_duration". '
 
-- `-print-stage`
+- `--print-stage`
 Print per-stage benchmark metrics for --omni serving when stage metrics are returned by the server. Disabled by default.
 
 - `--save-result`
@@ -312,7 +312,7 @@ vllm bench serve \
   --ignore-eos \
   --percentile-metrics ttft,tpot,itl \
   --random-output-len 2 \
-  --extra_body '{"modalities": ["text"]}'
+  --extra-body '{"modalities": ["text"]}'
 ```
 
 If successful, you will see the following output:
@@ -390,7 +390,7 @@ vllm bench serve --omni \
   --ignore-eos \
   --percentile-metrics ttft,tpot,itl,e2el,audio_ttfp,audio_rtf,ttfc,tpoc,icl \
   --random-output-len 900 \
-  --extra_body '{"modalities": ["text","audio"]}' \
+  --extra-body '{"modalities": ["text","audio"]}' \
   --print-stage
 ```
 

docs/community/governance.md

@@ -33,16 +33,20 @@ Lead maintainers are responsible for the overall direction and strategy of the p
 
 Committers have write access and merge rights. They typically have deep expertise in specific areas of this project and shepherd the community contributions:
 
+- [@alex-jw-brooks](https://github.com/alex-jw-brooks): Frontend, Configuration, and Entrypoint Support
+- [@amy-why-3459](https://github.com/amy-why-3459): Omni Modality Model and World Model Support
 - [@david6666666](https://github.com/david6666666): Quantization and Community Relationship
-- [@gcanlin](https://github.com/gcanlin): Hardware plugin and NPU integration
+- [@gcanlin](https://github.com/gcanlin): Hardware plugin, NPU integration, and TTS Model Support
 - [@Isotr0py](https://github.com/Isotr0py): Diffusion and Quantization
 - [@linyueqian](https://github.com/linyueqian): TTS and Omni Support
 - [@lishunyang12](https://github.com/lishunyang12): Quantization and Configuration
 - [@princepride](https://github.com/princepride): Diffusion and Omni Support
 - [@RuixiangMa](https://github.com/RuixiangMa): Diffusion models, parallel, cache, and docs
 - [@SamitHuang](https://github.com/SamitHuang): RL, Diffusion, and cache
+- [@Sy0307](https://github.com/Sy0307): TTS Model and AR Engine Support
 - [@tzhouam](https://github.com/tzhouam): Engine and New Model Support
 - [@wtomin](https://github.com/wtomin): Diffusion models, parallel, and docs
+- [@xuechendi](https://github.com/xuechendi): Diffusion and Intel GPU Support
 - [@ZeldaHuang](https://github.com/ZeldaHuang): Omni Support
 - [@ZJY0516](https://github.com/ZJY0516): Diffusion attention backend, kernel fusion, and CustomOp
 - [@yuanheng-zhao](https://github.com/yuanheng-zhao): Diffusion cache, offload, and Omni Support
@@ -53,7 +57,7 @@ This section breaks down diffusion-related responsibilities. If you have PRs tou
 
 ### Runtime-related
 
-- Diffusion models: @RuixiangMa, @wtomin
+- Diffusion models: @RuixiangMa, @wtomin, @xuechendi
 
 ### Optimization
 
@@ -61,6 +65,7 @@ This section breaks down diffusion-related responsibilities. If you have PRs tou
 - Attention backend: @ZJY0516
 - Cache: @yuanheng-zhao, @RuixiangMa, @SamitHuang
 - Offload: @yuanheng-zhao
+- Intel GPU support: @xuechendi
 - Quantization: @lishunyang12, @david6666666
 - Kernel fusion / communication-computation: @ZJY0516
 

docs/configuration/composable_parallel.md

@@ -0,0 +1,239 @@
+# Composable parallel strategies
+
+In vLLM-Omni, the `composable_parallel` system layers a declarative *parallel strategy* on top of the existing stage configs. A strategy YAML maps each pipeline stage (by its `model_stage` name) to a stack of axis declarations — one per parallelism dimension — that the engine translates into per-stage parallel sizing (`tensor_parallel_size`, `data_parallel_size`, `pipeline_parallel_size`, expert-parallel toggle, replica count) and, when relevant, an orchestrator-wide load-balancer policy.
+
+Strategies are intentionally narrow: each one only writes the axes it declares, leaving every other engine arg untouched. This makes a strategy file a small, reusable overlay you can swap independently of the deploy YAML (see `configuration/stage_configs.md`).
+
+!!! note
+    Composable parallel is **opt-in**. Passing `--strategy-config <path/to/strategy.yaml>` overlays the derived sizing onto the registry-merged stages *before* any CLI overrides are applied. When `--strategy-config` is omitted, stages remain exactly as their deploy YAML produces them.
+
+## Top-level schema reference
+
+A strategy file has a single top-level mapping, `strategies`, keyed by the `model_stage` name of each stage you want to overlay. Each value is a list of axis declarations (see [StrategySpec fields](#strategyspec-fields) below).
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `strategies` | `dict[str, list]` | required | — | Maps `model_stage` name (e.g. `thinker`, `talker`, `code2wav`, `dit`) to a list of axis declarations. The key MUST be the stage name, not the integer `stage_id`. See `vllm_omni/config/composable_parallel/strategy_loader.py`. |
+
+### StrategySpec fields
+
+Each entry in a stage's list becomes one `StrategySpec` (see `vllm_omni/config/composable_parallel/spec.py`). The loader fills in `routing` and `aggregation` from per-kind defaults, so the file stays compact.
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `axis` | str | required | — | Mesh-axis kind. One of `tp`, `dp`, `pp`, `ep`, `stage_replica` (wired today) or `sp_ulysses`, `sp_ring`, `cfg`, `vae_pp`, `hsdp`, `stage_pp`, `cp` (reserved; declaring one raises `AxisTranslationError` at translate time). See `MeshAxisKind` in `vllm_omni/config/composable_parallel/spec.py`. |
+| `size` | int | required | — | Axis degree. Must be `> 0`. |
+| `routing` | str | optional | per-kind default | Stateless routing policy. Accepted only for `dp` / `stage_replica`; other kinds reject it. Values: `random`, `round_robin`, `least_queue`. (`hash` is recognised but currently raises `NotImplementedError`.) See `RouteByStage` in `vllm_omni/config/composable_parallel/routing.py`. |
+| `l1_owner` | str | optional | per-kind default | Overrides which layer owns request routing for this axis. `engine` for `tp`/`dp`/`pp`/`ep`, `delegated` for `stage_replica`. Recorded into `shard_extension`. |
+| `name` | str | optional | axis kind | Human-readable label for this spec, used in error messages. |
+
+Two additional `StrategySpec` slots — `layer_hook_specs` and `kernel_specs` — let advanced authors bind layer-walker hook IDs (`LayerHookSpec`) or kernel IDs (`KernelSpec`) onto an axis. These are not exposed by the YAML loader today and are intended for programmatic construction. See `vllm_omni/config/composable_parallel/spec.py` for the dataclasses.
+
+### CLI flags
+
+| 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`
+
+The set of recognised axis kinds is the `MeshAxisKind` enum in `vllm_omni/config/composable_parallel/spec.py`:
+
+```yaml
+# Translatable today (see `_SUPPORTED_KINDS` in translator.py).
+axis: tp              # tensor parallel — sets tensor_parallel_size
+axis: dp              # engine data parallel — sets data_parallel_size (vLLM's intra-engine DP LB)
+axis: pp              # pipeline parallel — sets pipeline_parallel_size
+axis: ep              # dense expert parallel — sets enable_expert_parallel=True; size must equal tp*dp
+axis: stage_replica   # omni-coordinator-level replica fan-out — sets num_replicas + omni_lb_policy
+
+# Reserved (raise AxisTranslationError today; declared in the type system for forward compat).
+axis: sp_ulysses
+axis: sp_ring
+axis: cfg
+axis: vae_pp
+axis: hsdp
+axis: stage_pp
+axis: cp
+```
+
+### `routing`
+
+The full closed hierarchy lives in `vllm_omni/config/composable_parallel/routing.py` (`Broadcast`, `PartitionByHash`, `ShardSequence`, `ShardSpatial`, `RouteByExpert`, `PipelineMicrobatch`, `DuplicateWithCondTag`, `RouteByStage`). For YAML authors only `RouteByStage` is exposed as a `routing:` string, and only for `dp` / `stage_replica` axes (other axes have fixed routing — `tp`/`ep` broadcast, `pp` microbatch). Recognised policies:
+
+```yaml
+routing: random         # uniform-random across replicas/DP ranks
+routing: round_robin    # cycle through replicas/DP ranks (the default for dp/stage_replica)
+routing: least_queue    # send to the replica with the smallest backlog
+# routing: hash         # recognised but raises NotImplementedError; no key-stable balancer yet
+```
+
+### `aggregation`
+
+Aggregation is filled in automatically by the loader (`_default_aggregation` in `vllm_omni/config/composable_parallel/strategy_loader.py`) from the axis kind, so users do not author it:
+
+| Axis kind | Aggregation pattern |
+|-----------|---------------------|
+| `tp`, `ep` | `TakeRank` (all ranks agree; pick one) |
+| `dp`, ep (in some flows) | `Union` (disjoint replica results) |
+| `pp` | `StitchPipeline` |
+| `stage_replica` | `FanInByStage` |
+
+Full pattern hierarchy: `TakeRank`, `Union`, `GatherDim`, `AllGather`, `StitchSpatial`, `StitchPipeline`, `Combine`, `FanInByStage` (see `vllm_omni/config/composable_parallel/aggregation.py`).
+
+## Precedence
+
+From highest to lowest, when the same axis is touched by multiple layers:
+
+1. **CLI overrides** (`--tensor-parallel-size`, `--stage-overrides`, `--omni-lb-policy`, etc.). A CLI value always wins; a warning is emitted whenever it overrides a strategy-declared axis (see `_reconcile_strategy_with_cli` in `vllm_omni/config/config_factory.py`).
+2. **Strategy YAML** (`--strategy-config`). Derived sizing is written onto the merged stages with *conflict-on-explicit* semantics — if the deploy YAML already set a value to something different, application raises `StrategyApplyError`.
+3. **Deploy YAML** (`--deploy-config` or the bundled `vllm_omni/deploy/<model_type>.yaml`).
+4. **Parser defaults.**
+
+After CLI overrides land, the device-layout guard re-runs (`check_device_layout`, called from `_reconcile_strategy_with_cli`) against the *effective* `tp * dp * pp * num_replicas` and the resolved `devices` string, so a `devices` value passed through `--stage-overrides` or a `--tensor-parallel-size` value that conflicts with the strategy's world size cannot slip past the pre-spawn check.
+
+## Worked example
+
+### Example 1 — `strategy_tp2.yaml`
+
+Shard the `thinker` stage with TP=2 on top of the bundled Qwen2.5-Omni deploy YAML. Routing is fixed (broadcast) for TP, so no `routing:` field is needed.
+
+```yaml
+# examples/offline_inference/qwen2_5_omni/strategy_tp2.yaml
+strategies:
+  thinker:
+    - axis: tp
+      size: 2
+```
+
+Launch:
+
+```bash
+python3 end2end.py --query-type text --num-prompts 6 \
+    --strategy-config strategy_tp2.yaml \
+    --stage-overrides '{"0": {"devices": "0,1"}, "1": {"devices": "2"}, "2": {"devices": "2"}}'
+```
+
+Effective `OmniParallelConfig` per stage after the overlay (see `OmniParallelConfig` in `vllm_omni/config/composable_parallel/translator.py`):
+
+| Stage (`model_stage`) | `tensor_parallel_size` | `data_parallel_size` | `pipeline_parallel_size` | `stage_replica_size` | `omni_lb_policy` |
+|-----------------------|------------------------|----------------------|--------------------------|----------------------|------------------|
+| `thinker`             | **2** (from strategy)  | 1                    | 1                        | 1                    | —                |
+| `talker`              | 1                      | 1                    | 1                        | 1                    | —                |
+| `code2wav`            | 1                      | 1                    | 1                        | 1                    | —                |
+
+### Example 2 — `strategy_stage_replica.yaml`
+
+Replicate the `thinker` stage across two independent engines and let omni's StagePool round-robin requests across them.
+
+```yaml
+# examples/offline_inference/qwen2_5_omni/strategy_stage_replica.yaml
+strategies:
+  thinker:
+    - axis: stage_replica
+      size: 2
+      routing: round_robin
+```
+
+Launch:
+
+```bash
+python3 end2end.py --query-type text --num-prompts 6 \
+    --strategy-config strategy_stage_replica.yaml \
+    --stage-overrides '{"0": {"devices": "0,1"}, "1": {"devices": "2"}, "2": {"devices": "2"}}'
+```
+
+Effective per-stage config and the derived orchestrator knob:
+
+| Stage (`model_stage`) | `tensor_parallel_size` | `stage_replica_size` | `omni_lb_policy` (derived) |
+|-----------------------|------------------------|----------------------|----------------------------|
+| `thinker`             | 1                      | **2**                | **`round-robin`**          |
+| `talker`              | 1                      | 1                    | —                          |
+| `code2wav`            | 1                      | 1                    | —                          |
+
+The derived `omni_lb_policy = round-robin` is **not** written into any stage's engine args. Instead it is surfaced on `StrategyApplyResult.omni_lb_policy` and applied once at orchestrator construction by `AsyncOmniEngine._apply_strategy_lb_policy` (see `vllm_omni/engine/async_omni_engine.py`, lines 1107–1134). If you also pass `--omni-lb-policy round-robin` the values must match; passing a different value raises `Conflicting load-balancer policy`.
+
+## Per-field deep-dive
+
+### `name`
+
+Default: the axis kind (e.g. `"tp"`).
+
+Human-readable label that appears in `AxisTranslationError` and `StrategyApplyError` messages. Set it to disambiguate two stages declaring the same axis kind when scanning logs.
+
+### `mesh_axis` (`axis` + `size`)
+
+Default: no default — both fields are required.
+
+`axis` chooses the parallelism dimension; `size` is its degree. Five kinds are wired end-to-end today (`tp`, `dp`, `pp`, `ep`, `stage_replica`); the remaining kinds in `MeshAxisKind` are reserved and raise `AxisTranslationError` at translate time so a typo cannot silently become a no-op (see `_SUPPORTED_KINDS` in `vllm_omni/config/composable_parallel/translator.py`). For `ep`, the size MUST equal `tensor_parallel_size * data_parallel_size`.
+
+### `routing`
+
+Default: per-kind — `RouteByStage(round_robin)` for `dp` and `stage_replica`; `PipelineMicrobatch()` for `pp`; `Broadcast()` for `tp`/`ep` and any other kind.
+
+User-facing string only on `dp` and `stage_replica` axes. The four recognised values are `random`, `round_robin`, `least_queue`, `hash`; `hash` is rejected today with `NotImplementedError`. See `RouteByStage` in `vllm_omni/config/composable_parallel/routing.py`.
+
+### `l1_owner`
+
+Default: per-kind — `engine` for `tp`/`dp`/`pp`/`ep`, `delegated` for `stage_replica`.
+
+Overrides which layer owns request routing along this axis. `engine` means vLLM realizes routing intra-engine; `delegated` means the omni coordinator owns routing across independent replicas. Set this only when overriding the per-kind default in `_DEFAULT_L1_OWNER` (`vllm_omni/config/composable_parallel/translator.py`); invalid values fail fast in `_resolve_l1_owner`.
+
+### `stage_replica` and `omni_lb_policy`
+
+`stage_replica` is *not* a vLLM world dimension; it spins up `size` independent engine replicas of one stage, coordinated by the omni `StagePool` load balancer. The axis writes both:
+
+- The stage's `num_replicas` (a per-stage deploy knob).
+- The pipeline-wide `omni_lb_policy` string (an orchestrator-level knob).
+
+Because `omni_lb_policy` is read once at `AsyncOmniEngine` construction — not from per-stage engine args — only one value is allowed across the whole pipeline. If two stages declare conflicting policies, application raises (see `apply_strategy_specs` in `vllm_omni/config/composable_parallel/apply.py`, lines 253–260). To set this knob manually instead, use `--omni-lb-policy` on the CLI; see [the model stage docs](stage_configs.md) for how stage replicas plug into the orchestrator at runtime.
+
+### `model_stage` keys
+
+Default: no default — required for every entry under `strategies:`.
+
+Strategy keys MUST be `model_stage` *names* (strings), the same labels stage configs use for `engine_args.model_stage` (see [`model_stage` in `stage_configs.md`](stage_configs.md#engine_argsmodel_stage)). Integer `stage_id` values are not accepted; the resolver matches on `model_stage` exactly (`_resolve_stage` in `vllm_omni/config/composable_parallel/apply.py`).
+
+## Common pitfalls
+
+!!! warning "Using `stage_id` integers as keys"
+    Strategy keys are `model_stage` names (str), not integer stage IDs. A file that uses an integer index will raise at apply time:
+
+    ```text
+    StrategyApplyError: strategy role model_stage=0 did not match any stage;
+    available stages: 'thinker'(id=0), 'talker'(id=1), 'code2wav'(id=2)
+    ```
+
+    Use the `model_stage` name (e.g. `thinker`) instead.
+
+!!! 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:
+
+    ```text
+    StrategyApplyError: role 'talker' derives omni_lb_policy='least-queue-length'
+    but role 'thinker' already derived 'round-robin'. The load-balancer policy is
+    pipeline-wide; only one value is allowed.
+    ```
+
+    Make the policies match, drop one, or supply the value explicitly via `--omni-lb-policy`.

docs/configuration/pd_disaggregation.md

@@ -123,7 +123,7 @@ After inserting the extra thinker stage, renumber the remaining stages:
     runtime:
       devices: "2"
     engine_input_source: [1]
-    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker
+    sync_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_token_only
 
   - stage_id: 3
     runtime:
@@ -131,7 +131,7 @@ After inserting the extra thinker stage, renumber the remaining stages:
     engine_args:
       max_num_seqs: 1
     engine_input_source: [2]
-    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav
+    sync_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav_token_only
 ```
 
 Compared with the default Qwen3-Omni config:

docs/configuration/stage_configs.md

@@ -1,11 +1,11 @@
-# Stage configs for vLLM-Omni
+# Deploy and legacy stage configurations
 
-In vLLM-Omni, the target model is separated into multiple stages, which are processed by different LLMEngines, DiffusionEngines or other types of engines. Depending on different types of stages, such as Autoregressive (AR) stage or Diffusion transformer (DiT) stage, each can choose corresponding schedulers, model workers to load with the Engines in a plug-in fashion.
+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.
 
 !!! 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`. The bundled defaults have been verified on 1xH100 for Qwen2.5-Omni and 2xH100 for Qwen3-Omni. Models that have not yet migrated to the new schema continue to use the legacy `vllm_omni/model_executor/stage_configs/<model>.yaml` files via `--stage-configs-path`.
 
-## New deploy schema reference
+## Deploy configuration schema
 
 The new deploy schema lives under `vllm_omni/deploy/` and is paired with a frozen `PipelineConfig` registered by the model's `pipeline.py`. Each deploy YAML has these top-level fields:
 
@@ -82,14 +82,14 @@ stages:
     input_connectors:  {from_stage_0: shm}
 ```
 
-### CLI flags introduced in this refactor
+### CLI flags
 
 | Flag | Description |
 |------|-------------|
-| `--deploy-config PATH` | Load a new-schema deploy YAML. Takes precedence over `--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 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. |
 | `--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.** Accepts legacy `stage_args` yamls and (auto-detected) new deploy yamls; emits a deprecation warning. Migrate to `--deploy-config`. To be removed in a follow-up PR. |
+| `--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
 
@@ -146,7 +146,7 @@ CUDA_VISIBLE_DEVICES=1 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
 
 From highest to lowest:
 
-1. Per-stage flags (`--stage-overrides` JSON, `--stage-<id>-<key>` if registered)
+1. Per-stage overrides (`--stage-overrides` JSON)
 2. Explicit global CLI flags (`--gpu-memory-utilization 0.85`, etc.)
 3. Platform section (`platforms.npu.stages`, etc.) on top of the base `stages:`
 4. Overlay YAML (via `base_config:`) on top of the base YAML
@@ -210,7 +210,7 @@ Effective config per stage after the merge:
 | 1 | `max_model_len` | `16384` | global CLI |
 | 2 | (all defaults) | — | base YAML (no overrides apply) |
 
-Therefore, as a core part of vLLM-Omni, the stage configs for a model have several main functions:
+Therefore, as a core part of vLLM-Omni, a model's pipeline and deployment configurations have several main functions:
 
 - Claim partition of stages and their corresponding class implementation in `model_executor/models`.
 - The disaggregated configuration for each stage and the communication topology among them.
@@ -219,16 +219,16 @@ Therefore, as a core part of vLLM-Omni, the stage configs for a model have sever
 - Default input parameters.
 
 To override specific parameters, explicitly inject the customized configuration schema
-in both online and offline instantiation flows. Prioritize the `--deploy-config` flag
-when loading the new-schema deploy YAML schemas, reserving the `--stage-configs-path` parameter
+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.
 
 Examples:
 
-For offline (Assume necessary dependencies have been imported):
+For offline inference (assuming the necessary dependencies have been imported):
 ```python
 model_name = "Qwen/Qwen2.5-Omni-7B"
-omni = Omni(model=model_name, stage_configs_path="/path/to/custom_stage_configs.yaml")
+omni = Omni(model=model_name, deploy_config="/path/to/deploy_config.yaml")
 ```
 
 For online serving:
@@ -242,11 +242,14 @@ Legacy online serving:
 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 stage configs, and we welcome all feedbacks from both community users and developers to help us shape the development!
+    We are actively iterating on the definition of deployment configurations, and we welcome feedback from users and developers.
+
+## Legacy stage configuration reference
+
+The remainder of this page documents the deprecated `stage_args` schema for models that have not migrated to deploy configurations. The following historical Qwen2.5-Omni configuration illustrates that schema; current Qwen2.5-Omni deployments should use `vllm_omni/deploy/qwen2_5_omni.yaml` instead.
 
-Below is a specific example of stage_configs.yaml in Qwen2.5-omni.
 ```python
-# stage config for running qwen2.5-omni with AsyncOmniEngine + Orchestrator runtime.
+# Historical stage configuration for Qwen2.5-Omni.
 stage_args:
   - stage_id: 0 # mark the unique id for each stage
     runtime: # The disaggregated configuration
@@ -290,7 +293,6 @@ stage_args:
       enable_prefix_caching: false
       engine_output_type: latent
     engine_input_source: [0]
-    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen2_5_omni.thinker2talker
     default_sampling_params:
       temperature: 0.9
       top_p: 0.8
@@ -339,7 +341,7 @@ runtime:
 
 ```
 
-## Stage Configuration Arguments
+## Legacy stage configuration arguments
 
 Each stage in the `stage_args` list contains the following configuration options:
 
@@ -379,6 +381,20 @@ The maximum number of sequences for concurrent processing in this stage. For LLM
 
 Default: `1`
 
+#### `engine_args.request_batch_max_wait_ms`
+
+The maximum time, in milliseconds, that a diffusion request-mode stage may wait
+before the first `schedule()` of a new scheduler wave so compatible requests can
+accumulate for request-level batching. This only applies to diffusion pipelines
+that support request-level batching with `step_execution` disabled.
+
+Use this together with `max_num_seqs > 1` for bursty serving traffic. `0`
+disables admission waiting and preserves the lowest first-request latency.
+For diffusion request-level batching tuning, see
+[Request-Level Batching](../user_guide/diffusion/request_batching.md).
+
+Default: `0.0`
+
 ### `engine_args`
 
 Engine arguments for configuring the LLM engine, diffusion engine, or other engine types used by this stage.

docs/contributing/DOCS_GUIDE.md

@@ -67,6 +67,21 @@ docs/
 └── stylesheets/         # Custom CSS
 ```
 
+## Naming Model Examples
+
+Offline inference and online serving examples for the same model use the same
+directory name and the shared display name in
+`examples/model_display_names.yml`. Use these title forms:
+
+- `# <Model>: Offline inference`
+- `# <Model>: Online serving`
+
+The documentation generator uses the full title for the page H1 and the shared
+display name alone for navigation, and fails the build if a mapped README uses
+a different H1. Keep checkpoint identifiers in commands and prose rather than
+in the display name, and do not rename an existing example directory solely to
+adjust its title because the directory defines its public documentation URL.
+
 ## Publishing Documentation
 
 ### GitHub Pages (Recommended)

docs/contributing/ci/CI_5levels.md

@@ -67,7 +67,7 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     <tr>
       <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 validation</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 interfaces (using random lightweight models)</td>
       <td><code>core_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
@@ -89,7 +89,7 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     <tr>
       <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 with real weights + online/offline key scenarios + basic accuracy validation</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 interfaces (using random lightweight models)</td>
       <td><code>advanced_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
@@ -113,9 +113,9 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     <tr>
       <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/low-priority models: function and doc testing</td>
-      <td>Low-priority features (using random lightweight models)</td>
-      <td>Low-priority interfaces (using random lightweight models)</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 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>
@@ -144,7 +144,7 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     <tr>
       <td><strong>L5</strong><br>(Stability & Reliability)</td>
       <td>Online: long-term stability test + reliability test</td>
-      <td>Long-term stability and reliability testing for high-priority models</td>
+      <td>Long-term stability and reliability testing for high-priority models<br>Low-priority models: function and doc testing</td>
       <td>/</td>
       <td>Invalid-parameter validation for high-priority interfaces</td>
       <td><code>slow and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
@@ -446,7 +446,11 @@ L2 level testing builds upon L1 by introducing GPU resources and verifying that
     -   ***Time Cost***: Execution time is controlled within ***15 minutes*** to ensure fast feedback.
 -   ***L2 (Basic End-to-End Testing)***:
 -   -   ***Scope***: Covers two basic deployment scenarios: `online` (serving) and `offline` (inference).
-    -   ***Focus***: Uses `dummy` models or lightweight real models to verify that the entire chain from request input to result output works normally, including output data structure, streaming (stream) support, etc. Also includes some unit tests that require launching independent service instances.
+    -   ***Focus***: Uses `dummy` weights (via deploy YAML patching at `core_model`) or lightweight real models to verify that the entire chain from request input to result output works normally, including output data structure, streaming (stream) support, and **cheap payload checks** at `--run-level core_model` (see below). Also includes some unit tests that require launching independent service instances.
+    -   ***L2 response validation (`core_model`)***: Implemented in `tests/helpers/assertions.py` and invoked by `OpenAIClientHandler` based on `--run-level`. At L2 we require **request success** plus minimal output sanity—not full accuracy:
+        -   **Speech / TTS** (`assert_audio_speech_response`): decoded audio must be present and non-empty (or exceed `min_audio_bytes` when set in `request_config`); `response_format` must match the returned content-type (e.g. `wav`, `pcm`). Whisper transcript similarity, PCM HNR, and preset-voice gender checks run only at L3+.
+        -   **Diffusion** (`assert_diffusion_response`): at least one non-empty image, video, or audio artifact. Resolution/frame-count parity and other deep checks run only at L3+.
+        -   **Omni multimodal** (`assert_omni_response`): L2 asserts successful completion; keyword, transcript, and cross-modal similarity checks run only at L3+.
     -   ***Characteristic***: Requires ***GPU*** resources to perform model computations.
 
 ### 1.3 Test Directory and Execution Files
@@ -555,6 +559,11 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 -   1.  ***Inference Functionality***: Ensures real models can perform forward computation normally and return results.
     2.  ***Accuracy Compliance***: Verifies that the model's evaluation metrics (e.g., accuracy) meet the expected baseline, preventing code changes from introducing accuracy issues.
     3.  ***Important Performance***: Verifies whether performance (e.g., P99 latency, throughput) in core scenarios meets preset thresholds.
+-   ***L3 response validation (`advanced_model`)***: At `--run-level advanced_model`, `tests/helpers/assertions.py` adds semantic checks on top of L2 payload gates (see Chapter 1):
+    -   **Speech / TTS** (`assert_audio_speech_response`): Whisper transcript of returned audio vs `request_config["input"]` (cosine similarity &gt; 0.9 when input text is set); optional `min_audio_bytes` floor; PCM harmonic-to-noise ratio when `response_format` is `pcm`; **preset voice gender** via `_assert_preset_voice_gender_from_audio` when `voice` matches a known preset in `_PRESET_VOICE_GENDER_MAP` (pitch/F0-based classifier; skipped for unknown voices or `pcm` output).
+    -   **Omni multimodal** (`assert_omni_response`): non-empty text/audio outputs; `key_words` in transcript or text; text–audio similarity / containment; preset **`speaker`** gender check (same helper as TTS).
+    -   **Diffusion** (`assert_*_diffusion_response`): image/video dimension and frame-count parity with request parameters where configured.
+    -   **Accuracy suites**: pixel/video similarity and metric baselines under `/tests/e2e/accuracy/` (separate from inline helper assertions).
 
 ### 2.3 Test Directory and Execution Files
 
@@ -582,9 +591,9 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 
     **Explanation**:
 
-    @pytest.mark.advanced_model: Marks the test as L3 merge level, indicating deep validation with real models. @pytest.mark.full_model: Marks L4 nightly-only suites (e.g. `test_*_expansion.py`, doc examples).
+    @pytest.mark.advanced_model: Marks the test as L3 merge level (`--run-level advanced_model`): real weights, Whisper/similarity/keyword checks, diffusion deep checks, and **preset voice/speaker gender** validation where applicable. @pytest.mark.full_model: Marks L4 nightly-only suites (e.g. `test_*_expansion.py`, doc examples).
 
-    @pytest.mark.core_model: Marks the test as L1 or L2 level, indicating that this test case validates the basic functionality of the core model. It uses mock weights and only checks if the relevant interface functions correctly.
+    @pytest.mark.core_model: Marks the test as L1 or L2 level. At `--run-level core_model`, validation is limited to request success plus cheap payload checks (e.g. non-empty audio bytes, response format/content-type, non-empty diffusion outputs)—not Whisper, keyword, or accuracy gates. Deploy YAML may use `load_format: dummy` for fast PR feedback.
 
     @pytest.mark.parametrize: A parameterization decorator that allows abstracting test data into parameters, enabling reuse of the same test logic across different data configurations. indirect=True indicates that parameters will be passed to the fixture for processing.
 
@@ -680,7 +689,7 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 
     **Single Request**: The comment clearly states this is a single-request completion test. For concurrent testing, it can be extended to multiple requests using request_num = n.
 
-    **Implicit Validation**: The `send_omni_request` and `send_diffusion_request` methods internally includes validation logic dynamically selected based on the --run-level parameter: core_model performs basic validation, while advanced_model and full_model perform deep validation.
+    **Implicit Validation**: `send_omni_request`, `send_audio_speech_request`, and `send_diffusion_request` call `assert_*_response` helpers with the session `--run-level`. At `core_model`, checks are limited to success plus cheap payload sanity (non-empty audio/media, format/content-type, optional `min_audio_bytes`). At `advanced_model` and `full_model`, deep validation adds Whisper transcripts, keyword/similarity, diffusion dimension checks, PCM HNR, etc.
 
     **Audio output debugging**: Deep validation may transcribe returned audio via `convert_audio_bytes_to_text` (Whisper). If an audio keyword or text–audio similarity assertion fails, set `VLLM_OMNI_KEEP_REQUEST_MEDIA=1` before running pytest to keep the intermediate WAV files for inspection (see [Test helper environment variables](#test-helper-environment-variables)).
 

docs/contributing/ci/test_guide.md

@@ -12,12 +12,37 @@ When running test cases, you may need to install the following dependencies:
 
 ```bash
 uv pip install ".[dev]"
-apt-get install -y ffmpeg
+apt-get install -y espeak-ng jq
 ```
 
 ## Running Tests
 Our test scripts use the pytest framework. First, please use `git clone https://github.com/vllm-project/vllm-omni.git` to download the vllm-omni source code. Then, in the root directory of vllm-omni, you can run the following commands in your local test environment to execute the corresponding test cases.
 
+### CI job runners (L2–L4): logs, timing, and timeouts
+
+[`tools/run_ready_jobs.sh`](https://github.com/vllm-project/vllm-omni/blob/main/tools/run_ready_jobs.sh), [`tools/run_merge_jobs.sh`](https://github.com/vllm-project/vllm-omni/blob/main/tools/run_merge_jobs.sh), and [`tools/nightly/run_nightly_jobs.sh`](https://github.com/vllm-project/vllm-omni/blob/main/tools/nightly/run_nightly_jobs.sh) share the same run-time behavior (via [`tools/run_jobs_common.sh`](https://github.com/vllm-project/vllm-omni/blob/main/tools/run_jobs_common.sh)):
+
+**Log layout** (default under `logs/ready_jobs`, `logs/merge_jobs`, or `logs/nightly_jobs`; override with `--log-dir`):
+
+| Path | Description |
+|------|-------------|
+| `jobs/*.sh` | Generated per-step bash wrappers |
+| `jobs/.job_timeouts` | Job key → `timeout_in_minutes` from Buildkite YAML (when set) |
+| `<job_key>.log` | Tee output for each job |
+| `timing_summary.log` | Per-job duration and total wall time after the run completes |
+
+**Timing:** while each job runs, stderr prints `finished in …` or `failed after …`. When all jobs finish, a summary is printed and written to `timing_summary.log`, for example:
+
+```
+=== Job timing summary ===
+  TTS_VoxCPM2_Test  25m 40s  OK
+  Omni_Qwen3-Omni_Test  8m 05s  TIMED OUT
+Total wall time: 33m 45s (2 jobs)
+Failed jobs: 1/2
+```
+
+**Timeouts:** if a Buildkite step defines `timeout_in_minutes`, the runner wraps that job with `timeout ${N}m` (aligned with CI). Steps without `timeout_in_minutes` rely on any inline `timeout …` already present in the extracted pytest command. On timeout the process exits with code `124` and the summary marks the job as `TIMED OUT`.
+
 === "L1 level"
 
     ```bash
@@ -28,61 +53,109 @@ Our test scripts use the pytest framework. First, please use `git clone https://
 
 === "L2 level"
 
+    **Recommended:** run CI-aligned jobs from the repo root via [`tools/run_ready_jobs.sh`](https://github.com/vllm-project/vllm-omni/blob/main/tools/run_ready_jobs.sh). The script reads [`.buildkite/test-ready.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-ready.yml), generates per-step bash wrappers, runs pytest, and tees logs under `logs/ready_jobs/` (see [CI job runners](#ci-job-runners-l2l4-logs-timing-and-timeouts) for timing and timeout behavior). Requires `bash`, `python3`, and PyYAML (`pip install pyyaml`).
+
+    ```bash
+    # All L2 and L1 jobs (default: every pytest step in test-ready.yml)
+    bash tools/run_ready_jobs.sh
+
+    # Skip L1-style "Simple Test" steps in test-ready.yml (group or "Simple ·" labels)
+    bash tools/run_ready_jobs.sh --skip-simple
+    bash tools/run_ready_jobs.sh --skip-simple --model-type diffusion
+
+    # Preview extracted commands without running
+    bash tools/run_ready_jobs.sh --dry-run
+
+    # Filter by model area (OR semantics): omni | tts | diffusion | all
+    bash tools/run_ready_jobs.sh --model-type omni
+    bash tools/run_ready_jobs.sh --model-type tts,diffusion
+
+    # Match a Buildkite label substring (e.g. one model suite)
+    bash tools/run_ready_jobs.sh --model-type tts --label-substr "VoxCPM2"
+
+    ```
+
+    **Ad hoc:** run a single test file or marker expression manually:
+
     ```bash
     cd tests
-    pytest -s -v -m "core_model and not cpu" --run-level=core_model
-    ```
-    If you only want to run a specific test case, you can use:
-    ```bash
     pytest -s -v test_xxxx.py --run-level=core_model
+    pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
     ```
-    If you only want to run specific test cases on a particular platform, you can use:
-    ```bash
-    pytest -s -v -m "core_model and distributed_cuda and L4"  --run-level=core_model
-    ```
-    The latest test commands for various test suites can be found in the [pipeline](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-ready.yml).
 
 === "L3 level"
 
+    **Recommended:** run CI-aligned jobs from the repo root via [`tools/run_merge_jobs.sh`](https://github.com/vllm-project/vllm-omni/blob/main/tools/run_merge_jobs.sh). The script reads [`.buildkite/test-merge.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-merge.yml), generates per-step bash wrappers, runs pytest, and tees logs under `logs/merge_jobs/` (see [CI job runners](#ci-job-runners-l2l4-logs-timing-and-timeouts)). Requires `bash`, `python3`, and PyYAML (`pip install pyyaml`).
+
     ```bash
-    pytest -s -v -m "advanced_model" --run-level=advanced_model
+    # All L3 and L1 jobs (default: every pytest step in test-merge.yml)
+    bash tools/run_merge_jobs.sh
+
+    # Skip L1-style "Simple Test" steps in test-merge.yml (group or "Simple ·" labels)
+    bash tools/run_merge_jobs.sh --skip-simple
+    bash tools/run_merge_jobs.sh --skip-simple --model-type omni
+
+    # Preview extracted commands without running
+    bash tools/run_merge_jobs.sh --dry-run
+
+    # Filter by model area (OR semantics): omni | tts | diffusion | all
+    bash tools/run_merge_jobs.sh --model-type diffusion
+    bash tools/run_merge_jobs.sh --model-type omni,tts
+
+    # Match a Buildkite label substring
+    bash tools/run_merge_jobs.sh --model-type diffusion --label-substr "Wan22"
+
     ```
-    If you only want to run a specific test case, you can use:
+
+    **Ad hoc:** run a single test file or marker expression manually:
+
     ```bash
     pytest -s -v test_xxxx.py --run-level=advanced_model
+    pytest -s -v -m "advanced_model and distributed_cuda and L4" --run-level=advanced_model
     ```
-    If you only want to run specific test cases on a particular platform, you can use:
-    ```bash
-    pytest -s -v -m "advanced_model and distributed_cuda and L4"  --run-level=advanced_model
-    ```
-    The latest L3 test commands for various test suites can be found in the [pipeline](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-merge.yml).
 
 
 === "L4 level"
 
+    **Recommended:** run CI-aligned nightly jobs from the repo root via [`tools/nightly/run_nightly_jobs.sh`](https://github.com/vllm-project/vllm-omni/blob/main/tools/nightly/run_nightly_jobs.sh). The script reads [`.buildkite/test-nightly.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-nightly.yml), generates per-step bash wrappers, runs pytest (perf jobs first, then aggregates perf JSON into Excel), and tees logs under `logs/nightly_jobs/` (see [CI job runners](#ci-job-runners-l2l4-logs-timing-and-timeouts); `generate_nightly_perf_excel` is included in the timing summary). Requires `bash`, `python3`, and PyYAML (`pip install pyyaml`).
+
     ```bash
-    cd tests
-    pytest -s -v -m "full_model" --run-level=full_model
+    # All nightly jobs (default: test-type all, model-type all)
+    bash tools/nightly/run_nightly_jobs.sh
+
+    # Preview extracted commands without running
+    bash tools/nightly/run_nightly_jobs.sh --dry-run
+
+    # Test kind (--test-type, OR semantics): perf | acc | function | stability | local | all
+    bash tools/nightly/run_nightly_jobs.sh --test-type function
+    bash tools/nightly/run_nightly_jobs.sh --test-type perf,acc
+
+    # Model area (--model-type, OR semantics): omni | tts | diffusion | all
+    bash tools/nightly/run_nightly_jobs.sh --test-type function --model-type omni
+    bash tools/nightly/run_nightly_jobs.sh --test-type perf --model-type diffusion
+
+    # Stability scripts (tests/dfx/stability/scripts/) and local marker tests
+    bash tools/nightly/run_nightly_jobs.sh --test-type stability --model-type omni
+    bash tools/nightly/run_nightly_jobs.sh --test-type local --model-type tts
     ```
-    If you only want to run a specific test case, you can use:
-    ```bash
-    pytest -s -v test_xxxx.py --run-level=full_model
-    ```
-    If you only want to run specific test cases on a particular platform, you can use:
-    ```bash
-    pytest -s -v -m "full_model and distributed_cuda and L4"  --run-level=full_model
-    ```
-    Note: To run performance tests (defaults to ``test_qwen_omni.json``; use ``--test-config-file tests/dfx/perf/tests/test_tts.json`` for TTS):
-    ```bash
-    pytest -s -v tests/dfx/perf/scripts/run_benchmark.py
-    ```
-    The latest L4 (nightly) test commands use the `full_model` marker and `--run-level full_model` (see [test-nightly.yml](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-nightly.yml) and [test-nightly-diffusion.yml](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-nightly-diffusion.yml)). Example:
+
+    Perf steps (label contains ``Perf Test``) run before other jobs; Excel output is written under ``logs/``. Diffusion nightly steps may also appear in [test-nightly-diffusion.yml](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-nightly-diffusion.yml) on CI.
+
+    **Ad hoc:** run a single test file or marker expression manually:
 
     ```bash
     cd tests
+    pytest -s -v test_xxxx.py --run-level=full_model
+    pytest -s -v -m "full_model and distributed_cuda and L4" --run-level=full_model
     pytest -s -v -m "full_model and (omni or tts) and H100" --run-level=full_model
     ```
 
+    To run a standalone perf benchmark (defaults to ``test_qwen_omni.json``; use ``--test-config-file tests/dfx/perf/tests/test_tts.json`` for TTS):
+
+    ```bash
+    pytest -s -v tests/dfx/perf/scripts/run_benchmark.py
+    ```
+
 === "L5 level"
 
     L5 includes stability and reliability testing. Typical commands:

docs/contributing/model/adding_diffusion_model.md

@@ -349,12 +349,14 @@ class YourModelPipeline(nn.Module):
 - def __call__(
 + def forward(
     self,
-+   req: OmniDiffusionRequest,  # ← Add request parameter here
++   req: DiffusionRequestBatch,  # ← Add request-batch parameter here
 - ):
-+ ) -> DiffusionOutput:  # ← Add return type
++ ) -> list[DiffusionOutput]:  # ← Add return type
 ```
 
-[`OmniDiffusionRequest`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/request/#vllm_omni.diffusion.request.OmniDiffusionRequest) is a dataclass that contains the **prompts** and **sampling parameters** [`OmniDiffusionSamplingParams`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/inputs/data/#vllm_omni.inputs.data.OmniDiffusionSamplingParams) for the diffusion pipeline execution. It also contains a request_id for other components to trace this request and its outputs.
+[`OmniDiffusionRequest`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/request/#vllm_omni.diffusion.request.OmniDiffusionRequest) is a dataclass that contains one **prompt** and the **sampling parameters** [`OmniDiffusionSamplingParams`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/inputs/data/#vllm_omni.inputs.data.OmniDiffusionSamplingParams) for one logical diffusion request. It also contains a request_id for other components to trace this request and its outputs. Before pipeline execution, the runner wraps one or more independent requests into `DiffusionRequestBatch`.
+
+[`DiffusionRequestBatch`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/worker/request_batch/#vllm_omni.diffusion.worker.request_batch.DiffusionRequestBatch) exposes compatibility properties such as `prompts`, `sampling_params`, and `request_id`. Pipelines that can execute the whole request batch in one forward pass should set `supports_request_batch = True`; other pipelines still receive a single-request batch and return a one-element output list.
 
 See some parameters in `OmniDiffusionSamplingParams` as follows:
 
@@ -367,35 +369,36 @@ See some parameters in `OmniDiffusionSamplingParams` as follows:
 **Extract parameters from request:**
 
 ```python
-from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.data import DiffusionOutput
+from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 
 def forward(
     self,
-    req: OmniDiffusionRequest,
-) -> DiffusionOutput:
-    # Extract prompts from request
-    if req.prompts is not None:
-        prompt = [
-            p if isinstance(p, str) else (p.get("prompt") or "")
-            for p in req.prompts
-        ]
+    req: DiffusionRequestBatch,
+) -> list[DiffusionOutput]:
+    # Extract prompts from the request batch
+    prompts = [
+        p if isinstance(p, str) else (p.get("prompt") or "")
+        for p in req.prompts
+    ]
 
-    # Extract sampling parameters
+    # Extract common sampling parameters
     sampling_params = req.sampling_params
     num_inference_steps = sampling_params.num_inference_steps or 50
     guidance_scale = sampling_params.guidance_scale or 7.5
     height = sampling_params.height or (self.default_sample_size * self.vae_scale_factor)
     width = sampling_params.width or (self.default_sample_size * self.vae_scale_factor)
 
-    # For image editing pipelines, extract images from multi_modal_data
-    if hasattr(req, 'multi_modal_data') and req.multi_modal_data:
-        input_images = req.multi_modal_data.get('image', [])
+    # For image editing pipelines, extract media from each prompt dict
+    input_images = []
+    for p in req.prompts:
+        multi_modal_data = p.get("multi_modal_data", {}) if isinstance(p, dict) else {}
+        input_images.append(multi_modal_data.get("image"))
 
     # ... rest of generation logic
 ```
 
-For an image editing model, an example `OmniDiffusionRequest` is like:
+For an image editing model, the request `prompt` can be a dict like:
 ```python
 {
     "prompt": "turn this cat to a dog",
@@ -472,12 +475,12 @@ def get_your_model_pre_process_func(
     def pre_process_func(
         request: OmniDiffusionRequest,
         ):
-        for i, prompt in enumerate(request.prompts):
-            multi_modal_data = prompt.get("multi_modal_data", {}) if not isinstance(prompt, str) else None
-            raw_image = multi_modal_data.get("image", None) if multi_modal_data is not None else None
-            # image pre-processing
-            # after pre-processing, update the request attributes
-            ...
+        prompt = request.prompt
+        multi_modal_data = prompt.get("multi_modal_data", {}) if not isinstance(prompt, str) else None
+        raw_image = multi_modal_data.get("image", None) if multi_modal_data is not None else None
+        # image pre-processing
+        # after pre-processing, update the request attributes
+        ...
         return request
 
     return pre_process_func
@@ -635,7 +638,7 @@ mkdir -p examples/online_serving/your_model_name
 
 - Script: `examples/offline_inference/your_model_name/end2end.py`
   - Parse args like BAGEL (`--model`, `--modality`, optional `--image-path`, `--steps`, etc.)
-  - Use `from vllm_omni.entrypoints.omni import Omni` (or `OmniDiffusion` if your model is diffusion-only)
+  - Use `from vllm_omni.entrypoints.omni import Omni` for both multi-stage and diffusion-only models
   - Save outputs (images/audio/video/text) with deterministic filenames (e.g., `output_0_0.png`)
 - Doc: `examples/offline_inference/your_model_name/README.md`
   - Include at least one runnable command, e.g.:
@@ -923,11 +926,11 @@ When implementing a new pipeline, avoid putting all logic inside a single functi
 
 For example:
 ```
-def forward(self, req: OmniDiffusionRequest):
+def forward(self, req: DiffusionRequestBatch) -> list[DiffusionOutput]:
     prompt_embeds = self.encode_prompt(req)
     latents = self.diffuse(prompt_embeds, req)
     images = self.vae.decode(latents)
-    return DiffusionOutput(output=images)
+    return [DiffusionOutput(output=images)]
 ```
 This allows the timing utility to measure each stage (e.g., encode_prompt, diffuse, vae.decode) separately and helps identify performance bottlenecks more easily.
 

docs/contributing/model/adding_omni_model.md

@@ -475,92 +475,64 @@ thinker_hidden_states = output.multimodal_output["24"]
 
 ### Implementation Example
 
-Create stage transition processors in `vllm_omni/model_executor/stage_input_processors/your_model_name.py`:
+Create stage transition processors in `vllm_omni/model_executor/stage_input_processors/your_model_name.py`. Each inter-stage edge should provide a **coherent processor set** rather than a single monolithic function:
+
+| Suffix | Role | Runs when |
+|--------|------|-----------|
+| `*_full_payload` | Worker-side payload producer | `async_chunk=false`; accumulates tensors and ships via connector |
+| `*_async_chunk` | Scheduler-side streaming producer | `async_chunk=true`; emits per-chunk payloads |
+| `*_token_only` | Orchestrator placeholder builder | `async_chunk=false`; allocates downstream prompt slots only |
 
 ```python
-# qwen3_omni.py
+# qwen3_omni.py (Thinker → Talker, non-async path)
 
-def thinker2talker(
-    stage_list: list[Any],
-    engine_input_source: list[int],
+def thinker2talker_token_only(
+    source_outputs: list[Any],
     prompt: OmniTokensPrompt | TextPrompt | None = None,
     requires_multimodal_data: bool = False,
+    streaming_context: Any | None = None,
 ) -> list[OmniTokensPrompt]:
-    """
-    Process thinker outputs to create talker inputs.
-
-    Args:
-        stage_list: List of stage objects
-        engine_input_source: Source stage IDs (typically [0] for thinker)
-        prompt: Original prompt data
-
-    Returns:
-        List of OmniTokensPrompt for talker stage
-    """
-    source_stage_id = engine_input_source[0]
-    thinker_outputs = stage_list[source_stage_id].engine_outputs
-    talker_inputs = []
-
-    for thinker_output in thinker_outputs:
-        output = thinker_output.outputs[0]
-        # Extract thinker embeddings and hidden states
-        thinker_prefill_embeddings = output.multimodal_output["0"].float().clone().detach().cuda()
-        thinker_hidden_states = output.multimodal_output["24"].float().clone().detach().cuda()
-
-        info = {
-            "thinker_prefill_embeddings": thinker_prefill_embeddings,
-            "thinker_hidden_states": thinker_hidden_states,
-            "thinker_sequences": thinker_output.prompt_token_ids + output.token_ids,
-            "thinker_input_ids": thinker_output.prompt_token_ids,
-        }
-
-        talker_inputs.append(
-            OmniTokensPrompt(
-                prompt_token_ids=[0] * computed_length,
-                additional_information=info,
-                multi_modal_data=None,
-            )
-        )
-
-    return talker_inputs
+    """Allocate talker prefill slots; bulk tensors arrive via the connector."""
+    ...
 
 
-def talker2code2wav(
-    stage_list: list[Any],
-    engine_input_source: list[int],
-    prompt: OmniTokensPrompt | TextPrompt | None = None,
-    requires_multimodal_data: bool = False,
-) -> list[OmniTokensPrompt]:
-    """
-    Process talker outputs to create code2wav inputs.
-    """
-    source_stage_id = engine_input_source[0]
-    talker_outputs = stage_list[source_stage_id].engine_outputs
-    code2wav_inputs = []
+def thinker2talker_full_payload(
+    transfer_manager: Any,
+    pooling_output: dict[str, Any],
+    request: OmniEngineCoreRequest,
+) -> dict[str, Any] | None:
+    """Pack accumulated thinker hidden states into OmniPayload for stage-1."""
+    ...
 
-    for talker_output in talker_outputs:
-        output = talker_output.outputs[0]
-        # Extract codec codes
-        codec_codes = (
-            output.multimodal_output["code_predictor_codes"]
-            .to(torch.long)
-            .transpose(0, 1)
-            .cpu()
-            .to(torch.long)
-            .reshape(-1)
-            .tolist()
-        )
 
-        code2wav_inputs.append(
-            OmniTokensPrompt(
-                prompt_token_ids=codec_codes,
-                multi_modal_data=None,
-            )
-        )
-
-    return code2wav_inputs
+def thinker2talker_async_chunk(
+    transfer_manager: Any,
+    multimodal_output: OmniPayload | dict[str, Any],
+    request: OmniEngineCoreRequest,
+    is_finished: bool = False,
+) -> OmniPayloadStruct | None:
+    """Stream thinker rows to talker while async_chunk is enabled."""
+    ...
 ```
 
+Wire these in `pipeline.py`:
+
+```python
+StagePipelineConfig(
+    stage_id=0,
+    custom_process_next_stage_input_func=f"{_PROC}.thinker2talker_full_payload",
+    async_chunk_process_next_stage_input_func=f"{_PROC}.thinker2talker_async_chunk",
+    ...
+),
+StagePipelineConfig(
+    stage_id=1,
+    sync_process_input_func=f"{_PROC}.thinker2talker_token_only",
+    ...
+),
+```
+
+Do **not** add a no-suffix `thinker2talker` when a `sync_process_input_func` is already declared — `_select_processor_funcs()` always prefers the `*_token_only` hook in non-async mode, so the bare function would never run. See `docs/design/rfc_stage_input_processors_refactor.md` for the full contract.
+
 ## Testing
 
 For comprehensive testing guidelines, please refer to the [Test File Structure and Style Guide](../ci/tests_style.md).

docs/contributing/profiling.md

@@ -286,3 +286,54 @@ Recommended viewers:
 - Nsight Systems GUI for CUDA kernel timelines
 
 For upstream background on the underlying vLLM profiling infrastructure, see the [vLLM profiling guide](https://docs.vllm.ai/en/stable/contributing/profiling/).
+
+## 7. Orchestrator Monitor
+
+> **Warning:** Diagnostic only. Adds lightweight counters on the orchestrator poll loop and per-replica queue depth. Disable in production unless you are actively debugging orchestrator saturation.
+
+Multi-stage omni pipelines route client-facing outputs and inter-stage connector traffic through a single-process orchestrator. When that loop is saturated (for example by large multimodal payloads on the poll path), TTFT and stability can degrade even when GPU stages are healthy. The orchestrator monitor records time-series signals to confirm whether the orchestrator or downstream stage queues are the bottleneck.
+
+Enable on the stage-0 (orchestrator) process:
+
+```bash
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
+  --stage-id 0 \
+  --enable-orch-monitor
+```
+
+Optional output path override:
+
+```bash
+export VLLM_OMNI_ORCH_MONITOR_PATH=/tmp/vllm_omni_orch_monitor.json
+```
+
+If unset, the monitor writes `./vllm_omni_orch_monitor_<MMDDHHMM>.json` at shutdown.
+
+### Recorded metrics
+
+Each 1-second window records:
+
+| Series | Description |
+|---|---|
+| `windows.duration_s` | Wall time covered by the window |
+| `windows.loop_idle` / `windows.loop_active` | Orchestrator poll-loop iterations with no work vs. active forwarding |
+| `replicas.<stage,replica>.outputs_queue_size` | MP client `outputs_queue` backlog for that replica |
+| `replicas.<stage,replica>.inflight` | Requests currently bound/routed to the replica |
+
+On shutdown the server also logs a short summary (`loop_active_pct`, per-replica queue averages/maxima).
+
+### Relationship to other diagnostics
+
+This monitor is intentionally separate from the existing profiling tools:
+
+| Tool | Scope | Output |
+|---|---|---|
+| `--enable-diffusion-pipeline-profiler` | Diffusion stage functions (`vae.decode`, `diffuse`, …) | Per-request `stage_durations` / logs |
+| `--enable-ar-profiler` | AR stage generation time | Per-request `stage_durations` |
+| `profiler_config` (`torch` / `cuda`) | GPU/CPU kernels inside a stage worker | `trace.json`, Nsight, operator tables |
+| Prometheus `/metrics` (`vllm:omni_*`) | Pipeline SLOs and cross-stage transfer | Continuous scrape endpoint |
+| `--enable-orch-monitor` | Orchestrator poll loop + replica queue backlog | Single JSON file at shutdown |
+
+The orchestrator monitor does not use `torch.profiler` because the bottleneck signal lives in the orchestrator process (poll-loop duty cycle and queue depth), not inside stage workers. It complements Prometheus metrics when you need a post-run time series for a benchmark or regression run.
+
+Process liveness monitors (`monitor_engine_liveness`, worker/engine-core monitors) are unrelated: they detect crashed workers, not orchestrator load.

docs/design/architecture_overview.md

@@ -13,7 +13,7 @@ This document outlines the architectural design for vLLM-Omni.
 
 The primary goal of the vLLM-Omni project is to build the fastest and easiest-to-use open-source Omni-Modality model inference & serving engine. vLLM-Omni extends the original vLLM, which was created to support large language models for text-based autoregressive (AR) generation tasks. vLLM-Omni is designed to support:
 
-* **Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, and video, alongside text.
+* **Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, video, and action trajectories, alongside text.
 * **Non-Autoregressive Structure:** Support model structure beyond autoregressive, especially Diffusion Transformer (DiT), which is widely used in visual and audio generation.
 * **Integration with vLLM Core:** Maintain compatibility and leverage existing vLLM key modules and optimizations where applicable.
 * **Extensibility:** Design a modular and flexible architecture that can easily accommodate new modalities, model architectures, and output formats.

docs/design/feature/cfg_parallel.md

@@ -126,16 +126,16 @@ Call `self.diffuse` in your pipeline's forward function:
 
 ```python
 import torch.nn as nn
+from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
+
 class YourModelPipeline(nn.Module, CFGParallelMixin):
     def forward(
         self,
-        prompt: str,
-        negative_prompt: str | None = None,
-        guidance_scale: float = 3.5,
-        num_inference_steps: int = 50,
-        **kwargs,
-    ):
-        # Encode prompts, Initialize latents, Get timesteps
+        req: DiffusionRequestBatch,
+    ) -> list[DiffusionOutput]:
+        # Read prompts and sampling params from the request
+        ...
+        # Encode prompts, initialize latents, get timesteps
         ...
         # Run diffusion loop (calls the mixin's diffuse method)
         latents = self.diffuse(

docs/design/feature/diffusion_continuous_batching.md

@@ -38,9 +38,13 @@ With continuous batching enabled:
 The current implementation is conservative:
 
 - only compatible requests are batched together
-- request-mode diffusion still runs with `max_num_seqs=1`
 - per-request progress and completion remain independent
 
+Here, "continuous batching" means the step-wise path enabled by
+`step_execution=True`. Request-mode `DiffusionRequestBatch` is static
+request-level batching for one full pipeline `forward()` call; it does not
+admit or remove requests between denoise steps.
+
 ## Enablement
 
 Use `--step-execution` as the feature gate, then increase `--max-num-seqs`
@@ -72,7 +76,7 @@ which is built from shape-sensitive and CFG-sensitive sampling fields. This is
 the core correctness rule for batching: requests are only co-batched when they
 share the same denoise tensor contract.
 
-There are two important details:
+There are three important details:
 
 - `num_inference_steps` is not part of the key, so requests with different
   total step counts can still share a batch
@@ -88,8 +92,10 @@ key also covers LoRA identity (`lora_int_id`, `lora_scale`), so requests
 targeting different adapters or scales run in separate batches and the
 worker can activate exactly one adapter per step.
 
-The current batching unit is one `OmniDiffusionRequest`. Requests with
-multiple prompts do not participate in batching today.
+The scheduler batching unit is one logical `OmniDiffusionRequest`. In the
+step-wise path, runtime tensor batching is represented as `StepInputBatch`. For
+request-mode prompt semantics, see
+[Request-Level Batching](../../user_guide/diffusion/request_batching.md).
 
 ## Runner
 
@@ -126,17 +132,15 @@ request-local scheduler state and outputs.
 the background loop and async add-request path needed for multiple requests to
 accumulate in the scheduler.
 
-This is supporting infrastructure, not the main design point. The batching
-behavior is defined by scheduler-side compatibility gating and runner-side
-batch packing.
+When `step_execution=True`, the engine routes work through the step-wise
+executor path. The continuous batching behavior is defined by scheduler-side
+compatibility gating and runner-side `StepInputBatch` packing.
 
 ## Current Limitations
 
 - Experimental feature; use `max_num_seqs=1` for the older conservative path.
 - Only native pipelines that already support `step_execution=True`.
-- Request-mode diffusion still clamps `max_num_seqs` back to `1`.
 - Only homogeneous batches keyed by `SamplingParamsKey` are supported.
-- Multi-prompt requests are not batched.
 - `cache_backend`, KV transfer, and other request-mode extras are not wired
   into the batched step-wise path yet.
 - Future work can relax the current same-shape restriction with richer

docs/design/feature/diffusion_request_level_batching.md

@@ -0,0 +1,175 @@
+# Request-Level Batching for Diffusion
+
+This document describes the request-mode batching path for diffusion pipelines.
+For end-user enablement and tuning, see
+[Request-Level Batching](../../user_guide/diffusion/request_batching.md).
+
+This is separate from
+[Continuous Batching for Step-Wise Diffusion](diffusion_continuous_batching.md).
+Request-level batching runs one full pipeline `forward()` over a static batch of
+compatible requests. Step-wise continuous batching admits work between denoise
+steps when `step_execution=True`.
+
+## Why It Helps
+
+The request-level design avoids coupling several logical requests to one request
+object. This keeps request identity, abort/error handling, and per-request
+metadata unambiguous while still allowing one fused pipeline forward pass for
+bursty or concurrent traffic.
+
+## Overview
+
+With request-level batching enabled:
+
+- each `OmniDiffusionRequest` contains one `prompt` and one `request_id`
+- the scheduler groups compatible waiting requests into one scheduler wave
+- `DiffusionRequestBatch` wraps the scheduled requests for pipeline `forward()`
+- batch-capable pipelines return `list[DiffusionOutput]`, one output per
+  request
+- `BatchRunnerOutput` maps each result back to its original `request_id`
+
+Pipelines opt in with `supports_request_batch = True` and a `forward()` method
+that accepts `DiffusionRequestBatch` and returns `list[DiffusionOutput]`.
+Pipelines that do not opt in keep the existing per-request execution path.
+
+## Enablement
+
+Request-level batching is the request-mode path, so `step_execution` must remain
+disabled. Increase `max_num_seqs` above `1` to let the scheduler keep multiple
+compatible requests active:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni \
+  --port 8091 \
+  --max-num-seqs 4
+```
+
+For bursty online ingress, `request_batch_max_wait_ms` can add a bounded
+admission wait before the first `schedule()` of a scheduler wave:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni \
+  --port 8091 \
+  --max-num-seqs 4 \
+  --request-batch-max-wait-ms 20
+```
+
+`request_batch_max_wait_ms=0` disables this wait and is the default.
+
+## Request Contract
+
+`OmniDiffusionRequest` represents one logical request. It owns one prompt,
+sampling parameters, request id, and request-local metadata. Runtime batches are
+formed by the scheduler and represented separately from the request payload.
+
+Runtime batching is represented by:
+
+- [`DiffusionSchedulerOutput`](gh-file:vllm_omni/diffusion/sched/interface.py)
+  for scheduled request ids and request payloads
+- [`DiffusionRequestBatch`](gh-file:vllm_omni/diffusion/worker/request_batch.py)
+  for the pipeline-facing request batch
+- [`BatchRunnerOutput`](gh-file:vllm_omni/diffusion/worker/utils.py) for
+  per-request results
+
+`DiffusionRequestBatch` intentionally exposes compatibility properties such as
+`prompts`, `sampling_params`, `request_id`, and `kv_sender_info` so migrated
+pipelines can stay close to upstream code while using a batch-aware contract.
+
+## Scheduler
+
+The scheduler derives its capacity from `max_num_seqs` through
+`max_num_running_reqs`. It exposes waiting/running queue counters so the engine
+can decide whether admission wait is useful before scheduling a new wave.
+
+Batch compatibility is controlled by
+[`SamplingParamsKey`](gh-file:vllm_omni/diffusion/sched/interface.py). The key
+contains shape-sensitive and guidance-sensitive fields, including output count
+and LoRA identity. Requests with incompatible shapes, CFG settings, output
+counts, LoRA adapters, or LoRA scales are kept in separate batches.
+
+Admission is conservative:
+
+- the scheduler only batches compatible requests
+- FIFO ordering is preserved
+- an incompatible request at the head of the waiting queue blocks later
+  compatible requests
+
+## Engine
+
+[`DiffusionEngine`](gh-file:vllm_omni/diffusion/diffusion_engine.py) resolves
+request-batch capability during initialization from the configured pipeline
+class, including custom pipeline classes.
+
+The capability check uses the pipeline class attribute
+`supports_request_batch = True`. Pipelines that set this attribute must implement
+a request-batch-compatible `forward()` contract and return one
+`DiffusionOutput` per request; the runner validates that return shape at runtime.
+
+When the selected pipeline is batch-capable and `step_execution=False`, request
+mode routes scheduler waves through the batch executor path. Otherwise it keeps
+the per-request executor path.
+
+The optional admission wait runs only when:
+
+- request batching is supported
+- `step_execution=False`
+- `request_batch_max_wait_ms > 0`
+- no requests are currently running
+
+The wait exits early when the waiting queue reaches capacity, when the queue is
+stable for a short window, when the deadline expires, or when the engine stops.
+
+## Executor And Runner
+
+The executor exposes two request-mode entries:
+
+- `execute_request`: one worker call per scheduled request
+- `execute_batch`: one worker call for the whole `DiffusionSchedulerOutput`
+
+On the batch path, the worker builds a `DiffusionRequestBatch` and runs the
+pipeline once. Request-local setup remains per request:
+
+- KV transfer metadata
+- random generator and seed handling
+- request output/error/abort mapping
+
+Shared batch setup happens once per batch when possible:
+
+- cache refresh
+- LoRA activation for the homogeneous adapter key
+- pipeline `forward(req_batch)`
+
+Large tensor IPC still uses the shared-memory packing path. The packer traverses
+both normal `RunnerOutput.result` wrappers and nested batch results so batched
+outputs do not fall back to pickle IPC for tensor payloads.
+
+## Current Limitations
+
+- Only pipelines that declare the request-batch contract use fused batch
+  execution.
+- Batches are homogeneous under `SamplingParamsKey`; heterogeneous resolution or
+  incompatible guidance settings do not co-batch yet.
+- FIFO scheduling can reduce batching opportunities when an incompatible
+  request is at the front of the queue.
+- `request_batch_max_wait_ms` improves burst coalescing but can add latency to
+  the first request in a scheduler wave. Keep it small for latency-sensitive
+  serving.
+- Step-wise continuous batching is documented separately and only applies when
+  `step_execution=True`.
+
+## Related Files
+
+- Request object and request batch:
+  [`vllm_omni/diffusion/request.py`](gh-file:vllm_omni/diffusion/request.py)
+- Scheduler interface:
+  [`vllm_omni/diffusion/sched/interface.py`](gh-file:vllm_omni/diffusion/sched/interface.py)
+- Scheduler base:
+  [`vllm_omni/diffusion/sched/base_scheduler.py`](gh-file:vllm_omni/diffusion/sched/base_scheduler.py)
+- Engine:
+  [`vllm_omni/diffusion/diffusion_engine.py`](gh-file:vllm_omni/diffusion/diffusion_engine.py)
+- Worker runner:
+  [`vllm_omni/diffusion/worker/diffusion_model_runner.py`](gh-file:vllm_omni/diffusion/worker/diffusion_model_runner.py)
+- Executor interface:
+  [`vllm_omni/diffusion/executor/abstract.py`](gh-file:vllm_omni/diffusion/executor/abstract.py)
+- Tests:
+  [`tests/diffusion/test_diffusion_engine.py`](gh-file:tests/diffusion/test_diffusion_engine.py)

docs/design/feature/vae_parallel.md

@@ -448,6 +448,42 @@ Complete examples in the codebase:
 
 ---
 
+## Spatially-Sharded Decode (Wan)
+
+The tile-parallel executor above assigns independent spatial **tiles** to ranks. The Wan VAE additionally supports a **spatially-sharded decode** backend, selected through `DiffusionParallelConfig.vae_parallel_mode` (`"spatial_shard_height"` or `"spatial_shard_width"`; default is `"tile"`).
+
+### How it differs from tile parallel
+
+| Aspect | Tile parallel (`"tile"`) | Spatially-sharded (`"spatial_shard_height"`/`"spatial_shard_width"`) |
+|--------|--------------------------|-----------------------------------------------|
+| Unit of work | Independent overlapping tiles | A single global feature map sharded along H or W |
+| Cross-rank communication | Gather tiles to rank 0, stitch + blend | Per-conv **halo exchange** of boundary rows/cols (P2P) |
+| Output assembly | Blend overlapping tiles | All-gather shards on rank 0, trim padding (matches `broadcast_result=False`) |
+| Scope | Decode + encode | Decode only |
+
+Spatial-shard decode swaps the decoder's spatial convolutions/padding for halo-exchanging variants (`WanDistConv2d`, `WanDistCausalConv3d`, `WanDistZeroPad2d`) so each rank only holds a shard of the activations but still sees the correct receptive field at shard boundaries. Implementation lives in `vllm_omni/diffusion/distributed/autoencoders/wan_spatial_shard.py`.
+
+### Wiring
+
+`vae_parallel_mode` flows through the same path as `vae_patch_parallel_size`:
+
+```text
+serve.py (--vae-parallel-mode) / OmniEngineArgs
+  -> DiffusionParallelConfig.vae_parallel_mode
+  -> registry.py: model.vae.set_parallel_size(vae_pp_size, mode=...)
+  -> DistributedVaeExecutor.parallel_mode
+  -> DistributedAutoencoderKLWan.tiled_decode dispatch
+```
+
+`DistributedAutoencoderKLWan._spatial_shard_decode_enabled()` gates the path: it requires distributed decode to be enabled, a 5D latent, and `vae_patch_parallel_size == DiT group size`. Otherwise it logs a warning and falls back to tile-parallel decode.
+
+### Notes
+
+- The decoder is patched **in place** the first time spatial-shard decode runs and is bound to a single split dimension for the lifetime of the VAE instance.
+- Numerical correctness vs. single-GPU decode is covered by `tests/diffusion/distributed/test_wan_spatial_shard.py::test_spatial_shard_decode_matches_reference` (multi-GPU, nightly `full_model` + `distributed_cuda`).
+
+---
+
 ## Summary
 
 Adding VAE Patch Parallel support to diffusion model:

docs/design/index.md

@@ -11,6 +11,7 @@ This section contains design documents and architecture specifications for vLLM-
 - [Disaggregated Inference](feature/disaggregated_inference.md)
 - [Ray-based Execution](feature/ray_based_execution.md)
 - [Adding Step Execution Support for Diffusion Pipelines](feature/diffusion_step_execution.md)
+- [Request-Level Batching for Diffusion](feature/diffusion_request_level_batching.md)
 - [Continuous Batching for Step-Wise Diffusion](feature/diffusion_continuous_batching.md)
 
 ## Infrastructure Design Documents

docs/design/module/dit_module.md

@@ -199,7 +199,7 @@ class _BaseScheduler(SchedulerInterface):
 - **Shared cleanup logic**: Duplicate-request checks, finish handling, and state removal are centralized instead of
   duplicated in each policy.
 
-- **Current constraint boundary**: `_BaseScheduler` derives `max_num_running_reqs` from `max_num_seqs`, but request-mode diffusion is still clamped back to `1` by the engine. The step-wise path can keep this above `1` for compatible-request batching.
+- **Current constraint boundary**: `_BaseScheduler` derives `max_num_running_reqs` from `max_num_seqs`. Request-mode diffusion can use that capacity for compatible independent requests when the configured pipeline declares `supports_request_batch = True`; step-wise diffusion uses the same scheduler capacity for compatible step batches.
 
 #### 2.4 Current `RequestScheduler` Policy
 
@@ -215,7 +215,7 @@ class RequestScheduler(_BaseScheduler):
 
 - **FIFO request scheduling**: Waiting requests are promoted in queue order.
 
-- **Single-request admission**: `RequestScheduler` still admits one active request at a time because request-mode execution completes a whole request per dispatch.
+- **Compatible request admission**: `RequestScheduler` admits waiting requests while capacity remains and the request's `SamplingParamsKey` is compatible with the active batch. Request-mode execution keeps each logical request independent, while batch-capable pipelines receive the scheduled requests as a runner-side `DiffusionRequestBatch`.
 
 - **Executor result feedback**: `update_from_output()` converts executor output into `FINISHED_COMPLETED` or `FINISHED_ERROR` and returns finished request ids.
 
@@ -235,7 +235,12 @@ while True:
 
 - **No scheduler-owned IPC**: Scheduler no longer talks to workers directly.
 
-- **Split concurrency model**: Request-mode diffusion remains single-active-request, while the step-wise path can keep multiple compatible requests running and advance them independently between denoise steps.
+- **Split concurrency model**: Request-mode diffusion can schedule a static
+  batch of compatible independent requests for one full pipeline forward when
+  the pipeline supports request-level batching; the step-wise path can also
+  admit or remove compatible requests between denoise steps. See
+  [Request-Level Batching](../feature/diffusion_request_level_batching.md) and
+  [Continuous Batching for Step-Wise Diffusion](../feature/diffusion_continuous_batching.md).
 
 ---
 
@@ -465,7 +470,7 @@ class Attention(nn.Module):
                  role="self", role_category=None, ...):
         # Resolve backend for this role from the active OmniDiffusionConfig
         config = get_current_diffusion_config_or_none()
-        attention_config = config.attention_config if config is not None else None
+        attention_config = config.diffusion_attention_config if config is not None else None
         attn_backend_cls, spec = get_attn_backend_for_role(
             role=role,
             head_size=head_size,
@@ -488,7 +493,7 @@ These backends provide the **kernel implementations** for attention computation.
 
 #### Backend Selection Mechanism
 
-Selection is driven by `AttentionConfig` on `OmniDiffusionConfig`, which carries a global `default` plus a `per_role` map of `AttentionSpec` entries. A role string is resolved in this order:
+Selection is driven by `OmniDiffusionConfig.diffusion_attention_config`, which carries a global `default` plus a `per_role` map of `AttentionSpec` entries. A role string is resolved in this order:
 
 ```python
 def get_attn_backend_for_role(role, head_size, attention_config=None, role_category=None):
@@ -904,7 +909,7 @@ def initialize_model_parallel(
 
 ```
 1. User Request
-   └─> OmniDiffusion.generate(prompt)
+   └─> Omni.generate(prompt)
        └─> Prepare OmniDiffusionRequest
            └─> DiffusionEngine.step(requests)
 

docs/features/custom_pipeline.md

@@ -10,7 +10,7 @@ This guide demonstrates how to use the newly added features for extending vLLM-O
 Three main features enable custom pipeline extension:
 
 1. **`WorkerWrapperBase`**: A wrapper class that enables dynamic worker extension with custom functionality
-2. **`load_format`**: A parameter that controls how diffusion models are loaded, including support for custom pipelines
+2. **`diffusion_load_format`**: A parameter that controls how diffusion models are loaded, including support for custom pipelines
 3. **`CustomPipelineWorkerExtension`**: An extension class that enables pipeline re-initialization with custom implementations
 
 ## Features
@@ -27,15 +27,15 @@ Three main features enable custom pipeline extension:
 
 **Location:** `vllm_omni/diffusion/worker/diffusion_worker.py`
 
-### load_format Parameter
+### diffusion_load_format parameter
 
-The `load_format` parameter controls how diffusion models are loaded. It supports the following values:
+The `diffusion_load_format` parameter controls the initial diffusion model load. The relevant values are:
 
 - **`"default"`**: Standard model loading using the model registry (default behavior)
-- **`"custom_pipeline"`**: Load a custom pipeline class specified by `custom_pipeline_name`
-- **`"dummy"`**: Skip model loading (useful for testing or when pipeline will be initialized separately)
+- **`"dummy"`**: Skip the initial model load; use this with `custom_pipeline_args["pipeline_class"]` for the custom pipeline workflow below
+- **`"diffusers"`**: Load through the Hugging Face Diffusers adapter
 
-**Location:** `vllm_omni/diffusion/model_loader/diffusers_loader.py`
+**Location:** `vllm_omni/diffusion/data.py` (`OmniDiffusionConfig`)
 
 ### CustomPipelineWorkerExtension
 
@@ -54,20 +54,24 @@ Create a custom pipeline class that extends an existing pipeline. In this exampl
 
 ```python
 # custom_pipeline.py
-from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.data import OmniDiffusionConfig
 from vllm_omni.diffusion.models.qwen_image.pipeline_qwen_image_edit import QwenImageEditPipeline
+from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 import torch
 
 class CustomPipeline(QwenImageEditPipeline):
     def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = ""):
         super().__init__(od_config=od_config, prefix=prefix)
 
-    def forward(self, req, prompt=None, negative_prompt=None, **kwargs):
+    def forward(self, req: DiffusionRequestBatch):
+        # Optionally customize sampling parameters
+        actual_num_steps = req.sampling_params.num_inference_steps or 50
+        req.sampling_params.num_inference_steps = actual_num_steps
+
         # Call parent's forward to get normal output
-        output = super().forward(req=req, prompt=prompt, negative_prompt=negative_prompt, **kwargs)
+        output = super().forward(req=req)
 
         # Add custom trajectory data
-        actual_num_steps = req.sampling_params.num_inference_steps or kwargs.get('num_inference_steps', 50)
         output.trajectory_timesteps = torch.linspace(1000, 0, actual_num_steps, dtype=torch.float32)
         output.trajectory_latents = torch.randn(actual_num_steps, 1, 16, 64, 64, dtype=torch.float32)
 

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, and 0.23.0. 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, 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).
 
 === "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==0.23.0 --torch-backend=auto
+uv pip install vllm==0.25.0 --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 0.23.0 release version of the library
+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
 
 ```bash
-uv pip install vllm==0.23.0 --torch-backend=auto
+uv pip install vllm==0.25.0 --torch-backend=auto
 ```
 
-The 0.23.0 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 0.25.0 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.
 
 #### 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 v0.23.0
+git checkout v0.25.0
 ```
 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 v0.23.0; the wheel filename has no `+cu130` suffix)
-export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.23.0/vllm-0.23.0-cp38-abi3-manylinux_2_28_x86_64.whl
+#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
 ```
 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:v0.23.0 \
+    vllm/vllm-omni:v0.25.0 \
     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==0.23.0+rocm722 --extra-index-url https://wheels.vllm.ai/rocm/0.23.0/rocm722
+uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/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 0.23.0 release version of the library
+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
 
 ```bash
-uv pip install vllm==0.23.0+rocm722 --extra-index-url https://wheels.vllm.ai/rocm/0.23.0/rocm722
+uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723
 ```
 
-The pre-built 0.23.0 vLLM wheel targets ROCm 7.2.2. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.
+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.
 
 #### 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 v0.23.0
+git checkout v0.25.0
 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:v0.23.0 \
+  vllm/vllm-omni-rocm:v0.25.0 \
   --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:v0.23.0
+  vllm/vllm-omni-rocm:v0.25.0
 ```
 
 # --8<-- [end:pre-built-images]

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

@@ -1,19 +1,16 @@
 # --8<-- [start:requirements]
 
-For detailed hardware and software requirements, please refer to the [vllm-ascend installation documentation](https://docs.vllm.ai/projects/ascend/en/latest/installation.html).
+For detailed hardware and software requirements, please refer to the [vLLM-Ascend installation documentation](https://docs.vllm.ai/projects/ascend/en/latest/installation.html).
 
 # --8<-- [end:requirements]
 # --8<-- [start:installation-release]
 
-The recommended way to use vLLM-Omni on NPU is through the vllm-ascend pre-built Docker images:
+The recommended way to use vLLM-Omni on NPU is through the vLLM-Ascend pre-built Docker images:
 
 ```bash
-# Update the vllm-ascend image
-# Atlas A2:
-# export IMAGE=quay.io/ascend/vllm-ascend:v0.18.0rc1
-# Atlas A3:
-# export IMAGE=quay.io/ascend/vllm-ascend:v0.18.0rc1-a3
-export IMAGE=quay.io/ascend/vllm-ascend:v0.18.0rc1
+# 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
 docker run --rm \
     --name vllm-omni-npu \
     --shm-size=1g \
@@ -31,11 +28,11 @@ docker run --rm \
     -v /etc/ascend_install.info:/etc/ascend_install.info \
     -v /root/.cache:/root/.cache \
     -p 8000:8000 \
-    -it $IMAGE bash
+    -it quay.io/atlas-ci/vllm-ascend:0.25.0-a3 bash
 
 # Inside the container, install vLLM-Omni from source
 cd /vllm-workspace
-git clone -b v0.18.0 https://github.com/vllm-project/vllm-omni.git
+git clone -b v0.25.0 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 .
@@ -87,14 +84,11 @@ Supported images as following.
 Here's an example deployment command that has been verified on 4 x NPUs:
 
 ```bash
-# Atlas A2:
-# export IMAGE=quay.io/ascend/vllm-omni:v0.18.0
-# Atlas A3:
-# export IMAGE=quay.io/ascend/vllm-omni:v0.18.0-a3
-export IMAGE=quay.io/ascend/vllm-omni:v0.18.0
+# Atlas A2: quay.io/ascend/vllm-omni:v0.25.0
+# Atlas A3: quay.io/ascend/vllm-omni:v0.25.0-a3
 docker run --rm \
-    --name vllm-omni-npu \
-    --shm-size=1g \
+    --name vllm-omni-a3 \
+    --shm-size=4g \
     --device /dev/davinci0 \
     --device /dev/davinci1 \
     --device /dev/davinci2 \
@@ -106,10 +100,17 @@ docker run --rm \
     -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
     -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
     -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
+    --device /dev/davinci_manager \
+    --device /dev/devmm_svm \
+    --device /dev/hisi_hdc \
+    -v /usr/local/dcmi:/usr/local/dcmi \
+    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
+    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
     -v /etc/ascend_install.info:/etc/ascend_install.info \
-    -v /root/.cache:/root/.cache \
-    -p 8000:8000 \
-    -it $IMAGE bash
+    -v ~/.cache:/root/.cache \
+    -p 8091:8091 \
+    -it quay.io/ascend/vllm-omni:v0.25.0-a3 bash
 ```
 
 !!! tip

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==0.23.0 --torch-backend=auto
+uv pip install vllm==0.25.0 --torch-backend=auto
 
 # On ROCm
-uv pip install vllm==0.23.0+rocm722 --extra-index-url https://wheels.vllm.ai/rocm/0.23.0/rocm722
+uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723
 
 git clone https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
@@ -31,11 +31,10 @@ uv pip install -e .
 
 For additional installation methods — please see the [installation guide](installation/README.md).
 
-
 !!! note
     It is important to install the same major & minor version of vLLM and vLLM Omni, otherwise things may not work as expected. If the versions are misaligned, you will see a warning when you import vLLM Omni.
 
-    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 < `0.23.0` and vLLM Omni `0.23.0`, 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 < `0.25.0` and vLLM Omni `0.25.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.
 
 ## Offline Inference
 
@@ -52,14 +51,14 @@ if __name__ == "__main__":
     images[0].save("coffee.png")
 ```
 
-You can pass a list of prompts and wait for them to process altogether, shown below.
+You can pass a list of prompts and wait for the independent requests to finish,
+as shown below.
 
 !!! info
 
-    However, it is not currently recommended to do so
-    because not all models support batch inference,
-    and batch requesting mostly does not provide significant performance improvement (despite the impression that it does).
-    This feature is primarily for the sake of interface compatibility with vLLM and to allow for future improvements.
+    For diffusion pipelines, each prompt becomes a separate logical request.
+    The runtime may automatically batch compatible in-flight requests through
+    the scheduler and runner.
 
 ```python
 from vllm_omni.entrypoints.omni import Omni
@@ -88,9 +87,9 @@ if __name__ == "__main__":
 
 !!! info
 
-    For diffusion pipelines, the stage config field `stage_args.[].engine_args.max_num_seqs` is 1 by default, and the input
-    list is sliced into single-item requests before feeding into the diffusion pipeline. For models that do internally support
-    batched inputs, you can [modify this configuration](../configuration/stage_configs.md) to let the model accept a longer batch of prompts.
+    For diffusion request-level batching controls such as `max_num_seqs` and
+    `request_batch_max_wait_ms`, see
+    [Request-Level Batching](../user_guide/diffusion/request_batching.md).
 
 For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)
 

docs/mkdocs/hooks/generate_examples.py

@@ -16,8 +16,35 @@ ROOT_DIR_RELATIVE = "../../../../.."
 EXAMPLE_DIR = ROOT_DIR / "examples"
 EXAMPLE_DOC_DIR = ROOT_DIR / "docs/user_guide/examples"
 NAV_FILE = ROOT_DIR / "docs/.nav.yml"
+MODEL_DISPLAY_NAMES_FILE = EXAMPLE_DIR / "model_display_names.yml"
 MAX_INLINE_MATERIAL_SIZE = 8 * 1024
 
+SERVING_MODE_TITLES = {
+    "offline_inference": "Offline inference",
+    "online_serving": "Online serving",
+}
+
+
+def load_model_display_names() -> dict[str, str]:
+    try:
+        with open(MODEL_DISPLAY_NAMES_FILE, encoding="utf-8") as f:
+            model_display_names = yaml.safe_load(f)
+    except (OSError, yaml.YAMLError) as exc:
+        raise ValueError(f"Failed to load MODEL_DISPLAY_NAMES_FILE at {MODEL_DISPLAY_NAMES_FILE}: {exc}") from exc
+
+    if not isinstance(model_display_names, dict) or not all(
+        isinstance(key, str) and isinstance(value, str) for key, value in model_display_names.items()
+    ):
+        raise ValueError(
+            "MODEL_DISPLAY_NAMES_FILE "
+            f"at {MODEL_DISPLAY_NAMES_FILE} must contain a YAML mapping "
+            "of string model IDs to string display names"
+        )
+    return model_display_names
+
+
+MODEL_DISPLAY_NAMES = load_model_display_names()
+
 
 def fix_case(text: str) -> str:
     subs = {
@@ -54,6 +81,7 @@ class Example:
         main_file (Path): The main file in the directory.
         other_files (list[Path]): list of other files in the directory.
         title (str): The title of the document.
+        nav_title (str): The concise title used in navigation.
 
     Methods:
         __post_init__(): Initializes the main_file, other_files, and title attributes.
@@ -78,6 +106,16 @@ class Example:
     def is_code(self) -> bool:
         return self.main_file.suffix != ".md"
 
+    @property
+    def model_display_name(self) -> str | None:
+        if self.category not in SERVING_MODE_TITLES:
+            return None
+        return MODEL_DISPLAY_NAMES.get(self.path.stem)
+
+    @property
+    def nav_title(self) -> str:
+        return self.model_display_name or self.title
+
     def determine_main_file(self) -> Path:
         """
         Determines the main file in the given path.
@@ -151,13 +189,25 @@ class Example:
         return [file for file in self.path.rglob("*") if is_other_file(file)]
 
     def determine_title(self) -> str:
+        model_display_name = self.model_display_name
         if not self.is_code:
             # Specify encoding for building on Windows
             with open(self.main_file, encoding="utf-8") as f:
                 first_line = f.readline().strip()
             match = re.match(r"^#\s+(?P<title>.+)$", first_line)
+            if model_display_name:
+                expected_title = f"{model_display_name}: {SERVING_MODE_TITLES[self.category]}"
+                actual_title = match.group("title") if match else first_line
+                if actual_title != expected_title:
+                    raise ValueError(
+                        f"Model example title mismatch in {self.main_file}: "
+                        f"expected '# {expected_title}', got {first_line!r}"
+                    )
+                return expected_title
             if match:
                 return match.group("title")
+        elif model_display_name:
+            raise ValueError(f"Mapped model example must use a Markdown README: {self.path}")
         return fix_case(self.path.stem.replace("_", " ").title())
 
     def fix_relative_links(self, content: str) -> str:
@@ -300,7 +350,7 @@ def update_nav_file(examples: list[Example]):
         for example in category_examples:
             doc_path = EXAMPLE_DOC_DIR / example.category / f"{example.path.stem}.md"
             rel_path = doc_path.relative_to(ROOT_DIR / "docs")
-            category_items.append({example.title: rel_path.as_posix()})
+            category_items.append({example.nav_title: rel_path.as_posix()})
 
         if category_items:
             # Format category name (e.g., "offline_inference" -> "Offline Inference")

docs/models/supported_models.md

@@ -34,6 +34,7 @@ th {
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` | ✅︎ | ✅︎ | | |
 | `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | ✅︎ | | | |
 | `WanPipeline` | Wan2.1-T2V, Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, `Wan-AI/Wan2.1-T2V-14B-Diffusers`, `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `Cosmos3OmniDiffusersPipeline` | Cosmos3 T2I, T2V, I2V, V2V, T2V with sound, action policy | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |

docs/serving/audio_generate_api.md

@@ -66,7 +66,7 @@ Content-Type: application/json
 |-----------|------|---------|-------------|
 | `input` | string | **required** | Text prompt describing the audio to generate |
 | `model` | string | server's model | Model to use (optional, should match server if specified) |
-| `response_format` | string | "wav" | Audio format: wav, mp3, flac, pcm, aac, opus |
+| `response_format` | string | "wav" | Audio format: wav, mp3, flac, pcm, opus |
 | `speed` | float | 1.0 | Playback speed (0.25 - 4.0) |
 
 #### Diffusion Parameters
@@ -90,7 +90,6 @@ Returns binary audio data with the appropriate `Content-Type` header:
 | `mp3` | `audio/mpeg` |
 | `flac` | `audio/flac` |
 | `pcm` | `audio/pcm` |
-| `aac` | `audio/aac` |
 | `opus` | `audio/opus` |
 
 ## Examples
@@ -285,7 +284,7 @@ Pydantic validation failure (e.g. invalid `response_format`, `speed` out of rang
     "detail": [
         {
             "type": "literal_error",
-            "msg": "Input should be 'wav', 'pcm', 'flac', 'mp3', 'aac' or 'opus'",
+            "msg": "Input should be 'wav', 'pcm', 'flac', 'mp3' or 'opus'",
             ...
         }
     ]

docs/serving/image_edit_api.md

@@ -252,7 +252,7 @@ Validation errors (missing required fields):
 
 ```bash
 # Check if server is responding
-curl -X http://localhost:8000/v1/images/edit \
+curl http://localhost:8000/v1/images/edits \
   -F "prompt='test'"
 ```
 

docs/serving/image_generation_api.md

@@ -138,7 +138,7 @@ Content-Type: application/json
 | `model` | string | server's model | Model to use (optional, should match server if specified) |
 | `n` | integer | 1 | Number of images to generate (1-10) |
 | `size` | string | model defaults | Image dimensions in WxH format (e.g., "1024x1024", "512x512") |
-| `response_format` | string | "b64_json" | Response format (only "b64_json" supported) |
+| `response_format` | string | "b64_json" | Response format (`"b64_json"` or `"file"`) |
 | `user` | string | null | User identifier for tracking |
 
 #### vllm-omni Extension Parameters

docs/serving/speech_api.md

@@ -109,7 +109,7 @@ Content-Type: application/json
 | `input` | string | **required** | The text to synthesize into speech |
 | `model` | string | server's model | Model to use (optional, should match server if specified) |
 | `voice` | string | "vivian" | Speaker name (e.g., vivian, ryan, aiden) |
-| `response_format` | string | "wav" | Audio format: wav, mp3, flac, pcm, aac, opus |
+| `response_format` | string | "wav" | Audio format: wav, mp3, flac, pcm, opus |
 | `speed` | float | 1.0 | Playback speed (0.25-4.0) |
 
 #### vLLM-Omni Extension Parameters
@@ -122,7 +122,8 @@ Content-Type: application/json
 | `max_new_tokens` | integer | 2048 | Maximum tokens to generate |
 | `initial_codec_chunk_frames` | integer | null | Per-request initial chunk size override for TTFA tuning. When null, IC is computed dynamically based on server load. |
 | `non_streaming_mode` | bool | null | Qwen3-TTS prompt construction mode override. Does not affect HTTP response streaming or async-chunk pipelining. When null, Qwen3-TTS uses model defaults: Base=false, CustomVoice/VoiceDesign=true. |
-| `stream` | bool | false | Stream raw PCM chunks as they are decoded (requires `response_format="pcm"`) |
+| `stream` | bool | false | When true, stream OpenAI `speech.audio.*` SSE events (requires `response_format="pcm"` or `"wav"`). For raw PCM/WAV byte streaming, set `stream_format="audio"`. |
+| `stream_format` | string | null | Streaming output format. `"audio"` streams raw audio bytes as they are decoded; `"sse"` streams OpenAI `speech.audio.*` Server-Sent Events. If omitted, `stream=true` selects SSE and `stream=false` remains non-streaming. See [Response Format](#response-format). |
 
 **Supported languages:** Only applicable to Qwen3-TTS. Derived from the model configuration (`talker_config.codec_language_id` in the checkpoint's `config.json`), plus `Auto`, which is always accepted. Official Qwen3-TTS checkpoints support: Auto, Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian.
 
@@ -136,7 +137,57 @@ Content-Type: application/json
 
 ### Response Format
 
-Returns binary audio data with appropriate `Content-Type` header (e.g., `audio/wav`).
+The response shape depends on the streaming parameters:
+
+**Non-streaming (default).** With `stream=false` and no `stream_format`, returns the
+complete clip as binary audio data with an appropriate `Content-Type` header (e.g.
+`audio/wav`). The raw-bytes body has no JSON carrier, so no `usage` is reported.
+
+**Raw audio stream** (`stream_format="audio"`). Streams raw audio bytes (PCM or
+WAV) as they are decoded.
+
+Both streaming modes (`stream_format="audio"` and `"sse"`) require
+`response_format="pcm"` or `"wav"`, and `speed` must be `1.0` (or omitted).
+
+**SSE stream** (`stream=true` or `stream_format="sse"`). Streams [OpenAI
+`speech.audio.*` Server-Sent Events](https://platform.openai.com/docs/api-reference/audio-streaming).
+Each event has an `event:` line and a JSON `data:` line:
+
+- `speech.audio.delta` — a base64 audio chunk:
+
+    ```json
+    { "type": "speech.audio.delta", "audio": "<base64>", "response_format": "pcm" }
+    ```
+
+- `speech.audio.done` — terminal event, carrying token `usage`:
+
+    ```json
+    {
+        "type": "speech.audio.done",
+        "usage": {
+            "input_tokens": 119,
+            "output_tokens": 77,
+            "total_tokens": 196,
+            "input_token_details": { "text_tokens": 18, "audio_tokens": 101 }
+        }
+    }
+    ```
+
+- `speech.audio.error` — emitted instead of `speech.audio.done` if generation fails:
+
+    ```json
+    { "type": "speech.audio.error", "error": { "message": "...", "type": "server_error", "param": null, "code": 500 } }
+    ```
+
+The `usage` object on `speech.audio.done` is the same shape returned per item by the
+[batch endpoint](#batch-speech-generation):
+
+- `input_tokens` = `text_tokens` + `audio_tokens`
+    - `text_tokens`: tokens of the synthesized text (`input` plus `instructions`)
+    - `audio_tokens`: reference-audio codec frames, non-zero only for in-context
+      voice cloning (Base task); `0` for CustomVoice/VoiceDesign or x-vector-only
+- `output_tokens`: generated codec tokens
+- `total_tokens` = `input_tokens` + `output_tokens`
 
 ### Voices Endpoint
 
@@ -490,7 +541,13 @@ Each item in the `items` array requires only `input` (the text). All other field
             "index": 0,
             "status": "success",
             "audio_data": "<base64-encoded audio>",
-            "media_type": "audio/wav"
+            "media_type": "audio/wav",
+            "usage": {
+                "input_tokens": 119,
+                "output_tokens": 77,
+                "total_tokens": 196,
+                "input_token_details": { "text_tokens": 18, "audio_tokens": 101 }
+            }
         },
         {
             "index": 1,
@@ -504,6 +561,18 @@ Each item in the `items` array requires only `input` (the text). All other field
 }
 ```
 
+Each successful item carries a `usage` object (errored items omit it):
+
+- `input_tokens` = `text_tokens` + `audio_tokens`
+    - `text_tokens`: tokens of the synthesized text (`input` plus `instructions`)
+    - `audio_tokens`: reference-audio codec frames, non-zero only for in-context
+      voice cloning (Base task); `0` for CustomVoice/VoiceDesign or x-vector-only
+- `output_tokens`: generated codec tokens
+- `total_tokens` = `input_tokens` + `output_tokens`
+
+This is the same `usage` object emitted on the terminal `speech.audio.done` event
+of the single endpoint's [SSE stream](#response-format) (`stream_format="sse"`).
+
 ### Examples
 
 **Basic batch with shared defaults:**

docs/user_guide/diffusion/attention_backends.md

@@ -20,6 +20,8 @@ The full set of backends and their platform defaults is in the **Backend Options
 | `TORCH_SDPA` | PyTorch `scaled_dot_product_attention` with the default backend dispatcher. Most conservative; always available. |
 | `SAGE_ATTN` | SageAttention 2.2 — INT8-quantized attention with FP16 accumulation. Lossy but typically visually indistinguishable on diffusion outputs. Requires `sageattention`. |
 | `SAGE_ATTN_3` | Requires `sageattn3` from `SageAttention/sageattention3_blackwell`. CUDA only, intended for Blackwell GPUs, with GQA/MQA requests falling back to PyTorch SDPA. |
+| `FLASH_ATTN_HUB` | FlashAttention 2 from HuggingFace `kernels` library. Useful for train/rollout alignment. |
+| `FLASH_ATTN_3_HUB` | FlashAttention 3 from HuggingFace `kernels` library. CUDA Hopper (sm_90+) only; falls back to `FLASH_ATTN_HUB` on older GPUs. |
 
 
 ## Configuration
@@ -81,7 +83,7 @@ When constructing `OmniDiffusionConfig` directly:
 from vllm_omni.diffusion.data import AttentionConfig, AttentionSpec, OmniDiffusionConfig
 
 config = OmniDiffusionConfig(
-    attention_config=AttentionConfig(
+    diffusion_attention_config=AttentionConfig(
         default=AttentionSpec(backend="FLASH_ATTN"),
         per_role={
             "cross": AttentionSpec(backend="TORCH_SDPA"),
@@ -210,6 +212,36 @@ Notes:
 - `SAGE_ATTN_3` is only selected on CUDA when `sageattn3` is importable and the GPU is Blackwell-class.
 - SageAttention3's Blackwell kernel assumes `Hq == Hkv`. In vLLM-Omni, GQA/MQA diffusion requests fall back to PyTorch SDPA for correctness.
 
+## HuggingFace Kernels Hub Backends
+
+To achieve perfect numerical consistency between **training** (typically using HuggingFace Diffusers with Hub kernels) and **serving/rollout** (using vLLM-Omni), you can use the Hub-based attention backends. This eliminates numerical drift / sampling divergence caused by executing different local kernel versions during rollout.
+
+The following backend options are supported:
+- `FLASH_ATTN_HUB` (HuggingFace `kernels-community/flash-attn2`)
+- `FLASH_ATTN_3_HUB` (HuggingFace `kernels-community/flash-attn3`, Hopper sm_90+ only)
+
+### Installation
+
+To use these backends, you must install the `kernels` library:
+
+```bash
+pip install kernels==0.14.1
+```
+
+If the `kernels` library is not available in the environment, vLLM-Omni will log a warning and fall back gracefully to the corresponding local backend implementations (`FLASH_ATTN`). On CUDA GPUs below Hopper (compute capability < 9.0), `FLASH_ATTN_3_HUB` falls back to `FLASH_ATTN_HUB`.
+
+### Usage
+
+Select a Hub backend using the global CLI flag or environment variables:
+
+```bash
+# Environment variable
+export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN_HUB
+
+# CLI flag
+vllm-omni serve <model> --diffusion-attention-backend FLASH_ATTN_HUB
+```
+
 ## Usage Examples
 
 ### Default (auto-route)

docs/user_guide/diffusion/cpu_offload_diffusion.md

@@ -33,7 +33,7 @@ m = Omni(model="Wan-AI/Wan2.2-T2V-A14B-Diffusers", enable_cpu_offload=True)
 
 **CLI:**
 ```bash
-vllm-omni serve diffusion Wan-AI/Wan2.2-T2V-A14B-Diffusers --enable-cpu-offload
+vllm serve Wan-AI/Wan2.2-T2V-A14B-Diffusers --omni --enable-cpu-offload
 ```
 
 ### To Support a Model
@@ -82,6 +82,33 @@ mutual exclusion: when one group runs, the other moves to CPU.
 - Support single GPU only for now
 
 
+### Component offloading for split models (e.g. Cosmos3)
+
+Some models split their transformer into mutually-exclusive *components* that run
+in different phases of a single forward pass rather than as separate pipeline
+components -- e.g. Cosmos3's understanding (reasoner) component runs once per
+generation while the generation (generator) component runs every denoising step.
+Such models have no separate text encoder to swap against, so the transformer
+owns a small model-local offload path and wraps each phase with
+`with self._offload_context(name):`
+
+```python
+class Cosmos3VFMTransformer(nn.Module):
+    def forward(self, ...):
+        with self._offload_context("reasoner"):
+            ...  # understanding pass, runs once
+        with self._offload_context("generator"):
+            ...  # denoising pass, runs every step
+```
+
+Model-level offloading then keeps exactly one component GPU-resident at a time
+(the other on CPU), reusing the same `SequentialOffloadHook` `.to()` movers. The
+pipeline opts in by exposing `enable_omni_model_cpu_offload` (which drives the
+transformer's `enable_model_cpu_offload` and pins the VAE). Layerwise offloading
+works for these models too -- each component declares its own block container via
+`_layerwise_offload_blocks_attrs`.
+
+
 ## Layerwise (Blockwise) Offloading
 
 ### How It Works
@@ -119,10 +146,10 @@ m = Omni(model="Wan-AI/Wan2.2-I2V-A14B-Diffusers", enable_layerwise_offload=True
 **CLI:**
 ```bash
 # Text-to-video
-vllm-omni serve diffusion Wan-AI/Wan2.2-T2V-A14B-Diffusers --enable-layerwise-offload
+vllm serve Wan-AI/Wan2.2-T2V-A14B-Diffusers --omni --enable-layerwise-offload
 
 # Or image-to-video
-vllm-omni serve diffusion Wan-AI/Wan2.2-I2V-A14B-Diffusers --enable-layerwise-offload
+vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers --omni --enable-layerwise-offload
 ```
 
 ### To Support a Model
@@ -145,9 +172,9 @@ class Flux2Transformer2DModel(nn.Module):
 ```
 
 ### Limitations
-- Cold start latency increases because of
-    1) components are loaded to CPU first at the very first during initialization,
-    2) weight consolidation and pinning
+- Cold start latency increases because offloaded components must be moved to CPU
+  during setup; layerwise offload may add extra weight consolidation and pinning
+  work.
 - Performance depends on compute cost and H2D bandwidth as well
 - Support single GPU only for now
 
@@ -178,11 +205,18 @@ Both strategies use vLLM-Omni's hook registry system (`HookRegistry` and `ModelH
 
 ```
 OffloadBackend (base class)
-├── ModelLevelOffloadBackend → uses SequentialOffloadHook
+├── ModelLevelOffloadBackend → uses SequentialOffloadHook (.to() swap)
+│                              (delegates to a pipeline's enable_omni_model_cpu_offload
+│                               for split models like Cosmos3)
 └── LayerWiseOffloadBackend → uses LayerwiseOffloadHook
 ```
 
-Factory function `get_offload_backend()` selects the appropriate backend based on configuration.
+Factory function `get_offload_backend()` selects the appropriate backend based on
+configuration.
+
+For split models, `ModelLevelOffloadBackend.enable()` detects a pipeline's
+`enable_omni_model_cpu_offload` hook and delegates to it; Cosmos3 then swaps its
+reasoner/generator components inside the model forward pass.
 
 
 ## Supported Models

docs/user_guide/diffusion/frame_interpolation.md

@@ -47,7 +47,8 @@ For supported Wan2.2 pipelines, the execution order is:
 1. Diffusion worker finishes denoising and decodes the raw video tensor.
 2. Worker-side model-specific post-processing runs.
 3. If frame interpolation is enabled, RIFE interpolates the decoded video
-   tensor on the worker side and records a FPS multiplier in `custom_output`.
+   tensor on the worker side and records a FPS multiplier in
+   `metadata.video.video_fps_multiplier`.
 4. The API server receives the already-interpolated video and only performs
    MP4 export.
 

docs/user_guide/diffusion/parallelism/overview.md

@@ -10,7 +10,7 @@ This guide covers the parallelism methods in vLLM-Omni for speeding up diffusion
 | **[Sequence Parallelism](sequence_parallel.md)**   | Splits sequence dimension across GPUs (Ulysses-SP, Ring-Attention, or hybrid) for high-resolution images and videos |
 | **[CFG-Parallel](cfg_parallel.md)**                | Runs CFG positive/negative branches on separate GPUs for ~1.8x speedup on guided generation                         |
 | **[Pipeline Parallelism](pipeline_parallel.md)**   | Splits the denoising transformer block-wise across sequential GPU stages to reduce per-GPU model memory             |
-| **[VAE Patch Parallelism](vae_patch_parallel.md)** | Distributes VAE decode spatially across GPUs to reduce peak VAE memory                                              |
+| **[VAE Parallelism](vae_parallelism.md)** | Distributes VAE decode spatially across GPUs to reduce peak VAE memory                                              |
 | **[HSDP](hsdp.md)**                                | Shards full model weights via PyTorch FSDP2 to enable large-model inference on memory-constrained GPUs              |
 | **[Expert Parallelism](expert_parallel.md)**       | Shards MoE expert blocks across GPUs for MoE models (e.g. HunyuanImage3.0)                                          |
 

docs/user_guide/diffusion/parallelism/vae_parallelism.md

@@ -1,4 +1,4 @@
-# VAE Patch Parallelism Guide
+# VAE Parallelism Guide
 
 
 ## Table of Content
@@ -15,7 +15,7 @@
 
 ## Overview
 
-VAE Patch Parallelism distributes the VAE (Variational AutoEncoder) decode/encode computation across multiple GPUs by splitting the latent space into spatial tiles or patches. Each GPU processes a subset of tiles in parallel, significantly reducing peak memory consumption during the VAE decode stage while maintaining output quality.
+VAE parallelism distributes VAE (Variational AutoEncoder) decode/encode work across multiple GPUs. This guide covers VAE patch/tile parallelism, which splits latent space into spatial tiles or patches, and Wan spatial-shard decode, which shards decoder feature maps along height or width.
 
 This is particularly useful for:
 - **High-resolution image generation** where VAE decode can become a memory bottleneck
@@ -25,7 +25,7 @@ This is particularly useful for:
 See supported models list in [Supported Models](../../diffusion_features.md#supported-models).
 
 
-VAE Patch Parallelism uses two strategies based on image size:
+VAE patch parallelism uses two strategies based on image size:
 
 | Strategy | Use Case | How It Works | Overlap Handling | Output Quality |
 |----------|----------|--------------|------------------|----------------|
@@ -113,6 +113,7 @@ In `DiffusionParallelConfig`:
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
 | `vae_patch_parallel_size` | int | 1 | Number of GPUs for VAE patch/tile parallelism. Set to 2 or higher to enable. Should typically match `tensor_parallel_size` as they share the same process group. |
+| `vae_parallel_mode` | str | `"tile"` | VAE parallel decode strategy: `"tile"` (default tile/patch parallel decode), `"spatial_shard_height"`, or `"spatial_shard_width"` (spatially-sharded decode, Wan only). See [Spatially-Sharded Decode](#spatially-sharded-decode-wan). |
 
 Additional requirements:
 
@@ -125,6 +126,51 @@ Additional requirements:
 
 ---
 
+## Spatially-Sharded Decode (Wan)
+
+The default `vae_parallel_mode="tile"` distributes whole tiles across ranks. For the **Wan** VAE there is an alternative decode strategy, **spatially-sharded decode**, selected via `vae_parallel_mode="spatial_shard_height"` or `vae_parallel_mode="spatial_shard_width"`.
+
+Instead of assigning independent tiles to ranks, spatial-shard decode shards the decoder feature maps along the height (`spatial_shard_height`) or width (`spatial_shard_width`) dimension and exchanges halo rows/columns between neighboring ranks around the spatial convolutions. This keeps the receptive field correct across shard boundaries, so the result matches the single-GPU decode within numerical tolerance.
+
+```python
+from vllm_omni import Omni
+from vllm_omni.diffusion.data import DiffusionParallelConfig
+
+omni = Omni(
+    model="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    parallel_config=DiffusionParallelConfig(
+        tensor_parallel_size=2,
+        vae_patch_parallel_size=2,               # must match the DiT group size
+        vae_parallel_mode="spatial_shard_width", # or "spatial_shard_height"
+    ),
+)
+```
+
+Or from the CLI / serving entrypoint:
+
+```bash
+vllm serve Wan-AI/Wan2.1-T2V-1.3B-Diffusers --omni \
+    --tensor-parallel-size 2 \
+    --vae-patch-parallel-size 2 \
+    --vae-parallel-mode spatial_shard_width
+```
+
+**Constraints and behavior:**
+
+- Spatial-shard decode is **decode-only** and currently implemented for the **Wan** VAE. Other models ignore `spatial_shard_*` modes.
+- It requires `vae_patch_parallel_size` to **match the DiT process group size**. If it does not, the VAE logs a warning and **falls back to tile-parallel decode** at runtime.
+- `spatial_shard_height` and `spatial_shard_width` are mutually exclusive for a given VAE instance (the decoder is patched in place for a single split dimension).
+
+For end-to-end latency/throughput, launch serving with the desired `vae_parallel_mode` and use the existing diffusion serving benchmark:
+
+```bash
+python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
+    --endpoint /v1/videos --dataset random --task t2v --num-prompts 1 \
+    --height 480 --width 832 --num-frames 17 --max-concurrency 1
+```
+
+---
+
 ## Best Practices
 
 ### When to Use

docs/user_guide/diffusion/request_batching.md

@@ -0,0 +1,110 @@
+# Request-Level Batching
+
+Request-level batching lets diffusion serving combine multiple compatible
+logical requests into one pipeline `forward()` call. Each prompt remains a
+separate request with its own `request_id`, sampling parameters, seed, output,
+error, and abort state. The scheduler decides which requests can run together.
+
+!!! warning "Prompt List Semantics"
+
+    Diffusion request-level batching does not support a top-level packed
+    list-prompt request. Submit multiple prompts as independent requests and let
+    the scheduler batch compatible in-flight requests. Multimodal payloads stay
+    inside a single prompt dict, for example
+    `{"prompt": "...", "multi_modal_data": {"image": image}}`.
+
+## Enablement
+
+Increase `max_num_seqs` above `1` to allow the request scheduler to keep more
+than one compatible request active:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni \
+  --port 8091 \
+  --max-num-seqs 4
+```
+
+For bursty online traffic, you can also set a small admission wait window. This
+lets the engine wait briefly before the first `schedule()` of a new wave so
+nearby compatible requests can arrive and share the same fused forward pass:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni \
+  --port 8091 \
+  --max-num-seqs 4 \
+  --request-batch-max-wait-ms 20
+```
+
+`--request-batch-max-wait-ms 0` is the default and disables admission waiting,
+so there is no added wait latency.
+
+For deploy YAMLs, configure the diffusion stage engine args:
+
+```yaml
+stage_args:
+  - stage_id: 0
+    stage_type: diffusion
+    engine_args:
+      max_num_seqs: 4
+      request_batch_max_wait_ms: 20
+```
+
+## Compatibility
+
+Only pipelines that declare request-batch support use the fused request-batch
+path. The engine validates that the pipeline `forward()` uses the request-batch
+contract and returns `list[DiffusionOutput]`. Pipelines that do not support this
+contract do not use fused `pipeline.forward(batch)`; scheduled requests are
+executed through per-request worker calls.
+
+The scheduler batches only compatible requests. Compatibility is based on
+shape-sensitive and guidance-sensitive sampling fields, including resolution,
+frame count, CFG settings, output count, and LoRA identity. Requests with
+different LoRA adapters or scales are kept in separate batches so the worker
+activates one adapter per batch.
+
+Request-level batching applies when `step_execution=False`. For the separate
+step-wise runtime, see [Step Execution](step_execution.md).
+
+## Tuning
+
+- `max_num_seqs` caps the number of active compatible requests in one scheduler
+  wave.
+- `request_batch_max_wait_ms` is an upper bound on extra admission wait before a
+  new wave starts. Keep it small for latency-sensitive serving; values such as
+  `10` to `50` ms are a practical starting range for bursty HTTP ingress.
+- `0` disables admission waiting and preserves the lowest first-request latency.
+- FIFO ordering is conservative: an incompatible request at the front of the
+  waiting queue can block later compatible requests from joining the current
+  batch.
+
+## Python API
+
+When constructing `Omni`, pass the same engine arguments:
+
+```python
+from vllm_omni.entrypoints.omni import Omni
+
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    max_num_seqs=4,
+    request_batch_max_wait_ms=20.0,
+)
+
+outputs = omni.generate(
+    [
+        "a cup of coffee on a table",
+        "a toy dinosaur on a sandy beach",
+        "a fox waking up in bed and yawning",
+    ]
+)
+```
+
+`Omni.generate([...])` submits each list item as its own logical diffusion
+request. The runtime may batch those requests internally when their sampling
+parameters are compatible.
+
+## For Contributors
+
+For implementation details and model-author guidance, see
+[Request-Level Batching for Diffusion](../../design/feature/diffusion_request_level_batching.md).

docs/user_guide/diffusion_features.md

@@ -61,7 +61,7 @@ Memory optimization methods help reduce GPU memory usage, enabling inference on
 |--------|-------------|----------|
 | **[CPU Offload](diffusion/cpu_offload_diffusion.md)** | Offloads model components to CPU memory | Limited VRAM, large models on consumer GPUs |
 | **[Quantization](quantization/overview.md)** | Reduces transformer stages from BF16 to FP8/INT8/etc. | Limited VRAM, minimal accuracy loss    |
-| **[VAE Patch Parallelism](diffusion/parallelism/vae_patch_parallel.md)** | Distributes VAE decode tiling across GPUs | High-resolution generation with reduced VAE memory peak |
+| **[VAE Parallelism](diffusion/parallelism/vae_parallelism.md)** | Distributes VAE decode work across GPUs | High-resolution generation with reduced VAE memory peak |
 
 ### Extensions
 
@@ -75,13 +75,17 @@ Extension methods add specialized capabilities to diffusion models beyond standa
 
 ### Execution Modes
 
-Execution modes control how the diffusion pipeline processes denoise steps.
+Execution modes control how the diffusion pipeline processes requests and
+denoise steps.
 
 | Method | Description | Best For |
 |--------|-------------|----------|
+| **[Request-Level Batching](diffusion/request_batching.md)** | Scheduler batches compatible independent diffusion requests into one pipeline forward pass | Bursty online serving and multi-request throughput |
 | **[Step Execution](diffusion/step_execution.md)** | Per-step denoise execution with mid-request abort support | Request cancellation between denoise steps, fine-grained execution control |
 
-**Note:** Step execution is currently supported by QwenImagePipeline only. See [Supported Models](#supported-models) for details.
+**Note:** Request-level batching is available for pipelines that declare the
+request-batch forward contract. Step execution is currently supported by
+QwenImagePipeline only. See [Supported Models](#supported-models) for details.
 
 ### Quantization Methods
 
@@ -101,7 +105,7 @@ The following tables show which models support each feature:
 
 > Notes:
 
-> 1. CPU Offload has two methods: Module-wise (default for models with DiT + text encoder) and Layerwise. The tables below show **Layerwise support** only.
+> 1. CPU Offload has two methods: Module-wise (default for models with DiT + text encoder) and Layerwise. The tables below show **Layerwise support** only. Split models like Cosmos3 (no separate text encoder) swap their reasoner/generator components for module-wise offload; see the [CPU Offload Guide](diffusion/cpu_offload_diffusion.md).
 > 2. The **💾Quantization** column is collapsed for readability. See [Quantization Overview](quantization/overview.md) for per-method and per-model support details.
 
 ### ImageGen
@@ -117,19 +121,20 @@ The following tables show which models support each feature:
 | **GLM-Image**            |     ❌     |     ❌      |           ❌           |       ✅        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Hidream-I1-Full**        |     ❌     |     ❌      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **HunyuanImage3**        |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ✅        |        ❌         |
+| **Krea 2**               |     ❌     |     ✅      |           ❌           |       ❌        |         ❌         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LongCat-Image**        |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **LongCat-Image-Edit**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **MagiHuman**            |     ❌     |     ❌      |           ❌           |       ❓        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **MammothModa2(T2I)**    |     ❌     |     ❌      |           ❌           |       ❌        |         ❌         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Nextstep_1(T2I)**      |     ❓     |     ❓      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
-| **OmniGen2**             |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
+| **OmniGen2**             |     ❌     |     ✅      |           ✅           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Ovis-Image**           |     ❌     |     ✅      |           ❌           |       ✅        |         ❌         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Qwen-Image**           |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ✅        |        ✅         |
 | **Qwen-Image-2512**      |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ✅        |        ✅         |
 | **Qwen-Image-Edit**      |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Qwen-Image-Edit-2509** |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |        ✅ (decode)         |          ✅           |       ❌        |        ❌         |
 | **Qwen-Image-Layered**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
-| **SenseNova-U1**         |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
+| **SenseNova-U1**         |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Stable-Diffusion-XL**  |     ❌     |     ❌      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Stable-Diffusion3.5**  |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Z-Image**              |     ✅     |     ✅      |           ✅           |       ❓        |   ✅ (TP=2 only)   |          ❌          |   ✅    |             ❌             |      ✅ (decode)      |       ✅        |        ❌         |
@@ -139,14 +144,15 @@ The following tables show which models support each feature:
 > Notes:
 > 1. Nextstep_1(T2I) does not support cache acceleration methods such as TeaCache or Cache-DiT.
 > 2. `Tongyi-MAI/Z-Image-Turbo` and `SII-GAIR/daVinci-MagiHuman-Base-1080p` are distilled models with minimal NFEs; CFG-Parallel is not necessary.
-> 3. Cosmos3 T2I uses `Cosmos3OmniDiffusersPipeline` with `modalities=["image"]`. Model-level CPU offload is not supported; use layerwise offload.
+> 3. Cosmos3 T2I uses `Cosmos3OmniDiffusersPipeline` with `modalities=["image"]`. Model-level CPU offload swaps the nested UND reasoner and GEN generator pathways; layerwise offload remains available for blockwise GEN/UND offload.
+> 4. Krea 2 currently supports single-GPU inference plus LoRA, Cache-DiT, HSDP, CPU/layerwise offload, and VAE-patch-parallel (decode). TP/SP/CFG-Parallel are not yet wired. The few-step distilled (Turbo) checkpoint uses `is_distilled=true` (fixed timestep shift `mu=1.15`); generate at 2048x2048 by default with `num_inference_steps≈8` and `guidance_scale=0`. The Raw checkpoint uses 1024x1024, `num_inference_steps=28`, and `guidance_scale=4.5`.
 
 ### VideoGen
 
 | Model                        | ⚡TeaCache | ⚡Cache-DiT | 🔀SP (Ulysses & Ring) | 🔀CFG-Parallel | 🔀Tensor-Parallel | Pipeline-Parallel | 🔀HSDP | 💾CPU Offload (Layerwise) | 💾VAE-Patch-Parallel | 💾Quantization | 🔄Step Execution |
 |------------------------------|:---------:|:----------:|:---------------------:|:--------------:|:-----------------:|:-----------------:|:------:|:-------------------------:|:--------------------:|:--------------:|:----------------:|
 | **Wan2.2**                   |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ✅         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ❌        |        ❌         |
-| **Wan2.2-S2V**               |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ❌    |             ✅             |  ✅ (encode/decode)   |       ❌        |        ❌         |
+| **Wan2.2-S2V**               |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ❌        |        ❌         |
 | **Wan2.1-VACE**              |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2**                    |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **LTX-2.3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ❌    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
@@ -270,7 +276,7 @@ Measured on NVIDIA H800:
 **Memory Optimization:**
 
 - **[CPU Offload Guide](diffusion/cpu_offload_diffusion.md)** - Offload model components to CPU, reduce GPU memory usage
-- **[VAE Patch Parallelism Guide](diffusion/parallelism/vae_patch_parallel.md)** - Distribute VAE decode tiling across GPUs for high-resolution images
+- **[VAE Parallelism Guide](diffusion/parallelism/vae_parallelism.md)** - Distribute VAE decode work across GPUs for high-resolution images and videos
 - **[Quantization Overview](quantization/overview.md)** - Overview of quantization methods for diffusion, multi-stage omni/TTS, and multi-stage diffusion models
 
 **Extensions:**

docs/user_guide/examples/offline_inference/bagel.md

@@ -257,7 +257,7 @@ python end2end.py --model ByteDance-Seed/BAGEL-7B-MoT \
 | :------- | :--- | :------ | :---------- |
 | `--deploy-config` | string | `None` | Path to deploy YAML (auto-detected if omitted) |
 | `--stage-configs-path` | string | `None` | [Deprecated] Legacy path to `stage_args` YAML; prefer `--deploy-config` |
-| `--worker-backend` | choice | `process` | `process` or `ray` |
+| `--worker-backend` | choice | `multi_process` | `multi_process` or `ray` |
 | `--ray-address` | string | `None` | Ray cluster address |
 | `--quantization` | string | `None` | Quantization method (e.g. `fp8`) |
 | `--log-stats` | flag | `False` | Enable statistics logging |

docs/user_guide/examples/offline_inference/glm_image.md

@@ -26,18 +26,22 @@ Stage 0 (AR Model)                Stage 1 (Diffusion)
 
 ```python
 from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 if __name__ == "__main__":
     omni = Omni(model="zai-org/GLM-Image")
     outputs = omni.generate(
         "A photorealistic mountain landscape at sunset",
-        sampling_params={
-            "height": 1024,
-            "width": 1024,
-            "num_inference_steps": 50,
-            "guidance_scale": 1.5,
-            "seed": 42,
-        },
+        sampling_params_list=[
+            omni.default_sampling_params_list[0],
+            OmniDiffusionSamplingParams(
+                height=1024,
+                width=1024,
+                num_inference_steps=50,
+                guidance_scale=1.5,
+                seed=42,
+            ),
+        ],
     )
     outputs[0].request_output.images[0].save("output.png")
 ```
@@ -46,6 +50,7 @@ if __name__ == "__main__":
 
 ```python
 from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 if __name__ == "__main__":
     omni = Omni(model="zai-org/GLM-Image")
@@ -56,13 +61,16 @@ if __name__ == "__main__":
                 "image": "input.png",
             },
         },
-        sampling_params={
-            "height": 1024,
-            "width": 1024,
-            "num_inference_steps": 50,
-            "guidance_scale": 1.5,
-            "seed": 42,
-        },
+        sampling_params_list=[
+            omni.default_sampling_params_list[0],
+            OmniDiffusionSamplingParams(
+                height=1024,
+                width=1024,
+                num_inference_steps=50,
+                guidance_scale=1.5,
+                seed=42,
+            ),
+        ],
     )
     outputs[0].request_output.images[0].save("output.png")
 ```

docs/user_guide/examples/offline_inference/image_to_video.md

@@ -3,7 +3,9 @@
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_video>.
 
 
-This example demonstrates how to generate videos from images using Wan2.2 Image-to-Video models with vLLM-Omni's offline inference API.
+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
+offline inference API.
 
 ## Local CLI Usage
 
@@ -51,13 +53,41 @@ python image_to_video.py \
   --output i2v_output.mp4
 ```
 
+### LTX-2.3
+
+```bash
+python image_to_video.py \
+  --model dg845/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 a Diffusers-format checkpoint such as `dg845/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.
+
 Key arguments:
 
-- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V).
+- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V, or
+  LTX-2/LTX-2.3).
 - `--image`: Path to input image (required).
 - `--prompt`: Text description of desired motion/animation.
-- `--height/--width`: Output resolution (auto-calculated from image if not set). Dimensions should be multiples of 16.
-- `--num-frames`: Number of frames (default 81).
+- `--height/--width`: Output resolution (auto-calculated from image if not set).
+  Wan dimensions should be multiples of 16; LTX dimensions should be multiples
+  of 32.
+- `--num-frames`: Number of frames (model-specific default; LTX-style models
+  work best with `8k + 1`).
 - `--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.
@@ -65,6 +95,7 @@ Key arguments:
 - `--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 50).
 - `--fps`: Frames per second for the saved MP4 (requires `diffusers` export_to_video).
+- `--audio-sample-rate`: fallback audio sample rate for embedded audio.
 - `--output`: Path to save the generated video.
 - `--vae-use-slicing`: Enable VAE slicing for memory optimization.
 - `--vae-use-tiling`: Enable VAE tiling for memory optimization.

docs/user_guide/examples/offline_inference/mammothmodal2_preview.md

@@ -7,29 +7,22 @@ Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inf
 
 Download model
 ```bash
-hf bytedance-research/MammothModa2-Preview --local-dir ./MammothModa2-Preview
+hf download bytedance-research/MammothModa2-Preview --local-dir ./MammothModa2-Preview
 ```
 
 ### Text-to-Image (T2I)
 
-```bash
-python examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_t2i.py \
-  --model ./MammothModa2-Preview \
-  --stage-config ./vllm_omni/model_executor/stage_configs/mammoth_moda2.yaml \
-  --prompt "A stylish woman riding a motorcycle in NYC, movie poster style" \
-  --height 1024 \
-  --width 1024 \
-  --num-inference-steps 50 \
-  --text-guidance-scale 4.0 \
-  --out output.png
-```
+Text-to-image now runs through the shared offline image example
+(`examples/offline_inference/text_to_image/text_to_image.py`). See the recipe
+`recipes/MammothModa2/MammothModa2-Preview.md` for the full command and the
+`extra_body` knobs (`text_guidance_scale`, `cfg_range`, `num_inference_steps`).
 
 ### Image Summary
 
 ```bash
 python examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_summarize.py \
   --model ./MammothModa2-Preview \
-  --stage-config ./vllm_omni/model_executor/stage_configs/mammoth_moda2_ar.yaml \
+  --deploy-config ./vllm_omni/deploy/mammoth_moda2_ar.yaml \
   --question "Summarize this image." \
   --image ./image.png
 ```
@@ -40,7 +33,3 @@ python examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_s
     ``````py
     --8<-- "examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_summarize.py"
     ``````
-??? abstract "run_mammothmoda2_t2i.py"
-    ``````py
-    --8<-- "examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_t2i.py"
-    ``````

docs/user_guide/examples/offline_inference/mimo_audio.md

@@ -1,4 +1,4 @@
-# MiMo-Audio Offline Inference
+# MiMo-Audio: Offline inference
 
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/mimo_audio>.
 

docs/user_guide/examples/offline_inference/qwen2_5_omni.md

@@ -1,4 +1,4 @@
-# Qwen2.5-Omni
+# Qwen2.5-Omni: Offline inference
 
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen2_5_omni>.
 

docs/user_guide/examples/offline_inference/qwen3_omni.md

@@ -1,4 +1,4 @@
-# Qwen3-Omni
+# Qwen3-Omni: Offline inference
 
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen3_omni>.
 

docs/user_guide/examples/offline_inference/text_to_image.md

@@ -160,9 +160,11 @@ python examples/offline_inference/text_to_image/text_to_image.py \
   --output flux2-dev.png
 ```
 
-### Batch Requests (Multiple Prompts)
+### Multiple Prompts
 
-You can pass multiple prompts in a single `generate` call.
+You can pass multiple prompts in a single `generate` call. For diffusion
+pipelines, each prompt is submitted as a separate logical request; compatible
+requests may be automatically batched by the scheduler and runner.
 
 ```python
 from vllm_omni.entrypoints.omni import Omni
@@ -181,17 +183,8 @@ if __name__ == "__main__":
 
 !!! info
 
-    Not all models support batch inference, and batch requesting mostly does not provide significant
-    performance improvement. This feature is primarily for interface compatibility with vLLM and to
-    allow for future improvements.
-
-!!! info
-
-    For diffusion pipelines, the stage config field `stage_args.[].runtime.max_batch_size` is 1 by
-    default, and the input list is sliced into single-item requests before feeding into the diffusion
-    pipeline. For models that do internally support batched inputs, you can
-    [modify this configuration](https://github.com/vllm-project/vllm-omni/tree/main/configuration/stage_configs.md) to let the model accept a
-    longer batch of prompts.
+    For diffusion request-level batching controls such as `max_num_seqs`, see
+    [Request-Level Batching](../../diffusion/request_batching.md).
 
 ### Negative Prompts
 

docs/user_guide/examples/offline_inference/text_to_video.md

@@ -12,6 +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 |
+| `dg845/LTX-2.3-Diffusers` | 384x512 | 25 | 20 | 4.0 | 96GB-class GPU |
 | `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 |
 
@@ -50,6 +51,30 @@ python text_to_video.py \
   --output ltx2_out.mp4
 ```
 
+### LTX-2.3
+
+```bash
+python text_to_video.py \
+  --model dg845/LTX-2.3-Diffusers \
+  --model-class-name LTX23Pipeline \
+  --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
+```
+
+Use a Diffusers-format checkpoint such as `dg845/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.
+
 ### HunyuanVideo-1.5 (480p)
 
 ```bash
@@ -110,6 +135,8 @@ 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.
 - `--prompt`: text description (string).
 - `--height/--width`: output resolution. Default depends on model.
 - `--num-frames`: number of frames. Default depends on model.
@@ -124,7 +151,8 @@ 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).
+- `--audio-sample-rate`: audio sample rate for embedded audio (when the
+  pipeline returns audio; LTX-2.3 outputs 48kHz audio).
 - `--quantization`: quantization method (`fp8` for FP8, `gguf` for GGUF).
 - `--flow-shift`: scheduler flow_shift parameter.
 

docs/user_guide/examples/online_serving/aura_omni.md

@@ -1,4 +1,4 @@
-# AURA Omni Pipeline
+# AURA Omni: Online serving
 
 `aura_omni` wires ASR, AURA, and Qwen3-TTS into one vLLM-Omni pipeline:
 

docs/user_guide/examples/online_serving/diffusers_pipeline_adapter.md

@@ -9,7 +9,8 @@ vLLM-Omni supports running diffusion models with the diffusers backend, directly
 
 The diffusers backend is a black-box adapter. Its primary focus is to serve diffusion models online.
 Currently, the following features are NOT yet supported.
-It is not guaranteed whether they will be supported in the future.
+Community contributions are welcome to add these features when they can be
+implemented with clear Diffusers or vLLM-Omni behavior.
 
 - CFG parallel execution
 - Sequence parallel execution
@@ -66,6 +67,35 @@ This is suitable for sampling parameters not available through the vLLM-Omni int
 When a parameter is available in the vLLM-Omni interface, it will be adapted here.
 But if that parameter is simultaneously set in both the vLLM-Omni interface and `diffusers_call_kwargs`, the **former** will take precedence (because it is set at request time).
 
+### Quantization
+
+Use `diffusers_load_kwargs` for Diffusers-native quantization options. If
+`diffusers_load_kwargs["quantization_config"]` is provided as a dictionary, the
+diffusers backend builds a Diffusers quantization config and lets Diffusers
+validate it before calling `DiffusionPipeline.from_pretrained()`.
+
+The diffusers backend also provides a small compatibility shortcut for
+vLLM-Omni quantization configs when `diffusers_load_kwargs` does not already
+contain `quantization_config`. Currently this maps online/dynamic `fp8` and
+online/dynamic `int8` to Diffusers/TorchAO dynamic quantization for transformer
+components such as `transformer` or `transformer_2`. This path requires
+`torchao` to be installed.
+
+For example, the CLI can request dynamic FP8 through the vLLM-Omni interface:
+
+```bash
+vllm serve "Qwen/Qwen-Image" \
+    --omni \
+    --diffusion-load-format diffusers \
+    --quantization-config '{"method": "fp8"}'
+```
+
+Other vLLM-Omni quantization methods, such as `gguf`, `modelopt`, `mxfp4`,
+`mxfp8`, serialized checkpoints, static FP8 configs, and layer-name skip lists
+such as `ignored_layers`, are intentionally not translated by this compatibility
+shortcut. Use Diffusers-native configuration through `diffusers_load_kwargs` or
+a native vLLM-Omni pipeline for those cases.
+
 ### Attention Backends
 
 The diffusers backend converts

docs/user_guide/examples/online_serving/mimo_audio.md

@@ -1,4 +1,4 @@
-# Online serving Example of vLLM-Omni for MiMo-Audio
+# MiMo-Audio: Online serving
 
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/mimo_audio>.
 

docs/user_guide/examples/online_serving/qwen2_5_omni.md

@@ -1,4 +1,4 @@
-# Qwen2.5-Omni
+# Qwen2.5-Omni: Online serving
 
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen2_5_omni>.
 

docs/user_guide/examples/online_serving/qwen3_omni.md

@@ -1,4 +1,4 @@
-# Qwen3-Omni
+# Qwen3-Omni: Online serving
 
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen3_omni>.
 

docs/user_guide/examples/online_serving/text_to_audio.md

@@ -139,7 +139,7 @@ Content-Type: application/json
 |-----------|------|---------|-------------|
 | `input` | string | **required** | Text prompt describing the audio to generate |
 | `model` | string | server's model | Model to use (optional, should match server if specified) |
-| `response_format` | string | "wav" | Audio format: wav, mp3, flac, pcm, aac, opus |
+| `response_format` | string | "wav" | Audio format: wav, mp3, flac, pcm, opus |
 | `speed` | float | 1.0 | Playback speed (0.25 - 4.0) |
 | `audio_length` | float | null | Audio duration in seconds (max ~47s for `stable-audio-open-1.0`) |
 | `audio_start` | float | 0.0 | Audio start time in seconds |

docs/user_guide/examples/online_serving/text_to_speech.md

@@ -79,7 +79,7 @@ response = client.audio.speech.create(
 response.stream_to_file("output.wav")
 ```
 
-Streaming PCM output (where supported) — set `stream=true` with `response_format="pcm"`:
+Streaming PCM output (where supported) — set `stream=true`, `stream_format="audio"`, and `response_format="pcm"`:
 
 ```bash
 curl -X POST http://localhost:8091/v1/audio/speech \
@@ -88,6 +88,7 @@ curl -X POST http://localhost:8091/v1/audio/speech \
         "input": "Hello, how are you?",
         "voice": "default",
         "stream": true,
+        "stream_format": "audio",
         "response_format": "pcm"
     }' --no-buffer | play -t raw -r 24000 -e signed -b 16 -c 1 -
 ```
@@ -414,10 +415,11 @@ curl -X POST http://localhost:8091/v1/audio/speech \
         "voice": "vivian",
         "language": "English",
         "stream": true,
+        "stream_format": "audio",
         "response_format": "pcm"
     }' --no-buffer | play -t raw -r 24000 -e signed -b 16 -c 1 -
 ```
-Streaming requires `response_format="pcm"` and `async_chunk: true` on the stage config (default in `qwen3_tts.yaml`). `speed` is not supported when streaming.
+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:
@@ -496,7 +498,7 @@ After startup, `/v1/audio/voices` lists `alice`, and `/v1/audio/speech` can use
 ```bash
 python voxcpm2/gradio_demo.py
 ```
-Uses an AudioWorklet-based player adapted from the Qwen3-TTS demo for gap-free playback. Audio is streamed from the OpenAI Speech endpoint with `stream=true`.
+Uses an AudioWorklet-based player adapted from the Qwen3-TTS demo for gap-free playback. Raw PCM audio is streamed from the OpenAI Speech endpoint with `stream=true` and `stream_format="audio"`.
 
 ---
 

docs/user_guide/quantization/autoround.md

@@ -3,7 +3,7 @@
 ## Overview
 
 [AutoRound](https://github.com/intel/auto-round) produces pre-quantized
-checkpoints for LLMs, VLMs, and diffusion models. vLLM-Omni reads the
+checkpoints for LLMs, VLMs, diffusion models, and world models. vLLM-Omni reads the
 checkpoint's `config.json` and auto-detects
 `quantization_config.quant_method = "auto-round"`.
 
@@ -36,6 +36,13 @@ guide. AutoRound is Intel-supported.
 | Wan2.2-T2V | `Intel/Wan2.2-T2V-A14B-Diffusers-int4-AutoRound` | Diffusion transformer | W4A16 | GPTQ-Marlin or Intel-supported AutoRound backend |
 | Wan2.2-TI2V | `Intel/Wan2.2-TI2V-5B-Diffusers-int4-AutoRound` | Diffusion transformer | W4A16 | GPTQ-Marlin or Intel-supported AutoRound backend |
 
+### World Model (Cosmos3)
+
+| Model | Checkpoint | Scope | Scheme | Backend |
+|-------|------------|-------|--------|---------|
+| Cosmos3-Nano | `Intel/Cosmos3-Nano-int4-AutoRound` | World-model transformer | W4A16 | GPTQ-Marlin or Intel-supported AutoRound backend |
+| Cosmos3-Super | `Intel/Cosmos3-Super-int4-AutoRound` | World-model transformer | W4A16 | GPTQ-Marlin or Intel-supported AutoRound backend |
+
 ### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)
 
 | Model | Checkpoint | Scope | Scheme | Backend |

docs/user_guide/quantization/bitsandbytes.md

@@ -0,0 +1,120 @@
+# BitsAndBytes W4 Quantization
+
+## Overview
+
+BitsAndBytes 4-bit quantization supports weight-only NF4/FP4 diffusion transformer
+inference on CUDA GPUs. It quantizes BF16/FP16 weights at load time; activations
+stay in BF16/FP16. No pre-quantized checkpoint is required.
+
+This is an online (dynamic) path: load the normal HuggingFace checkpoint and
+quantize during model loading via the `bitsandbytes` CUDA kernels.
+
+## Hardware Support
+
+| Device | Support |
+|--------|---------|
+| NVIDIA CUDA GPU (SM 75+) | ✅ |
+| NVIDIA Blackwell GPU (SM 100+) | ✅ |
+| NVIDIA Ada/Hopper GPU (SM 89+) | ✅ |
+| NVIDIA Ampere GPU (SM 80+) | ✅ |
+| AMD ROCm | ❌ |
+| Intel XPU | ❌ |
+| Ascend NPU | ❌ |
+
+Legend: `✅` supported, `❌` unsupported. Non-CUDA platforms raise in
+`get_quant_method()`; CUDA requires compute capability 7.5+ (`SM 75+`).
+
+Requires the optional `bitsandbytes` package (`pip install bitsandbytes`).
+
+## Model Type Support
+
+### Diffusion Model (Qwen-Image, Wan2.2)
+
+| Model | HF models | CUDA | Mode | Recommendation |
+|-------|-----------|:----:|------|----------------|
+| Z-Image | `Tongyi-MAI/Z-Image-Turbo` | Yes | Online W4 weight-only | All heavy linear layers; sensitive embedders stay BF16 |
+| Qwen-Image | `Qwen/Qwen-Image`, `Qwen/Qwen-Image-2512` | Not validated | Online W4 weight-only | Compare vs BF16 before enabling |
+| Wan2.2 | Wan2.2 diffusion pipelines | Not validated | Online W4 weight-only | Validate before enabling in docs |
+
+Other diffusion models may work if their transformer uses supported linear
+layers, but they are not validated in this guide.
+
+### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)
+
+| Model | Scope | Status | Notes |
+|-------|-------|--------|-------|
+| Qwen3-Omni | Thinker language-model stage | Not validated | Prefer checkpoint-supported ModelOpt FP8 or AutoRound paths |
+| Qwen3-TTS | TTS language-model stage | Not validated | No BitsAndBytes TTS stage support is documented |
+
+### Multi-Stage Diffusion Model (BAGEL, GLM-Image)
+
+| Model | Scope | Status | Notes |
+|-------|-------|--------|-------|
+| BAGEL | Stage-specific transformer or DiT module | Not validated | Requires explicit stage routing |
+| GLM-Image | Stage-specific transformer or DiT module | Not validated | Requires quality comparison with BF16 |
+
+## Configuration
+
+Python API:
+
+```python
+from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+omni = Omni(model="Tongyi-MAI/Z-Image-Turbo", quantization="bitsandbytes")
+
+omni_with_skips = Omni(
+    model="Tongyi-MAI/Z-Image-Turbo",
+    quantization_config={
+        "method": "bitsandbytes",
+        "ignored_layers": ["to_out"],
+    },
+)
+
+outputs = omni.generate(
+    "A cup of coffee on the table",
+    OmniDiffusionSamplingParams(num_inference_steps=50),
+)
+```
+
+CLI:
+
+```bash
+python text_to_image.py --model Tongyi-MAI/Z-Image-Turbo --quantization bitsandbytes
+python text_to_image.py --model Tongyi-MAI/Z-Image-Turbo --quantization bitsandbytes --ignored-layers "to_out"
+vllm serve Tongyi-MAI/Z-Image-Turbo --omni --quantization bitsandbytes
+```
+
+## Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `method` | str | - | Quantization method (`"bitsandbytes"`) |
+| `quant_type` | str | `"nf4"` | 4-bit data type: `"nf4"` (recommended) or `"fp4"` |
+| `compress_statistics` | bool | `True` | Double-quantize block scaling statistics for better accuracy |
+| `ignored_layers` | list[str] | `[]` | Layer name patterns to keep in BF16/FP16 |
+
+## Validation and Notes
+
+On Z-Image-Turbo (single GPU, 1024×1024, 50 steps), BitsAndBytes W4 typically
+reduces peak VRAM from roughly 24.5 GiB (BF16) to roughly 17 GiB. Compare output
+quality against a BF16 baseline before enabling on new models.
+
+Multi-GPU tensor parallelism (`tensor_parallel_size` > 1) is not validated for
+BitsAndBytes in diffusion models; each rank quantizes its own weight shard
+independently.
+
+If quality regresses, use `ignored_layers` to keep sensitive projections in BF16
+(for example `to_out` or `w2`).
+
+```python
+omni = Omni(
+    model="Tongyi-MAI/Z-Image-Turbo",
+    quantization="bitsandbytes",
+    cache_backend="tea_cache",
+    cache_config={"rel_l1_thresh": 0.2},
+)
+```
+
+Only add a new model to the supported table after comparing the BitsAndBytes
+output against a BF16 baseline and documenting any required `ignored_layers`.

docs/user_guide/quantization/fp8.md

@@ -44,10 +44,16 @@ pip install vllm-omni[quack]
 pip install 'quack-kernels[cu13]' --extra-index-url https://download.pytorch.org/whl/cu130
 ```
 
-It is enabled automatically once installed (no flag needed) and is **Blackwell-only**:
-on Hopper/Ada the CUTLASS FP8 kernel already fuses bias, so quack is not used there.
-Set `VLLM_OMNI_USE_QUACK_FP8=0` to force the FlashInfer path. If `quack-kernels` is
-not installed, FP8 still works — it just keeps the unfused FlashInfer path.
+It is enabled automatically once installed (no flag needed) and is **datacenter
+Blackwell only** (`sm_100` / `sm_101` / `sm_103`, compute capability `10.x`, e.g.
+B200): quack's CuteDSL GEMM uses the 5th-gen `tcgen05` tensor-core MMA, which exists
+only on those parts. On Hopper/Ada the CUTLASS FP8 kernel already fuses bias, and on
+workstation/consumer Blackwell (`sm_120` / `sm_121`, compute capability `12.x`, e.g.
+RTX PRO 6000 / RTX 50-series) `tcgen05` is absent — so quack is **not** auto-enabled
+there and FlashInfer's native FP8 path is used instead. Set
+`VLLM_OMNI_USE_QUACK_FP8=1` to force quack on, or `VLLM_OMNI_USE_QUACK_FP8=0` to force
+the FlashInfer path. If `quack-kernels` is not installed, FP8 still works — it just
+keeps the unfused FlashInfer path.
 
 #### Compile cache and warmup
 

docs/user_guide/quantization/gguf.md

@@ -9,6 +9,15 @@ for the transformer.
 
 GGUF is static quantization: the quantized weights are produced before serving.
 
+!!! note
+    GGUF support has migrated to OOT [vllm-gguf-plugin](https://github.com/vllm-project/vllm-gguf-plugin). Make sure you have GGUF plugin installed before serving a GGUF model.
+
+Before serving a GGUF model, make sure to install the [vllm-gguf-plugin](https://github.com/vllm-project/vllm-gguf-plugin):
+
+```bash
+uv pip install vllm-gguf-plugin>=0.0.3
+```
+
 ## Hardware Support
 
 | Device | Support |

docs/user_guide/quantization/mxfp4.md

@@ -39,7 +39,7 @@ vLLM-Omni provides two quantization methods with different scale structures:
 | NVIDIA Blackwell GPU (SM 100+) | ⭕ |
 | NVIDIA Ada/Hopper GPU (SM 89+) | ⭕ |
 | NVIDIA Ampere GPU (SM 80+) | ⭕ |
-| AMD ROCm | ⭕ |
+| AMD ROCm (gfx950 / MI355X) | ✅ |
 | Intel XPU | ⭕ |
 | Ascend NPU (Atlas 950 A5) | ✅ |
 

docs/user_guide/quantization/overview.md

@@ -20,7 +20,7 @@ type has a different quantization scope.
 | NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
 | NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
 | NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ⭕ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
-| AMD ROCm | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ❌ |
+| AMD ROCm | ⭕ | ⭕ | ⭕ | ⭕ | ✅ | ⭕ | ⭕ | ❌ |
 | Intel XPU | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
 | Ascend NPU | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
 

examples/model_display_names.yml

@@ -0,0 +1,12 @@
+# Canonical display names for model examples that have both offline and online pages.
+# The docs generator combines these names with the serving mode for each page H1.
+aura_omni: AURA Omni
+covo_audio: Covo-Audio-Chat
+dynin_omni: Dynin-Omni
+lance: Lance
+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/audiox/README.md

@@ -1,40 +0,0 @@
-# AudioX offline inference
-
-Generate audio with the [AudioX](https://zeyuet.github.io/AudioX/) MMDiT diffusion
-pipeline (`AudioXPipeline`). Six tasks: `t2a`, `t2m`, `v2a`, `v2m`, `tv2a`, `tv2m`.
-
-## Prerequisites
-
-Download a vLLM-Omni weight bundle (component-sharded safetensors):
-
-```bash
-huggingface-cli download zhangj1an/AudioX --local-dir ./audiox_weights
-```
-
-The Hugging Face id `zhangj1an/AudioX` also works directly without prefetching.
-
-## Usage
-
-```bash
-# Text-to-audio only (default uses zhangj1an/AudioX from the Hub):
-python end2end.py --tasks t2a
-
-# All six tasks against a local bundle and a sample video for v2*/tv2*:
-python end2end.py \
-  --model ./audiox_weights \
-  --video https://zeyuet.github.io/AudioX/static/samples/V2M/1XeBotOFqHA.mp4
-
-# Subset of tasks, custom seed and steps:
-python end2end.py --tasks t2a tv2a --num-inference-steps 100 --seed 0
-```
-
-## Arguments
-
-- `--model`: HF id or local bundle path (default: `zhangj1an/AudioX`).
-- `--tasks`: any subset of `t2a t2m v2a v2m tv2a tv2m` (default: all).
-- `--video`: video file/URL — required for `v2*` and `tv2*`.
-- `--reference-audio`: optional audio prompt (audio-conditioned generation).
-- `--num-inference-steps`, `--guidance-scale`, `--seed`, `--seconds-total`,
-  `--sample-rate`, `--output-dir`: generation knobs.
-
-Outputs land in `<output-dir>/<task>.wav` as 16-bit stereo WAV.

examples/offline_inference/audiox/end2end.py

@@ -1,110 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""End-to-end AudioX offline example covering the 6 t2*/v2*/tv2* tasks.
-
-Provide a directory with the **vLLM-Omni AudioX safetensors bundle** (e.g. from
-``zhangj1an/AudioX`` on Hugging Face)::
-
-    huggingface-cli download zhangj1an/AudioX --local-dir ./audiox_weights
-    python end2end.py --model ./audiox_weights
-    python end2end.py --model ./audiox_weights --tasks t2a tv2a
-"""
-
-from __future__ import annotations
-
-import argparse
-import time
-from pathlib import Path
-
-import soundfile
-import torch
-import torchaudio.functional as TF
-
-from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-from vllm_omni.platforms import current_omni_platform
-
-ROOT = Path(__file__).resolve().parent
-
-SAMPLE_PROMPTS: dict[str, str] = {
-    "t2a": "Fireworks burst twice, followed by a period of silence before a clock begins ticking.",
-    "t2m": "Uplifting ukulele tune for a travel vlog",
-    "v2a": "",
-    "v2m": "",
-    "tv2a": "drum beating sound and human talking",
-    "tv2m": "uplifting music matching the scene",
-}
-
-ALL_TASKS = ("t2a", "t2m", "v2a", "v2m", "tv2a", "tv2m")
-VIDEO_TASKS = frozenset({"v2a", "v2m", "tv2a", "tv2m"})
-TEXT_TASKS = frozenset({"t2a", "t2m", "tv2a", "tv2m"})
-
-
-def parse_args() -> argparse.Namespace:
-    p = argparse.ArgumentParser(description="AudioX offline end-to-end (6 t2*/v2*/tv2* tasks).")
-    p.add_argument("--model", default="zhangj1an/AudioX", help="HF id or local AudioX bundle path.")
-    p.add_argument("--tasks", nargs="+", default=list(ALL_TASKS), choices=ALL_TASKS)
-    p.add_argument("--video", default="", help="Video path / URL (required for v2*/tv2*).")
-    p.add_argument("--reference-audio", default="", help="Optional audio prompt for audio-conditioned generation.")
-    p.add_argument("--output-dir", default=str(ROOT / "audiox_task_outputs"))
-    p.add_argument("--num-inference-steps", type=int, default=250)
-    p.add_argument("--seconds-total", type=float, default=10.0)
-    p.add_argument("--guidance-scale", type=float, default=6.0)
-    p.add_argument("--seed", type=int, default=42)
-    p.add_argument("--sample-rate", type=int, default=48000, help="Output WAV rate (resampled if != model rate).")
-    return p.parse_args()
-
-
-def save_wav(audio: torch.Tensor, path: Path, sample_rate: int) -> None:
-    """Write 16-bit PCM WAV. ``audio`` is ``[channels, samples]`` float in [-1, 1]."""
-    path.parent.mkdir(parents=True, exist_ok=True)
-    soundfile.write(str(path), audio.clamp(-1.0, 1.0).cpu().T.numpy(), sample_rate, subtype="PCM_16")
-
-
-def main() -> None:
-    args = parse_args()
-
-    omni = Omni(model=args.model, model_class_name="AudioXPipeline")
-
-    for task in args.tasks:
-        if task in VIDEO_TASKS and not args.video:
-            raise SystemExit(f"task={task!r} requires --video")
-        prompt = SAMPLE_PROMPTS[task] if task in TEXT_TASKS else ""
-        extra: dict = {"audiox_task": task, "seconds_start": 0.0, "seconds_total": float(args.seconds_total)}
-        if task in VIDEO_TASKS:
-            extra["video_path"] = args.video
-        if args.reference_audio:
-            extra["audio_path"] = args.reference_audio
-
-        generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
-        t0 = time.perf_counter()
-        outputs = omni.generate(
-            prompt,
-            OmniDiffusionSamplingParams(
-                generator=generator,
-                guidance_scale=args.guidance_scale,
-                num_inference_steps=args.num_inference_steps,
-                seed=args.seed,
-                extra_args=extra,
-            ),
-        )
-        audio = outputs[0].request_output.multimodal_output.get("audio")
-        if audio is None:
-            raise RuntimeError(f"No audio produced for task {task!r}")
-        audio = torch.as_tensor(audio).detach().cpu().float()
-        if audio.ndim == 3:
-            audio = audio[0]
-
-        model_sr = int(outputs[0].request_output.multimodal_output.get("audio_sample_rate") or 44100)
-        if model_sr != args.sample_rate:
-            audio = TF.resample(audio, model_sr, args.sample_rate)
-
-        out_path = Path(args.output_dir) / f"{task}.wav"
-        save_wav(audio, out_path, args.sample_rate)
-        print(f"[{task}] saved {out_path} ({time.perf_counter() - t0:.2f}s)")
-
-    omni.close()
-
-
-if __name__ == "__main__":
-    main()

examples/offline_inference/aura_omni/README.md

@@ -1,4 +1,4 @@
-# AURA Omni
+# AURA Omni: Offline inference
 
 This example runs the native AURA Omni pipeline offline:
 

examples/offline_inference/covo_audio/README.md

@@ -1,4 +1,4 @@
-# Covo-Audio-Chat (Offline Inference)
+# Covo-Audio-Chat: Offline inference
 
 ## Setup
 

examples/offline_inference/custom_pipeline/image_to_image/custom_pipeline.py

@@ -2,14 +2,12 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import logging
-from typing import Any
 
-import PIL.Image
 import torch
 
-from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.data import OmniDiffusionConfig
 from vllm_omni.diffusion.models.qwen_image.pipeline_qwen_image_edit import QwenImageEditPipeline
-from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 
 logger = logging.getLogger(__name__)
 
@@ -18,58 +16,14 @@ class CustomPipeline(QwenImageEditPipeline):
     def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = ""):
         super().__init__(od_config=od_config, prefix=prefix)
 
-    def forward(
-        self,
-        req: OmniDiffusionRequest,
-        prompt: str | list[str] | None = None,
-        negative_prompt: str | list[str] | None = None,
-        image: PIL.Image.Image | torch.Tensor | None = None,
-        true_cfg_scale: float = 4.0,
-        height: int | None = None,
-        width: int | None = None,
-        num_inference_steps: int = 50,
-        sigmas: list[float] | None = None,
-        guidance_scale: float = 1.0,
-        num_images_per_prompt: int = 1,
-        generator: torch.Generator | list[torch.Generator] | None = None,
-        latents: torch.Tensor | None = None,
-        prompt_embeds: torch.Tensor | None = None,
-        prompt_embeds_mask: torch.Tensor | None = None,
-        negative_prompt_embeds: torch.Tensor | None = None,
-        negative_prompt_embeds_mask: torch.Tensor | None = None,
-        output_type: str | None = "pil",
-        attention_kwargs: dict[str, Any] | None = None,
-        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
-        max_sequence_length: int = 512,
-    ) -> DiffusionOutput:
+    def forward(self, req: DiffusionRequestBatch):
         """Forward pass for image editing with dummy trajectory data."""
-        # Call parent's forward to get the normal output
-        output = super().forward(
-            req=req,
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            image=image,
-            true_cfg_scale=true_cfg_scale,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            sigmas=sigmas,
-            guidance_scale=guidance_scale,
-            num_images_per_prompt=num_images_per_prompt,
-            generator=generator,
-            latents=latents,
-            prompt_embeds=prompt_embeds,
-            prompt_embeds_mask=prompt_embeds_mask,
-            negative_prompt_embeds=negative_prompt_embeds,
-            negative_prompt_embeds_mask=negative_prompt_embeds_mask,
-            output_type=output_type,
-            attention_kwargs=attention_kwargs,
-            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
-            max_sequence_length=max_sequence_length,
-        )
+        # Customize num_inference_steps that best suits your model
+        actual_num_steps = req.sampling_params.num_inference_steps or 50
+        req.sampling_params.num_inference_steps = actual_num_steps
 
-        # Get actual num_inference_steps used
-        actual_num_steps = req.sampling_params.num_inference_steps or num_inference_steps
+        # Call parent's forward to get the normal output
+        output = super().forward(req=req)
 
         # Create dummy trajectory data
         dummy_trajectory_latents = torch.randn(actual_num_steps, 1, 16, 64, 64, dtype=torch.float32)

examples/offline_inference/dynin_omni/README.md

@@ -1,4 +1,4 @@
-# Dynin-Omni Offline End2End Example
+# Dynin-Omni: Offline inference
 
 This folder contains a unified offline inference entrypoint:
 

examples/offline_inference/image_to_image/image_edit.py

@@ -138,6 +138,15 @@ def parse_args() -> argparse.Namespace:
         required=True,
         help="Path(s) to input image file(s) (PNG, JPG, etc.). Can specify multiple images.",
     )
+    parser.add_argument(
+        "--deploy-config",
+        type=str,
+        default=None,
+        help=(
+            "Path to a deploy YAML. Required for multi-stage image-edit pipelines "
+            "whose deploy config is not auto-loaded."
+        ),
+    )
     parser.add_argument(
         "--prompt",
         type=str,
@@ -349,7 +358,12 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--enforce-eager",
         action="store_true",
-        help="Disable torch.compile and force eager execution.",
+        default=None,
+        help=(
+            "Disable torch.compile and force eager execution. Left unset (None) "
+            "so it is only forwarded when explicitly given; "
+            "otherwise the per-stage deploy YAML value wins."
+        ),
     )
     parser.add_argument(
         "--vae-use-slicing",
@@ -468,7 +482,7 @@ def main():
         }
 
     # Initialize Omni with appropriate pipeline
-    omni = Omni(
+    omni_kwargs: dict[str, Any] = dict(
         model=args.model,
         enable_layerwise_offload=args.enable_layerwise_offload,
         vae_use_slicing=args.vae_use_slicing,
@@ -476,11 +490,15 @@ def main():
         cache_backend=args.cache_backend,
         cache_config=cache_config,
         parallel_config=parallel_config,
-        enforce_eager=args.enforce_eager,
         enable_cpu_offload=args.enable_cpu_offload,
         enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
         profiler_config=args.profiler_config,
     )
+    if args.enforce_eager is not None:
+        omni_kwargs["enforce_eager"] = args.enforce_eager
+    if args.deploy_config:
+        omni_kwargs["deploy_config"] = args.deploy_config
+    omni = Omni(**omni_kwargs)
     model_class_name = get_model_class_name(omni)
     declared_extra_body_params = get_extra_body_params(model_class_name)
     print("Pipeline loaded")

examples/offline_inference/image_to_video/README.md

@@ -1,16 +1,142 @@
 # Image-To-Video
 
-This shared example generates videos from images with VACE, Wan2.2, LTX2,
-HunyuanVideo-1.5, Cosmos3, and other compatible pipelines.
+This shared example generates videos from images with VACE, Wan2.2,
+LTX-2/LTX-2.3, HunyuanVideo-1.5, Cosmos3, and other compatible pipelines.
 
-## Local CLI Usage
+- `image_to_video.py`: command-line script for single video generation with advanced options.
 
-Download the example image:
+## Table of Contents
+
+- [Overview](#overview)
+- [Prerequisites](#prerequisites)
+- [Quick Start](#quick-start)
+- [Key Arguments](#key-arguments)
+- [More CLI Examples](#more-cli-examples)
+- [Advanced Features](#advanced-features)
+- [FAQ](#faq)
+
+## Overview
+
+This folder provides a unified CLI script for image-to-video generation using vLLM-Omni diffusion/video pipelines. The script selects practical defaults for supported model families while still exposing common sampling, memory, and parallelism options.
+
+### Supported Models
+
+| Model | Default Resolution | Default Frames | Default Steps | Guidance | VRAM Notes |
+| ----- | ------------------ | -------------- | ------------- | -------- | ---------- |
+| `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 |
+
+!!! 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.
+
+Default model: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`.
+
+## Prerequisites
+
+Download the example image used in the snippets below:
 
 ```bash
-wget https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg
+curl -L -o cherry_blossom.jpg https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg
 ```
 
+## Quick Start
+
+### Python API
+
+Single-prompt generation using TI2V-5B (lightweight):
+
+```python
+import PIL.Image
+import torch
+
+from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+if __name__ == "__main__":
+    image = PIL.Image.open("cherry_blossom.jpg").convert("RGB")
+    image = image.resize((576, 320))
+
+    omni = Omni(
+        model="Wan-AI/Wan2.2-TI2V-5B-Diffusers",
+        flow_shift=12.0,
+    )
+    outputs = omni.generate(
+        {
+            "prompt": "Cherry blossoms swaying gently in the breeze, petals falling",
+            "multi_modal_data": {"image": image},
+        },
+        OmniDiffusionSamplingParams(
+            height=320,
+            width=576,
+            num_frames=17,
+            num_inference_steps=20,
+            guidance_scale=4.0,
+            generator=torch.Generator(device="cuda").manual_seed(42),
+        ),
+    )
+    from diffusers.utils import export_to_video
+    frames = outputs[0].request_output.images
+    export_to_video(frames, "quick_test_i2v.mp4", fps=16)
+```
+
+### Local CLI Usage
+
+Quick test using TI2V-5B with a small resolution and few frames:
+
+```bash
+python image_to_video.py \
+  --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
+  --image cherry_blossom.jpg \
+  --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \
+  --height 320 \
+  --width 576 \
+  --num-frames 17 \
+  --guidance-scale 4.0 \
+  --num-inference-steps 20 \
+  --flow-shift 12.0 \
+  --fps 16 \
+  --output quick_test_i2v.mp4
+```
+
+## Key Arguments
+
+| 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`) |
+| `--image` | str | (required) | Path to input image |
+| `--prompt` | str | `""` | Text description of desired motion/animation |
+| `--negative-prompt` | str | `""` | Optional list of artifacts to suppress |
+| `--seed` | int | `42` | Random seed for deterministic sampling |
+| `--guidance-scale` | float | `5.0` | CFG scale |
+| `--guidance-scale-high` | float | `None` | Separate CFG for high-noise stage (MoE only) |
+| `--height` | int | auto | Video height (auto-calculated from image if not set). Multiples of 16 |
+| `--width` | int | auto | Video width (auto-calculated from image if not set). Multiples of 16 |
+| `--num-frames` | int | `81` | Number of frames |
+| `--num-inference-steps` | int | `50` | Number of denoising steps |
+| `--boundary-ratio` | float | `0.875` | Boundary split ratio for two-stage MoE models |
+| `--flow-shift` | float | `5.0` | Scheduler flow shift (5.0 for 720p, 12.0 for 480p) |
+| `--sample-solver` | str | `unipc` | Wan2.2 sampling solver (`unipc` or `euler` for Lightning/Distill) |
+| `--fps` | int | `None` | Frames per second for the saved MP4 |
+| `--frame-rate` | float | `None` | Generation frame rate for pipelines that require it (e.g., LTX2) |
+| `--output` | str | `i2v_output.mp4` | Path to save the generated video |
+| `--vae-use-slicing` | flag | off | Enable VAE slicing for memory optimization |
+| `--vae-use-tiling` | flag | off | Enable VAE tiling for memory optimization |
+| `--enable-cpu-offload` | flag | off | Enable CPU offloading for diffusion models |
+| `--enable-layerwise-offload` | flag | off | Enable layerwise offloading on DiT modules |
+| `--cfg-parallel-size` | int | `1` | Set to `2` to enable CFG Parallel |
+| `--tensor-parallel-size` | int | `1` | Tensor parallel size (effective for models that support TP, e.g. LTX2) |
+| `--ulysses-degree` | int | `1` | Ulysses sequence parallel degree |
+| `--ring-degree` | int | `1` | Ring sequence parallel degree |
+| `--cache-backend` | str | `None` | Cache backend: `cache_dit` or `tea_cache` |
+| `--use-hsdp` | flag | off | Enable Hybrid Sharded Data Parallel |
+| `--hsdp-shard-size` | int | `-1` | GPUs per shard group (-1 auto-calculates) |
+| `--hsdp-replicate-size` | int | `1` | Number of replica groups for HSDP |
+
+## More CLI Examples
+
 ### Wan2.2-I2V-A14B-Diffusers (MoE)
 
 ```bash
@@ -18,7 +144,7 @@ python image_to_video.py \
   --model Wan-AI/Wan2.2-I2V-A14B-Diffusers \
   --image cherry_blossom.jpg \
   --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \
-  --negative-prompt "<optional quality filter>" \
+  --negative-prompt "low quality, blurry" \
   --height 480 \
   --width 832 \
   --num-frames 48 \
@@ -28,7 +154,7 @@ python image_to_video.py \
   --boundary-ratio 0.875 \
   --flow-shift 12.0 \
   --fps 16 \
-  --output i2v_output.mp4
+  --output i2v_wan_moe.mp4
 ```
 
 ### Wan2.2-TI2V-5B-Diffusers (Unified)
@@ -38,7 +164,7 @@ python image_to_video.py \
   --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
   --image cherry_blossom.jpg \
   --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \
-  --negative-prompt "<optional quality filter>" \
+  --negative-prompt "low quality, blurry" \
   --height 480 \
   --width 832 \
   --num-frames 48 \
@@ -46,9 +172,91 @@ python image_to_video.py \
   --num-inference-steps 40 \
   --flow-shift 12.0 \
   --fps 16 \
-  --output i2v_output.mp4
+  --output i2v_wan_ti2v.mp4
 ```
 
+### HunyuanVideo-1.5 I2V (480p)
+
+```bash
+python image_to_video.py \
+  --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v \
+  --image cherry_blossom.jpg \
+  --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \
+  --height 480 \
+  --width 832 \
+  --num-frames 121 \
+  --guidance-scale 6.0 \
+  --flow-shift 5.0 \
+  --num-inference-steps 50 \
+  --fps 24 \
+  --enable-cpu-offload \
+  --vae-use-tiling \
+  --vae-use-slicing \
+  --output hunyuan_i2v.mp4
+```
+
+### LTX2 Image-to-Video
+
+```bash
+python image_to_video.py \
+  --model /path/to/LTX-2 \
+  --model-class-name LTX2ImageToVideoPipeline \
+  --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
+```
+
+## Advanced Features
+
+### CFG Parallel
+
+Set `--cfg-parallel-size 2` to enable CFG Parallel for faster inference on multi-GPU setups.
+See more examples in the [cfg_parallel user guide](../../../docs/user_guide/diffusion/parallelism/cfg_parallel.md).
+
+### Cache Acceleration
+
+Use `--cache-backend cache_dit` for Cache-DiT acceleration or `--cache-backend tea_cache` for Timestep Embedding Aware Cache:
+
+```bash
+python image_to_video.py \
+  --model Wan-AI/Wan2.2-I2V-A14B-Diffusers \
+  --image cherry_blossom.jpg \
+  --prompt "Cherry blossoms swaying gently in the breeze" \
+  --cache-backend cache_dit \
+  --output i2v_cached.mp4
+```
+
+### LTX-2.3
+
+```bash
+python image_to_video.py \
+  --model dg845/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 a Diffusers-format checkpoint such as `dg845/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
@@ -66,15 +274,19 @@ python image_to_video.py \
 
 Key arguments:
 
-- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V).
+- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V,
+  LTX-2/LTX-2.3, 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.
 - `--reference-image`: Optional reference image; repeat it to provide multiple references.
 - `--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.
 - `--prompt`: Text description of desired motion/animation.
-- `--height/--width`: Output resolution (auto-calculated from image if not set). Dimensions should be multiples of 16.
-- `--num-frames`: Number of frames (default: model-specific — Wan 81, LTX2 121, Cosmos3 189).
+- `--height/--width`: Output resolution (auto-calculated from image if not set).
+  Wan dimensions should be multiples of 16; LTX dimensions should be multiples
+  of 32.
+- `--num-frames`: Number of frames (model-specific default; LTX-style models
+  work best with `8k + 1`; Cosmos3 defaults to 189).
 - `--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.
@@ -82,6 +294,7 @@ Key arguments:
 - `--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).
+- `--audio-sample-rate`: fallback audio sample rate for embedded audio.
 - `--output`: Path to save the generated video.
 - `--vae-use-slicing`: Enable VAE slicing for memory optimization.
 - `--vae-use-tiling`: Enable VAE tiling for memory optimization.
@@ -191,3 +404,11 @@ The VACE T2V command is documented in the shared
 
 For Wan2.2 LightX2V-converted local Diffusers directories and related LoRA
 assets, see the [LoRA guide](../../../docs/user_guide/diffusion/lora.md#wan22-lightx2v-offline-assembly).
+
+## FAQ
+
+**OOM errors**: Try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage. For very large models, add `--enable-cpu-offload` or `--enable-layerwise-offload`.
+
+**Auto-calculated resolution**: If `--height` and `--width` are not provided, the script calculates output dimensions from the input image while maintaining aspect ratio and targeting 480 x 832 area (or 512 x 768 for LTX2).
+
+**Wan2.2 MoE vs unified**: `I2V-A14B` is a larger Mixture-of-Experts model with separate low/high-noise DiT stages (use `--guidance-scale-high` and `--boundary-ratio`). `TI2V-5B` is a smaller unified T2V+I2V model that does not need these extra arguments.

examples/offline_inference/image_to_video/image_to_video.py

@@ -2,7 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 """
-Image-to-Video generation example using Wan2.2 I2V/TI2V models, LTX2, HunyuanVideo-1.5, or Wan2.1 VACE.
+Image-to-Video generation example using Wan2.2 I2V/TI2V models, LTX2/LTX-2.3,
+HunyuanVideo-1.5, Cosmos3, or Wan2.1 VACE.
 
 Supports:
 - Wan2.2-I2V-A14B-Diffusers: MoE model with CLIP image encoder
@@ -27,6 +28,13 @@ Usage:
         --num-frames 121 --num-inference-steps 40 --guidance-scale 4.0 \
         --frame-rate 24 --fps 24 --output ltx2_i2v.mp4
 
+    # LTX-2.3 image-to-video
+    python image_to_video.py --model dg845/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 \
+        --frame-rate 24 --fps 24 --output ltx23_i2v.mp4
+
     # HunyuanVideo-1.5 I2V (480p)
     python image_to_video.py --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v \
         --image input.jpg --prompt "A cat playing with yarn" \
@@ -79,17 +87,20 @@ parse_profiler_config = functools.partial(parse_json_object, flag_name="--profil
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
-        description="Generate a video from one or more images (Wan2.2, LTX2, HunyuanVideo-1.5, or Wan2.1 VACE)."
+        description=(
+            "Generate a video from one or more images "
+            "(Wan2.2, LTX2/LTX-2.3, HunyuanVideo-1.5, Cosmos3, or Wan2.1 VACE)."
+        )
     )
     parser.add_argument(
         "--model",
         default="Wan-AI/Wan2.2-I2V-A14B-Diffusers",
-        help="Diffusers I2V model ID or local path (Wan2.2, HunyuanVideo-1.5, or Wan2.1 VACE).",
+        help="Diffusers I2V model ID or local path (Wan2.2, LTX2/LTX-2.3, HunyuanVideo-1.5, Cosmos3, or Wan2.1 VACE).",
     )
     parser.add_argument(
         "--model-class-name",
         default=None,
-        help="Override model class name (e.g., LTX2ImageToVideoPipeline).",
+        help="Override model class name (e.g., LTX2ImageToVideoPipeline or LTX23ImageToVideoPipeline).",
     )
     parser.add_argument("--image", help="Path to the first-frame or source image.")
     parser.add_argument("--last-image", help="Path to a last-frame condition (used by models such as VACE).")
@@ -113,7 +124,10 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--width", type=int, default=None, help="Video width (auto-calculated from image if not set).")
     parser.add_argument("--num-frames", type=int, default=None, help="Number of frames. Default: model-specific.")
     parser.add_argument(
-        "--num-inference-steps", type=int, default=None, help="Sampling steps. Default: model-specific."
+        "--num-inference-steps",
+        type=int,
+        default=None,
+        help="Sampling steps. Default: model-specific.",
     )
     parser.add_argument("--boundary-ratio", type=float, default=0.875, help="Boundary split ratio for MoE models.")
     parser.add_argument(
@@ -206,7 +220,7 @@ def parse_args() -> argparse.Namespace:
         "--quantization",
         type=str,
         default=None,
-        choices=["fp8", "mxfp8", "mxfp4", "mxfp4_dualscale", "int8", "gguf"],
+        choices=["fp8", "mxfp8", "mxfp4", "mxfp4_dualscale", "int8"],
         help="Quantization method for the transformer. mxfp8: W8A8 MXFP8 (NPU). mxfp4: W4A4 MXFP4 (NPU). mxfp4_dualscale: W4A4 MXFP4 dual-scale + BF16 fallback mixed (NPU). fp8: online FP8 (GPU).",
     )
 
@@ -312,10 +326,8 @@ 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 = "ltx2" in model_name or (model_class_name and "ltx2" in model_class_name.lower())
+    is_ltx2 = model_class_name in {"LTX2ImageToVideoPipeline", "LTX23ImageToVideoPipeline"}
     is_cosmos = "cosmos" in model_name or (model_class_name is not None and "cosmos" in model_class_name.lower())
-    if model_class_name is None and is_ltx2:
-        model_class_name = "LTX2ImageToVideoPipeline"
 
     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
@@ -340,7 +352,15 @@ def main():
             16,
         )
     elif is_ltx2:
-        d_fps, d_guidance, d_num_frames, d_steps, d_flow_shift, d_max_area, d_mod = 24, 4.0, 121, 40, 5.0, 512 * 768, 32
+        d_fps, d_guidance, d_num_frames, d_steps, d_flow_shift, d_max_area, d_mod = (
+            24,
+            4.0,
+            121,
+            40,
+            5.0,
+            512 * 768,
+            32,
+        )
     else:  # Wan2.2 / HunyuanVideo-1.5
         d_fps, d_guidance, d_num_frames, d_steps, d_flow_shift, d_max_area, d_mod = 16, 5.0, 81, 50, 5.0, 480 * 832, 16
 
@@ -462,7 +482,7 @@ def main():
         f" tensor_parallel_size={args.tensor_parallel_size}, vae_patch_parallel_size={args.vae_patch_parallel_size},"
         f" pipeline_parallel_size={args.pipeline_parallel_size}"
     )
-    print(f"  Video size: {args.width}x{args.height}")
+    print(f"  Video size: {width}x{height}")
     print(f"{'=' * 60}\n")
 
     prompt_dict = build_image_to_video_prompt(
@@ -509,6 +529,7 @@ def main():
     print(f"Total generation time: {generation_time:.4f} seconds ({generation_time * 1000:.2f} ms)")
 
     audio = None
+    audio_sample_rate = args.audio_sample_rate
     if isinstance(frames, list):
         frames = frames[0] if frames else None
 
@@ -519,11 +540,13 @@ def main():
             )
         if frames.multimodal_output and "audio" in frames.multimodal_output:
             audio = frames.multimodal_output["audio"]
+            audio_sample_rate = frames.multimodal_output.get("audio_sample_rate", audio_sample_rate)
         if frames.is_pipeline_output and frames.request_output is not None:
             inner_output = frames.request_output
             if isinstance(inner_output, OmniRequestOutput):
                 if inner_output.multimodal_output and "audio" in inner_output.multimodal_output:
                     audio = inner_output.multimodal_output["audio"]
+                    audio_sample_rate = inner_output.multimodal_output.get("audio_sample_rate", audio_sample_rate)
                 frames = inner_output
         if isinstance(frames, OmniRequestOutput):
             if frames.images:
@@ -531,6 +554,7 @@ def main():
                     frames, audio = frames.images[0]
                 elif len(frames.images) == 1 and isinstance(frames.images[0], dict):
                     audio = frames.images[0].get("audio")
+                    audio_sample_rate = frames.images[0].get("audio_sample_rate", audio_sample_rate)
                     frames = frames.images[0].get("frames") or frames.images[0].get("video")
                 else:
                     frames = frames.images
@@ -543,6 +567,7 @@ def main():
             frames, audio = first_item
         elif isinstance(first_item, dict):
             audio = first_item.get("audio")
+            audio_sample_rate = first_item.get("audio_sample_rate", audio_sample_rate)
             frames = first_item.get("frames") or first_item.get("video")
         elif isinstance(first_item, list):
             frames = first_item
@@ -551,6 +576,7 @@ def main():
         frames, audio = frames
     elif isinstance(frames, dict):
         audio = frames.get("audio")
+        audio_sample_rate = frames.get("audio_sample_rate", audio_sample_rate)
         frames = frames.get("frames") or frames.get("video")
 
     if frames is None:
@@ -669,7 +695,7 @@ def main():
             frames_u8,
             audio_np,
             fps=float(fps),
-            audio_sample_rate=args.audio_sample_rate,
+            audio_sample_rate=audio_sample_rate,
         )
         with open(str(output_path), "wb") as f:
             f.write(video_bytes)

examples/offline_inference/lance/README.md

@@ -1,4 +1,4 @@
-# Lance (ByteDance) — offline inference
+# Lance: Offline inference
 
 [Lance](https://huggingface.co/bytedance-research/Lance) is a 3B unified
 autoregressive + diffusion multimodal model on a Qwen2.5-VL backbone. It is

examples/offline_inference/lance/end2end.py

@@ -293,16 +293,19 @@ def main():
 
     if args.modality in ("img2text", "video2text"):
         for i, req_output in enumerate(outputs):
-            text = getattr(req_output, "output", None) or getattr(req_output, "text", None)
-            if text is None and hasattr(req_output, "custom_output"):
-                text = (req_output.custom_output or {}).get("text_output")
+            multimodal_output = getattr(req_output, "multimodal_output", {}) or {}
+            metadata = multimodal_output.get("metadata", {}) if isinstance(multimodal_output, dict) else {}
+            text_metadata = metadata.get("text", {}) if isinstance(metadata, dict) else {}
+            text = (multimodal_output.get("text") if isinstance(multimodal_output, dict) else None) or (
+                text_metadata.get("text_output") if isinstance(text_metadata, dict) else None
+            )
+            text = text or getattr(req_output, "output", None) or getattr(req_output, "text", None)
             print(f"[Output {i}] {text}")
         return
 
     if args.modality in ("text2video", "video2video", "image2video"):
         for i, req_output in enumerate(outputs):
-            custom = getattr(req_output, "custom_output", None) or {}
-            frames = custom.get("video_frames")
+            frames = getattr(req_output, "images", None)
             if not frames:
                 print(f"[Output {i}] no video frames returned")
                 continue

examples/offline_inference/lance/gradio_demo.py

@@ -835,12 +835,15 @@ def run_task(
     print(f"[unified] task={task} {width}x{height} frames={num_frames} cfg={cfg} steps={steps} seed={seed}", flush=True)
 
     outputs = list(omni.generate(prompts=formatted, sampling_params_list=params))
-    custom = getattr(outputs[0], "custom_output", None) or {}
-    # Lance/Bagel return text at outputs[0].output (string) or
-    # custom_output["text_output"].
+    multimodal_output = getattr(outputs[0], "multimodal_output", {}) or {}
+    metadata = multimodal_output.get("metadata", {}) if isinstance(multimodal_output, dict) else {}
+    text_metadata = metadata.get("text", {}) if isinstance(metadata, dict) else {}
     text_out = None
     if task in {"x2t_image", "x2t_video"}:
-        text_out = custom.get("text_output") or getattr(outputs[0], "output", None)
+        text_out = (multimodal_output.get("text") if isinstance(multimodal_output, dict) else None) or (
+            text_metadata.get("text_output") if isinstance(text_metadata, dict) else None
+        )
+        text_out = text_out or getattr(outputs[0], "output", None)
         if not isinstance(text_out, str):
             text_out = None
 
@@ -856,13 +859,11 @@ def run_task(
             gr.update(value=str(text_out or "(empty)"), visible=True),
         )
     if task in {"t2v", "i2v", "video_edit"}:
-        frames = custom.get("video_frames")
+        frames = getattr(outputs[0], "images", None)
         if frames is None:
             raise gr.Error("No video frames in output.")
         return (gr.update(visible=False), gr.update(value=_save_video(frames), visible=True), gr.update(visible=False))
-    # image tasks — orchestrator strips ``outputs[0].output`` across the
-    # IPC boundary but preserves ``custom_output``, so prefer the latter.
-    img_out = custom.get("image") or getattr(outputs[0], "output", None)
+    img_out = (getattr(outputs[0], "images", None) or [None])[0] or getattr(outputs[0], "output", None)
     if img_out is None:
         raise gr.Error("No image in output.")
     return (gr.update(value=_save_image(img_out), visible=True), gr.update(visible=False), gr.update(visible=False))