Merge branch 'main' into codex-split-attention-offload-guides

Update docs/design/feature/offloader/README.md
Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
2026-08-15 07:00:22 +08:00 · 2026-08-13 23:37:08 +08:00 · 2026-08-13 23:37:01 +08:00 · 2026-08-12 11:35:07 +08:00 · 2026-08-12 09:30:34 +08:00 · 2026-08-12 08:55:41 +08:00
487 changed files with 5342 additions and 50434 deletions
@@ -76,9 +76,6 @@ steps:
          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
          - tests/e2e/offline_inference/test_qwen3_tts_customvoice.py
          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
        commands:
@@ -95,9 +92,6 @@ steps:
          - tests/e2e/online_serving/test_qwen3_tts_base.py
          - tests/e2e/offline_inference/test_qwen3_tts_base.py
          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
          - .buildkite/common/scripts/run_cov_split.sh
@@ -115,15 +109,27 @@ steps:
              -- -m 'advanced_model and cuda' --run-level 'advanced_model'
        mirror_hardwares: l4_1

+      - label: "TTS · Higgs-Audio-V3 Test"
+        source_file_dependencies:
+          - 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_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'
+            "
+        mirror_hardwares: l4_1
+
      - label: "Omni · Qwen3-Omni Test"
        source_file_dependencies:
          - tests/e2e/offline_inference/test_qwen3_omni.py
          - tests/e2e/online_serving/test_qwen3_omni.py
          - vllm_omni/model_executor/models/qwen3_omni/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
-          - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
          - vllm_omni/deploy/qwen3_omni_moe.yaml
        timeout_in_minutes: 50
@@ -135,11 +141,6 @@ steps:
        source_file_dependencies:
          - tests/e2e/offline_inference/test_minicpmo_4_5.py
          - tests/e2e/online_serving/test_minicpmo_4_5.py
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
          - vllm_omni/deploy/minicpmo_4_5.yaml
        timeout_in_minutes: 50
        commands:
@@ -161,35 +162,70 @@ steps:
          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
          - vllm_omni/experimental/fullduplex/
          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
          - vllm_omni/core/sched/
          - vllm_omni/outputs/multimodal_accumulation.py
          - vllm_omni/engine/stage_init_utils.py
          - vllm_omni/engine/async_omni_engine.py
-          - vllm_omni/engine/orchestrator.py
          - vllm_omni/entrypoints/cli/serve.py
-          - vllm_omni/entrypoints/openai/api_server.py
-          - vllm_omni/entrypoints/openai/duplex_capability.py
-          - vllm_omni/config/stage_config.py
        timeout_in_minutes: 50
        commands:
          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'advanced_model and cuda' --run-level 'advanced_model'
        mirror_hardwares: h100_1

+      - label: "TTS · VoxCPM2 Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_voxcpm2_tts.py
+          - tests/e2e/online_serving/test_voxcpm2_tts.py
+          - vllm_omni/model_executor/models/voxcpm2/
+          - vllm_omni/deploy/voxcpm2.yaml
+        timeout_in_minutes: 30
+        commands:
+          - |
+            timeout 30m bash -c "
+              export VLLM_LOGGING_LEVEL=DEBUG
+              export VLLM_WORKER_MULTIPROC_METHOD=spawn
+              pytest -s -v tests/e2e/offline_inference/test_voxcpm2_tts.py tests/e2e/online_serving/test_voxcpm2_tts.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+            "
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · Qwen Image Edit Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_edit.py
+          - vllm_omni/diffusion/models/qwen_image/
+        timeout_in_minutes: 20
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_qwen_image_edit.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+        mirror_hardwares: h100_1
+      - label: "Diffusion · Bagel Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_bagel.py
+          - tests/e2e/online_serving/test_bagel.py
+          - vllm_omni/model_executor/models/bagel/
+          - vllm_omni/diffusion/models/bagel/
+          - vllm_omni/model_executor/stage_input_processors/bagel.py
+          - vllm_omni/deploy/bagel.yaml
+          - .buildkite/common/scripts/run_cov_split.sh
+          - pyproject.toml
+        # 20 -> 40: the coverage split runs the model twice.
+        timeout_in_minutes: 40
+        commands:
+          - |
+            .buildkite/common/scripts/run_cov_split.sh \
+              --model-id bagel \
+              --offline tests/e2e/offline_inference/test_bagel.py \
+              --online tests/e2e/online_serving/test_bagel.py \
+              -- -m 'advanced_model and cuda' --run-level 'advanced_model'
+        mirror_hardwares: h100_1
+
      - label: "Diffusion · Qwen Image Test"
        source_file_dependencies:
          - tests/e2e/online_serving/test_qwen_image.py
          - vllm_omni/diffusion/models/qwen_image/
-          - vllm_omni/diffusion/models/dmd2/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
        commands:
          - |
            timeout 40m bash -c '
@@ -198,12 +234,23 @@ steps:
            '
        mirror_hardwares: h100_1

+      - label: "Diffusion · Qwen Image Layered Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_layered.py
+          - vllm_omni/diffusion/models/qwen_image/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_qwen_image_layered.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+            "
+        mirror_hardwares: h100_1
+
      - label: "Diffusion · Wan22 Test"
        source_file_dependencies:
          - tests/e2e/offline_inference/test_wan22_t2v.py
          - tests/e2e/online_serving/test_wan22_t2v.py
          - vllm_omni/diffusion/models/wan2_2/
-          - vllm_omni/diffusion/models/dmd2/
        commands:
          - |
            timeout 40m bash -c "
@@ -211,3 +258,21 @@ steps:
              pytest -s -v tests/e2e/offline_inference/test_wan22_t2v.py tests/e2e/online_serving/test_wan22_t2v.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
            "
        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Z Image Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_z_image.py
+          - tests/e2e/online_serving/test_z_image.py
+          - vllm_omni/diffusion/models/z_image/
+        timeout_in_minutes: 30
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_z_image.py tests/e2e/online_serving/test_z_image.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+        mirror_hardwares: l4_1
+      - label: "Diffusion · FLUX.2 Klein Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/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 -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+        mirror_hardwares: l4_1
@@ -159,7 +159,19 @@ steps:
          - 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
+          - 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
+        mirror_hardwares: l4_1
+
+      - label: ":full_moon: TTS · Accuracy Test · VoxCPM2"
+        timeout_in_minutes: 30
+        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_voxcpm2_tts_expansion.py -m 'full_model and cuda and tts' --run-level 'full_model'
+            "
        mirror_hardwares: l4_1

      - label: ":full_moon: TTS · Perf Test"
@@ -176,6 +188,35 @@ steps:
            exit $$EXIT
        mirror_hardwares: h100_2

+      - label: ":full_moon: TTS · VoxCPM2 · Perf Test"
+        key: nightly-tts-performance-voxcpm2
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_voxcpm2.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - 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
+        mirror_hardwares: h100_1
+
  - group: ":card_index_dividers: Tiny Model Tests [Multi-GPU]"
    key: nightly-tiny-model-test-group
    depends_on: upload-nightly-pipeline
@@ -205,7 +246,8 @@ steps:
      build.pull_request.labels includes "nightly-test" ||
      build.pull_request.labels includes "diffusion-x2iat-test"
    steps:
-      # Use explicit file shards so single-card cases do not reserve 2+ GPUs.
+      # Use explicit file shards so single-card cases do not reserve 2+ GPUs,
+      # and Bagel feature coverage is not duplicated via a broad tests/e2e sweep.

      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Single-GPU"
        timeout_in_minutes: 120
@@ -213,6 +255,15 @@ steps:
          - >-
            pytest -sv
            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_boogu_image.py
+            tests/e2e/online_serving/test_boogu_image_edit.py
+            tests/e2e/online_serving/test_boogu_image_expansion.py
+            tests/e2e/online_serving/test_boogu_image_edit_expansion.py
+            tests/e2e/online_serving/test_bagel_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"
        mirror_hardwares: h100_1
@@ -223,14 +274,47 @@ 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"
        mirror_hardwares: h100_2

+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Layered/LongCat/FLUX"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_qwen_image_layered_expansion.py
+            -m "full_model and diffusion and H100 and distributed_cuda"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Bagel"
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_bagel_expansion.py
+            -m "full_model and diffusion and H100 and distributed_cuda"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - 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
+            tests/e2e/offline_inference/test_glm_image_autoround_w4a16_expansion.py
+            -m "full_model and diffusion and H100"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
        timeout_in_minutes: 120
        commands:
-          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
+          - pytest -sv tests/e2e/ -k "not test_wan and not test_bagel_expansion and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
        mirror_hardwares: l4_4

      - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
@@ -243,7 +327,7 @@ steps:
        timeout_in_minutes: 180
        commands:
          - export VLLM_HTTP_TIMEOUT_KEEP_ALIVE=120
-          - pytest -s -v tests/e2e/accuracy/test_qwen_image.py --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/test_qwen_image*.py --run-level full_model
          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2i_matches_diffusers' -m full_model --run-level full_model
        mirror_hardwares: h100_1

@@ -271,6 +355,70 @@ steps:
            exit $$EXIT
        mirror_hardwares: h100_4

+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit"
+        key: nightly-diffusion-x2iat-performance-qwen-image-edit
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - export CACHE_DIT_VERSION=1.5.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_4
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit-2511"
+        key: nightly-diffusion-x2iat-performance-qwen-image-edit-2511
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - export CACHE_DIT_VERSION=1.5.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_4
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Layered"
+        key: nightly-diffusion-x2iat-performance-qwen-image-layered
+        timeout_in_minutes: 120
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - export CACHE_DIT_VERSION=1.5.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · BAGEL"
+        key: nightly-diffusion-x2iat-performance-bagel
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - export CACHE_DIT_VERSION=1.5.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
  - group: ":card_index_dividers: Diffusion X2V Model Test"
    key: nightly-diffusion-x2v-group
    depends_on: upload-nightly-pipeline
@@ -292,6 +440,12 @@ steps:
          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda" --run-level "full_model"
        mirror_hardwares: h100_2

+      - label: ":full_moon: Diffusion X2V · LingBot Video MoE Function Test"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_lingbot_video_moe.py -k "video_generation" -m "full_model and cuda" --run-level "full_model"
+        mirror_hardwares: h100_1
+
      - label: ":full_moon: Diffusion X2V · Doc Test"
        timeout_in_minutes: 60
        commands:
@@ -302,6 +456,7 @@ steps:
        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_ltx.py -m "full_model and cuda" --run-level "full_model"
        mirror_hardwares: h100_2

      - label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
@@ -322,8 +477,16 @@ steps:
        commands:
          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m full_model --run-level full_model
          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/test_video_streaming_output_similarity.py -m full_model --run-level full_model
        mirror_hardwares: h100_2

+      - label: ":full_moon: Diffusion X2V · LTX Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v 'tests/e2e/accuracy/ltx/test_ltx_official_similarity.py::test_ltx_matches_official[ltx2_3_i2v]' -m full_model --run-level full_model
+          - pytest -s -v 'tests/e2e/accuracy/ltx/test_ltx_official_similarity.py::test_ltx_matches_official[ltx23_two_stage_layer_fused_i2v]' -m full_model --run-level full_model
+        mirror_hardwares: h100_1
+
      - label: ":full_moon: Diffusion X2V · Perf Test"
        key: nightly-diffusion-x2v-performance
        timeout_in_minutes: 180
@@ -399,6 +562,16 @@ steps:
          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and L4' --run-level "full_model"
        mirror_hardwares: l4_1

+  - label: ":bar_chart: Testcase Statistics"
+    key: nightly-testcase-statistics
+    timeout_in_minutes: 120
+    depends_on: upload-nightly-pipeline
+    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"
+    mirror_hardwares: h100_1
+
  # NOTE: ":email: Nightly Collection & Email" is deprecated and will be removed soon.
  # Do not add new depends_on entries or extend this step.
  - label: ":email: Nightly Collection & Email"
@@ -409,20 +582,32 @@ steps:
      - nightly-omni-performance-vllm-text
      - nightly-omni-performance-multi-replicas
      - nightly-tts-performance
+      - nightly-tts-performance-higgs-audio-v3
      - nightly-diffusion-x2iat-performance-qwen-image
+      - nightly-diffusion-x2iat-performance-qwen-image-edit
+      - nightly-diffusion-x2iat-performance-qwen-image-edit-2511
+      - nightly-diffusion-x2iat-performance-qwen-image-layered
+      - nightly-diffusion-x2iat-performance-bagel
      - nightly-diffusion-x2v-performance
+      - nightly-testcase-statistics
    if: build.env("NIGHTLY") == "1" && build.env("EMAIL_DISTRIBUTION") == "1"
    commands:
      - pip install openpyxl
      - export DEFAULT_INPUT_DIR=tests/dfx/perf/results
      - export DEFAULT_OUTPUT_DIR=tests/dfx/perf/results
      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance-higgs-audio-v3
      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-no-async-chunk
      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-async-chunk
      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-vllm-text
      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-multi-replicas
      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-edit
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-edit-2511
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-layered
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-bagel
      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.html" . --step nightly-testcase-statistics
      - python tools/nightly/generate_nightly_perf_excel.py
      - python tools/nightly/generate_nightly_perf_html.py
      - python tools/nightly/send_nightly_email.py --report-file "tests/dfx/perf/results/*.xlsx, tests/dfx/perf/results/*.html"
@@ -169,10 +169,6 @@ steps:
        source_file_dependencies:
          - tests/e2e/online_serving/test_qwen3_omni.py
          - vllm_omni/model_executor/models/qwen3_omni/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
-          - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
          - vllm_omni/deploy/qwen3_omni_moe.yaml
        commands:
@@ -185,11 +181,6 @@ steps:
      - label: "Omni · MiniCPM-o 4.5 Offline Test"
        source_file_dependencies:
          - tests/e2e/offline_inference/test_minicpmo_4_5.py
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
          - vllm_omni/deploy/minicpmo_4_5.yaml
        commands:
          - |
@@ -201,11 +192,6 @@ steps:
      - label: "Omni · MiniCPM-o 4.5 Online Test"
        source_file_dependencies:
          - tests/e2e/online_serving/test_minicpmo_4_5.py
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
          - vllm_omni/deploy/minicpmo_4_5.yaml
        commands:
          - |
@@ -224,24 +210,16 @@ steps:
          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
          - vllm_omni/experimental/fullduplex/
          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
          - vllm_omni/core/sched/
          - vllm_omni/outputs/multimodal_accumulation.py
          - vllm_omni/engine/stage_init_utils.py
          - vllm_omni/engine/async_omni_engine.py
-          - vllm_omni/engine/orchestrator.py
          - vllm_omni/entrypoints/cli/serve.py
-          - vllm_omni/entrypoints/openai/api_server.py
-          - vllm_omni/entrypoints/openai/duplex_capability.py
-          - vllm_omni/config/stage_config.py
        commands:
          - |
            timeout 20m bash -c "
@@ -261,13 +239,25 @@ steps:
          - pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_ready.json -m 'core_model and cuda'
        mirror_hardwares: h100_1

+      - label: "TTS · VoxCPM2 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_voxcpm2_tts.py
+          - vllm_omni/model_executor/models/voxcpm2/
+          - vllm_omni/deploy/voxcpm2.yaml
+        timeout_in_minutes: 30
+        commands:
+          - |
+            timeout 30m bash -c "
+              export VLLM_LOGGING_LEVEL=DEBUG
+              export VLLM_WORKER_MULTIPROC_METHOD=spawn
+              pytest -s -v tests/e2e/online_serving/test_voxcpm2_tts.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: l4_1
+
      - label: "TTS · Qwen3-TTS CustomVoice Test"
        source_file_dependencies:
          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
        commands:
@@ -282,9 +272,6 @@ steps:
        source_file_dependencies:
          - tests/e2e/online_serving/test_qwen3_tts_base.py
          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
        commands:
@@ -297,11 +284,52 @@ steps:
            "
        mirror_hardwares: l4_1

+      - label: "TTS · Higgs-Audio-V3 Test"
+        source_file_dependencies:
+          - 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_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 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · Qwen Image Edit Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_edit.py
+          - vllm_omni/diffusion/models/qwen_image/
+        commands:
+          - |
+            timeout 20m bash -c "
+              pytest -s -v tests/e2e/online_serving/test_qwen_image_edit.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Bagel Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_bagel.py
+          - vllm_omni/model_executor/models/bagel/
+          - vllm_omni/diffusion/models/bagel/
+          - vllm_omni/model_executor/stage_input_processors/bagel.py
+          - vllm_omni/deploy/bagel.yaml
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_bagel.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
+
      - label: "Diffusion · Wan22 Test"
        source_file_dependencies:
          - tests/e2e/online_serving/test_wan22_t2v.py
          - vllm_omni/diffusion/models/wan2_2/
-          - vllm_omni/diffusion/models/dmd2/
        commands:
          - |
            timeout 40m bash -c "
@@ -314,9 +342,6 @@ steps:
        source_file_dependencies:
          - tests/e2e/online_serving/test_cosmos3.py
          - vllm_omni/diffusion/models/cosmos3/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py
-          - vllm_omni/experimental/world_models/
-          - vllm_omni/model_extras/cosmos3.py
        commands:
          - |
            timeout 40m bash -c "
@@ -329,8 +354,6 @@ steps:
        source_file_dependencies:
          - tests/e2e/online_serving/test_qwen_image.py
          - vllm_omni/diffusion/models/qwen_image/
-          - vllm_omni/diffusion/models/dmd2/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
        commands:
          - |
            timeout 40m bash -c "
@@ -338,3 +361,15 @@ steps:
              pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'core_model' --run-level 'core_model'
            "
        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Qwen Image Layered Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_layered.py
+          - vllm_omni/diffusion/models/qwen_image/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_qwen_image_layered.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
@@ -30,6 +30,14 @@ steps:
      - pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
    mirror_hardwares: h100_4

+  - label: "Reliability Test - voxcpm2"
+    timeout_in_minutes: 180
+    depends_on: upload-weekly-pipeline
+    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
+    commands:
+      - pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m "slow"
+    mirror_hardwares: l4_1
+
  - group: "Reliability Test - Invalid parameters Test"
    depends_on: upload-weekly-pipeline
    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
@@ -100,22 +108,6 @@ steps:
            --run-level "full_model"
        mirror_hardwares: h100_1

-      # Own step: the image has no NanoCodec, so this file import-skips out of
-      # the mark-selected TTS steps above. The overrides keep nemo_toolkit[tts]
-      # from downgrading transformers; see
-      # examples/offline_inference/text_to_speech/README.md.
-      - label: "TTS · Gepard-1.0 · L4"
-        timeout_in_minutes: 60
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - printf '%s\n' "transformers>=5.5.3" "huggingface-hub>=1.0" > /tmp/gepard-overrides.txt
-          - uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" "nemo_toolkit[tts]==2.7.3" --overrides /tmp/gepard-overrides.txt
-          - >-
-            pytest -s -v tests/e2e/offline_inference/test_gepard_tts.py
-            -m "tts and cuda"
-            --run-level "full_model"
-        mirror_hardwares: l4_1
-
      - label: "Diffusion · L4"
        timeout_in_minutes: 60
        commands:
@@ -141,4 +133,4 @@ steps:
            pytest -sv
            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
            --run-level "full_model"
-        mirror_hardwares: h100_4
+        mirror_hardwares: h100_2
@@ -69,12 +69,6 @@ steps:
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
          HF_TOKEN: "${HF_TOKEN}"
        commands:
-          # Pin the Seed-TTS scorer to the second card: _get_eval_device() otherwise
-          # auto-picks npu:0, which the omni server under test already fills with its
-          # KV cache, so Whisper OOMs on `.to(device)` and every clip reports
-          # "ASR / WER failed".
-          - export SEED_TTS_WER_EVAL=1
-          - export SEED_TTS_EVAL_DEVICE=npu:1
          - |
            set +e
            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and npu and A3" --run-level full_model
@@ -102,6 +96,33 @@ steps:
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$EXIT
+  - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
+    key: nightly-diffusion-x2iat-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
+    steps:
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit-2511"
+        key: nightly-diffusion-x2iat-performance-qwen-image-edit-2511
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_8
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export CACHE_DIT_VERSION=1.5.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
  - group: ":card_index_dividers: Diffusion I2I Model Test"
    key: nightly-HunyuanImage3-x2i-test-group
    depends_on: upload-nightly-pipeline
@@ -1,15 +1,4 @@
 steps:
-  - label: "Platforms Test"
-    key: ready-platforms-test
-    depends_on: upload-ready-pipeline
-    mirror_hardwares: a3_npu_2
-    commands:
-      - >-
-        pytest -s -v
-        tests/platforms/npu/test_minicpmo_code2wav_npugraph.py
-        -m 'core_model and npu and A3 and omni'
-        --run-level 'core_model'
-
  - group: ":card_index_dividers: Omni Model Test"
    key: ready-omni-group
    depends_on: upload-ready-pipeline
@@ -254,7 +254,6 @@ outputs/
 results/
 generated/
 output_*/
-/tests/e2e/accuracy/artifacts/

 # Configuration overrides
 configs/local.yaml
@@ -66,7 +66,7 @@ repos:
      entry: python tools/pre_commit/check_tts_adapter.py
      language: python
      pass_filenames: false
-      files: ^(vllm_omni/entrypoints/openai/serving_speech\.py|tools/pre_commit/check_tts_adapter\.py)$
+      files: ^(vllm_omni/entrypoints/openai/(serving_speech\.py|tts_adapters/__init__\.py)|tools/pre_commit/check_tts_adapter\.py)$
      # The budget-is-now-slack reminder is printed on a PASSING run, and
      # pre-commit hides output from passing hooks unless the hook is verbose.
      verbose: true
@@ -43,7 +43,6 @@ nav:
    - Overview: features/README.md
    - Runtime and Stage Execution:
      - Execution Modes and Streaming: user_guide/diffusion/execution_modes.md
-      - Prefill-Decode Disaggregation (experimental): features/pd_disaggregation.md
      - Sleep Mode: features/sleep_mode.md
    - Quantization:
      - Overview: user_guide/quantization/overview.md
@@ -117,7 +116,6 @@ nav:
        - Async Diffusion Output: design/feature/async_diffusion_output.md
        - Async Omni Output Materialization: design/feature/omni_async_output_materialization.md
        - Automatic Prefix Caching: design/feature/prefix_caching.md
-        - Realtime AR-Diffusion: design/feature/realtime_ar_diffusion.md
      - Communication:
        - OmniConnector Implementations:
          - Mooncake Store Connector: design/feature/omni_connectors/mooncake_store_connector.md
@@ -14,6 +14,10 @@ For introduction, please check [Introduction for stage config](./stage_configs.m

 - **[GPU Memory Calculation and Configuration](./gpu_memory_utilization.md)** - Guide on how to calculate memory requirements and set up `gpu_memory_utilization` for optimal performance

+## Multi-Stage Recipes
+
+- **[Prefill-Decode Disaggregation](./pd_disaggregation.md)** - How to derive a PD-aware Qwen3-Omni stage config from the default config without introducing another bundled YAML
+
 ## Optimization Features

 - **[Diffusion Features Overview](../user_guide/diffusion_features.md)** - Complete overview of all diffusion model features and supported models
@@ -0,0 +1,171 @@
+# Prefill-Decode (PD) Disaggregation
+
+PD disaggregation splits the Qwen3-Omni thinker into separate prefill and decode
+stages so prompt processing and token generation can run on different workers.
+
+This is documented as a stage-config recipe instead of a bundled YAML because the
+deployment-specific values usually change per environment:
+
+- GPU placement
+- `tensor_parallel_size`
+- connector backend and connector ports
+- connector IPs or bootstrap addresses
+
+Start from the [default Qwen3-Omni stage config](gh-file:vllm_omni/deploy/qwen3_omni_moe.yaml)
+and copy it to your own file, for example `qwen3_omni_pd.yaml`. Then apply the
+changes below.
+
+## Requirements
+
+- 3+ GPUs for a basic layout: prefill, decode, and talker+code2wav
+- A KV connector supported by vLLM, such as `MooncakeConnector`
+- Matching `tensor_parallel_size` on the prefill and decode thinker stages
+
+## 1. Split the thinker into prefill and decode stages
+
+Replace the original thinker stage with two stages:
+
+```yaml
+stage_args:
+  - stage_id: 0
+    stage_type: llm
+    is_prefill_only: true
+    runtime:
+      devices: "0"
+    engine_args:
+      max_num_seqs: 16
+      model_stage: thinker
+      model_arch: Qwen3OmniMoeForConditionalGeneration
+      worker_type: ar
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      gpu_memory_utilization: 0.9
+      enforce_eager: true
+      trust_remote_code: true
+      engine_output_type: latent
+      distributed_executor_backend: "mp"
+      enable_prefix_caching: false
+      max_num_batched_tokens: 32768
+      hf_config_name: thinker_config
+      tensor_parallel_size: 1
+      kv_transfer_config:
+        kv_connector: "MooncakeConnector"
+        kv_role: "kv_producer"
+        kv_rank: 0
+        kv_parallel_size: 2
+        kv_connector_extra_config:
+          mooncake_bootstrap_port: 25201
+    final_output: false
+    is_comprehension: true
+    default_sampling_params:
+      temperature: 0.4
+      top_p: 0.9
+      top_k: 1
+      max_tokens: 2048
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.05
+
+  - stage_id: 1
+    stage_type: llm
+    is_decode_only: true
+    runtime:
+      devices: "1"
+    engine_args:
+      max_num_seqs: 64
+      model_stage: thinker
+      model_arch: Qwen3OmniMoeForConditionalGeneration
+      worker_type: ar
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      gpu_memory_utilization: 0.9
+      enforce_eager: true
+      trust_remote_code: true
+      engine_output_type: latent
+      distributed_executor_backend: "mp"
+      enable_prefix_caching: false
+      max_num_batched_tokens: 32768
+      hf_config_name: thinker_config
+      tensor_parallel_size: 1
+      kv_transfer_config:
+        kv_connector: "MooncakeConnector"
+        kv_role: "kv_consumer"
+        kv_rank: 1
+        kv_parallel_size: 2
+        kv_connector_extra_config:
+          mooncake_bootstrap_port: 25202
+    engine_input_source: [0]
+    final_output: true
+    final_output_type: text
+    is_comprehension: true
+    default_sampling_params:
+      temperature: 0.4
+      top_p: 0.9
+      top_k: 1
+      max_tokens: 2048
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.05
+```
+
+Notes:
+
+- `is_prefill_only: true` marks the thinker stage that only saves KV.
+- `is_decode_only: true` marks the thinker stage that resumes from remote KV.
+- `kv_transfer_config` is required on both stages.
+- The orchestrator forces the prefill stage to run with `max_tokens=1`, so the
+  prefill side only processes the prompt and exports KV.
+
+## 2. Shift the downstream stages by one index
+
+After inserting the extra thinker stage, renumber the remaining stages:
+
+```yaml
+  - stage_id: 2
+    runtime:
+      devices: "2"
+    engine_input_source: [1]
+    sync_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_token_only
+
+  - stage_id: 3
+    runtime:
+      devices: "2"
+    engine_args:
+      max_num_seqs: 1
+    engine_input_source: [2]
+    sync_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav_token_only
+```
+
+Compared with the default Qwen3-Omni config:
+
+- the talker becomes stage `2` instead of stage `1`
+- the code2wav stage becomes stage `3` instead of stage `2`
+- the talker now reads from decode stage `1`
+
+## 3. Add runtime edges for the four-stage pipeline
+
+```yaml
+runtime:
+  enabled: true
+  edges:
+    - from: 0
+      to: 1
+    - from: 1
+      to: 2
+    - from: 2
+      to: 3
+```
+
+## 4. Launch with your custom config
+
+```bash
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
+    --deploy-config /path/to/qwen3_omni_pd.yaml
+```
+
+## Operational Notes
+
+- `MooncakeConnector` does not support heterogeneous TP sizes across the PD
+  pair. Keep prefill and decode at the same `tensor_parallel_size`.
+- If the thinker requires TP=2, both thinker stages must use TP=2 and be given
+  separate GPU sets, for example `"0,1"` for prefill and `"2,3"` for decode.
+- Choose connector ports and addresses that match your deployment. The values
+  shown above are examples only.
@@ -38,12 +38,11 @@ Each entry under `stages:` accepts any `StageDeployConfig` field directly (no ne
 |-------|------|----------|---------|-------------|
 | `stage_id` | int | required | — | Stage identity; matched against `PipelineConfig.stages[*].stage_id`. |
 | `max_num_seqs` | int | optional | `64` | Max concurrent sequences per stage. |
-| `gpu_memory_utilization` | float | optional | `0.9` | Per-stage total memory target; used for automatic KV-cache sizing. |
-| `kv_cache_memory_bytes` | int \| null | optional | `null` | Explicit per-rank KV-cache budget in bytes; overrides automatic sizing. |
+| `gpu_memory_utilization` | float | optional | `0.9` | Per-stage memory budget. |
 | `tensor_parallel_size` | int | optional | `1` | TP degree for this stage. |
 | `enforce_eager` | bool | optional | `false` | Disable CUDA graphs. |
-| `max_num_batched_tokens` | int | optional | `32768` | Per-stage prefill/token budget; also contributes to the native maximum in-flight token limit. |
-| `max_model_len` | int \| null | optional | `null` | Per-sequence context or KV length; `-1` enables native cache-capacity auto-fitting, while values above the HF default auto-set `VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`. |
+| `max_num_batched_tokens` | int | optional | `32768` | Prefill budget. |
+| `max_model_len` | int \| null | optional | `null` | Per-stage context length (auto-sets `VLLM_ALLOW_LONG_MAX_MODEL_LEN=1` when larger than HF default). |
 | `async_scheduling` | bool \| null | optional | `null` | Per-stage async scheduling toggle. |
 | `devices` | str | optional | `"0"` | `CUDA_VISIBLE_DEVICES`-style device list. |
 | `output_connectors` | dict \| null | optional | `null` | Keyed by `to_stage_<n>`; values are names registered under top-level `connectors:`. |
@@ -329,13 +329,11 @@ On PR builds, `upload_pipeline.py` logs `skip '…' (no changes under …)` for

 ### Per-model coverage

-Pilot (v1): one Merge-tier (L3) job in `.buildkite/cuda/test-merge.yml` uploads
-per-model, per-entry-mode coverage as Buildkite artifacts — "TTS · Qwen3-TTS Base
-Test". It runs on a single GPU so the pilot is cheap to reproduce. (A second
-diffusion pilot, "Diffusion · Bagel Test", was retired from merge; reintroduce
-another diffusion coverage job if a second pilot is needed again.) This is a
-pilot, not a rollout: no dashboard/visualization lives in this repo, and nothing
-is gated on coverage.
+Pilot (v1): two Merge-tier (L3) jobs in `.buildkite/cuda/test-merge.yml` upload
+per-model, per-entry-mode coverage as Buildkite artifacts — "Diffusion · Bagel
+Test" and "TTS · Qwen3-TTS Base Test". Both run on a single GPU so the pilot is
+cheap to reproduce. This is a pilot, not a rollout: no dashboard/visualization
+lives in this repo, and nothing is gated on coverage.

 **Naming convention**: `coverage-<model_id>-<mode>-<step_id>.xml.gz`, where
 `<model_id>` is the model's directory name under
@@ -412,8 +410,7 @@ runs a coverage job needs the same.
 List both `run_cov_split.sh` and `pyproject.toml` in every opted-in job's
 `source_file_dependencies` — both change what the job measures, so without them a
 change there is filtered out of normal PR builds and only surfaces in a later
-nightly. `tests/buildkite/test_upload_pipeline.py` covers the filter behavior with
-a synthetic job (it does not pin real merge labels).
+nightly. `tests/buildkite/test_upload_pipeline.py` asserts this for the pilots.
 Editing only the surrounding CI YAML still does not schedule them, so a PR that
 touches just the wiring needs a full E2E run (or the commands run on a GPU host)
 to produce artifacts. When checking a new model's
@@ -184,6 +184,9 @@ Failed jobs: 1/2
    # Reliability: HunyuanImage DiT (H100 × 4)
    pytest -s -v dfx/reliability/test_reliability_hunyuan_image.py -m slow

+    # Reliability: VoxCPM2 (L4)
+    pytest -s -v dfx/reliability/test_reliability_voxcpm2.py -m slow
+
    ```

    The latest L5 CI jobs (reliability + invalid-parameter weekly steps) are in [test-weekly.yml](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/cuda/test-weekly.yml).
@@ -562,6 +562,7 @@ L5 level testing focuses on the performance of model services under ***long-runn
    -   `test_reliability_qwen3_omni.py` — Qwen3-Omni chat / multimodal reliability (GPU OOM, process kill, recovery, error contract under `--async-chunk` vs default).
    -   `test_reliability_wan22.py` — Wan2.2 I2V video API reliability (`/v1/videos` under OOM and process kill, recovery).
    -   `test_reliability_hunyuan_image.py` — HunyuanImage-3.0-Instruct DiT-only reliability (`/v1/images/generations` under OOM and process kill; deploy `hunyuan_image3_dit.yaml`, H100 × 4).
+    -   `test_reliability_voxcpm2.py` — VoxCPM2 TTS reliability (`/v1/audio/speech` under OOM and process kill; deploy `voxcpm2.yaml`, L4 × 1).
    -   `helpers.py` — Shared primitives used by current suites: raw HTTP probes for `/v1/chat/completions` and `/health`, OpenAI-style error parsing, GPU OOM sidecar (`inject_gpu_oom` / `stop_gpu_oom_hogs`), and `pgrep`-based process-kill injector construction (`make_process_kill_fault_injector`).
    -   `conftest.py` — `fault_injector` and `omni_server_after_fault` / `omni_server_after_fault_function` fixtures to run a callable **after** the server is ready.
    -   `README.md` — Short local run commands for this directory.
@@ -661,12 +662,13 @@ Reliability tests are **short fault-injection** integration runs (L5 **(b)** in
 | `test_reliability_qwen3_omni.py` | Qwen3-Omni: OOM vs **text vs text+audio** error contract, large multimodal chat under OOM, concurrent pressure, **SIGKILL** on `VLLM::Worker`, `/health` → 503 + fast-fail + concurrent chat; optional OOM recovery scenario (may be skipped while tracked in issues). |
 | `test_reliability_wan22.py` | Wan2.2 I2V: large `/v1/videos` under OOM, **SIGKILL** on `multiprocessing.spawn` chain, health / fast-fail / concurrent video requests; optional recovery test (may be skipped). |
 | `test_reliability_hunyuan_image.py` | HunyuanImage DiT-only: large `/v1/images/generations` under OOM, **SIGKILL** on `vLLM-Omni::` workers and serve/tree targets, health / fast-fail / concurrent image requests; some OOM/recovery cases may be skipped while tracked in issues. |
+| `test_reliability_voxcpm2.py` | VoxCPM2: `/v1/audio/speech` under OOM (error contract), **SIGKILL** on `VLLM::` workers and serve/tree targets, health / fast-fail / concurrent speech requests; some OOM cases may be skipped while tracked in issues. |
 | `README.md` | Minimal run / collect examples. |

 ##### Parametrization and markers

 - Each test module defines a **`RELIABILITY_SCENARIOS`** list (`test_name`, `server_params`: model, `stage_config_name` or diffusion `server_args`, etc.). **`create_reliability_omni_server_params()`** in `tests/dfx/conftest.py` resolves stage paths (including XPU substitutions where applicable) and builds **`OmniServerParams`** lists consumed by **`@pytest.mark.parametrize(..., indirect=True)`** on `omni_server` or `omni_server_function`.
- Cases are tagged **`@pytest.mark.slow`** for weekly / selective CI. GPU-heavy suites use **`@hardware_test(res={"cuda": "H100"}, num_cards=...)`** (Qwen3-Omni **2**× H100; Wan2.2 **1**× H100; HunyuanImage DiT **4**× H100).
+- Cases are tagged **`@pytest.mark.slow`** for weekly / selective CI. GPU-heavy suites use **`@hardware_test(res={"cuda": "H100"}, num_cards=...)`** or **`@hardware_test(res={"cuda": "L4"}, num_cards=1)`** (Qwen3-Omni **2**× H100; Wan2.2 **1**× H100; HunyuanImage DiT **4**× H100; VoxCPM2 **1**× L4).
 - **Process-kill** tests use **`@pytest.mark.skipif(os.name == "nt", ...)`** because injection uses POSIX **`pgrep` / `kill`**.

 ##### CI trigger
@@ -678,11 +680,13 @@ Weekly Buildkite (`.buildkite/cuda/test-weekly.yml`) runs one step per model sui
 | Reliability Test - qwen3-omni | `test_reliability_qwen3_omni.py` | H100 × 2 (`mithril-h100-pool`) |
 | Reliability Test - wan22 | `test_reliability_wan22.py` | H100 × 2 (`mithril-h100-pool`) |
 | Reliability Test - hunyuan-image | `test_reliability_hunyuan_image.py` | H100 × 4 (`mithril-h100-pool`) |
+| Reliability Test - voxcpm2 | `test_reliability_voxcpm2.py` | L4 × 1 (`gpu_1_queue`) |

 ```bash
 pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m "slow"
 pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m "slow"
 pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
+pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m "slow"
 ```

 ##### Local commands
@@ -692,6 +696,7 @@ pytest --collect-only tests/dfx/reliability
 pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m slow
 pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m slow
 pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m slow
+pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m slow
 ```

 ##### Adding a new model suite
@@ -704,4 +709,4 @@ pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m slow
 </details>

 -   -   ***Stability***: `pytest -s -v tests/dfx/stability/scripts/test_stability_qwen3_omni.py` or `pytest -s -v tests/dfx/stability/scripts/test_stability_wan22.py` (or add `test_stability_<model>.py` alongside a matching JSON config)
-    -   ***Reliability***: `pytest -s -v tests/dfx/reliability/test_reliability_<model>.py -m slow` (current suites: `qwen3_omni`, `wan22`, `hunyuan_image`; add `test_reliability_<suite>.py` and a matching step in `.buildkite/cuda/test-weekly.yml` for new models)
+    -   ***Reliability***: `pytest -s -v tests/dfx/reliability/test_reliability_<model>.py -m slow` (current suites: `qwen3_omni`, `wan22`, `hunyuan_image`, `voxcpm2`; add `test_reliability_<suite>.py` and a matching step in `.buildkite/cuda/test-weekly.yml` for new models)
@@ -4,9 +4,6 @@
 > `experimental/ar_diffusion` contract. It follows the ownership and lifecycle
 > direction in [#5137](https://github.com/vllm-project/vllm-omni/issues/5137),
 > but it is not yet part of the stable module-design hierarchy.
->
-> The end-to-end request, identity, failure, and memory contracts are documented
-> in [Realtime AR-Diffusion sessions](feature/realtime_ar_diffusion.md).

 `ARDiffusionEngine` is an opt-in diffusion backend for pipelines that keep
 autoregressive attention KV across requests. The engine only selects
@@ -54,13 +51,14 @@ time, and its inherited batch/step entry points also fail explicitly. Supporting
 those paths requires a batch-aware state-binding contract rather than silently
 bypassing `bind_ar_diffusion_state()`.

-The implementation may retain multiple sessions per model instance. It selects
-the largest feasible capacity up to the pipeline's `session_capacity`, based on
-self-attention pages, cross-attention allocations, scratch, and declared
-model-owned state. One session is admitted whenever it fits actual free device
-memory; `gpu_memory_fraction` controls additional residency. Requests beyond
-that capacity evict the least-recently-used session through the same lifecycle
-hook used by explicit close and failure cleanup.
+The current implementation retains one session per model instance. A request
+for a different `session_id` closes the resident session through the same LRU
+lifecycle used by explicit close and failure cleanup before allocating the new
+one. Self-attention and cross-attention memory are therefore sized for one
+resident session even if the pipeline declares a larger `session_capacity`.
+This prioritizes rollout throughput and prevents block-pool exhaustion before a
+count-based eviction can run. Multi-session residency can be added later with a
+memory-backed capacity calculation or allocation-pressure eviction.

 Scratch storage is also capability-driven. `frames_per_block` reserves space for
 an uncommitted current video block, while `max_scratch_tokens_per_branch`
@@ -117,22 +117,7 @@ After (async D2H):

 ### Batch Split

-When `execute_model_batch` returns a `COMPUTE_DONE` with a single `async_output_id` for the entire batch, the executor splits it into per-request `async_output_id`s (format `{batch_id}/{request_id}`) via `_batch_split_map`. When `OUTPUT_READY` arrives, the pump extracts per-request results from the batch output and resolves each request's output future independently. If `OUTPUT_READY` arrives before `execute_batch` registers the split map, `execute_batch` directly adopts the early output.
-
-### Reliability, Lifecycle & Timeout Behavior
-
-1. **Drain Before Memory Release (`drain_async_outputs` / `_ASYNC_OUTPUT_DRAIN_TIMEOUT_S`)**:
-   When workers transition into sleep states (`handle_sleep_task`) or invoke memory-releasing methods, `WorkerProc.drain_async_outputs()` blocks up to `_ASYNC_OUTPUT_DRAIN_TIMEOUT_S` (10.0s) until all in-flight background D2H/SHM packing tasks finish. This acts as a synchronization barrier preventing worker sleep cycles from releasing device tensors while the side CUDA stream is still actively reading them. Draining typically completes within 10–50 ms under normal operation.
-
-2. **Timeout Diagnostics (`_ASYNC_OUTPUT_TIMEOUT`)**:
-   The engine waits for `wait_output_ready()` with a timeout of `_ASYNC_OUTPUT_TIMEOUT` (30.0s). If a timeout occurs, the executor logs detailed diagnostic bookkeeping (pending futures, cached outputs, and batch split maps) to identify whether the stall occurred in the worker, pump, or waiter.
-
-3. **Queue Serialization & Atomic Future Resolution**:
-   - `result_mq` writes are serialized with `_result_mq_lock` to protect the single-writer `MessageQueue` against concurrent writes from the worker main loop (`COMPUTE_DONE`) and background thread (`OUTPUT_READY`).
-   - `ResultPumpThread` unpacks shared-memory payloads first, and then atomically resolves waiting futures or populates `_completed_outputs` under `_futures_lock`, avoiding race conditions with concurrent `wait_output_ready()` invocations.
-
-4. **Storage-Aware IPC Packing**:
-   `pack_diffusion_output_shm()` evaluates `max(view_bytes, storage_bytes)` against `_SHM_TENSOR_THRESHOLD` (1 MB) to ensure that per-request slices sharing a large batch storage are moved to POSIX shared memory rather than serialized through pickle over the MessageQueue inline buffer.
+When `execute_model_batch` returns a `COMPUTE_DONE` with a single `async_output_id` for the entire batch, the executor splits it into per-request `async_output_id`s (format `{batch_id}/{request_id}`) via `_batch_split_map`. When `OUTPUT_READY` arrives, the pump extracts per-request results from the batch output and resolves each request's output future independently.

 ## Model Coverage

@@ -146,7 +131,6 @@ All models running in request-mode (`step_execution=False`, the default) automat
 |---|---|---|
 | **HunyuanImage-3.0** | Image | `False` |
 | **Qwen-Image** | Image | `True` |
-| **LTX-2.3** | Video / Audio | `False` |

 Other models with `step_execution=False` are also supported but not yet verified.

@@ -4,54 +4,20 @@ This document describes distributed layerwise offload (DLO) for diffusion
 models. DLO keeps only a small number of DiT blocks on the accelerator and
 streams the remaining blocks from host memory. The distributed backend can
 either shard those host-side weights across an existing parallel group or keep
-complete rank-local block sources and avoid an additional collective.
+the standard loader's rank-local weights and avoid an additional collective.

 For user-facing commands, see the
 [distributed layerwise offloading guide](../../../user_guide/diffusion/offloader/distributed_layerwise_offload.md)
 and the [Cosmos3 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/cosmos3/Cosmos3-DistOffload.md).

-## Feature compatibility
-
-Host-storage optimization and runtime compatibility are separate decisions.
-When direct checkpoint mmap is unavailable, DLO can still use tensors produced
-by the ordinary loader. "Compatibility path" below means that fallback is
-implemented but has less end-to-end coverage than the primary path.
-
-Legend: ✅ supported, ⚠️ compatibility path or limited validation, ❌ unsupported.
-
-| Feature | DLO + AllGather | DLO without AllGather |
-|---|---|---|
-| **DP** | ✅ Primary path; host weights are sharded across the DP group. | ✅ Each DP rank streams complete rank-local blocks. |
-| **SP** | ✅ When DP=1, DLO uses the SP group for weight sharding. | ✅ SP remains active without a DLO weight collective. |
-| **TP > 1** | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. |
-| **HSDP** | ❌ Rejected to avoid double-sharding parameters. | ⚠️ Limited end-to-end coverage. |
-| **Per-tensor online FP8 linears** | ✅ Ordinary loader finalizes weights and scales before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
-| **Other online quantization methods** | ❌ Rejected until runtime packing and scale layouts are validated. | ⚠️ Allowed through the ordinary loader; validation is method-specific. |
-| **Model-level or standard layerwise CPU offload** | ❌ Disabled because DLO takes priority. | ❌ Disabled because DLO takes priority. |
-| **Resident leading layers** | ❌ Rejected. | ✅ Requires eligible resident paths in the model's `OffloadPlan`. |
-
-See [Parallelism compatibility](#parallelism-compatibility) and
-[Request and loading constraints](#request-and-loading-constraints) for the
-detailed contracts and validation boundaries.
-
 ## Status

 DLO is implemented for multi-device diffusion execution. The default
 AllGather path is the primary path for DP and SP deployments. The
-`--dlo-no-use-allgather` path streams complete blocks independently and adds no
-DLO weight collective.
-
-Host storage is selected separately from the transfer protocol. The loader can
-produce a direct-checkpoint mmap plan for a proven-compatible runtime layout;
-otherwise it uses the ordinary loader. Consequently, no-AllGather replicas on
-the same node can share immutable checkpoint pages when direct mmap is
-selected, while the ordinary-loader fallback still keeps a private runtime
-copy per process.
-
-The Phase A shared-mmap support boundary is TP1. TP greater than one is an
-ordinary-loader compatibility path: DLO can consume the resulting TP-local
-tensors, but those configurations do not use checkpoint mmap and must not be
-used to claim shared-mmap host-memory savings.
+`--dlo-no-use-allgather` path is a rank-local compatibility mode: it is useful
+for standard-loader sharding, workstation bring-up, and systems where an
+additional DLO collective is undesirable, but it does not reduce host weight
+storage across ranks.

 The compatibility matrix below describes the current implementation. The
 unit-level guards are covered, but not every parallelism combination has a
@@ -75,36 +41,6 @@ TP is deliberately not used as DLO's AllGather group. HSDP has its own
 parameter-sharding lifecycle and is not allowed to be sharded a second time by
 DLO's AllGather path.

-### The loader owns host-weight planning
-
-Before it decides whether ordinary weight materialization can be skipped, the
-diffusion loader builds one `HostWeightPlan`. A direct-checkpoint mmap plan is
-accepted only when preflight proves all of the following:
-
- every required DiT parameter and persistent buffer has exactly one source;
- runtime names, checkpoint keys, shapes, and dtypes match;
- the runtime topology is TP1 without HSDP or online quantization; and
- every custom loader operation is represented by a loader-owned checkpoint
-  adapter.
-
-The exact plan object is handed to DLO. The backend does not rescan checkpoint
-files, repeat the capability decision, or reconstruct names from its block
-topology. If preflight fails, the loader materializes weights normally and DLO
-consumes those runtime tensors.
-
-The plan owns only dedicated DiT component sources. If a pipeline also exposes
-ordinary sources for a text encoder or another non-DiT component, the loader
-still consumes those sources and includes their loaded names in its strict
-coverage check. Only the source prefixes covered by the plan skip ordinary
-materialization. A source that mixes DiT and non-DiT weights fails closed to
-the complete ordinary-loader path because it cannot be skipped safely as a
-unit.
-
-This boundary keeps checkpoint semantics out of DLO and avoids model-pipeline
-flags such as `_supports_mmap_loading` or parameter attributes for mmap-only
-transforms. Model-specific direct-layout knowledge, when required, lives in a
-checkpoint adapter beside the ordinary loader.
-
 ### AllGather path

 With the default `dlo_use_allgather=True`, each rank stores approximately
@@ -125,15 +61,6 @@ Buffers:    [current slot]       [prefetch slot]       [current slot]
 The backend uses two shared device buffers, so accelerator weight residency is
 bounded by the largest streamed blocks rather than the complete model.

-When direct checkpoint mmap is selected, the checkpoint mappings are only the
-source used to prepare each rank's persistent shard. They can be closed after
-shard preparation. Across the AllGather group, those private shards total
-approximately one runtime model copy.
-
-An effective DLO group size of one performs no collective, even when
-`dlo_use_allgather=True`; it follows the rank-local transfer path described
-below.
-
 When DP is greater than one, the engine can process one request per DP rank in
 the same denoising wave. Because AllGather is a collective, all participating
 requests must take the same execution path at every denoising step.
@@ -141,20 +68,9 @@ requests must take the same execution path at every denoising step.
 ### Rank-local path without DLO AllGather

 With `--dlo-no-use-allgather`, DLO forces its internal offload shard size to
-one and streams complete blocks using H2D copies only. The host backing may be
-either a loader-approved checkpoint mapping or ordinary runtime tensors.
-
-For direct mmap, each process retains immutable safetensors views and uses two
-bounded pinned host staging slots. Processes on the same node that map the same
-files share physical checkpoint pages through the OS page cache. This removes
-the persistent private full-model copy per pure-DP process, but each process
-still packs and transfers every complete block. Sharing is node-local; each
-node has its own page cache.
-
-When direct mmap preflight fails, the regular model loader remains responsible
-for preparing each rank's weights, including TP-local tensors or HSDP-managed
-parameters. In that fallback, each pure-DP process keeps a private full runtime
-copy.
+one. The regular model loader remains responsible for preparing each rank's
+weights, including TP-local tensors or HSDP-managed parameters. DLO then
+streams those rank-local tensors block by block using H2D copies only.

 This mode means:

@@ -164,18 +80,21 @@ This mode means:
  not shard weights across SP ranks.
 - TP/HSDP/SP collectives, if configured, are not disabled by this flag; only
  DLO's additional weight AllGather is disabled.
- Pure DP deployments share one checkpoint-backed copy per node when direct
-  mmap is selected; the ordinary-loader fallback keeps one private runtime
-  copy per rank.
+- Pure DP deployments keep a full host-side model copy per rank, subject to
+  shared OS page-cache behavior.
 - The scheduler does not require a synchronized DP request wave for DLO.

+This path is intentionally not implemented by reusing the AllGather mmap
+loader. It relies on the standard loader so model-specific TP/HSDP transforms
+remain intact.
+
 ## Parallelism compatibility

 | Parallelism | DLO + AllGather | DLO without AllGather |
 |---|---|---|
-| **DP** | Supported primary path. DLO shards host weights across the DP group and can run DP multi-concurrency. | Supported rank-local path. Compatible TP1 replicas can share checkpoint pages on each node; fallback runtime tensors remain private. |
+| **DP** | Supported primary path. DLO shards host weights across the DP group and can run DP multi-concurrency. | Supported rank-local path. DP replicas remain independent; no DLO weight sharding or cross-DP DLO collective. |
 | **SP** | Supported in the implementation. With DP=1, DLO uses the SP group for host-weight sharding; SP still shards sequence/activation work. | SP remains active, but DLO keeps standard-loader rank-local weights and adds no SP weight collective. |
-| **TP > 1** | Outside the Phase A shared-mmap support scope. The loader falls back before mutation, preserves TP-local layouts, and DLO may apply DP/SP host sharding to those ordinary runtime tensors. | Outside the Phase A shared-mmap support scope. The ordinary TP-aware loader produces rank-local tensors, which DLO streams without an additional weight collective; DP replicas retain private runtime storage. |
+| **TP > 1** | Unsupported in the DLO mmap path. TP-aware loader callbacks are bypassed, so the backend rejects this configuration when it enters that path. | Standard loading is retained and TP-local tensors can be streamed. This is the intended compatibility path, but it still needs broader model and hardware validation. |
 | **HSDP** | Rejected. HSDP has already sharded parameters, so DLO AllGather would double-shard them. | Accepted by configuration. HSDP owns parameter sharding and its own gathers; DLO only stages rank-local parameters. End-to-end coverage is limited. |

 ### Combined dimensions
@@ -203,18 +122,11 @@ AllGather DP multi-concurrency requires:
 The no-AllGather path does not impose these DLO-specific synchronized-wave
 requirements.

-Direct checkpoint mmap can back either transfer path. It is currently limited
-to proven TP1, non-HSDP, non-online-quantized layouts. Other layouts use the
-ordinary loader. Per-tensor online FP8 linears can use DLO AllGather after the
-ordinary loader finalizes their runtime weights and scales; DLO then shards and
-reconstructs those tensors with their recorded layouts. Other online methods
-must use `--dlo-no-use-allgather` or disable online quantization until their
-runtime layouts are validated.
-
-A normalized runtime mmap cache, built through the ordinary loader, is the
-proposed general mechanism for sharing transformed TP or quantized layouts.
-That cache and its publication/lifecycle protocol are intentionally outside
-this phase; see [RFC #6195](https://github.com/vllm-project/vllm-omni/issues/6195).
+The mmap loader is used only by the supported DLO+AllGather path when the
+model has a compatible checkpoint layout. Online quantization is incompatible
+with that sharded mmap path; use `--dlo-no-use-allgather` or disable online
+quantization. Models that do not meet the mmap requirements use the regular
+loader path.

 ## Validation coverage

@@ -222,77 +134,15 @@ Current source-level validation includes:

 - HSDP + DLO + AllGather rejection;
 - HSDP + DLO without AllGather acceptance at configuration level;
- loader preflight fallback for TP, HSDP, online quantization, unknown custom
-  loaders, missing keys, and shape/dtype mismatches;
- ordinary-loader fallback for per-tensor online FP8 linears followed by DLO
-  sharding of finalized weights and scales;
- exact loader-to-backend plan transfer and ordinary-loader fallback;
- rank-local mmap source retention, bounded two-slot staging, and adapter
-  transforms without parameter-side flags;
+- TP rejection in the DLO+AllGather mmap path;
 - resident-layer requests requiring no-AllGather;
 - DP request-wave validation for denoising-step compatibility;
 - sharding, double-buffer, AllGather-size, and heterogeneous-block regression
  tests.

-### B300 parallel-topology smoke matrix
-
-A four-GPU B300 smoke test covered MiniMax-H3 FL2VA with the same prompt, seed,
-CUDNN attention backend, 256x256 output, two denoising steps, and
-`dlo_resident_layers=0`. The TP2 rows used DiT DP2xTP2 with the text encoder and
-VAEs at TP1. They validate the ordinary-loader fallback only, not direct mmap
-or shared-mmap host-memory savings.
-
-| Configuration | Result | Warm E2E | Peak device memory | Host PSS |
-|---|---:|---:|---:|---:|
-| DP4xTP1 AllGather | Passed, 4 concurrent requests | 2.87 s / 4 requests | 13.84 GiB | 211.99 GiB |
-| DP4xTP1 no-AllGather | Passed, 1 request | 15.02 s | 13.23 GiB | 187.77 GiB |
-| DP2xTP2 AllGather | Passed, 2 concurrent requests | 4.16 s / 2 requests | 12.50 GiB | 211.97 GiB |
-| DP2xTP2 no-AllGather | Passed, 1 request | 3.51 s | 11.88 GiB | 314.01 GiB |
-
-Within each topology, the AllGather and no-AllGather video and audio outputs
-were byte-identical. All four runs completed without an `ERROR` or traceback
-and released their device allocations. For DP4xTP1, no-AllGather direct mmap
-reduced total PSS by 24.22 GiB (11.4%) and `Private_Dirty` from 211.33 to
-125.32 GiB (40.7%) relative to AllGather. For DP2xTP2, preflight selected the
-ordinary loader as designed; no-AllGather PSS was 314.01 GiB, about 48% above
-AllGather, because DP replicas did not share checkpoint-backed runtime
-weights. This is a functional and memory smoke test, not a production-quality
-performance or output-quality benchmark.
-
-### Host-memory measurement
-
-A two-worker MiniMax-H3 FL2VA measurement on one L20X node compared the
-ordinary-loader fallback with direct mmap. Both runs used
-DP=2, TP=1, no DLO AllGather, BF16 weights, two denoising steps, and a
-256x256 four-second request. The ordinary-loader workers were sampled after
-initialization. The mmap workers were sampled after one completed request, so
-the checkpoint working set had been faulted into the page cache; this is the
-more conservative point for mmap.
-
-The values below come from `/proc/<worker>/smaps_rollup` and include the whole
-worker, not only the DiT. The stable rank-to-rank difference comes from other
-pipeline components, so each worker should be compared with the same worker in
-the other storage mode.
-
-| Worker | Ordinary RSS | mmap RSS | Ordinary PSS | mmap PSS | PSS reduction |
-|---|---:|---:|---:|---:|---:|
-| DP worker 0 | 168.27 GiB | 132.76 GiB | 167.84 GiB | 101.43 GiB | 66.40 GiB |
-| DP worker 1 | 116.19 GiB | 79.97 GiB | 115.73 GiB | 48.64 GiB | 67.09 GiB |
-| **Two-worker total** | — | — | **283.56 GiB** | **150.08 GiB** | **133.48 GiB (47.1%)** |
-
-The direct-mmap workers each reported 62.45 GiB `Shared_Clean` but only
-31.20 GiB `Pss_File`, which is the proportional charge expected when the same
-resident checkpoint pages are mapped by two workers. `Private_Dirty` also fell
-from 167.53 to 70.24 GiB for worker 0 and from 115.40 to 17.44 GiB for worker
-1, a reduction of about 97–98 GiB per worker. RSS understates this benefit
-because it counts a shared physical page in every process that maps it; summed
-PSS is the appropriate node-memory comparison.
-
-The highest-value missing coverage is broader end-to-end numerical and
-lifecycle comparison against ordinary layerwise offload for DP+SP,
-HSDP+SP+no-AllGather, and TP greater than one across additional models and
-target CUDA/NCCL or CANN/HCCL hardware. That broader TP coverage does not
-change the Phase A direct-mmap TP1 support boundary.
+The highest-value missing coverage is end-to-end numerical comparison against
+ordinary layerwise offload for DP+SP, TP+no-AllGather, and HSDP+SP+no-AllGather
+on the target CUDA/NCCL or CANN/HCCL hardware.

 ## Recommendations

@@ -300,9 +150,7 @@ change the Phase A direct-mmap TP1 support boundary.
  scaling path.
 - Use **SP + DLO AllGather** for long-sequence workloads when DP concurrency is
  not the goal.
- Use **no-AllGather** when independent replica execution is required. TP1
-  direct-mmap deployments can share checkpoint pages per node; other layouts
-  retain the ordinary loader's private host memory behavior and are outside
-  the Phase A shared-mmap support scope.
+- Use **no-AllGather** to bring up TP or workstation PCIe configurations, with
+  the expectation of higher host-memory use and lower validation confidence.
 - Prefer **HSDP alone** for production HSDP deployments until the combined
  HSDP + DLO no-AllGather path has broader end-to-end coverage.
@@ -76,12 +76,6 @@ equivalent. `auto-round` and `auto_round` use the Omni INC/AutoRound adapter.
 ModelOpt checkpoint metadata is detected from its method, producer metadata, or
 `quant_algo`, then mapped to the corresponding vLLM ModelOpt configuration.

-Unless the input is a per-component dictionary, the resolved configuration is
-passed unchanged to every quantization-aware component constructed by the
-pipeline. It affects only layers supported by that component's quantization
-method; it does not rewrite arbitrary `torch.nn` modules. A per-component
-dictionary narrows this global scope through runtime layer-prefix routing.
-
 The factory uses Omni-specific overrides for methods whose diffusion behavior
 or platform dispatch is not provided by the vLLM registry. The current override
 set includes INT8, BitsAndBytes, MXFP8, MXFP4, MXFP4 dual-scale, and INC/
@@ -160,11 +154,9 @@ quantizable vLLM layers. Each method configuration is responsible for:
 Model code must keep precision-sensitive or unsupported modules unquantized.
 The shared `safe_quant_config()` helper is used by model integrations for
 normalization and modulation layers where a generic FP8 configuration would be
-unsafe. Encoders exposed through the quantization factory receive a global
-configuration just like the main transformer, but only their supported vLLM
-quantizable layers are affected. Ordinary `torch.nn` encoder layers, VAEs,
-schedulers, and tokenizers are not rewritten automatically. A component map can
-select a narrower scope, but it cannot add quantization support to a layer.
+unsafe. Text encoders, audio/vision encoders, VAEs, schedulers, and tokenizers
+are not implicitly quantized just because the main transformer is quantized;
+they require explicit component routing and method support.

 Weight quantization and quantized KV cache are separate paths. The former is
 resolved through `quantization_config`; runtime attention/KV-cache quantization
@@ -190,15 +182,12 @@ configured parallelism strategy:
 - `WeightsMapper` transformations must be applied before matching ignored-layer
  lists or component prefixes.

-Distributed layerwise offload has an additional boundary: direct checkpoint
-mmap cannot prepare online-quantized tensors because it bypasses the ordinary
-loader that creates their runtime weights and scales. Per-tensor online FP8
-linears can still use DLO AllGather through the ordinary loader: each rank first
-finalizes the FP8 tensors, then DLO shards those runtime tensors for
-reconstruction during prefetch. Other online methods remain limited to
-`--dlo-no-use-allgather` until their packing and scale layouts are validated.
-See the [DLO design document](offloader/distributed_layerwise_offload.md) for
-the full compatibility matrix.
+Distributed layerwise offload has an additional boundary: its sharded mmap
+AllGather path is not compatible with online quantization because the path
+expects already materialized checkpoint weights. Use the rank-local
+`--dlo-no-use-allgather` path or a compatible pre-quantized checkpoint when
+combining these features. See the [DLO design document](offloader/distributed_layerwise_offload.md)
+for the full compatibility matrix.

 ## Adding a quantization backend

@@ -1,213 +0,0 @@
-# Realtime AR-Diffusion sessions
-
-> **Status:** experimental. This document describes the internal execution
-> contract implemented under `vllm_omni.experimental.ar_diffusion`. It does not
-> define a public HTTP or WebSocket schema.
-
-## Scope
-
-Realtime AR-Diffusion serves world models that generate one autoregressive
-latent block per request while retaining model state across requests. The
-generic runtime owns identity, event ordering, session lifecycle, routing, and
-KV allocation. A model adapter owns model-specific controls and conditioning.
-
-LingBot World v2 is the first single-KV-branch integration. DreamZero uses the
-same runner capability with two logical CFG branches.
-
-## Identities
-
-Four identifiers have distinct lifetimes:
-
-| Identifier | Lifetime | Owner | Contract |
-| --- | --- | --- | --- |
-| `session_id` | One persistent world | Session manager | Selects runner KV and model-owned state. |
-| `event_id` | One control/prompt update | Transport adapter | Unique and monotonically increasing within a session, including across reset. |
-| `chunk_index` | One committed AR block position | Session | Contiguous from zero; reset starts again at zero. |
-| `request_id` | One chunk execution | Session | Correlates the tick with its returned metadata. |
-
-`AsyncOmni` keeps its normal UUID-suffixed engine routing ID. The tick
-`request_id` remains inside the immutable AR-Diffusion snapshot and the
-standard output metadata, while caller-visible `OmniRequestOutput.request_id`
-continues to use AsyncOmni's existing external-ID contract. Protocol identity
-therefore does not require a private entrypoint override.
-
-## Request lifecycle
-
-1. `ARDiffusionSession.accept_event()` validates and queues an immutable
-   `ARDiffusionSessionEvent`. Prompt and opaque control tracks may be updated in
-   the same event.
-2. `ARDiffusionSession.next_chunk()` serializes chunk execution and calls
-   `_build_tick_locked()`. The resulting `ARDiffusionTickRequest` is a snapshot
-   of the current prompt, reduced controls, ordered event IDs, and chunk index.
-3. `ARDiffusionOmniTickConsumer.execute_tick()` copies the stage sampling
-   parameters, stores the typed tick in
-   `sampling_params.extra_args["ar_diffusion_tick"]`, constructs the normal Omni
-   prompt through its `prompt_provider`, and invokes `AsyncOmni.generate()`.
-4. `AsyncOmni` allocates its normal unique engine routing ID and submits one
-   `OmniDiffusionRequest` through the orchestrator.
-5. `ARDiffusionModelRunner._request_session()` parses the typed tick.
-   `_get_or_create_session()` obtains runner-owned paged KV, and
-   `bind_ar_diffusion_state()` exposes it to the pipeline only for the duration
-   of `execute_model()`.
-6. The model pipeline validates the tick and generates exactly one latent
-   block. LingBot interprets camera controls in
-   `LingBotCameraControlReducer` and
-   `LingBotWorldCausalDMDPipeline.forward()`; the generic runtime does not parse
-   camera or action schemas.
-7. The pipeline returns one `DiffusionOutput` with the generated payload and an
-   `ARDiffusionChunkMetadata` mapping under `metadata["ar_diffusion"]`.
-   `AsyncOmni` exposes it as
-   `OmniRequestOutput.multimodal_output["metadata"]["ar_diffusion"]`.
-8. The consumer parses the standard envelope. The session commits its reducer
-   state, prompt, controls, event IDs, and next chunk index only when returned
-   metadata exactly equals the submitted tick snapshot.
-
-The metadata envelope is:
-
-```json
-{
-  "metadata": {
-    "ar_diffusion": {
-      "session_id": "world-7",
-      "request_id": "ar-world-7-3-...",
-      "chunk_index": 3,
-      "applied_event_ids": [10, 11]
-    }
-  }
-}
-```
-
-## Transaction and failure semantics
-
-Accepted events are not acknowledged as applied until the model output and
-metadata are validated. Reducer `prepare()` is speculative; reducer `commit()`
-and the session snapshot form one logical commit. A model, metadata, or reducer
-failure leaves the chunk index and queued events uncommitted and transitions the
-session to `FAILED`.
-
-After a tick failure, worker state is closed because a forward may have
-partially modified or already released KV. The same chunk is not retried in
-place. A caller must explicitly reset the session, starting again at chunk
-zero, or close it.
-
-Explicit close follows this state machine:
-
-```text
-ACTIVE or FAILED
-       |
-       v
-    CLOSING ---- worker cleanup succeeds ----> CLOSED
-       |
-       +------- worker cleanup fails --------> CLEANUP_FAILED
-                                                   |
-                                                   +-- close retry --> CLOSING
-```
-
-`CLEANUP_FAILED` is a local tombstone. The session manager retains it, rejects
-creation of the same `session_id`, and rejects reset. Only an idempotent close
-retry that succeeds removes the session. Transport disconnect uses the same
-path.
-
-## KV ownership and capacity
-
-`ARDiffusionKVCacheSpec` is the pipeline-owned immutable geometry contract. It
-declares:
-
- TP-local self-attention geometry, AR block size, sink, and recent window;
- logical KV branches and their worker-local slots;
- fixed-length cross-attention caches;
- scratch tokens required by an uncommitted forward;
- persistent model-owned CUDA bytes per resident session; and
- the requested resident-session capacity.
-
-The runner computes:
-
-```text
-required(capacity) =
-    scratch
-  + managed_self_attention_pages(capacity)
-  + capacity * cross_attention_bytes_per_session
-  + capacity * model_owned_state_bytes_per_session
-```
-
-`gpu_memory_fraction` is a soft expansion budget:
-
-```text
-configured_budget = available_device_bytes * gpu_memory_fraction
-effective_budget = max(configured_budget, required(1))
-```
-
-If `required(1)` exceeds actual available device memory, initialization fails.
-Otherwise the runner always admits one viable session and uses the configured
-fraction to select additional resident sessions up to the model-declared cap.
-All model-owned reservations are deducted before the paged self-attention pool
-is allocated.
-
-LingBot reports its persistent full-horizon image-condition tensor. DreamZero
-reports the measured 603 MiB per-session Wan VAE causal-convolution state.
-
-## Concurrency and routing limits
-
-The current runner executes one request at a time:
-
- `max_num_seqs` must be one;
- request batching and diffusion step execution are rejected;
- one AR block is generated per engine request; and
- an AR-Diffusion stage must have exactly one replica.
-
-The single-replica restriction is intentional. Multi-replica support requires
-session-affine routing so every tick, reset, and close reaches the worker that
-owns the session. Random or round-robin routing is not correct.
-
-Multiple resident sessions may share one runner. Capacity selection accounts
-for all persistent pools, and the runner performs LRU eviction through the same
-model lifecycle hook used by explicit close.
-
-## Model boundary
-
-The generic runtime transports controls as opaque
-`ARDiffusionControlInput(track, schema, data)` values. Model adapters validate
-and reduce those values. This keeps target/velocity, key state, pose,
-intrinsics, and other model-specific conventions out of the session protocol.
-
-For LingBot World v2:
-
- `lingbot_world/actions.py` parses key-state and trajectory controls;
- `lingbot_world/camera.py` loads or constructs camera trajectories and Plücker embeddings;
- `lingbot_world/transformer.py` implements checkpoint-compatible causal attention; and
- `lingbot_world/pipeline.py` constructs conditioning, owns small non-KV session state, and
-  produces one block plus the standard metadata envelope.
-
-## Non-goals
-
-This contract does not currently provide:
-
- a public HTTP or WebSocket request schema (the structured-interaction
-  frontend is tracked separately in
-  [#5527](https://github.com/vllm-project/vllm-omni/pull/5527));
- camera/action semantics shared by every world model;
- session migration or replication across workers;
- retry of an ambiguous partially executed chunk;
- cross-stage KV transfer; or
- stateful streaming VAE decode.
-
-Those features can be layered on top without adding LingBot-specific identity
-or lifecycle fields to the generic runtime.
-
-## Required regression coverage
-
-CPU contract tests cover:
-
- separation of tick correlation IDs from internal engine routing IDs;
- event ordering, metadata equality, and reducer fail-closed commit;
- close/disconnect failure tombstones and cleanup retry;
- capacity one under the default fraction for shipped LingBot geometry;
- capacity expansion under a tuned fraction;
- DreamZero model-owned state accounting;
- runner session reuse, reset, close, LRU eviction, and forward cleanup; and
- LingBot model registration, imports, camera/action reduction, and one-block
-  output metadata.
-
-GPU validation must additionally exercise real model loading, action input,
-prompt switching, rolling-window replacement, reset, close, finite output
-latents, and exact metadata/request identity.
@@ -22,7 +22,6 @@ implementation contract; it is not, by itself, a general support claim.
 - [Async Diffusion Output](feature/async_diffusion_output.md)
 - [Async Omni Output Materialization](feature/omni_async_output_materialization.md)
 - [Automatic Prefix Caching in Omni Models](feature/prefix_caching.md)
- [Realtime AR-Diffusion Sessions](feature/realtime_ar_diffusion.md)

 ### Communication

@@ -18,8 +18,8 @@ validation_paths:
  - tests/config/**
 upstream_refs:
  - vllm.config
-last_reviewed: 2026-08-11
-last_verified_commit: e356708da4cd39992405686dacfc32b18fcccc7f
+last_reviewed: 2026-08-07
+last_verified_commit: 3d7fc3b9ba3cac88d579d4dc35b78b0b641675fc
 ---

 # vLLM-Omni configuration
@@ -39,66 +39,11 @@ The PR review proposal to capture environment-derived values in configuration
 objects at initialization is an open design question, not a current
 invariant.

-## Current structured-stage reuse behavior
-
-The current RFC #4021 implementation uses upstream vLLM config classes as the
-schema source for AR and generation stages:
-
- `OmniStageLoadConfig` inherits `vllm.config.LoadConfig`.
- `OmniStageCacheConfig` inherits `vllm.config.CacheConfig`.
- `OmniStageSchedulerConfig` inherits `vllm.config.SchedulerConfig`.
- `OmniStageParallelConfig` inherits `vllm.config.ParallelConfig`.
-
-This inheritance preserves the existing load, cache, scheduler, and parallel
-concern boundaries while reducing duplicate downstream declarations. It does
-not, by itself, make every inherited field an effective vLLM-Omni runtime
-option. Construction and runtime consumption are separate surfaces:
-
-| Surface | Current behavior |
-| --- | --- |
-| Structured schema | Upstream dataclass fields are inherited. Their defaults, default factories, and applicable Pydantic validation can participate when the structured object is constructed. |
-| Omni input ownership | Pipeline/deploy/CLI construction continues to accept only fields with an existing structured owner. Direct construction of an inherited sub-config can expose a wider upstream schema. |
-| Engine projection | For AR and generation stages, reusable fields are discovered by intersecting each upstream config dataclass with upstream `EngineArgs`. Only constructor-explicit values are emitted; inherited defaults remain deferred to terminal materialization. |
-| Terminal materialization | The engine-owning process constructs the final upstream `VllmConfig` and performs model-, platform-, rank-, port-, and backend-dependent initialization. |
-
-The generated projection maps include known upstream naming differences such
-as `cache_dtype` to `kv_cache_dtype`, `policy` to `scheduling_policy`, and
-`data_parallel_master_ip` to `data_parallel_address`. A field added to both an
-upstream concern config and `EngineArgs` therefore enters the AR/generation
-projection without requiring a second downstream allowlist. Explicit inherited
-fields that have no projection raise an error rather than being silently
-dropped.
-
-Ownership exclusions remain deliberate. Stage topology owns `scheduler_cls`,
-cache owns `disable_hybrid_kv_cache_manager`, and Omni runtime owns
-`distributed_executor_backend` and `worker_cls`; vLLM's private API-process
-fields are terminal internals. These exclusions prevent one input from being
-constructed or projected through two config concerns. Effective-engine-argument
-tests cover both the dynamic mapping and this exclusion boundary.
-
-`CompilationConfig` and `ProfilerConfig` are direct structured-stage fields:
-`stage.compilation_config` and `stage.profiler_config`. They accept mapping/YAML
-inputs and complete upstream value objects. Mapping inputs are validated and
-materialized as their concrete upstream types when the structured config is
-constructed, so downstream consumers see one resolved representation.
-Prebuilt upstream objects retain their type across the structured projection
-boundary.
-
-Quantization remains on its existing Omni-owned transport contract. This
-reuse step does not adopt upstream `QuantizationConfigArgs`, because the
-current engine-specific quantization split needs to be resolved separately.
-
-Diffusion keeps its existing engine-field ownership and projection surface.
-Shared Python inheritance must not be treated as evidence that an LLM-only
-parallel input is effective for a diffusion stage.
-
 ## Safe-change guide while deferred

 Changes should trace every affected producer to its runtime consumer and test
 the structured, legacy, CLI, and deployment paths that are actually supported.
-For inherited vLLM fields, tests should separately cover constructor behavior
-and effective `EngineArgs` projection. Do not infer a stable contract from
-this placeholder page.
+Do not infer a stable contract from this placeholder page.

 ## Promotion gate

@@ -20,19 +20,13 @@ an implementation contract has a user-facing workflow.
 | Choose serial, batched, step-wise, or streaming diffusion execution | [Execution Modes and Streaming](../user_guide/diffusion/execution_modes.md) | [Diffusion Continuous Batching](../design/feature/diffusion_continuous_batching.md), [Async Diffusion Output](../design/feature/async_diffusion_output.md) |
 | Reclaim stage memory without restarting the server | [Sleep Mode](sleep_mode.md) | Runtime lifecycle behavior is documented in the user guide |

-The [Prefill-Decode Disaggregation design reference](pd_disaggregation.md)
-describes the experimental Qwen3-Omni runtime topology and its current
-integration boundaries. It is not yet a supported launch recipe.
-
 Some runtime designs are deliberately not promoted as standalone User Guide
 features yet:

- Generic, OmniConnector-based
-  [Disaggregated Inference](../design/feature/disaggregated_inference.md) is not
-  yet a standalone User Guide feature. Its
+- [Disaggregated Inference](../design/feature/disaggregated_inference.md) and
  [OmniConnector implementations](../design/index.md#communication) describe
-  transport contracts, while practical connector configuration remains under
-  [Pipeline and deploy configurations](../configuration/stage_configs.md).
+  topology and transport contracts. Practical deployment configuration remains
+  under [Pipeline and deploy configurations](../configuration/stage_configs.md).
 - [Async Chunk](../design/feature/async_chunk.md) and
  [Async Omni Output Materialization](../design/feature/omni_async_output_materialization.md)
  are model- and pipeline-dependent. Use the selected model's deploy
@@ -101,7 +101,7 @@ outputs = omni.generate(
 )

 # Access custom trajectory data
-output = outputs[0]
+output = outputs[0].request_output
 print(f"Trajectory timesteps shape: {output.metrics['trajectory_timesteps'].shape}")
 print(f"Trajectory latents shape: {output.latents.shape}")
 ```
@@ -1,140 +0,0 @@
-# Prefill-Decode (PD) Disaggregation (experimental)
-
-!!! warning "Experimental design reference"
-    PD disaggregation is only partially integrated on the current `main` branch.
-    The runtime contains PD detection and routing scaffolding, but there is no
-    supported deploy overlay or end-to-end validated launch recipe yet. Treat this
-    page as a description of the current design, not as production guidance.
-
-Prefill-decode disaggregation splits the Qwen3-Omni Thinker into two logical
-stages: a prefill worker that processes the prompt and produces KV cache, and a
-decode worker that imports that cache and generates tokens. The Talker and
-Code2Wav stages remain downstream; PD does not disaggregate the entire
-Qwen3-Omni pipeline.
-
-## Current design
-
-### Runtime topology
-
-```mermaid
-flowchart LR
-    client["Client request"] --> orchestrator["Omni Orchestrator"]
-    orchestrator -->|"original prompt<br/>max_tokens = 1"| prefill["Thinker prefill<br/>KV producer"]
-    prefill -->|"KV blocks"| connector[("vLLM KV connector")]
-    prefill -->|"KV routing metadata"| orchestrator
-    orchestrator -->|"original prompt<br/>remote KV parameters"| decode["Thinker decode<br/>KV consumer"]
-    connector -->|"remote KV load"| decode
-    decode -->|"generated text"| client
-```
-
-The orchestrator owns the request lifecycle, while the vLLM KV connector owns
-the bulk KV transfer. The original prompt is submitted to both Thinker stages:
-the decode worker still needs the request tokens and multimodal features to
-construct its request, while the imported KV cache prevents it from repeating
-the completed prefill computation.
-
-For the full Qwen3-Omni pipeline, splitting the Thinker adds one logical stage:
-
-| Stage | Role | Output or handoff |
-| --- | --- | --- |
-| 0 | Thinker prefill | Saves prompt KV and prompt-side multimodal output |
-| 1 | Thinker decode | Loads remote KV, generates text, and produces decode-side conditioning |
-| 2 | Talker | Converts combined Thinker conditioning into codec codes |
-| 3 | Code2Wav | Converts codec codes into audio |
-
-Text remains a final output of the decode stage, and audio remains a final
-output of Code2Wav.
-
-### Request lifecycle
-
-1. At startup, PD detection looks for one stage marked `is_prefill_only` and
-   one downstream stage marked `is_decode_only`. The decode stage must list the
-   prefill stage as an input source.
-2. The orchestrator clones the Thinker sampling parameters for prefill, forces
-   `max_tokens=1`, clears stop conditions, and sets producer-side
-   `kv_transfer_params`. This makes prefill finish by length after exporting KV.
-3. When prefill finishes, the orchestrator captures its KV routing metadata and
-   Qwen multimodal output under the original request ID.
-4. The orchestrator submits the original prompt to decode with consumer-side
-   `kv_transfer_params`. Those parameters identify the transfer, prefill engine,
-   bootstrap endpoint, and, when required by the connector, remote request.
-5. The decode engine imports the KV cache and continues generation. The normal
-   Omni stage path then carries Thinker conditioning through Talker and
-   Code2Wav.
-
-### Control and data paths
-
-PD uses two different connector layers. They solve different handoffs and are
-not interchangeable.
-
-| Path | Payload | Owner |
-| --- | --- | --- |
-| PD control path | Request ID, original prompt, `transfer_id`, bootstrap address, engine ID, and connector-specific request metadata | Omni Orchestrator |
-| PD data path | Thinker attention KV blocks | vLLM KV connector |
-| Downstream model path | Thinker hidden states and embeddings, Talker codec codes, and audio tensors | vLLM-Omni stage connectors and processors |
-
-### Detection and validation
-
-The current runtime accepts one logical prefill/decode pair per pipeline. Each
-stage may use tensor parallelism, but both sides of the pair must satisfy these
-invariants:
-
- both stages define `kv_transfer_config`
- prefill uses `kv_producer` or `kv_both`
- decode uses `kv_consumer` or `kv_both`
- both stages use the same KV connector
- `kv_buffer_device` and `kv_buffer_size` agree when set on both stages
- both stages use the same `tensor_parallel_size`
-
-These checks happen before request processing so an inconsistent PD topology
-fails during engine initialization.
-
-## What is implemented today
-
-| Area | Current state |
-| --- | --- |
-| Pair detection and topology validation | Implemented in the shared PD helper |
-| Prefill sampling preparation | Implemented for sync and async entrypoints |
-| Prefill-to-decode routing | Implemented in the current Orchestrator |
-| Deploy-based configuration | Not yet migrated; the supported `stages:` deploy schema cannot currently expand Qwen3-Omni into the four-stage PD topology |
-| Qwen3 Thinker-to-Talker handoff | Bridge state and embedding-merge helpers exist, but the merge is not wired into the live stage processor |
-| Connector compatibility | Incomplete; the Orchestrator currently requires `remote_request_id`, while the pinned Mooncake connector completes prefill without returning that metadata |
-| Validation coverage | PD entrypoint tests and the PD deploy overlay in online end-to-end tests are temporarily disabled during migration |
-| Performance | No non-regression or throughput/latency result is established for the current path |
-
-Because these pieces are still converging, the previous legacy `stage_args` YAML
-and `vllm serve` command have been removed. The serve CLI now accepts the
-deploy-based `stages:` schema, so retaining the legacy recipe would imply a
-launch path that current `main` rejects.
-
-## Configuration contract under development
-
-The intended deploy integration starts from the
-[default Qwen3-Omni deploy config](gh-file:vllm_omni/deploy/qwen3_omni_moe.yaml)
-and expands its frozen three-stage pipeline into the four stages shown above.
-A complete integration must:
-
- split the Thinker definition without duplicating the Talker or Code2Wav model
-  contracts
- remap downstream stage IDs, input sources, and stage connectors
- inject producer and consumer `kv_transfer_config` values into the two Thinker
-  stages
- allocate separate device sets for prefill and decode
- preserve both final outputs: decode text and Code2Wav audio
- provide connector-specific routing metadata without hard-coding one connector
-  contract
-
-The required number of GPUs depends on tensor-parallel sizes and whether Talker
-and Code2Wav share a device. A basic TP=1 layout uses one device for prefill, one
-for decode, and one for the two downstream audio stages.
-
-## Implementation map
-
- PD detection, validation, and sampling-parameter preparation:
-  [`PDDisaggregationMixin`](gh-file:vllm_omni/entrypoints/pd_utils.py)
- startup detection and connector metadata:
-  [`AsyncOmniEngine`](gh-file:vllm_omni/engine/async_omni_engine.py)
- request-scoped prefill-to-decode routing:
-  [`Orchestrator`](gh-file:vllm_omni/engine/orchestrator.py)
- Qwen3 downstream conditioning helpers:
-  [Qwen3-Omni stage input processor](gh-file:vllm_omni/model_executor/stage_input_processors/qwen3_omni.py)
@@ -47,7 +47,7 @@ if __name__ == "__main__":
    omni = Omni(model="Tongyi-MAI/Z-Image-Turbo")
    prompt = "a cup of coffee on the table"
    outputs = omni.generate(prompt)
-    images = outputs[0].images
+    images = outputs[0].request_output.images
    images[0].save("coffee.png")
 ```

@@ -75,7 +75,8 @@ if __name__ == "__main__":
    ]
    omni_outputs = omni.generate(prompts)
    for i_prompt, prompt_output in enumerate(omni_outputs):
-        this_images = prompt_output.images
+        this_request_output = prompt_output.request_output
+        this_images = this_request_output.images
        for i_image, image in enumerate(this_images):
            image.save(f"p{i_prompt}-img{i_image}.jpg")
            print("saved to", f"p{i_prompt}-img{i_image}.jpg")
@@ -37,24 +37,16 @@ th {
 | `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` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `LingBotWorldCausalDMDPipeline` | LingBot-World 2.0 v1 (experimental) | `robbyant/lingbot-world-v2-14b-causal-fast-diffusers` | Experimental | | | | — |
 | `Cosmos3OmniDiffusersPipeline` | Cosmos3 T2I, T2V, I2V, V2V, T2V with sound, action policy | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `SanaWmPipeline` | SANA-WM | `BBBBruce/SANA-WM_bidirectional-stage1-diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/NVIDIA/SANA-WM.md) |
 | `Wan22S2VPipeline` | Wan2.2-S2V | `Wan-AI/Wan2.2-S2V-14B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   |   | — |
 | `LTX2Pipeline` | LTX-2 / LTX-2.3 one-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | | — |
 | `LTX2TwoStagePipeline` | LTX-2 / LTX-2.3 ordinary two-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` + matching Lightricks LoRA and upsampler | ✅︎ | ✅︎ | | | — |
-| `LTX2DistilledOneStagePipeline` | LTX-2 / LTX-2.3 merged-distilled one-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled`, `diffusers/LTX-2.3-Distilled-Diffusers` | ✅︎ | ✅︎ | | | — |
-| `LTX2DistilledTwoStagePipeline` | LTX-2 / LTX-2.3 merged-distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled`, `diffusers/LTX-2.3-Distilled-Diffusers` + matching Lightricks upsampler | ✅︎ | ✅︎ | | | — |
-| `LTX2Pipeline` | LTX-2.5 Full/SFT one-stage T2V and I2V | `Lightricks/LTX-2.5-Diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/LTX/LTX-2.5.md) |
-| `LTX2TwoStagePipeline` | LTX-2.5 Full/SFT two-stage T2V and I2V | `Lightricks/LTX-2.5-Diffusers` + `Lightricks/LTX-2.5` LoRA | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/LTX/LTX-2.5.md) |
-| `LTX2DistilledOneStagePipeline` | LTX-2.5 merged-distilled one-stage T2V and I2V | `Lightricks/LTX-2.5-Diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/LTX/LTX-2.5.md) |
-| `LTX2DistilledTwoStagePipeline` | LTX-2.5 merged-distilled two-stage T2V and I2V | `Lightricks/LTX-2.5-Diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/LTX/LTX-2.5.md) |
+| `LTX2DistilledPipeline` | LTX-2 / LTX-2.3 full-distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled`, `diffusers/LTX-2.3-Distilled-Diffusers` + `Lightricks/LTX-2.3` upsampler | ✅︎ | ✅︎ | | | — |
 | `LingBotVideoPipeline` | LingBot-Video dense and MoE T2I, T2V, TI2V | `robbyant/lingbot-video-dense-1.3b`, `robbyant/lingbot-video-moe-30b-a3b` | ✅︎ | | | | — |
 | `MiniMaxH3Pipeline` | MiniMax H3 T2VA, first/last-keyframe FL2VA, and mixed-reference Ref2VA | `MiniMaxAI/MiniMax-H3` | ✅︎ | ✅︎ | | | [Published](https://recipes.vllm.ai/MiniMaxAI/MiniMax-H3) |
 | `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Pi0Pipeline` | π0 (Pi-Zero) VLA | `lerobot/pi0_base` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/lerobot/Pi0.md) |
 | `HeliosPipeline`, `HeliosPyramidPipeline` | Helios | `BestWishYsh/Helios-Base`, `BestWishYsh/Helios-Mid`, `BestWishYsh/Helios-Distilled` | ✅︎ | ✅︎ | ✅︎ | | — |
 | `MagiHumanPipeline` | MagiHuman | `SII-GAIR/daVinci-MagiHuman-Base-1080p` | ✅︎ | ✅︎ | | | — |
 | `OvisImagePipeline` | Ovis-Image | `OvisAI/Ovis-Image` | ✅︎ | ✅︎ | | ✅︎ | — |
@@ -79,7 +71,6 @@ th {
 | `SoulXSingerPipeline` | SoulX-Singer (SVS) | `Soul-AILab/SoulX-Singer` | ✅︎ | | | | — |
 | `SoulXSingerSVCPipeline` | SoulX-Singer-SVC | `Soul-AILab/SoulX-Singer` (`model-svc.pt`) | ✅︎ | | | | — |
 | `AudioXPipeline` | AudioX | `zhangj1an/AudioX` | ✅︎ | ✅︎ | | | — |
-| `MiniMaxMusic3ForConditionalGeneration` | MiniMax Music 3 (text-to-music) | `MiniMaxAI/MiniMax-Music3` | ✅︎ | | | | — |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | | | | [Published](https://recipes.vllm.ai/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice) |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
@@ -107,7 +98,6 @@ th {
 |`DyninOmniForConditionalGeneration` | Dynin-Omni | `snu-aidas/Dynin-Omni` | ✅︎ | | | | — |
 | `MiniCPMO45OmniForConditionalGeneration` | MiniCPM-o 4.5 | `openbmb/MiniCPM-o-4_5` | ✅︎ | | ✅︎ | | — |
 | `ErnieImagePipeline` | ERNIE-Image | `baidu/ERNIE-Image`, `baidu/ERNIE-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `GepardTalkerForConditionalGeneration` | Gepard-1.0 | `nineninesix/gepard-1.0` | ✅︎ | | | | — |
 |`HiDreamImagePipeline` | HiDream-I1-Full | `HiDream-ai/HiDream-I1-Full` | ✅︎ | ✅︎ | | | — |

 The `Recipe` column links the corresponding deployment recipe. `Published` is
@@ -121,10 +111,6 @@ support metadata until their recipe is audited.

 ✅︎ indicates the model is supported on that backend. Empty cells mean not listed as supported on that backend.

-See the [LingBot-World 2.0 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Robbyant/LingBot-World-2.0.md)
-for offline and experimental realtime usage, supported controls, validation,
-and current limitations.
-
 <sup>H3</sup> MiniMax H3 AMD GPU support is validated on gfx942 (MI300X) and
 gfx950 (MI350) in BF16, using the AITER `FLASH_ATTN` diffusion attention backend.
 Other AMD SKUs (e.g. MI325X) are not listed until their
@@ -141,20 +141,7 @@ 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`). Because the raw-bytes body has no JSON carrier, successful
-non-streaming responses from non-diffusion speech servers report usage through
-response headers:
-
-| Header | Description |
-| --- | --- |
-| `x-vllm-omni-input-tokens` | Total input tokens (`text_tokens` + `audio_tokens`). |
-| `x-vllm-omni-output-tokens` | Generated codec/audio tokens. |
-| `x-vllm-omni-total-tokens` | `input_tokens` + `output_tokens`. |
-| `x-vllm-omni-input-text-tokens` | Tokens from the synthesized text (`input` plus `instructions`). |
-| `x-vllm-omni-input-audio-tokens` | Reference-audio codec frames, non-zero only for in-context voice cloning. |
-
-Diffusion-mode speech servers route through a separate response path and do not
-emit these headers.
+`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.
@@ -167,14 +167,6 @@ transfer options such as `control_path`, `control_guidance`,
 `num_conditional_frames`, `show_control_condition`, and `show_input`; see the
 Cosmos3 recipe for complete examples.

-HTTP redirects for `image_reference.image_url` follow vLLM's
-`VLLM_MEDIA_URL_ALLOW_REDIRECTS` setting. Before starting the server, set it to
-`1` (the default) to allow redirects or `0` to reject them. A redirect target
-can differ from the original host, and vLLM-Omni does not yet fully implement
-upstream vLLM's media URL allowlist protection. Deployments should therefore
-treat remote media URLs as untrusted and choose this setting as part of their
-URL access policy.
-
 ### Speech-to-Video

 For models that support audio-driven generation (e.g., Wan2.2-S2V), pass both
@@ -3,9 +3,7 @@
 Distributed layerwise offloading (DLO) extends block streaming to multi-device
 deployments. With AllGather enabled, each rank stores roughly `1 / dp_size` of
 the host weights and reconstructs each layer at runtime. Without AllGather,
-each rank streams a complete block independently. Compatible TP1 deployments
-can share checkpoint-backed host pages among processes on the same node;
-otherwise DLO streams the ordinary loader's rank-local tensors.
+each rank streams its standard-loader rank-local weights independently.

 See the [DLO feature design](../../../design/feature/offloader/distributed_layerwise_offload.md)
 for the implementation contract and compatibility matrix.
@@ -61,48 +59,21 @@ omni = Omni(
 | `--enable-distributed-layerwise-offload` | Enable DLO | `false` |
 | `--data-parallel-size N` | DP ranks and AllGather weight-sharding group | `1` |
 | `--dlo-use-allgather` | Shard host weights and reconstruct with AllGather | `true` |
-| `--dlo-no-use-allgather` | Stream complete rank-local blocks without a DLO weight collective | `false` |
+| `--dlo-no-use-allgather` | Keep standard-loader rank-local weights | `false` |
 | `--dlo-resident-layers N` | Keep N leading main-DiT blocks on device; requires no-AllGather and model-declared resident paths | `0` |

-## Host-weight loading
+## mmap weight loading

-The diffusion loader chooses host storage before DLO is enabled. It first
-attempts to build a complete, validated direct-checkpoint mmap plan. If names,
-coverage, shape, dtype, topology, or loader-callback compatibility cannot be
-proven, it runs the ordinary model loader instead. DLO consumes that result and
-does not make a second checkpoint-compatibility decision.
-
-The shared-mmap optimization in this phase is supported only with TP1. TP
-greater than one falls back before model mutation to ordinary TP-aware loading.
-DLO may still consume those TP-local tensors, but this is a compatibility path:
-it does not share checkpoint-backed runtime weights across DP replicas and
-provides no shared-mmap host-memory guarantee.
-
-The mmap plan skips only dedicated DiT weight sources. Other component sources,
-such as a text encoder loaded through the shared diffusion loader, continue to
-use their ordinary component loader. A checkpoint source that mixes DiT and
-non-DiT weights falls back completely rather than leaving an unplanned
-component uninitialized.
-
-With direct checkpoint mmap, the loader:
+The DLO plus AllGather path:

 1. saves non-persistent buffers such as RoPE frequencies;
 2. moves the normally created transformer to the meta device;
 3. loads checkpoint tensors as mmap views backed by the shared OS page cache;
-4. applies any loader-owned bounded layout adapters while packing blocks;
-5. restores saved non-persistent buffers; and
-6. preserves `post_load_weights()` and `validate_loaded_weights()` lifecycle
-   hooks.
+4. calls model-specific `post_load_weights()` conversions; and
+5. restores the saved non-persistent buffers.

-For AllGather with a group larger than one, each process copies only its
-persistent shard and then releases the source mapping. For no-AllGather, each
-process keeps the mapping open and packs complete blocks through two bounded
-pinned staging slots. Processes mapping the same files on one node share the
-immutable pages; no-AllGather still performs a complete-block H2D copy in each
-process.
-
-When the effective DLO group size is one, `dlo_use_allgather=True` does not
-perform a collective and uses the same rank-local transfer behavior.
+This avoids one full checkpoint RSS copy per rank and does not require
+model-specific loading code.

 ## Declarative topology

@@ -132,25 +103,16 @@ must enter each collective.

 ## Limitations

- Direct checkpoint mmap currently requires TP1. TP greater than one is
-  outside the Phase A shared-mmap support scope and falls back before model
-  mutation to the ordinary TP-aware loader. DLO can stream that runtime layout,
-  but it provides no shared-mmap host-memory benefit or guarantee.
+- Online FP8 quantization is rejected with the DLO plus AllGather mmap path.
+  Use `--dlo-no-use-allgather` or disable online quantization.
+- Tensor parallel size greater than one is rejected in the mmap path because
+  it bypasses TP-aware loader callbacks. The no-AllGather path retains the
+  standard loader and remains experimental with TP.
 - HSDP plus AllGather is rejected to avoid double sharding. HSDP without
  AllGather has limited end-to-end validation.
- Per-tensor online FP8 linears use the ordinary loader and can run with either
-  DLO transfer path. With AllGather, every rank temporarily materializes the
-  complete FP8 model in host memory before DLO retains only its shard. Other
-  online quantization methods require no-AllGather until their runtime layouts
-  are validated.
 - Resident leading layers require `--dlo-no-use-allgather` and a model
  `OffloadPlan` that declares eligible `resident_dit_paths`.
 - DP concurrency requires an explicit, identical inference-step count.

-Sharing transformed TP or quantized runtime layouts through a normalized mmap
-cache is a follow-up design in
-[RFC #6195](https://github.com/vllm-project/vllm-omni/issues/6195), not part of
-the direct-checkpoint path.
-
 See the [Cosmos3 DistOffload recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/cosmos3/Cosmos3-DistOffload.md)
 for an end-to-end example.
@@ -152,8 +152,7 @@ In `DiffusionParallelConfig`:
 **Constraints:**

 - `hsdp_replicate_size × hsdp_shard_size == world_size`
- HSDP cannot be used with Tensor Parallelism, ordinary Data Parallelism,
-  Pipeline Parallelism, or Expert Parallelism.
+- HSDP cannot be used with Tensor Parallelism (`tensor_parallel_size` must be 1)

 ---

@@ -161,21 +161,12 @@ The following tables show which models support each feature:
 | **Wan2.1-VACE**              |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2**                    |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2.3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
-| **LTX-2.5**                  |     ❌     | ❓ (one-stage) | ❓ (Ulysses only) | ✅ (Full only) |         ❓         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |    ❓ (FP8)     |        ❌         |
 | **Helios**                   |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ✅*        |
 | **HunyuanVideo-1.5 T2V I2V** |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **DreamID-Omni**             |     ❌     |     ❌      |           ❌           |       ✅        |         ❌         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Cosmos3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **LongCat-Video-Avatar-1.5** |     ❌     |     ❌      |           ❌           |       ❌        |         ❌         |         ❌         |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **MiniMax-H3**               | ✅ (FL2VA) |     ✅      |           ✅           |       ❌        |       ✅ (DiT/TE)  |         ❌         |   ✅    |             ✅             |       ✅ (tile)       |      ✅ (DiT)      |        ❌         |
-| **SANA-WM**                  |     ❌     |     ❌      |          ❌<sup>5</sup> |       ✅        |         ✅         |         ❌         |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
-
-> Notes:
-> 5. SANA-WM cannot support sequence parallelism: its bidirectional gated delta
->    recurrence carries state across frames, so a rank cannot denoise a slice of
->    the token sequence in isolation. Doing so would need a distributed scan or
->    an all-gather before every GDN block. The remaining ❌ columns are simply
->    unvalidated on this model, not known-broken.

 > **Step execution note:** Helios supports single-request step execution only;
 > use `max_num_seqs=1`.
@@ -183,7 +174,7 @@ The following tables show which models support each feature:
 **Frame Interpolation Support**

 - **Supported**: Wan2.2 text-to-video, image-to-video, and TI2V pipelines
- **Not supported**: Wan2.1-VACE, LTX-2, LTX-2.3, LTX-2.5, Helios, HunyuanVideo-1.5, DreamID-Omni, SANA-WM
+- **Not supported**: Wan2.1-VACE, LTX-2, LTX-2.3, Helios, HunyuanVideo-1.5, DreamID-Omni

 ### AudioGen

@@ -43,7 +43,7 @@ if __name__ == "__main__":
            ),
        ],
    )
-    outputs[0].images[0].save("output.png")
+    outputs[0].request_output.images[0].save("output.png")
 ```

 ## Image-to-Image (Image Editing)
@@ -72,7 +72,7 @@ if __name__ == "__main__":
            ),
        ],
    )
-    outputs[0].images[0].save("output.png")
+    outputs[0].request_output.images[0].save("output.png")
 ```

 ## Generation Parameters
@@ -55,7 +55,7 @@ if __name__ == "__main__":
    omni = Omni(model="Qwen/Qwen-Image")
    prompt = "a cup of coffee on the table"
    outputs = omni.generate(prompt)
-    images = outputs[0].images
+    images = outputs[0].request_output.images
    images[0].save("coffee.png")
 ```

@@ -179,7 +179,7 @@ if __name__ == "__main__":
    ]
    outputs = omni.generate(prompts)
    for i, output in enumerate(outputs):
-        output.images[0].save(f"{i}.jpg")
+        output.request_output.images[0].save(f"{i}.jpg")
 ```

 !!! info
@@ -207,7 +207,7 @@ if __name__ == "__main__":
        }
    ])
    for i, output in enumerate(outputs):
-        output.images[0].save(f"{i}.jpg")
+        output.request_output.images[0].save(f"{i}.jpg")
 ```

 You can also pass a negative prompt via the CLI argument `--negative-prompt`:
@@ -65,7 +65,7 @@ More failure modes (for example, network jitter and network interruption) will b

 ## Source of Conclusions

-The behaviors and conclusions above are summarized from current fault injection validation results on **`Qwen3-Omni`**, **`Wan2.2`**, and **`HunyuanImage-3.0-Instruct`** (DiT-only, `/v1/images/generations`). Automated coverage lives under `tests/dfx/reliability/` (`test_reliability_qwen3_omni.py`, `test_reliability_wan22.py`, `test_reliability_hunyuan_image.py`) and runs weekly via `.buildkite/cuda/test-weekly.yml`.
+The behaviors and conclusions above are summarized from current fault injection validation results on **`Qwen3-Omni`**, **`Wan2.2`**, **`HunyuanImage-3.0-Instruct`** (DiT-only, `/v1/images/generations`), and **`VoxCPM2`** (`/v1/audio/speech`). Automated coverage lives under `tests/dfx/reliability/` (`test_reliability_qwen3_omni.py`, `test_reliability_wan22.py`, `test_reliability_hunyuan_image.py`, `test_reliability_voxcpm2.py`) and runs weekly via `.buildkite/cuda/test-weekly.yml`.

 ### Example: `SIGTERM` fault injection log (Qwen3-Omni)

@@ -95,7 +95,7 @@ warmup_quack_fp8([(14040, 2048, 6144), (14040, 2048, 2048)])
 | HunyuanImage-3.0 | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | Yes | Yes | All layers; use the Hunyuan stage config for multi-stage runs | None | |
 | HunyuanVideo-1.5 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `720p_t2v`, `480p_i2v` | Yes | Yes | All layers | None | |
 | Cosmos3 | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | Yes | Not validated | All layers | None | |
-| MiniMax-H3 | `MiniMaxAI/MiniMax-H3` (`FL2VA` / `Ref2VA`) | Yes | Not validated | `quantization="fp8"` quantizes eligible DiT and text-encoder linears; mixed-precision input/output heads stay FP32 | None | ✅︎ |
+| MiniMax H3 | `MiniMaxAI/MiniMax-H3` | Yes | Not validated | DiT linears except patch, timestep, and final projections; not compatible with layerwise offload | None | |

 ### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)

@@ -138,39 +138,6 @@ outputs = omni.generate(
 )
 ```

-### Global and per-component scope
-
-A plain, global quantization configuration is passed unchanged to every
-quantization-aware component constructed by a pipeline. This includes an
-eligible encoder when that encoder is implemented with vLLM quantizable
-layers; it does not rewrite arbitrary `torch.nn` modules. A structured
-component map is the only way to narrow that scope. Pipeline integrations that
-do not yet expose an encoder through the quantization factory remain DiT-only.
-
-For a pipeline that exposes both a transformer and a quantization-aware text
-encoder, the scope is:
-
-| Configuration | Transformer | Text encoder | Components without supported quantizable layers |
-|---------------|-------------|--------------|-------------------------------------------------|
-| `quantization="fp8"` | FP8 | FP8 | checkpoint precision |
-| `{"transformer": {"method": "fp8"}}` | FP8 | checkpoint precision | checkpoint precision |
-| `{"text_encoder": {"method": "fp8"}}` | checkpoint precision | FP8 | checkpoint precision |
-
-Use `quantization_config` for component-selective Python configuration. For
-example, quantize only the text decoder:
-
-```python
-omni = Omni(
-    model="<your-model>",
-    quantization_config={"text_encoder": {"method": "fp8"}},
-)
-```
-
-Component keys are runtime prefixes exposed by the pipeline integration; common
-keys include `transformer` and `text_encoder`. Entries may be combined, and
-`ignored_layers` can keep named eligible layers in checkpoint precision. Check
-the model recipe for supported components and their runtime prefixes.
-
 CLI:

 ```bash
@@ -179,24 +146,6 @@ python text_to_image.py --model <your-model> --quantization fp8 --ignored-layers
 vllm serve <your-model> --omni --quantization fp8
 ```

-### Distributed layerwise offload
-
-Per-tensor online FP8 linears can be combined with DLO's default AllGather path:
-
-```bash
-vllm serve <your-model> \
-  --omni \
-  --quantization fp8 \
-  --enable-distributed-layerwise-offload
-```
-
-Direct checkpoint mmap is not used for this combination. Each rank runs the
-ordinary loader to create the final FP8 weights and scales before DLO shards
-them, so startup temporarily materializes the complete FP8 model in host memory
-on every rank. Runtime DLO host residency is sharded normally. Use
-`--dlo-no-use-allgather` when the synchronized AllGather request-wave contract
-does not fit the workload.
-
 ## Parameters

 | Parameter | Type | Default | Description |
@@ -24,6 +24,7 @@ from vllm.assets.video import VideoAsset, video_to_ndarrays
 from vllm.multimodal.media.audio import load_audio
 from vllm.utils.argparse_utils import FlexibleArgumentParser

+from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.model_executor.stage_input_processors.aura_omni import (
    DEFAULT_QWEN3_TTS_REF_TEXT,
@@ -227,6 +228,7 @@ def parse_args():
    parser.add_argument("--init-timeout", type=int, default=2000, help="Overall initialization timeout.")
    parser.add_argument("--stage-init-timeout", type=int, default=2000, help="Per-stage initialization timeout.")
    parser.add_argument("--log-stats", action="store_true", default=False, help="Enable stats logging.")
+    nullify_stage_engine_defaults(parser)
    return parser.parse_args()


@@ -53,6 +53,7 @@ from typing import Any
 import torch
 from PIL import Image

+from vllm_omni.diffusion.data import DiffusionParallelConfig
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.outputs import OmniRequestOutput
@@ -138,6 +139,12 @@ async def main():

    # ---- Torch setup ----
    generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
+    parallel_config = DiffusionParallelConfig(
+        ulysses_degree=args.ulysses_degree,
+        ring_degree=args.ring_degree,
+        cfg_parallel_size=args.cfg_parallel_size,
+        tensor_parallel_size=args.tensor_parallel_size,
+    )

    # ---- Cache Config ----
    if args.cache_backend == "cache_dit":
@@ -164,10 +171,7 @@ async def main():
        vae_use_tiling=args.vae_use_tiling,
        cache_backend=args.cache_backend,
        cache_config=cache_config,
-        ulysses_degree=args.ulysses_degree,
-        ring_degree=args.ring_degree,
-        cfg_parallel_size=args.cfg_parallel_size,
-        tensor_parallel_size=args.tensor_parallel_size,
+        parallel_config=parallel_config,
        enforce_eager=args.enforce_eager,
        enable_cpu_offload=args.enable_cpu_offload,
        diffusion_load_format="dummy",
@@ -1,284 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Generate a LingBot-World v2 video from an image and camera trajectory.
-
-The official checkpoint is licensed separately under CC BY-NC-SA and is
-restricted to non-commercial use. This vLLM-Omni integration remains
-Apache-2.0 licensed.
-
-Example:
-    python examples/offline_inference/diffusion/lingbot_world_v2.py \
-        --prompt "The camera moves slowly forward through the scene." \
-        --image /path/to/first_frame.png \
-        --action-dir /path/to/actions/forward \
-        --output lingbot_world_v2.mp4
-
-``--action-dir`` must contain ``poses.npy`` and ``intrinsics.npy``. The
-example configures its parent as the trusted action root and sends the
-contained directory through ``extra_args["action_path"]``.
-"""
-
-from __future__ import annotations
-
-import argparse
-import math
-from collections.abc import Sequence
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-
-_MODEL = "robbyant/lingbot-world-v2-14b-causal-fast-diffusers"
-_NUM_INFERENCE_STEPS = 4
-_MAX_SEQUENCE_LENGTH = 512
-_MAX_PIXEL_AREA = 480 * 832
-_MAX_RAW_FRAMES = 117
-_MAX_SOURCE_CAMERA_FRAMES = 4096
-_TEMPORAL_COMPRESSION = 4
-_LATENT_FRAMES_PER_BLOCK = 3
-_SPATIAL_ALIGNMENT = 16
-
-
-@dataclass(frozen=True)
-class LingBotPaths:
-    image: Path
-    action_dir: Path
-    action_root: Path
-    action_relative: Path
-    camera_frames: int
-    output: Path
-
-
-def _validate_camera_arrays(action_dir: Path) -> int:
-    """Validate the bounded camera contract before starting the engine."""
-
-    try:
-        poses = np.load(action_dir / "poses.npy", mmap_mode="r", allow_pickle=False)
-        intrinsics = np.load(action_dir / "intrinsics.npy", mmap_mode="r", allow_pickle=False)
-    except (OSError, TypeError, ValueError):
-        raise ValueError("Camera files must be valid numeric poses.npy and intrinsics.npy arrays.") from None
-    if poses.ndim != 3 or poses.shape[1:] != (4, 4):
-        raise ValueError("poses.npy must have shape [frames, 4, 4].")
-    if intrinsics.ndim != 2 or intrinsics.shape[1:] != (4,):
-        raise ValueError("intrinsics.npy must have shape [frames, 4].")
-    if poses.shape[0] != intrinsics.shape[0]:
-        raise ValueError("poses.npy and intrinsics.npy must contain the same frame count.")
-    if not 1 <= poses.shape[0] <= _MAX_SOURCE_CAMERA_FRAMES:
-        raise ValueError("Camera arrays must contain between 1 and 4096 source frames.")
-    if poses.dtype.kind not in "fiu" or intrinsics.dtype.kind not in "fiu":
-        raise ValueError("Camera arrays must contain real numeric values.")
-    if not np.isfinite(poses).all() or not np.isfinite(intrinsics).all():
-        raise ValueError("Camera arrays must contain only finite values.")
-    return int(poses.shape[0])
-
-
-def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Generate a LingBot-World v2 video from a first-frame image and camera trajectory."
-    )
-    parser.add_argument("--model", default=_MODEL, help="Official Hugging Face model ID or local checkpoint path.")
-    parser.add_argument("--prompt", required=True, help="Text description of the scene and desired motion.")
-    parser.add_argument("--image", required=True, help="Path to the first-frame image.")
-    parser.add_argument(
-        "--action-dir",
-        required=True,
-        help="Camera action directory containing poses.npy and intrinsics.npy.",
-    )
-    parser.add_argument("--height", type=int, default=480, help="Output height; must be divisible by 16.")
-    parser.add_argument("--width", type=int, default=832, help="Output width; must be divisible by 16.")
-    parser.add_argument(
-        "--num-frames",
-        type=int,
-        default=9,
-        help="Raw output frame count. Nine frames are one three-latent-frame causal block.",
-    )
-    parser.add_argument("--seed", type=int, default=42, help="Random seed.")
-    parser.add_argument(
-        "--tensor-parallel-size",
-        type=int,
-        default=1,
-        help="Number of GPUs used for tensor parallelism inside the DiT.",
-    )
-    parser.add_argument("--flow-shift", type=float, default=5.0, help="Positive FlowUniPC scheduler shift.")
-    parser.add_argument("--fps", type=int, default=16, help="Frames per second in the exported MP4.")
-    parser.add_argument("--output", default="lingbot_world_v2.mp4", help="Output MP4 path.")
-    parser.add_argument(
-        "--enforce-eager",
-        action="store_true",
-        help="Disable compilation and force eager execution.",
-    )
-    return parser.parse_args(argv)
-
-
-def resolve_cli_paths(args: argparse.Namespace) -> LingBotPaths:
-    """Canonicalize local inputs and construct a safe trusted-root mapping."""
-
-    image = Path(args.image).expanduser().resolve()
-    if not image.is_file():
-        raise ValueError("--image must point to an existing file.")
-
-    action_dir = Path(args.action_dir).expanduser().resolve()
-    if not action_dir.is_dir():
-        raise ValueError("--action-dir must point to an existing directory.")
-    if not all((action_dir / name).is_file() for name in ("poses.npy", "intrinsics.npy")):
-        raise ValueError("--action-dir must contain poses.npy and intrinsics.npy.")
-    camera_frames = _validate_camera_arrays(action_dir)
-
-    action_root = action_dir.parent.resolve()
-    try:
-        action_relative = action_dir.relative_to(action_root)
-    except ValueError:
-        raise ValueError("--action-dir must be contained by its canonical trusted action root.") from None
-    if not action_relative.parts:
-        raise ValueError("--action-dir must name a directory contained by a trusted action root.")
-
-    output = Path(args.output).expanduser().resolve()
-    return LingBotPaths(
-        image=image,
-        action_dir=action_dir,
-        action_root=action_root,
-        action_relative=action_relative,
-        camera_frames=camera_frames,
-        output=output,
-    )
-
-
-def _positive_finite(value: float, flag: str) -> float:
-    result = float(value)
-    if not math.isfinite(result) or result <= 0:
-        raise ValueError(f"{flag} must be a positive finite number.")
-    return result
-
-
-def build_omni_kwargs(
-    args: argparse.Namespace,
-    paths: LingBotPaths,
-) -> dict[str, Any]:
-    """Build Omni configuration without overriding checkpoint class discovery."""
-
-    if args.tensor_parallel_size <= 0:
-        raise ValueError("--tensor-parallel-size must be a positive integer.")
-    flow_shift = _positive_finite(args.flow_shift, "--flow-shift")
-    model_path = Path(args.model).expanduser()
-    model = str(model_path.resolve()) if model_path.exists() else args.model
-    return {
-        "model": model,
-        "flow_shift": flow_shift,
-        "tensor_parallel_size": args.tensor_parallel_size,
-        "enforce_eager": args.enforce_eager,
-        "model_config": {"lingbot_action_root": str(paths.action_root)},
-    }
-
-
-def build_request(
-    args: argparse.Namespace,
-    paths: LingBotPaths,
-) -> tuple[dict[str, Any], dict[str, Any]]:
-    """Build the prompt and fixed v1 causal-DMD sampling contract."""
-
-    prompt_text = args.prompt.strip()
-    if not prompt_text:
-        raise ValueError("--prompt must contain non-whitespace text.")
-    if args.height <= 0 or args.width <= 0:
-        raise ValueError("--height and --width must be positive integers.")
-    if args.height % _SPATIAL_ALIGNMENT or args.width % _SPATIAL_ALIGNMENT:
-        raise ValueError("--height and --width must each be divisible by 16.")
-    if args.height * args.width > _MAX_PIXEL_AREA:
-        raise ValueError("--height * --width must not exceed 480 * 832 pixels.")
-    if args.num_frames <= 0:
-        raise ValueError("--num-frames must be a positive integer.")
-    if args.num_frames > _MAX_RAW_FRAMES:
-        raise ValueError("--num-frames must not exceed 117 raw frames.")
-    if args.num_frames > paths.camera_frames:
-        raise ValueError("Camera arrays must contain at least --num-frames frames.")
-    if (args.num_frames - 1) % _TEMPORAL_COMPRESSION:
-        raise ValueError("--num-frames must satisfy (num_frames - 1) divisible by 4.")
-    latent_frames = (args.num_frames - 1) // _TEMPORAL_COMPRESSION + 1
-    if latent_frames % _LATENT_FRAMES_PER_BLOCK:
-        raise ValueError("--num-frames must map to whole three-frame latent blocks.")
-    if args.fps <= 0:
-        raise ValueError("--fps must be a positive integer.")
-    flow_shift = _positive_finite(args.flow_shift, "--flow-shift")
-
-    prompt = {
-        "prompt": prompt_text,
-        "multi_modal_data": {"image": str(paths.image)},
-    }
-    sampling_kwargs = {
-        "height": args.height,
-        "width": args.width,
-        "num_frames": args.num_frames,
-        "num_inference_steps": _NUM_INFERENCE_STEPS,
-        "max_sequence_length": _MAX_SEQUENCE_LENGTH,
-        "seed": args.seed,
-        "fps": args.fps,
-        "extra_args": {
-            "action_path": paths.action_relative.as_posix(),
-            "flow_shift": flow_shift,
-        },
-    }
-    return prompt, sampling_kwargs
-
-
-def extract_video_array(outputs: Any) -> np.ndarray:
-    """Unwrap one standard Omni diffusion video into ``[frames, H, W, C]``."""
-
-    if not isinstance(outputs, list) or len(outputs) != 1:
-        raise ValueError("Expected exactly one Omni output for the single LingBot request.")
-    output = outputs[0]
-    request_output = getattr(output, "request_output", None)
-    if request_output is not None and getattr(request_output, "images", None):
-        output = request_output
-
-    images = getattr(output, "images", None)
-    if not images:
-        multimodal_output = getattr(output, "multimodal_output", None) or {}
-        images = multimodal_output.get("video")
-    if images is None:
-        raise ValueError("No video frames found in Omni output.")
-    if isinstance(images, list) and len(images) == 1:
-        images = images[0]
-
-    video: np.ndarray = np.asarray(images)
-    if video.ndim == 5:
-        if video.shape[0] != 1:
-            raise ValueError(f"Expected one generated video, got batch shape {video.shape}.")
-        video = video[0]
-    if video.ndim != 4 or video.shape[-1] not in (3, 4):
-        raise ValueError(f"Expected video shape [frames, height, width, channels], got {video.shape}.")
-    return video
-
-
-def main(argv: Sequence[str] | None = None) -> Path:
-    args = parse_args(argv)
-    paths = resolve_cli_paths(args)
-    prompt, sampling_kwargs = build_request(args, paths)
-    paths.output.parent.mkdir(parents=True, exist_ok=True)
-
-    # Keep vLLM imports below pure CLI/path validation so ``--help`` and the
-    # helper tests work on machines without the native vLLM runtime.
-    from diffusers.utils import export_to_video
-
-    from vllm_omni.entrypoints.omni import Omni
-    from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-    omni_kwargs = build_omni_kwargs(args, paths)
-    omni = Omni(**omni_kwargs)
-    try:
-        outputs = omni.generate(
-            prompt,
-            OmniDiffusionSamplingParams(**sampling_kwargs),
-            use_tqdm=False,
-        )
-    finally:
-        omni.close()
-
-    frames = extract_video_array(outputs)
-    export_to_video(frames, str(paths.output), fps=args.fps)
-    print(f"Saved {frames.shape[0]} frames to {paths.output}")
-    return paths.output
-
-
-if __name__ == "__main__":
-    main()
@@ -1,238 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Run LingBot-World 2.0 as an in-process realtime AR-Diffusion session.
-
-This example exercises the internal session API. It is not a public HTTP or
-WebSocket protocol. Each JSONL event advances the world by one three-latent
-frame AR block and writes the latent plus its identity metadata. The current
-scoped implementation supports at most ten ticks per generation epoch because
-its image condition is bounded to 117 pixel frames; reset or create a session
-to start a new world.
-"""
-
-from __future__ import annotations
-
-import argparse
-import asyncio
-import json
-import math
-import time
-from collections.abc import Sequence
-from pathlib import Path
-from typing import Any
-
-_MODEL = "robbyant/lingbot-world-v2-14b-causal-fast-diffusers"
-_CAMERA_ACTION_SCHEMA = "lingbot.camera_actions.v1"
-_FRAMES_PER_BLOCK = 3
-# ((117 pixel frames - 1) / VAE temporal factor 4 + 1) / 3 latent frames.
-_MAX_REALTIME_TICKS = 10
-
-
-def _camera_event_data(frames: list[list[str]]) -> dict[str, Any]:
-    """Build the event-side script consumed by LingBotCameraControlReducer."""
-    return {"mode": "script", "frames": frames}
-
-
-def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Run an in-process realtime LingBot-World 2.0 session.")
-    parser.add_argument("--model", default=_MODEL, help="Hugging Face model ID or local checkpoint path.")
-    parser.add_argument("--image", required=True, help="Initial RGB image.")
-    parser.add_argument("--prompt", required=True, help="Initial scene prompt.")
-    parser.add_argument(
-        "--events",
-        required=True,
-        help="JSONL file with one prompt/action event per AR block; at most 10 events.",
-    )
-    parser.add_argument("--output-dir", required=True, help="Directory for chunk latents and metadata.")
-    parser.add_argument("--session-id", default="lingbot-world", help="Persistent world session identifier.")
-    parser.add_argument("--height", type=int, default=480)
-    parser.add_argument("--width", type=int, default=832)
-    parser.add_argument("--seed", type=int, default=42)
-    parser.add_argument("--gpu-memory-fraction", type=float, default=0.1)
-    parser.add_argument("--tensor-parallel-size", type=int, default=1)
-    parser.add_argument("--enforce-eager", action="store_true")
-    return parser.parse_args(argv)
-
-
-def _load_events(path: Path) -> list[dict[str, Any]]:
-    events: list[dict[str, Any]] = []
-    for line_number, raw_line in enumerate(path.read_text().splitlines(), start=1):
-        line = raw_line.strip()
-        if not line:
-            continue
-        try:
-            value = json.loads(line)
-        except json.JSONDecodeError as exc:
-            raise ValueError(f"events line {line_number} is not valid JSON: {exc.msg}.") from None
-        if not isinstance(value, dict):
-            raise ValueError(f"events line {line_number} must be a JSON object.")
-        event_id = value.get("event_id")
-        if isinstance(event_id, bool) or not isinstance(event_id, int) or event_id < 0:
-            raise ValueError(f"events line {line_number} requires a non-negative integer event_id.")
-        prompt = value.get("prompt")
-        if prompt is not None and (not isinstance(prompt, str) or not prompt.strip()):
-            raise ValueError(f"events line {line_number} prompt must be a non-empty string.")
-        frames = value.get("frames")
-        if frames is not None:
-            if not isinstance(frames, list) or len(frames) != _FRAMES_PER_BLOCK:
-                raise ValueError(f"events line {line_number} frames must contain exactly three lists.")
-            for frame in frames:
-                if not isinstance(frame, list) or any(not isinstance(action, str) for action in frame):
-                    raise ValueError(f"events line {line_number} frames must contain only action strings.")
-        if prompt is None and frames is None:
-            raise ValueError(f"events line {line_number} must update prompt and/or frames.")
-        events.append({"event_id": event_id, "prompt": prompt, "frames": frames})
-    if not events:
-        raise ValueError("events file must contain at least one event.")
-    if len(events) > _MAX_REALTIME_TICKS:
-        raise ValueError(
-            "events file must contain at most "
-            f"{_MAX_REALTIME_TICKS} events because the current LingBot realtime "
-            "image-condition horizon is 117 pixel frames."
-        )
-    event_ids = [event["event_id"] for event in events]
-    if event_ids != sorted(set(event_ids)):
-        raise ValueError("event_id values must be unique and strictly increasing.")
-    return events
-
-
-def _validate_args(args: argparse.Namespace) -> tuple[Path, Path, Path]:
-    image = Path(args.image).expanduser().resolve()
-    events = Path(args.events).expanduser().resolve()
-    output_dir = Path(args.output_dir).expanduser().resolve()
-    if not image.is_file():
-        raise ValueError("--image must point to an existing file.")
-    if not events.is_file():
-        raise ValueError("--events must point to an existing JSONL file.")
-    if not args.prompt.strip():
-        raise ValueError("--prompt must contain non-whitespace text.")
-    if args.height <= 0 or args.width <= 0 or args.height % 16 or args.width % 16:
-        raise ValueError("--height and --width must be positive multiples of 16.")
-    if args.tensor_parallel_size <= 0:
-        raise ValueError("--tensor-parallel-size must be positive.")
-    if not math.isfinite(args.gpu_memory_fraction) or not 0 < args.gpu_memory_fraction <= 1:
-        raise ValueError("--gpu-memory-fraction must be in (0, 1].")
-    return image, events, output_dir
-
-
-async def run(argv: Sequence[str] | None = None) -> Path:
-    args = parse_args(argv)
-    image, events_path, output_dir = _validate_args(args)
-    events = _load_events(events_path)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Imports stay below pure input validation so --help and helper tests do not
-    # require a CUDA-enabled vLLM installation.
-    import torch
-
-    from vllm_omni.diffusion.models.lingbot_world.actions import LingBotCameraControlReducer
-    from vllm_omni.entrypoints.async_omni import AsyncOmni
-    from vllm_omni.experimental.ar_diffusion.consumer import ARDiffusionOmniTickConsumer
-    from vllm_omni.experimental.ar_diffusion.session import (
-        ARDiffusionSessionEvent,
-        ARDiffusionSessionManager,
-        ARDiffusionWorkerLifecycle,
-    )
-    from vllm_omni.experimental.ar_diffusion.tick_protocol import ARDiffusionControlInput
-    from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-    engine = AsyncOmni(
-        model=args.model,
-        engine_backend="vllm_omni.experimental.ar_diffusion.engine.ARDiffusionEngine",
-        enforce_eager=args.enforce_eager,
-        tensor_parallel_size=args.tensor_parallel_size,
-        max_num_seqs=1,
-        model_config={
-            "ar_diffusion_height": args.height,
-            "ar_diffusion_width": args.width,
-            "ar_diffusion_kv_config": {
-                "gpu_memory_fraction": args.gpu_memory_fraction,
-                "warmup_cudagraph": True,
-            },
-        },
-    )
-    sampling = OmniDiffusionSamplingParams(
-        height=args.height,
-        width=args.width,
-        num_frames=9,
-        num_inference_steps=4,
-        max_sequence_length=512,
-        seed=args.seed,
-        output_type="latent",
-        extra_args={"flow_shift": 5.0},
-    )
-    consumer = ARDiffusionOmniTickConsumer(
-        engine,
-        prompt_provider=lambda tick: {
-            "prompt": tick.prompt,
-            "multi_modal_data": {"image": str(image)},
-        },
-        sampling_params_list=[sampling],
-        diffusion_stage_id=0,
-    )
-    manager = ARDiffusionSessionManager(
-        tick_consumer=consumer,
-        lifecycle=ARDiffusionWorkerLifecycle(engine, stage_ids=[0], timeout=180.0),
-        max_pending_events=32,
-        control_reducer_factory=LingBotCameraControlReducer,
-    )
-    session = await manager.create_session(args.session_id)
-    measurements: list[dict[str, Any]] = []
-    try:
-        for chunk_index, event in enumerate(events):
-            controls = ()
-            if event["frames"] is not None:
-                controls = (
-                    ARDiffusionControlInput(
-                        track="camera",
-                        schema=_CAMERA_ACTION_SCHEMA,
-                        data=_camera_event_data(event["frames"]),
-                    ),
-                )
-            await session.accept_event(
-                ARDiffusionSessionEvent(
-                    event_id=event["event_id"],
-                    prompt=event["prompt"]
-                    if event["prompt"] is not None
-                    else (args.prompt if chunk_index == 0 else None),
-                    controls=controls,
-                )
-            )
-            started = time.perf_counter()
-            output = await session.next_chunk()
-            elapsed = time.perf_counter() - started
-            if len(output.images) != 1 or not isinstance(output.images[0], torch.Tensor):
-                raise RuntimeError("Expected one latent tensor from each realtime LingBot chunk.")
-            latent = output.images[0].detach().float().cpu()
-            metadata = consumer.chunk_metadata(output).to_dict()
-            torch.save(latent, output_dir / f"chunk_{chunk_index:03d}.pt")
-            (output_dir / f"chunk_{chunk_index:03d}.json").write_text(
-                json.dumps(metadata, indent=2, sort_keys=True) + "\n"
-            )
-            measurements.append(
-                {
-                    "chunk_index": chunk_index,
-                    "latency_seconds": elapsed,
-                    "shape": list(latent.shape),
-                    "finite": bool(torch.isfinite(latent).all()),
-                    "metadata": metadata,
-                }
-            )
-            print(json.dumps(measurements[-1], sort_keys=True), flush=True)
-    finally:
-        try:
-            await manager.close_session(args.session_id)
-        finally:
-            engine.shutdown()
-
-    summary_path = output_dir / "summary.json"
-    summary_path.write_text(json.dumps({"chunks": measurements}, indent=2, sort_keys=True) + "\n")
-    return summary_path
-
-
-def main(argv: Sequence[str] | None = None) -> Path:
-    return asyncio.run(run(argv))
-
-
-if __name__ == "__main__":
-    main()
@@ -96,6 +96,7 @@ from typing import Any
 import torch
 from PIL import Image

+from vllm_omni.diffusion.data import DiffusionParallelConfig
 from vllm_omni.diffusion.utils.image_output import extract_images_from_outputs
 from vllm_omni.diffusion.utils.param_utils import apply_declared_extra_args
 from vllm_omni.entrypoints.omni import Omni
@@ -451,6 +452,14 @@ def main():

    generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)

+    parallel_config = DiffusionParallelConfig(
+        ulysses_degree=args.ulysses_degree,
+        ring_degree=args.ring_degree,
+        cfg_parallel_size=args.cfg_parallel_size,
+        tensor_parallel_size=args.tensor_parallel_size,
+        enable_expert_parallel=args.enable_expert_parallel,
+    )
+
    # Configure cache based on backend type
    cache_config = None
    if args.cache_backend == "cache_dit":
@@ -481,11 +490,7 @@ def main():
        vae_use_tiling=args.vae_use_tiling,
        cache_backend=args.cache_backend,
        cache_config=cache_config,
-        ulysses_degree=args.ulysses_degree,
-        ring_degree=args.ring_degree,
-        cfg_parallel_size=args.cfg_parallel_size,
-        tensor_parallel_size=args.tensor_parallel_size,
-        enable_expert_parallel=args.enable_expert_parallel,
+        parallel_config=parallel_config,
        enable_cpu_offload=args.enable_cpu_offload,
        enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
        profiler_config=args.profiler_config,
@@ -96,7 +96,7 @@ if __name__ == "__main__":
        ),
    )
    from diffusers.utils import export_to_video
-    frames = outputs[0].images
+    frames = outputs[0].request_output.images
    export_to_video(frames, "quick_test_i2v.mp4", fps=16)
 ```

@@ -56,6 +56,7 @@ import numpy as np
 import PIL.Image
 import torch

+from vllm_omni.diffusion.data import DiffusionParallelConfig
 from vllm_omni.diffusion.utils.param_utils import apply_declared_extra_args
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
@@ -67,7 +68,6 @@ from vllm_omni.model_extras import (
 from vllm_omni.model_extras import (
    get_extra_body_params,
    get_model_class_name,
-    should_preserve_reference_image_size,
 )
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.platforms import current_omni_platform
@@ -454,13 +454,7 @@ def main():

    media_inputs: dict[str, Any] = {}
    if image is not None:
-        preserve_image_size = should_preserve_reference_image_size(
-            model_class_name,
-            model=args.model,
-        )
-        media_inputs["image"] = (
-            image if preserve_image_size else image.resize((width, height), PIL.Image.Resampling.LANCZOS)
-        )
+        media_inputs["image"] = image.resize((width, height), PIL.Image.Resampling.LANCZOS)
    if last_image is not None:
        media_inputs["last_image"] = last_image.resize((width, height), PIL.Image.Resampling.LANCZOS)
    if mask_image is not None:
@@ -502,6 +496,17 @@ def main():
        }

    profiler_enabled = args.profiler_config is not None
+    parallel_config = DiffusionParallelConfig(
+        ulysses_degree=args.ulysses_degree,
+        ring_degree=args.ring_degree,
+        cfg_parallel_size=args.cfg_parallel_size,
+        tensor_parallel_size=args.tensor_parallel_size,
+        vae_patch_parallel_size=args.vae_patch_parallel_size,
+        use_hsdp=args.use_hsdp,
+        hsdp_shard_size=args.hsdp_shard_size,
+        hsdp_replicate_size=args.hsdp_replicate_size,
+        pipeline_parallel_size=args.pipeline_parallel_size,
+    )
    omni_kwargs = dict(
        model=args.model,
        enable_layerwise_offload=args.enable_layerwise_offload,
@@ -512,15 +517,7 @@ def main():
        diffusion_kv_cache_skip_steps=args.diffusion_kv_cache_skip_steps,
        diffusion_kv_cache_skip_layers=args.diffusion_kv_cache_skip_layers,
        enable_cpu_offload=args.enable_cpu_offload,
-        ulysses_degree=args.ulysses_degree,
-        ring_degree=args.ring_degree,
-        cfg_parallel_size=args.cfg_parallel_size,
-        tensor_parallel_size=args.tensor_parallel_size,
-        vae_patch_parallel_size=args.vae_patch_parallel_size,
-        use_hsdp=args.use_hsdp,
-        hsdp_shard_size=args.hsdp_shard_size,
-        hsdp_replicate_size=args.hsdp_replicate_size,
-        pipeline_parallel_size=args.pipeline_parallel_size,
+        parallel_config=parallel_config,
        enforce_eager=args.enforce_eager,
        model_class_name=model_class_name,
        cache_backend=args.cache_backend,
@@ -82,6 +82,9 @@ def parse_args():
    # ``max_num_seqs=1`` …) are passed as flat kwargs to ``Omni`` below
    # and ``create_default_diffusion`` materializes the stage config.

+    from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
+
+    nullify_stage_engine_defaults(p)
    return p.parse_args()


@@ -15,6 +15,7 @@ import imageio
 import numpy as np
 import torch

+from vllm_omni.diffusion.data import DiffusionParallelConfig
 from vllm_omni.diffusion.models.longcat_video.pipeline_longcat_video_avatar import (
    prepare_longcat_video_avatar_model_for_omni,
 )
@@ -235,6 +236,14 @@ def main():
        dtype="bfloat16",
        enforce_eager=args.enforce_eager,
        additional_config=additional_config,
+        parallel_config=DiffusionParallelConfig(
+            ulysses_degree=1,
+            ring_degree=1,
+            cfg_parallel_size=1,
+            tensor_parallel_size=1,
+            vae_patch_parallel_size=1,
+            pipeline_parallel_size=1,
+        ),
    )

    audio_input: str | dict[str, str] | None = args.audio
@@ -335,7 +335,7 @@ def main(args):
    print(f"use_tts (tts_bos): {use_tts}")

    for stage_outputs in omni_generator:
-        output = stage_outputs
+        output = stage_outputs.request_output
        if stage_outputs.final_output_type == "text":
            request_id = output.request_id
            text_output = output.outputs[0].text
@@ -1,32 +1,21 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Validate MiniMax-H3 request-mode and DLO lifecycle behavior.
-
-The DLO modes accept any positive data-parallel size.  With AllGather enabled,
-the script submits one complete DP wave and verifies recovery after an invalid
-wave.  It also checks that every mode shuts down all worker processes cleanly.
+"""Reproduce MiniMax-H3 request-mode and DLO/DP2 lifecycle behavior.

 Examples:

    VLLM_WORKER_MULTIPROC_METHOD=spawn \
    VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
    VLLM_OMNI_DLO_DP_WAVE_TIMEOUT=600 \
-    CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python examples/offline_inference/minimax_h3/dlo_lifecycle.py \
-        --model /path/to/MiniMax-H3/FL2VA --mode dlo --dp-size 4
-
-    # Rank-local mmap storage, with no DLO collective or DP wave scheduling.
-    VLLM_WORKER_MULTIPROC_METHOD=spawn \
-    VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
    CUDA_VISIBLE_DEVICES=0,1 \
-    python examples/offline_inference/minimax_h3/dlo_lifecycle.py \
-        --model /path/to/MiniMax-H3/FL2VA --mode dlo-no-allgather --dp-size 2
+    python examples/offline_inference/minimax_h3/dlo_dp2_lifecycle.py \
+        --model /path/to/MiniMax-H3/FL2VA --mode dlo-dp2

    VLLM_WORKER_MULTIPROC_METHOD=spawn \
    VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
    CUDA_VISIBLE_DEVICES=0,1 \
-    python examples/offline_inference/minimax_h3/dlo_lifecycle.py \
-        --model /path/to/MiniMax-H3/FL2VA --mode request --tp-size 2
+    python examples/offline_inference/minimax_h3/dlo_dp2_lifecycle.py \
+        --model /path/to/MiniMax-H3/FL2VA --mode request
 """

 from __future__ import annotations
@@ -51,33 +40,14 @@ DEFAULT_PROMPTS = (
 )


-def positive_int(value: str) -> int:
-    parsed = int(value)
-    if parsed < 1:
-        raise argparse.ArgumentTypeError("must be a positive integer")
-    return parsed
-
-
 def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--model", required=True, help="Path to MiniMax-H3/FL2VA")
    parser.add_argument(
        "--mode",
-        choices=("dlo", "dlo-no-allgather", "request"),
+        choices=("dlo-dp2", "request"),
        required=True,
-        help="DLO (with or without AllGather) or ordinary non-DLO request mode",
-    )
-    parser.add_argument(
-        "--dp-size",
-        type=positive_int,
-        default=2,
-        help="DLO data-parallel size (for example 1, 2, 4, or 8; default: 2)",
-    )
-    parser.add_argument(
-        "--tp-size",
-        type=positive_int,
-        default=2,
-        help="Tensor-parallel size used only by request mode (default: 2)",
+        help="DLO AllGather DP2 or ordinary non-DLO TP2 request mode",
    )
    parser.add_argument("--steps", type=int, default=2)
    parser.add_argument("--duration", type=float, default=5.0)
@@ -90,11 +60,10 @@ def parse_args() -> argparse.Namespace:


 def engine_kwargs(args: argparse.Namespace) -> dict[str, Any]:
-    is_dlo = args.mode in {"dlo", "dlo-no-allgather"}
    common: dict[str, Any] = {
        "model": args.model,
        "trust_remote_code": True,
-        "num_gpus": args.dp_size if is_dlo else args.tp_size,
+        "num_gpus": 2,
        "usp": 1,
        "ring": 1,
        "vae_parallel_mode": "tile",
@@ -105,22 +74,22 @@ def engine_kwargs(args: argparse.Namespace) -> dict[str, Any]:
        "stage_init_timeout": args.init_timeout,
        "init_timeout": args.init_timeout,
    }
-    if is_dlo:
+    if args.mode == "dlo-dp2":
        common.update(
            tensor_parallel_size=1,
-            data_parallel_size=args.dp_size,
+            data_parallel_size=2,
            text_encoder_tp_size=1,
            vae_patch_parallel_size=1,
            enable_distributed_layerwise_offload=True,
-            dlo_use_allgather=args.mode == "dlo",
+            dlo_use_allgather=True,
            dlo_resident_layers=0,
        )
    else:
        common.update(
-            tensor_parallel_size=args.tp_size,
+            tensor_parallel_size=2,
            data_parallel_size=1,
-            text_encoder_tp_size=args.tp_size,
-            vae_patch_parallel_size=args.tp_size,
+            text_encoder_tp_size=2,
+            vae_patch_parallel_size=2,
            enable_distributed_layerwise_offload=False,
        )
    return common
@@ -203,8 +172,7 @@ async def run(args: argparse.Namespace) -> dict[str, Any]:
        "engine_kwargs": engine_kwargs(args),
    }
    try:
-        uses_dp_wave = args.mode == "dlo" and args.dp_size > 1
-        if uses_dp_wave:
+        if args.mode == "dlo-dp2":
            started = time.perf_counter()
            asymmetric = await asyncio.gather(
                generate_one(
@@ -214,15 +182,12 @@ async def run(args: argparse.Namespace) -> dict[str, Any]:
                    prompt="",
                    seed=1001,
                ),
-                *(
-                    generate_one(
-                        engine,
-                        args,
-                        request_id=f"invalid-peer-{index}",
-                        prompt=DEFAULT_PROMPTS[index % len(DEFAULT_PROMPTS)],
-                        seed=1001 + index,
-                    )
-                    for index in range(1, args.dp_size)
+                generate_one(
+                    engine,
+                    args,
+                    request_id="invalid-peer",
+                    prompt=DEFAULT_PROMPTS[0],
+                    seed=1002,
                ),
                return_exceptions=True,
            )
@@ -230,18 +195,18 @@ async def run(args: argparse.Namespace) -> dict[str, Any]:
            summary["asymmetric_errors"] = [
                f"{type(result).__name__}: {result}" for result in asymmetric if isinstance(result, BaseException)
            ]
-            if len(summary["asymmetric_errors"]) != args.dp_size:
-                raise RuntimeError("Every request in the asymmetric DP wave must fail before dispatch")
+            if len(summary["asymmetric_errors"]) != 2:
+                raise RuntimeError("Both requests in the asymmetric DP wave must fail before dispatch")

        started = time.perf_counter()
-        request_count = args.dp_size if uses_dp_wave else 1
+        request_count = 2 if args.mode == "dlo-dp2" else 1
        outputs = await asyncio.gather(
            *(
                generate_one(
                    engine,
                    args,
                    request_id=f"recovery-{index}",
-                    prompt=DEFAULT_PROMPTS[index % len(DEFAULT_PROMPTS)],
+                    prompt=DEFAULT_PROMPTS[index],
                    seed=2000 + index,
                )
                for index in range(request_count)
@@ -67,7 +67,7 @@ def main() -> None:
    parser.add_argument("--output-dir", default="results/nemotron_voicechat")
    parser.add_argument("--system-prompt", default=DEFAULT_SYSTEM_PROMPT)
    parser.add_argument(
-        "--deploy-config",
+        "--stage-configs-path",
        default=None,
        help=(
            "Deploy yaml override. Pass vllm_omni/deploy/nemotron_labs_voicechat_streaming.yaml "
@@ -134,8 +134,8 @@ def main() -> None:
    ]

    omni_kwargs: dict = {}
-    if args.deploy_config:
-        omni_kwargs["deploy_config"] = args.deploy_config
+    if args.stage_configs_path:
+        omni_kwargs["stage_configs_path"] = args.stage_configs_path
    omni = Omni(model=str(ckpt), trust_remote_code=True, **omni_kwargs)
    outputs = omni.generate(inputs, sampling_params_list)

@@ -23,6 +23,7 @@ from typing import Any

 import torch

+from vllm_omni.diffusion.data import DiffusionParallelConfig
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.platforms import current_omni_platform
@@ -249,6 +250,17 @@ def main():
            "scm_steps_policy": "dynamic",
        }

+    parallel_config = DiffusionParallelConfig(
+        tensor_parallel_size=args.tensor_parallel_size,
+        cfg_parallel_size=args.cfg_parallel_size,
+        vae_patch_parallel_size=args.vae_patch_parallel_size,
+        ring_degree=args.ring_degree,
+        ulysses_degree=args.ulysses_degree,
+        use_hsdp=args.use_hsdp,
+        hsdp_shard_size=args.hsdp_shard_size,
+        hsdp_replicate_size=args.hsdp_replicate_size,
+    )
+
    profiler_enabled = args.profiler_config is not None

    omni_kwargs = dict(
@@ -259,14 +271,7 @@ def main():
        vae_use_tiling=args.vae_use_tiling,
        enable_cpu_offload=args.enable_cpu_offload,
        enable_layerwise_offload=args.enable_layerwise_offload,
-        tensor_parallel_size=args.tensor_parallel_size,
-        cfg_parallel_size=args.cfg_parallel_size,
-        vae_patch_parallel_size=args.vae_patch_parallel_size,
-        ring_degree=args.ring_degree,
-        ulysses_degree=args.ulysses_degree,
-        use_hsdp=args.use_hsdp,
-        hsdp_shard_size=args.hsdp_shard_size,
-        hsdp_replicate_size=args.hsdp_replicate_size,
+        parallel_config=parallel_config,
        enforce_eager=args.enforce_eager,
        enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
        cache_backend=args.cache_backend,
@@ -38,6 +38,7 @@ from typing import Any
 import numpy as np
 import torch

+from vllm_omni.diffusion.data import DiffusionParallelConfig
 from vllm_omni.diffusion.utils.param_utils import apply_declared_extra_args
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
@@ -305,13 +306,17 @@ def main():
    print(f"  Seed: {args.seed}")
    print(f"{'=' * 60}\n")

+    parallel_config = DiffusionParallelConfig(
+        use_hsdp=args.use_hsdp,
+        hsdp_shard_size=args.hsdp_shard_size,
+        hsdp_replicate_size=args.hsdp_replicate_size,
+    )
+
    # Initialize Omni. AudioX requires an explicit pipeline class; Stable Audio
    # is resolved from its config.
    omni_kwargs: dict[str, Any] = dict(
        model=args.model,
-        use_hsdp=args.use_hsdp,
-        hsdp_shard_size=args.hsdp_shard_size,
-        hsdp_replicate_size=args.hsdp_replicate_size,
+        parallel_config=parallel_config,
        cache_backend=args.cache_backend,
        cache_config=cache_config,
        enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
@@ -74,7 +74,7 @@ if __name__ == "__main__":
    omni = Omni(model="Qwen/Qwen-Image")
    prompt = "a cup of coffee on the table"
    outputs = omni.generate(prompt)
-    images = outputs[0].images
+    images = outputs[0].request_output.images
    images[0].save("coffee.png")
 ```

@@ -265,7 +265,7 @@ if __name__ == "__main__":
    ]
    outputs = omni.generate(prompts)
    for i, output in enumerate(outputs):
-        output.images[0].save(f"{i}.jpg")
+        output.request_output.images[0].save(f"{i}.jpg")
 ```

 !!! info
@@ -301,7 +301,7 @@ if __name__ == "__main__":
        }
    ])
    for i, output in enumerate(outputs):
-        output.images[0].save(f"{i}.jpg")
+        output.request_output.images[0].save(f"{i}.jpg")
 ```

 You can also pass a negative prompt via the CLI argument `--negative-prompt`:
@@ -16,7 +16,6 @@ list of supported architectures across all modalities, see
 |---|---|---|---|---|---|---|
 | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | 2 (talker + code2wav) | ✓ | ✓ | — | 24 kHz |
 | Fish Speech S2 Pro | `fishaudio/s2-pro` | dual-AR | ✓ | ✓ | — | 44.1 kHz |
-| Gepard-1.0 | `nineninesix/gepard-1.0` | single (native AR + NanoCodec) | — (zero-shot; cloning WIP) | — (serving WIP) | zero-shot | 22.05 kHz |
 | GLM-TTS | `zai-org/GLM-TTS` | 2 (AR + DiT) | ✓ (required) | ✓ | — | 24 kHz |
 | Ming-omni-tts | `inclusionAI/Ming-omni-tts-0.5B` | 2 (AR + audio VAE) | ✓ | ✓ | style / IP / dialect / TTA / podcast | 44.1 kHz |
 | Ming-flash-omni-TTS | `Jonathan1909/Ming-flash-omni-2.0` | single (talker only) | — (caption-controlled) | — | style / IP / basic captions | 44.1 kHz |
@@ -396,58 +395,6 @@ python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py \

 ---

-## Gepard-1.0
-
-Single-stage native AR TTS at 22.05 kHz. Pipeline: `Qwen3.5 backbone → 32 FSQ codebook heads (one frame/step) → NeMo NanoCodec`. Backbone runs under vLLM paged attention; the 32-head sampling + learned embedding feedback ride the native-AR runner hooks. `enforce_eager` (CUDA graph is a perf follow-up).
-
-### Prerequisites
-The NanoCodec decoder needs NeMo (NVIDIA Open Model License), installed separately:
-
-```bash
-uv pip install "nemo_toolkit[tts]==2.7.3" \
-    --overrides <(printf '%s\n' "transformers>=5.5.3" "huggingface-hub>=1.0")
-```
-
-The overrides are what make that command work. `nemo_toolkit[tts]` declares
-`transformers~=4.57.0`, so a plain install downgrades the version
-`requirements/common.txt` pins and takes `huggingface-hub` back to 0.x with it — after
-which `import vllm` fails. Both packages are named here rather than left to resolve on
-their own: transformers 5.x happens to require `huggingface-hub` 1.x, but relying on
-that would make the second repair an accident of the first. `--constraint` cannot be
-used instead, since it narrows a version range rather than overriding a dependency a
-package declares, and the two sets never intersect. NeMo 2.7.3 decodes correctly
-against transformers 5.x, so nothing is lost by holding it there.
-
-(The same clash is why this is documented here rather than declared in
-`pyproject.toml`: an extra resolves together with the base dependencies, so
-`vllm-omni[gepard]` would be unsatisfiable rather than opt-in. SoulX-Singer English
-SVS documents `nemo_toolkit[asr]` the same way.)
-
-On a host whose CUDA toolkit cannot build kernels for the GPU — no `nvcc`/`ninja`, or a
-consumer Blackwell (`sm_120`) card — also set `VLLM_USE_FLASHINFER_SAMPLER=0`. vLLM's
-sampler JIT-compiles through FlashInfer on first use, and where that build fails the engine
-dies inside `profile_run` long after every import has succeeded, which reads as a model
-failure rather than an environment one. The PyTorch sampler it falls back to costs nothing
-here.
-
-### Quick start (zero-shot, default voice)
-```bash
-python examples/offline_inference/text_to_speech/gepard/end2end.py \
-    --text "Hello, this is a Gepard demo."
-```
-
-### Voice cloning
-Not yet — PR1 is zero-shot only (the learned `null_prefix` default voice). Reference-audio cloning (`ref_compressor` speaker prefix) is a follow-up PR.
-
-### Notes
- Output: 22.05 kHz mono WAV.
- First run downloads two checkpoints, not one: the model itself, and the NanoCodec decoder it names in `codec_id` (`nvidia/nemo-nano-codec-22khz-1.89kbps-21.5fps` by default). Both are fetched automatically; expect the first startup to be correspondingly slower.
- Offline only for now; the `/v1/audio/speech` adapter is a follow-up PR.
- Deploy config: `vllm_omni/deploy/gepard.yaml` (the example's default; copy it and pass `--deploy-config` to change it).
- Generation length and reproducibility are stage settings, not script flags. One output token is one audio frame, so `max_tokens` in the YAML is the frame budget; `seed` makes the in-model 32-head sampling reproducible. The script deliberately passes no `SamplingParams`: one supplied by a caller replaces the stage defaults wholesale rather than merging, which would drop the pipeline's stop token and run every request to `max_tokens`.
- Text length: short texts are repeated internally to match the training layout (the checkpoint's `text_repetition` block); the upper bound is the stage's `max_model_len`, enforced by the engine. Empty text is rejected rather than voiced.
- `VLLM_GEPARD_GREEDY=1` swaps the 32-head Gumbel-max sampling for argmax, for reproducible comparisons that do not depend on a seed.
-
 ## VoxCPM2

 Single-stage native AR TTS at 48 kHz. Pipeline: `feat_encoder → MiniCPM4 → FSQ → residual_lm → LocDiT → AudioVAE`.
@@ -38,6 +38,17 @@ from vllm_omni.utils.tracking_parser import TrackingArgumentParser
 logger = logging.getLogger(__name__)

 DEFAULT_MODEL = "fishaudio/s2-pro"
+DEFAULT_DEPLOY_CONFIG = os.path.join(
+    os.path.dirname(__file__),
+    "..",
+    "..",
+    "..",
+    "..",
+    "vllm_omni",
+    "model_executor",
+    "stage_configs",
+    "fish_speech_s2_pro.yaml",
+)


 def build_prompt(
@@ -141,8 +152,7 @@ def _build_omni_kwargs(args, model_name: str) -> dict:
        log_stats=args.log_stats,
        stage_init_timeout=args.stage_init_timeout,
    )
-    if args.deploy_config:
-        omni_kwargs["deploy_config"] = args.deploy_config
+    omni_kwargs["deploy_config"] = args.deploy_config or DEFAULT_DEPLOY_CONFIG
    return omni_kwargs


@@ -1,139 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Offline inference example for Gepard-1.0 TTS via vLLM-Omni.
-
-Single-stage native-AR pipeline: a Qwen3.5 backbone samples one 32-code FSQ
-frame per step; the NeMo NanoCodec decodes the frames to a 22.05 kHz mono
-waveform. Zero-shot: the default learned voice, no reference audio.
-
-Generation length and seed are stage settings rather than flags here. One
-output token is one audio frame, so ``max_tokens`` in the deploy YAML is the
-frame budget; pass a copy of the YAML via ``--deploy-config`` to change it.
-
-Usage:
-  python end2end.py --text "Hello, this is Gepard speaking."
-"""
-
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import soundfile as sf
-import torch
-
-import vllm_omni
-from vllm_omni.utils.tracking_parser import TrackingArgumentParser
-
-os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
-
-from vllm_omni import Omni  # noqa: E402
-
-MODEL = "nineninesix/gepard-1.0"
-SAMPLE_RATE = 22050
-# Absolute: the loader hands the path straight to OmegaConf, which resolves a
-# bare name against the current working directory, not the package.
-DEFAULT_DEPLOY_CONFIG = str(Path(vllm_omni.__file__).resolve().parent / "deploy" / "gepard.yaml")
-
-
-def build_request(text: str) -> dict:
-    """Build an Omni request payload for Gepard (zero-shot, no ref audio).
-
-    The model only consumes the [speaker slots, SOT, text, EOT, SOS] layout, so
-    a bare text prompt would have no speaker slots and no SOS.
-    """
-    from transformers import AutoTokenizer
-
-    from vllm_omni.model_executor.models.gepard.configuration_gepard import GepardConfig
-    from vllm_omni.model_executor.models.gepard.prompt import build_gepard_prompt_ids
-
-    # From the checkpoint's own sidecar, which is what the worker builds its
-    # config from too. A layout that disagrees with the trained one does not
-    # fail, it just degrades the speech.
-    cfg = GepardConfig.from_checkpoint(MODEL)
-    tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
-    prompt_token_ids = build_gepard_prompt_ids(
-        tokenizer(text, add_special_tokens=False)["input_ids"],
-        config=cfg,
-    )
-    return {
-        "prompt_token_ids": prompt_token_ids,
-        "additional_information": {"text": [text]},
-    }
-
-
-def save_audio(waveform: torch.Tensor, path: str, sample_rate: int = SAMPLE_RATE) -> None:
-    sf.write(path, waveform.float().numpy(), sample_rate)
-    seconds = waveform.numel() / sample_rate
-    print(
-        f"  Saved {path} ({tuple(waveform.shape)}, {sample_rate} Hz, "
-        f"{seconds:.2f}s, peak {float(waveform.abs().max()):.3f})"
-    )
-
-
-def main(args) -> None:
-    omni = Omni(
-        model=MODEL,
-        deploy_config=args.deploy_config,
-        stage_init_timeout=args.stage_init_timeout,
-        init_timeout=args.init_timeout,
-    )
-
-    # No explicit SamplingParams: one supplied here would replace the stage
-    # defaults rather than merge over them, dropping the pipeline's stop token.
-    output_dir = Path(args.output_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    print(f"Synthesizing: {args.text!r}")
-    inputs = build_request(text=args.text)
-
-    for i, stage_outputs in enumerate(omni.generate(inputs)):
-        for j, out in enumerate(stage_outputs.outputs):
-            mm = out.multimodal_output
-            if mm is None:
-                print(f"  [req {i}] No audio output.")
-                continue
-            # The consolidation path renames "model_outputs" to "audio".
-            audio = mm.get("audio")
-            if audio is None:
-                audio = mm.get("model_outputs")
-            if isinstance(audio, list):
-                audio = audio[0] if len(audio) else None
-            if audio is None:
-                print(f"  [req {i}] No waveform in multimodal_output (keys: {list(mm)}).")
-                continue
-            sr_tensor = mm.get("sr")
-            sr = int(sr_tensor.item()) if hasattr(sr_tensor, "item") else SAMPLE_RATE
-            save_audio(audio.cpu(), str(output_dir / f"output_{i}_{j}.wav"), sr)
-
-    print("Done.")
-
-
-def parse_args():
-    parser = TrackingArgumentParser(description="Gepard-1.0 offline TTS inference (zero-shot)")
-    parser.add_argument("--text", default="Hello, this is Gepard speaking.", help="Text to synthesize.")
-    parser.add_argument(
-        "--output-dir",
-        default=os.path.join(
-            os.environ.get("XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache")),
-            "gepard_output",
-        ),
-        help="Directory for WAV outputs (default: ~/.cache/gepard_output).",
-    )
-    parser.add_argument(
-        "--deploy-config",
-        default=DEFAULT_DEPLOY_CONFIG,
-        help=(
-            "Path to the deploy YAML (default: the packaged vllm_omni/deploy/gepard.yaml). This must "
-            "stay set: without the YAML's `pipeline: gepard` pin the architectures fallback routes "
-            "Qwen3_5ForCausalLM to the diffusion registry. Copy it to change max_tokens or seed."
-        ),
-    )
-    # The upstream defaults are too tight for a cold start.
-    parser.add_argument("--stage-init-timeout", type=int, default=900)
-    parser.add_argument("--init-timeout", type=int, default=1800)
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    main(parse_args())
@@ -13,7 +13,6 @@ import yaml
 from transformers import AutoTokenizer
 from vllm.utils.argparse_utils import FlexibleArgumentParser

-from vllm_omni.model_executor.models.common.ming.speaker_extractor import SpeakerEmbeddingExtractor
 from vllm_omni.model_executor.models.ming_tts.config_ming_tts import (
    KEY_CFG,
    KEY_MAX_DECODE_STEPS,
@@ -22,6 +21,7 @@ from vllm_omni.model_executor.models.ming_tts.config_ming_tts import (
    SAMPLE_RATE,
 )
 from vllm_omni.model_executor.models.ming_tts.prompt_assembly import build_ming_dense_prompt
+from vllm_omni.model_executor.models.ming_tts.speaker_extractor import MingSpeakerEmbeddingExtractor

 try:
    from .runner import (
@@ -142,8 +142,7 @@ def _resolve_speaker_embedding(args, case: dict, paths: list[str]):
        return _load_speaker_embedding(args.speaker_embedding)
    if not (case.get("auto_extract_speaker_embeddings") or args.extract_speaker_embeddings) or not paths:
        return None
-    # Runs before the engine is built, so nothing has pre-fetched campplus.onnx.
-    embs = SpeakerEmbeddingExtractor(args.model, allow_download=True).extract_many(paths)
+    embs = MingSpeakerEmbeddingExtractor(args.model).extract_many(paths)
    if not embs:
        raise RuntimeError("Speaker extraction produced no embeddings")
    return embs[0] if len(embs) == 1 else torch.stack(embs, dim=0)
@@ -32,10 +32,11 @@ from end2end import _estimate_prompt_len
 from vllm.utils.argparse_utils import FlexibleArgumentParser

 from vllm_omni import Omni
+from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
 from vllm_omni.utils.forced_aligner import align, build_forced_aligner_config


-def _default_deploy_config() -> str:
+def _default_stage_config() -> str:
    repo_root = Path(__file__).resolve().parents[4]
    return str(repo_root / "vllm_omni" / "deploy" / "qwen3_tts.yaml")

@@ -108,7 +109,7 @@ def main(args: Any) -> None:
        "output_dir",
    ):
        omni_kwargs.pop(key, None)
-    omni_kwargs["deploy_config"] = args.deploy_config or _default_deploy_config()
+    omni_kwargs["stage_configs_path"] = args.stage_configs_path or _default_stage_config()
    omni_kwargs["log_stats"] = args.log_stats
    omni = Omni(**omni_kwargs)

@@ -155,7 +156,7 @@ def parse_args() -> Any:
        default=None,
        help="Optional YAML file for forced aligner settings (incl. gpu_memory_utilization)",
    )
-    parser.add_argument("--deploy-config", default=None, help="Qwen3-TTS deploy YAML")
+    parser.add_argument("--stage-configs-path", default=None, help="Qwen3-TTS deploy YAML")
    parser.add_argument("--text", default="Hello world.", help="Text to synthesize and align")
    parser.add_argument("--language", default="English", help="Qwen3-TTS language field")
    parser.add_argument("--speaker", default="Vivian", help="CustomVoice speaker name")
@@ -163,6 +164,7 @@ def parse_args() -> Any:
    parser.add_argument("--max-new-tokens", type=int, default=2048, help="TTS max_new_tokens")
    parser.add_argument("--output-dir", default="output_audio", help="Directory for WAV and JSON sidecar")
    parser.add_argument("--log-stats", action="store_true", default=False, help="Enable vLLM-Omni stats logging")
+    nullify_stage_engine_defaults(parser)
    return parser.parse_args()


@@ -10,6 +10,7 @@ from typing import Any
 import numpy as np
 import torch

+from vllm_omni.diffusion.data import DiffusionParallelConfig
 from vllm_omni.diffusion.utils.param_utils import apply_declared_extra_args
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
@@ -461,6 +462,17 @@ def main():
            "scm_steps_policy": "dynamic",
        }

+    # Configure parallel settings
+    parallel_config = DiffusionParallelConfig(
+        ulysses_degree=args.ulysses_degree,
+        ring_degree=args.ring_degree,
+        cfg_parallel_size=args.cfg_parallel_size,
+        tensor_parallel_size=args.tensor_parallel_size,
+        vae_patch_parallel_size=args.vae_patch_parallel_size,
+        pipeline_parallel_size=args.pipeline_parallel_size,
+        enable_expert_parallel=args.enable_expert_parallel,
+    )
+
    profiler_enabled = args.profiler_config is not None

    omni_kwargs = dict(
@@ -469,13 +481,7 @@ def main():
        vae_use_slicing=args.vae_use_slicing,
        vae_use_tiling=args.vae_use_tiling,
        enable_cpu_offload=args.enable_cpu_offload,
-        ulysses_degree=args.ulysses_degree,
-        ring_degree=args.ring_degree,
-        cfg_parallel_size=args.cfg_parallel_size,
-        tensor_parallel_size=args.tensor_parallel_size,
-        vae_patch_parallel_size=args.vae_patch_parallel_size,
-        pipeline_parallel_size=args.pipeline_parallel_size,
-        enable_expert_parallel=args.enable_expert_parallel,
+        parallel_config=parallel_config,
        enforce_eager=args.enforce_eager,
        model_class_name=model_class_name,
        cache_backend=args.cache_backend,
@@ -564,8 +570,6 @@ def main():
        num_frames=args.num_frames,
        extra_args=extra_args,
    )
-    if args.frame_rate is not None:
-        sampling_kwargs["frame_rate"] = args.frame_rate
    if args.guidance_scale_high is not None:
        sampling_kwargs["guidance_scale_2"] = args.guidance_scale_high

@@ -11,6 +11,7 @@ import numpy as np
 from PIL import Image
 from vllm.multimodal.media.audio import load_audio

+from vllm_omni.diffusion.data import DiffusionParallelConfig
 from vllm_omni.diffusion.utils.media_utils import mux_video_audio_bytes
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
@@ -196,6 +197,14 @@ def main() -> None:
            },
        )

+    parallel_config = DiffusionParallelConfig(
+        cfg_parallel_size=args.cfg_parallel_size,
+        tensor_parallel_size=args.tensor_parallel_size,
+        use_hsdp=args.use_hsdp,
+        hsdp_shard_size=args.hsdp_shard_size,
+        hsdp_replicate_size=args.hsdp_replicate_size,
+    )
+
    cache_config = None
    if args.cache_backend == "cache_dit":
        cache_config = {
@@ -213,11 +222,7 @@ def main() -> None:

    omni_kwargs = dict(
        model=args.model,
-        cfg_parallel_size=args.cfg_parallel_size,
-        tensor_parallel_size=args.tensor_parallel_size,
-        use_hsdp=args.use_hsdp,
-        hsdp_shard_size=args.hsdp_shard_size,
-        hsdp_replicate_size=args.hsdp_replicate_size,
+        parallel_config=parallel_config,
        model_type=args.model_type,
        enable_cpu_offload=args.enable_cpu_offload,
        enable_layerwise_offload=args.enable_layerwise_offload,
@@ -1,104 +0,0 @@
-# π0 (Pi-Zero) VLA — OpenPI realtime serving
-
-[π0](https://www.physicalintelligence.company/blog/pi0) is a Vision-Language-Action
-model from Physical Intelligence: multi-camera images + a language instruction +
-robot proprioceptive state → a continuous action chunk via flow-matching denoising
-(it does **not** emit text tokens). This example serves π0 over the OpenPI realtime
-websocket protocol at `/v1/realtime/robot/openpi`.
-
-## Install extras
-
-The core `pip install -e .` does not include the OpenPI client used here:
-
- `openpi-client` (from the [openpi](https://github.com/Physical-Intelligence/openpi)
-  repo: `pip install -e packages/openpi-client`), `websockets`, `msgpack`, `msgpack-numpy`
-
-## Weights
-
-π0 uses the LeRobot `lerobot/pi0_base` checkpoint (HF, ~13 GB). Either let the server
-download it (`MODEL=lerobot/pi0_base`) or point at a local copy
-(`MODEL=/path/to/pi0_base`).
-
-## Run the server
-
-```bash
-vllm serve lerobot/pi0_base --omni --port 8000 \
-    --served-model-name pi0 \
-    --deploy-config vllm_omni/deploy/pi0.yaml \
-    --enforce-eager --disable-log-stats
-```
-
-The deploy config (`vllm_omni/deploy/pi0.yaml`) declares a single diffusion stage
-(`Pi0Pipeline`), float32, `max_num_seqs: 1`, and the `policy_server_config` handshake
-metadata (3 cameras, `joint_position`, action horizon 50, action dim 32).
-
-### Commonly adjusted `model_config` keys
-
-These live under `stages[0].model_config` in `vllm_omni/deploy/pi0.yaml`:
-
-| Key | Default | Meaning |
-|---|---|---|
-| `chunk_size` | `50` | Action-chunk length (timesteps) the model predicts per inference. |
-| `num_inference_steps` | `10` | Flow-matching Euler denoising steps. |
-| `max_action_dim` | `32` | Action dimensionality (state/action are padded to this). |
-| `max_state_dim` | `32` | Proprioceptive-state dimensionality (zero-padded to this). |
-| `image_resolution` | `[224, 224]` | Per-camera input size (square; SigLIP). |
-| `tokenizer_max_length` | `48` | Max PaliGemma prompt tokens. |
-| `max_cameras` | `3` | Camera slots the model attends to (real + `-1`-padded). |
-| `image_feature_keys` | 3 `observation.images.*` keys | Camera order the model attends to. |
-| `image_key_map` | `{}` | Map raw obs camera keys → `image_feature_keys` (empty = verbatim). |
-
-The `policy_server_config` block below them is the OpenPI handshake metadata
-advertised to the client; keep its `action_horizon` / `action_dim` /
-`image_resolution` in sync with the `model_config` values above. The e2e test
-`tests/e2e/online_serving/test_pi0_expansion.py::test_pi0_openpi_online` connects to a
-live server and asserts the advertised metadata matches these `pi0.yaml` values.
-
-## Run the client
-
-```bash
-python examples/online_serving/pi0/openpi_client.py --host 127.0.0.1 --port 8000 \
-    --prompt "pick up the red block and place it in the bin"
-```
-
-It connects, prints the server metadata, sends robot observations, and prints the
-returned `[action_horizon, action_dim] = [50, 32]` action chunks. Replace the blank
-cameras / zero state in `_make_dummy_obs` with real frames (HWC uint8) and
-proprioceptive state to drive a robot.
-
-### Observation format
-
-The client sends a flat dict per inference:
-
-```python
-{
-    "observation.images.base_0_rgb":       np.uint8[H, W, 3],
-    "observation.images.left_wrist_0_rgb": np.uint8[H, W, 3],
-    "observation.images.right_wrist_0_rgb":np.uint8[H, W, 3],
-    "state":   np.float32[state_dim],   # zero-padded to max_state_dim=32 server-side
-    "prompt":  "pick up the red block",
-    "session_id": "<uuid>",             # accepted but ignored (π0 is stateless)
-}
-```
-
-Camera keys must match the server's `image_feature_keys` (the checkpoint's
-`input_features` order). If your robot uses different camera names, set
-`model_config.image_key_map` in `pi0.yaml` to map raw obs keys → feature keys.
-
-## Correctness
-
-π0's flow-matching kernel is bit-for-bit matched to the LeRobot `PI0Policy` reference
-(`max|Δ| = 7.15e-07`, CPU/float32, fixed noise; see
-`tests/diffusion/models/pi0/test_pi0_parity.py::test_pi0_vllm_omni_vs_lerobot`) and is
-version-stable across transformers releases (the version-stability checks in
-`tests/diffusion/models/pi0/test_pi0_units.py`). An OpenPI websocket online-serving e2e
-lives in `tests/e2e/online_serving/test_pi0_expansion.py::test_pi0_openpi_online`.
-
-## Limitations
-
- **Normalization stats**: `lerobot/pi0_base` uses identity normalization, which is
-  fully supported (state passes through, actions are returned in the model's space).
-  Per-dataset `norm_stats` declared in a checkpoint's `config.json` are honored, but
-  stats stored only in LeRobot's `policy_preprocessor.json` companion safetensors are
-  not yet loaded — a fine-tuned checkpoint that relies on those would need that bridge
-  added before its actions are in real-world units.
@@ -1,119 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Minimal OpenPI client for the π0 (Pi-Zero) VLA policy server.
-
-Connects to a running π0 server (see README.md), sends a single robot
-observation (3 cameras + state + language instruction), and prints the returned
-continuous action chunk ``[action_horizon, action_dim]``.
-
-The server speaks the OpenPI websocket protocol:
-    connect -> server sends msgpack(policy_server_config metadata)
-    infer   -> client sends msgpack(obs), server replies msgpack(actions ndarray)
-
-Usage:
-    python openpi_client.py --host 127.0.0.1 --port 8000 \
-        --prompt "pick up the red block"
-"""
-
-from __future__ import annotations
-
-import argparse
-import logging
-import sys
-import uuid
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-
-try:
-    # Avoid shadowing the installed ``openpi_client`` package with this dir.
-    example_dir = str(Path(__file__).resolve().parent)
-    removed = sys.path and sys.path[0] == example_dir
-    if removed:
-        sys.path.pop(0)
-    try:
-        from openpi_client.websocket_client_policy import WebsocketClientPolicy
-    finally:
-        if removed:
-            sys.path.insert(0, example_dir)
-except ImportError as exc:  # pragma: no cover - runtime dependency guard
-    raise ImportError("π0 OpenPI example requires `openpi-client`.") from exc
-
-logger = logging.getLogger("pi0_openpi_client")
-
-DEFAULT_HOST = "127.0.0.1"
-DEFAULT_PORT = 8000
-DEFAULT_PATH = "/v1/realtime/robot/openpi"
-DEFAULT_PROMPT = "pick up the red block and place it in the bin"
-
-# π0 / pi0_base camera identities (must match the server's image_feature_keys).
-CAMERA_KEYS = (
-    "observation.images.base_0_rgb",
-    "observation.images.left_wrist_0_rgb",
-    "observation.images.right_wrist_0_rgb",
-)
-STATE_DIM = 32
-
-
-def _make_dummy_obs(prompt: str, session_id: str, image_size: int = 224) -> dict[str, Any]:
-    """Build a single π0 observation with blank cameras + zero state.
-
-    Replace the zeros with real camera frames (HWC uint8) and proprioceptive
-    state to drive an actual robot.
-    """
-    obs: dict[str, Any] = {cam: np.zeros((image_size, image_size, 3), dtype=np.uint8) for cam in CAMERA_KEYS}
-    obs["state"] = np.zeros(STATE_DIM, dtype=np.float32)
-    obs["prompt"] = prompt
-    obs["session_id"] = session_id
-    return obs
-
-
-def _ws_uri(host: str, port: int, path: str) -> str:
-    """Build the full websocket URI. WebsocketClientPolicy takes the whole URI as
-    ``host`` (it has no separate path arg)."""
-    if host.startswith(("ws://", "wss://")):
-        return host
-    return f"ws://{host}:{port}{path}"
-
-
-def _as_action_array(response: Any) -> np.ndarray:
-    if isinstance(response, dict) and response.get("type") == "error":
-        raise RuntimeError(f"Inference failed: {response.get('message', response)}")
-    return np.asarray(response, dtype=np.float32)
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="π0 OpenPI client")
-    parser.add_argument("--host", default=DEFAULT_HOST)
-    parser.add_argument("--port", type=int, default=DEFAULT_PORT)
-    parser.add_argument("--path", default=DEFAULT_PATH)
-    parser.add_argument("--prompt", default=DEFAULT_PROMPT)
-    parser.add_argument("--num-steps", type=int, default=2, help="Inference observations to send.")
-    args = parser.parse_args()
-    logging.basicConfig(level=logging.INFO, format="%(message)s")
-
-    policy = WebsocketClientPolicy(host=_ws_uri(args.host, args.port, args.path))
-    metadata = dict(policy.get_server_metadata())
-    logger.info("Server metadata: %s", metadata)
-
-    session_id = str(uuid.uuid4())
-    for step in range(args.num_steps):
-        obs = _make_dummy_obs(args.prompt, session_id)
-        actions = _as_action_array(policy.infer(obs))
-        logger.info(
-            "[step %d] actions shape=%s mean=%.4f std=%.4f",
-            step,
-            actions.shape,
-            float(actions.mean()),
-            float(actions.std()),
-        )
-        if not np.isfinite(actions).all():
-            raise RuntimeError("Server returned non-finite actions.")
-    logger.info("Done — π0 OpenPI round-trip OK.")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
@@ -79,7 +79,6 @@ plugins:
        user_guide/diffusion/step_execution.md: user_guide/diffusion/execution_modes.md
        user_guide/diffusion/cpu_offload_diffusion.md: user_guide/diffusion/cpu_offload.md
        user_guide/quantization/index.md: user_guide/quantization/overview.md
-        configuration/pd_disaggregation.md: features/pd_disaggregation.md
        design/feature/diffusion_request_level_batching.md: design/feature/diffusion_continuous_batching.md
        design/feature/diffusion_step_execution.md: design/feature/diffusion_continuous_batching.md
        design/feature/cpu_offloading.md: design/feature/offloader/README.md
@@ -45,7 +45,7 @@ MusicGen-style delay pattern).
 **Online serving:**

 ```bash
-vllm serve bosonai/higgs-audio-v3-tts-4b \
+vllm-omni serve bosonai/higgs-audio-v3-tts-4b \
    --host 0.0.0.0 --port 8095 \
    --trust-remote-code --omni
 ```
@@ -1,175 +0,0 @@
-# LTX-2.5
-
-> Text-to-video and first-frame image-to-video generation with synchronized audio
-
-## Overview
-
-vLLM-Omni supports the gated
-[`Lightricks/LTX-2.5-Diffusers`](https://huggingface.co/Lightricks/LTX-2.5-Diffusers)
-checkpoint for T2V and first-frame I2V. Every output includes synchronized
-48 kHz stereo audio.
-
-The raw [`Lightricks/LTX-2.5`](https://huggingface.co/Lightricks/LTX-2.5)
-repository is not directly loadable with `--model`. It supplies the official
-upsampler and LoRA sidecars used by the Full/SFT two-stage pipeline, so accept
-both model licenses and authenticate before first use.
-
-## Choose a pipeline
-
-| Pipeline | Mode | Default size | Schedule |
-|---|---|---:|---:|
-| `LTX2Pipeline` | Full/SFT one-stage | 960x544 | 30 steps |
-| `LTX2TwoStagePipeline` | Full/SFT two-stage | 1920x1088 | 30 + 3 steps |
-| `LTX2DistilledOneStagePipeline` | Distilled one-stage | 960x544 | 8 steps |
-| `LTX2DistilledTwoStagePipeline` | Distilled two-stage | 1920x1088 | 8 + 3 steps |
-
-Both two-stage pipelines generate at half resolution, apply the official x2
-latent upsampler, and run a three-step refinement stage. Select the class with
-`--model-class-name`; no `--task-type` flag is required. Supplying one initial
-image selects I2V, while omitting it selects T2V.
-
-## Prerequisites
-
-```bash
-hf auth login
-export MODEL=Lightricks/LTX-2.5-Diffusers
-```
-
-Install matching vLLM and vLLM-Omni versions, and ensure `ffmpeg` and
-`ffprobe` are on `PATH`. I2V requires PyAV backed by an FFmpeg build with the
-`libx264` encoder.
-
-## Hardware
-
-| GPU | Status | Recommended scope |
-|---|---|---|
-| NVIDIA B300 | Verified | All four canonical pipelines |
-| NVIDIA B200 or H200 | Capacity-based recommendation; not yet verified | All four canonical pipelines |
-| NVIDIA GB200 or GB300 | Capacity-based recommendation; not yet verified | All four canonical pipelines |
-| NVIDIA H100 80 GB | FP8 recipe | Distilled one-stage at 960x544 |
-
-The 1920x1088 two-stage examples require about 114 GB of peak GPU memory.
-80 GB GPUs do not have enough safety margin for the canonical two-stage
-configuration; use a larger GPU or reduce the output configuration.
-
-### H100 80 GB FP8
-
-Use the distilled one-stage pipeline with FP8 and cuDNN attention:
-
-```bash
-vllm serve Lightricks/LTX-2.5-Diffusers \
-  --omni \
-  --model-class-name LTX2DistilledOneStagePipeline \
-  --quantization fp8 \
-  --diffusion-attention-backend CUDNN_ATTN \
-  --host 0.0.0.0 \
-  --port 8000
-```
-
-Use the T2V or I2V request below after the server is ready.
-
-## Offline inference
-
-Choose values from the pipeline table. For example, the distilled two-stage
-path uses:
-
-```bash
-export PIPELINE=LTX2DistilledTwoStagePipeline
-export WIDTH=1920
-export HEIGHT=1088
-export STEPS=8
-```
-
-### Text-to-video
-
-```bash
-python examples/offline_inference/text_to_video/text_to_video.py \
-  --model "${MODEL}" \
-  --model-class-name "${PIPELINE}" \
-  --prompt "A cinematic shot of a red fox walking through a snowy forest at dawn, the camera tracking alongside, snow crunching underfoot." \
-  --width "${WIDTH}" \
-  --height "${HEIGHT}" \
-  --num-frames 121 \
-  --num-inference-steps "${STEPS}" \
-  --frame-rate 24 \
-  --fps 24 \
-  --seed 42 \
-  --output ltx25-t2v.mp4
-```
-
-### First-frame image-to-video
-
-```bash
-python examples/offline_inference/image_to_video/image_to_video.py \
-  --model "${MODEL}" \
-  --model-class-name "${PIPELINE}" \
-  --image /absolute/path/to/first-frame.png \
-  --prompt "The red fox walks forward while the camera tracks alongside." \
-  --width "${WIDTH}" \
-  --height "${HEIGHT}" \
-  --num-frames 121 \
-  --num-inference-steps "${STEPS}" \
-  --frame-rate 24 \
-  --fps 24 \
-  --seed 42 \
-  --output ltx25-i2v.mp4
-```
-
-LTX-2.5 uses the official CRF-18 first-frame conditioning path by default.
-
-## Online serving
-
-Start one server for the selected pipeline:
-
-```bash
-vllm serve "${MODEL}" \
-  --omni \
-  --model-class-name "${PIPELINE}" \
-  --host 0.0.0.0 \
-  --port 8000 \
-  --stage-init-timeout 900
-```
-
-T2V request:
-
-```bash
-curl -sS --fail-with-body \
-  -X POST http://127.0.0.1:8000/v1/videos/sync \
-  -F 'prompt=A cinematic shot of a red fox walking through a snowy forest at dawn, the camera tracking alongside, snow crunching underfoot.' \
-  -o ltx25-online-t2v.mp4
-```
-
-For I2V, add exactly one first frame:
-
-```bash
-export FIRST_FRAME=/absolute/path/to/first-frame.png
-
-curl -sS --fail-with-body \
-  -X POST http://127.0.0.1:8000/v1/videos/sync \
-  -F "input_reference=@${FIRST_FRAME};type=image/png" \
-  -F 'prompt=The red fox walks forward while the camera tracks alongside.' \
-  -o ltx25-online-i2v.mp4
-```
-
-Restart the server after changing `PIPELINE` because each class selects a
-different weight layout and execution topology.
-
-## Constraints
-
- Online `width`, `height`, `num_frames`, `fps`, and `num_inference_steps`
-  are optional and use the selected pipeline defaults when omitted. `seed` is
-  optional and random when omitted.
- `num_frames` must be `8k+1` when overridden.
- One-stage width and height must be divisible by 32; two-stage final
-  dimensions must be divisible by 64.
- Full/SFT pipelines accept negative prompts. Distilled pipelines are
-  positive-only and reject negative prompts.
- I2V accepts exactly one initial image per prompt.
- The supported `--model` value is `Lightricks/LTX-2.5-Diffusers`.
-
-## References
-
- [LTX-2.5-Diffusers model card](https://huggingface.co/Lightricks/LTX-2.5-Diffusers)
- [Official LTX-2 repository](https://github.com/Lightricks/LTX-2)
- [Generic T2V example](../../examples/offline_inference/text_to_video/text_to_video.py)
- [Generic I2V example](../../examples/offline_inference/image_to_video/image_to_video.py)
@@ -8,12 +8,10 @@
 |---|---|---|
 | `LTX2Pipeline` | LTX-2 one-stage T2V/I2V | `Lightricks/LTX-2` |
 | `LTX2TwoStagePipeline` | LTX-2 ordinary two-stage T2V/I2V | `Lightricks/LTX-2` |
-| `LTX2DistilledOneStagePipeline` | LTX-2 merged-distilled one-stage T2V/I2V | `rootonchair/LTX-2-19b-distilled` |
-| `LTX2DistilledTwoStagePipeline` | LTX-2 merged-distilled two-stage T2V/I2V | `rootonchair/LTX-2-19b-distilled` |
+| `LTX2DistilledPipeline` | LTX-2 full-distilled two-stage T2V/I2V | `rootonchair/LTX-2-19b-distilled` |
 | `LTX2Pipeline` | LTX-2.3 one-stage T2V/I2V | `diffusers/LTX-2.3-Diffusers` |
 | `LTX2TwoStagePipeline` | LTX-2.3 ordinary two-stage T2V/I2V | `diffusers/LTX-2.3-Diffusers`<br>`Lightricks/LTX-2.3` |
-| `LTX2DistilledOneStagePipeline` | LTX-2.3 merged-distilled one-stage T2V/I2V | `diffusers/LTX-2.3-Distilled-Diffusers` |
-| `LTX2DistilledTwoStagePipeline` | LTX-2.3 merged-distilled two-stage T2V/I2V | `diffusers/LTX-2.3-Distilled-Diffusers`<br>`Lightricks/LTX-2.3` |
+| `LTX2DistilledPipeline` | LTX-2.3 full-distilled two-stage T2V/I2V | `diffusers/LTX-2.3-Distilled-Diffusers`<br>`Lightricks/LTX-2.3` |

 Repositories in the table are download units. A full pipeline repository
 contains the Transformer, text encoder, connectors, VAEs, vocoder, scheduler,
@@ -28,12 +26,9 @@ image selects I2V. Both one-stage repositories declare this class, so
 the raw `Lightricks/LTX-2.3` safetensors repository is not directly loadable.

 `LTX2TwoStagePipeline` samples the regular model at half resolution, upsamples,
-then refines with the distilled LoRA. `LTX2DistilledOneStagePipeline` uses a
-merged distilled Transformer without upsampling, while
-`LTX2DistilledTwoStagePipeline` uses it in both stages. All entries support
-T2V and I2V; select their class explicitly. The deprecated
-`LTX2DistilledPipeline` name remains an alias for
-`LTX2DistilledTwoStagePipeline`.
+then refines with the distilled LoRA. `LTX2DistilledPipeline` uses a fully
+merged distilled Transformer in both stages. Both entries support T2V and I2V;
+select their class explicitly.

 ## API Migration

@@ -57,8 +52,8 @@ The consolidation also removes these registry names without aliases:
 | `LTX23Pipeline` | `LTX2Pipeline`; checkpoint metadata selects LTX-2.3 |
 | `LTX2ImageToVideoPipeline` | `LTX2Pipeline` with `image=` |
 | `LTX23ImageToVideoPipeline` | `LTX2Pipeline` with `image=`; checkpoint metadata selects LTX-2.3 |
-| `LTX2TwoStagesPipeline` | `LTX2DistilledTwoStagePipeline` |
-| `LTX2ImageToVideoTwoStagesPipeline` | `LTX2DistilledTwoStagePipeline` with `image=` |
+| `LTX2TwoStagesPipeline` | `LTX2DistilledPipeline` |
+| `LTX2ImageToVideoTwoStagesPipeline` | `LTX2DistilledPipeline` with `image=` |

 Passing any second positional argument now raises `TypeError`. These changes
 affect direct Python callers and explicit `--model-class-name` overrides;
@@ -117,10 +112,10 @@ Start a two-stage checkpoint with its explicit class:

 ```bash
 vllm serve rootonchair/LTX-2-19b-distilled --omni \
-  --model-class-name LTX2DistilledTwoStagePipeline --stage-init-timeout 600
+  --model-class-name LTX2DistilledPipeline --stage-init-timeout 600
 # LTX-2.3 full-distilled; the v1.1 x2 upsampler is resolved when absent
 vllm serve diffusers/LTX-2.3-Distilled-Diffusers --omni \
-  --model-class-name LTX2DistilledTwoStagePipeline --stage-init-timeout 600
+  --model-class-name LTX2DistilledPipeline --stage-init-timeout 600
 # Ordinary LTX-2.3 two-stage
 vllm serve diffusers/LTX-2.3-Diffusers --omni \
  --model-class-name LTX2TwoStagePipeline \
@@ -81,7 +81,7 @@ MammothModa2 generation parameters as a JSON object through `--extra-body`:
 ```bash
 python examples/offline_inference/text_to_image/text_to_image.py \
  --model ./MammothModa2-Preview \
-  --deploy-config vllm_omni/deploy/mammoth_moda2.yaml \
+  --stage-configs-path vllm_omni/deploy/mammoth_moda2.yaml \
  --prompt "A stylish woman riding a motorcycle in NYC, movie poster style" \
  --height 1024 \
  --width 1024 \
@@ -132,44 +132,6 @@ ls -lh mammoth_t2i.png
 python -c "from PIL import Image; print(Image.open('mammoth_t2i.png').size)"
 ```

-### 1x AMD MI300X, MammothModa2 Preview
-
-#### Environment
-
- OS: Linux 6.8.0-134-generic, x86_64
- Container: official ROCm image built from `docker/Dockerfile.rocm`
- Python: 3.12.13
- PyTorch: 2.11.0+gitd0c8b1f
- Driver / runtime: AMD 6.19.14.31400000 / ROCm 7.2.53211
- GPU: one AMD Instinct MI300X, `gfx942:sramecc+:xnack-`, 191.69 GiB visible HBM
- vLLM version: 0.27.0+rocm723
- vLLM Omni version or commit: `73e1368c7bb940efe1a025859c9d6c8eeeb2e3f0`
- Installed vLLM Omni package metadata: `0.27.0rc2.dev44+g55abdade9.rocm`
-
-#### Offline Commands
-
-The checked run used the committed stage split, with `gpu_memory_utilization` set to 0.5 for AR and 0.3 for DiT:
-
-```bash
-python3 examples/offline_inference/text_to_image/text_to_image.py \
-    --model bytedance-research/MammothModa2-Preview \
-    --deploy-config vllm_omni/deploy/mammoth_moda2.yaml \
-    --prompt "A stylish woman riding a motorcycle in NYC, movie poster style" \
-    --height 1024 \
-    --width 1024 \
-    --seed 42 \
-    --extra-body '{"text_guidance_scale": 4.0, "cfg_range": [0.0, 1.0], "num_inference_steps": 50}' \
-    --enable-diffusion-pipeline-profiler \
-    --log-stats \
-    --output mammoth_t2i.png
-```
-
-#### Verification
-
-The first request took 85.224 seconds. The AR stage generated 4,161 visual tokens in 72.996 seconds, and the DiT stage took 12.163 seconds. AR weight loading used 21.4 GiB and took 8.250 seconds. DiT weight loading used 5.49 GiB and took 1.824 seconds. The largest one second whole device memory sample was 106.57 GiB, including the AR KV cache reserved by the 0.5 memory setting.
-
-The output was a valid 1024 by 1024 RGB PNG.
-
 ## MammothModa2-Dev unified inference

 MammothModa2-Dev uses a Qwen3-VL AR backbone, while MammothModa2-Preview uses
@@ -1,195 +0,0 @@
-# MiniMax-H3 on a single NPU 950PR
-
-This recipe runs MiniMax-H3 with online INT8 quantization on one NPU 950PR
-NPU (128 GB HiBL 1.0 HBM). It covers the single-card T2VA configuration at
-1024x576. For the eight-card Atlas 800I A3 BF16 route at 768P, see
-[MiniMax-H3-NPU.md](MiniMax-H3-NPU.md).
-
-## Capacity requirements
-
-| Resource | Requirement |
-| --- | ---: |
-| NPU | 1x NPU 950PR |
-| NPU HBM | 128 GiB (131,072 MiB reported by `npu-smi`) |
-| Observed HBM high-water mark | 118,442 MiB (90.4% of capacity) |
-| Checkpoint storage | 135 GiB per partition, local disk strongly preferred |
-| Container shared memory | 8 GiB, or a writable local filesystem for spill |
-
-The observed high-water mark leaves about 12.3 GiB of headroom, so this card is
-sized for exactly one resident task partition at 1024x576. Do not expect a
-larger output shape or concurrency greater than one to fit.
-
-Because a single 950PR holds the whole partition in HBM, this route does not
-need layerwise offload, and the 200 GiB system-RAM floor carried by the 72 GiB
-GPU recipes does not apply. The validated run completed inside a container
-limited to 32 GiB of RAM. Passing `--enable-layerwise-offload` on this card is
-actively harmful: it stages weights in non-reclaimable host memory and the
-container's OOM killer terminates the server with exit code -9.
-
-
-## Environment
-
- Host architecture: x86_64
-  driver: 25.7.rc1.6 (hal 7.35.23)
-  firmware: 9.0.0.105.229
- CANN toolkit: 9.1.0 (`/usr/local//cann-9.1.0`)
- npu-smi: 25.7.rc1.6
- Python: 3.12.13
- PyTorch: 2.10.0+cpu
- torch_npu: 2.10.0.post2
- vLLM: 0.26.0
- vLLM-Omni: 0.26.1.dev103+g584d78c67.npu (commit `584d78c6`)
-
-Install vLLM-Omni from a checkout with MiniMax-H3 support:
-
-```bash
-uv venv
-source .venv/bin/activate
-uv pip install -e .
-```
-
-## Start a server
-
-```bash
-export MODEL=/path/to/MiniMax-H3/FL2VA
-export PORT=8000
-export VLLM_WORKER_MULTIPROC_METHOD=spawn
-export VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400
-
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 1 \
-  --tensor-parallel-size 1 \
-  --usp 1 \
-  --ring 1 \
-  --text-encoder-tp-size 1 \
-  --vae-patch-parallel-size 1 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --init-timeout 14400 \
-  --stage-init-timeout 14400 \
-  --quantization int8
-```
-
-Do not pass `--diffusion-attention-backend CUDNN_ATTN`. cuDNN attention has no
-NPU implementation and fails at the first denoise step. Leave the backend
-unset: it resolves to `TORCH_SDPA`, or to `FLASH_ATTN` when MindIE-SD is
-installed (see [MiniMax-H3-NPU.md § Environment](MiniMax-H3-NPU.md#environment)).
-
-H3 is CFG-distilled, so `--cfg-parallel-size` must remain 1.
-
-## Validated evidence
-
-Measured on one NPU 950PR with the server command above plus
-`--enable-diffusion-pipeline-profiler`, generating a 5 s 1024x576 T2VA clip from
-the request in [§ T2VA request example](#t2va-request-example). MiniMax-H3
-requested 60 denoise steps and executed 59 denoise updates, so per-step latency
-is `denoise / 59`. `ffprobe` confirms every returned MP4 is 1024x576, 24 fps,
-124 video frames, 5.175 s, with a 32 kHz audio track of 165,600 samples.
-
-Five requests were issued back to back with no idle time between them. The card
-is measurably faster on the first request than in steady state, so both regimes
-are reported.
-
-| Stage | First request, cold card | Thermal steady state |
-| --- | ---: | ---: |
-| End-to-end request | 462.13 s | 506.38 s |
-| Text encode | 0.55 s | 0.05 s |
-| Denoise (59 updates) | 429.29 s | 471.74 s |
-| Per denoise update | 7,276 ms | 7,996 ms |
-| VAE decode | 8.43 s | 8.14 s |
-| Worker-to-server handoff | 24 s | 27 s |
-| MP4 muxing | 0.57 s | 0.50 s |
-| Server-reported `denoise_step_latency_ms` | 7,702 ms | 8,440 ms |
-| Peak HBM (`npu-smi`) | 118,442 MiB | 118,442 MiB |
-| Average NPU power | 566.9 W | 556.9 W |
-| Peak NPU temperature | 104 C | 104 C |
-
-Sample counts differ per row. The cold column is a single request. In the steady
-column, text encode, denoise and VAE decode are means over the four subsequent
-requests — denoise spanned 470.37 to 473.21 s, a 0.60% spread — while
-end-to-end, handoff and muxing are means over the two of those four that
-survived the handoff timeout described above.
-
-**Report the steady-state column.** The cold card completes denoise 9.9% faster
-while drawing only 1.8% more average power, and both regimes reach the same
-104 C ceiling, so the first request is buying a short window of higher clocks
-before the die saturates. Any benchmark that issues a single request against an
-idle card will overstate throughput by about 10%.
-
-**Do not read `denoise_step_latency_ms` as a per-step time.** The server divides
-the entire stage wall time by the 60 *requested* steps, so it absorbs text
-encoding, VAE decode and the 837 MiB handoff. All three successful requests
-satisfy `denoise_step_latency_ms == e2e_stage_wall_time_ms / 60` exactly. The
-per-update figure in the table is `diffuse / 59` from the pipeline profiler.
-
-Denoise accounts for 93% of the steady-state request and the handoff accounts
-for 5.3%, so keeping the transfer in shared memory should bring end-to-end down
-to roughly 480 s.
-
-Peak HBM is sampled externally with `npu-smi` at a 3 s interval. It is a
-reserved high-water mark rather than live allocation: it reads 111,272 MiB after
-weight loading and before the first request, and does not fall back after
-requests complete. Re-measure for longer outputs, a different output shape, or
-concurrency greater than one.
-
-### Startup
-
-Weight loading took **31 min 50 s** on the validated host (first log line
-08:04:59, `Application startup complete` 08:36:49), with the checkpoint on a
-shared GlusterFS network mount. This is why the server command sets
-`--init-timeout 14400` and `--stage-init-timeout 14400`; the stock 600 s and
-1800 s timeouts both abort mid-load with `TimeoutError` and exit code 143.
-Reduce both timeouts when the partition is staged on local disk.
-
-## T2VA request example
-
-```bash
-export API_URL="http://127.0.0.1:${PORT}/v1/videos/sync"
-
-curl -sS --max-time 1800 -X POST "${API_URL}" \
-  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
-  -F 'width=1024' \
-  -F 'height=576' \
-  -F 'aspect_ratio=16:9' \
-  -F 'fps=24' \
-  -F 'num_inference_steps=60' \
-  -F 'flow_shift=12' \
-  -F 'seed=1101' \
-  -F 'extra_params={"task":"t2va","duration":5,"audio_flow_shift":3.0}' \
-  -o t2va.mp4
-```
-
-## Known limitations
-
- INT8 online quantization is validated for T2VA on this card. Use BF16 for
-  FL2VA and Ref2VA, or re-measure before relying on it.
- Single-card serving loads one task partition at a time. For Ref2VA, stop the
-  server and restart it against the `Ref2VA` directory.
- Sustained load drives the die to 104 C and costs about 10% of denoise
-  throughput relative to a cold card. Warm the server with at least one full
-  request before measuring, and treat steady state as the reportable number.
- Requests can fail during result handoff on hosts where the IPC spill path is
-  slow. See [§ Result handoff and shared
-  memory](#result-handoff-and-shared-memory).
- Loading the checkpoint from a network mount takes over 30 minutes and forces
-  very large init timeouts. Stage the partition on local disk when possible.
- The configuration measured here is 1024x576 at 60 steps, which differs from
-  the 1344x768 at 50 steps used by the GPU recipes in this directory. Denoise
-  cost scales superlinearly with token count, so the numbers are not directly
-  comparable across recipes.
- The image ships a `triton` package whose  backend is not built
-  (`No module named 'triton._C.libtriton.'`). vLLM logs this as an error
-  at startup and disables Triton; the diffusion path does not need it and the
-  run completes normally.
-
-## Additional resources
-
- [MiniMax-H3.md](MiniMax-H3.md) — full GPU guide
- [MiniMax-H3-NPU.md](MiniMax-H3-NPU.md) — eight-card Atlas 800I A3 BF16 guide
- [Int8 quantization](../../docs/user_guide/quantization/int8.md)
- [Supported models](../../docs/models/supported_models.md)
- [Video API](../../docs/serving/videos_api.md)
@@ -357,19 +357,12 @@ No restart is needed: `task=fl2va` routes to `FL2VA/transformer`, while

 ### Online FP8 quantization

-MiniMax H3 supports online FP8 quantization of both the DiT and the Qwen3-VL
-text decoder. The checkpoint remains BF16 on disk; vLLM-Omni creates FP8
-weights at runtime and uses dynamic activation scaling during inference. By
-default, `--quantization fp8` quantizes eligible attention and MLP linears in
-the text decoder, token refiner, and main DiT blocks, as well as the DiT
-condition and AdaLN projections. The Qwen vision tower, embeddings, norms,
-RoPE, both VAEs, and the model's FP32 patch, timestep, and output projections
-keep checkpoint precision.
-
-To select a component, use `--diffusion-quantization-config` with
-`{"transformer":{"method":"fp8"}}` for DiT-only FP8 or
-`{"text_encoder":{"method":"fp8"}}` for text-decoder-only FP8. The two
-entries can be combined. The shorthand below enables both components.
+MiniMax H3 supports load-time FP8 quantization of the DiT. The checkpoint
+remains BF16 on disk; vLLM-Omni quantizes eligible weights while loading and
+uses dynamic activation scaling during inference. By default, attention and
+MLP linears in the token refiner and main DiT blocks, the condition
+projection, and all AdaLN projections use FP8. Patch, timestep, and final
+projections remain FP32; the text encoder and VAEs are unchanged.

 Add this option to an existing H3 server command:

@@ -377,28 +370,6 @@ Add this option to an existing H3 server command:
 --quantization fp8
 ```

-#### Single 96 GB GPU, no-offload capacity check
-
-Use the FL2VA-only partition for this capacity test. Loading the combined
-service would also load the Ref2VA DiT and would test a different memory
-budget. A no-offload capacity check should contain none of
-`--enable-cpu-offload`, `--enable-layerwise-offload`, or
-`--enable-distributed-layerwise-offload`. VAE tiling changes decode placement
-but does not offload model weights to the CPU.
-
-The run passes the capacity check when the server initializes, the request
-finishes without CUDA OOM or Xid errors, `peak_used_mib` remains below the
-card's reported `memory_total_mib`, and `ffprobe` reports H.264 video plus
-32 kHz stereo AAC audio. Report the measured headroom rather than assuming
-that every nominal 96 GB SKU exposes the same MiB total.
-
-As a capacity proxy only, the same five-second case on one B300 measured a
-92,946 MiB whole-device first-request peak and a 92,146 MiB worker peak. Its
-encode, diffuse, decode, and client wall times were 8.664 s, 134.504 s,
-6.016 s, and 151.583 s. These numbers suggest that a 96 GB card may fit, but
-they are not an RTX PRO 6000 validation: kernels, allocator behavior, and
-reported device capacity differ across GPUs.
-
 Use `ignored_layers` to keep any otherwise eligible linear in BF16. H3
 resolves the `transformer` component before constructing the DiT, so names do
 not start with `transformer.`. Entries are exact runtime linear prefixes; a
@@ -415,11 +386,10 @@ For example, keep the first main block's attention projections in BF16 with:
  '{"transformer":{"method":"fp8","ignored_layers":["blocks.0.attn.qkv_proj","blocks.0.attn.out_proj"]}}'
 ```

-The structured option replaces `--quantization fp8`. Online FP8 can be used
-with H3 layerwise offload and with both DLO transfer paths. The default
-AllGather path uses the ordinary loader to finalize FP8 weights and scales
-before sharding them across ranks. `--dlo-no-use-allgather` instead retains
-complete rank-local tensors and avoids the synchronized request-wave contract.
+The structured option replaces `--quantization fp8`. Online FP8 is currently
+incompatible with H3 layerwise offload because the offload path produces a
+weight stride rejected by the Cutlass FP8 kernel. Use resident FP8 with tensor
+parallelism and VAE tiling instead.

 ## AMD ROCm (gfx942 / gfx950)

@@ -780,13 +750,6 @@ audio spectral cosine 0.9589 (minimum 0.80), and audio RMS ratio 0.9342. The
 resident per-GPU peak was 68.52 GiB for BF16 and 53.51 GiB for FP8, a 22%
 reduction.

-For direct human inspection, see the external
-[BF16 versus global-FP8 comparison](https://lishunyang12.github.io/vllm-omni-rankings/scripts/minimax_h3_global_fp8_vs_bf16/),
-which includes matched five-second T2VA, I2VA, and Ref2VA videos. The
-[comparison sources](https://github.com/lishunyang12/vllm-omni-rankings/tree/main/scripts/minimax_h3_global_fp8_vs_bf16)
-also record per-task fidelity metrics and provenance without storing generated
-media in this repository.
-
 ## TeaCache acceleration

 TeaCache reuses DiT block residuals across denoising steps when consecutive
@@ -871,9 +834,7 @@ vllm serve "${MODEL_ROOT}/FL2VA" \
  H3 native `tile` mode only.
 - A U2 x Ring2 hybrid currently fails with an attention-mask length mismatch; use
  pure Ulysses.
- Online FP8 with DLO AllGather temporarily materializes the complete FP8 model
-  in host memory on every rank during startup before retaining only each rank's
-  shard. Size startup host memory for that transient peak.
+- Online FP8 is not compatible with layerwise offload.
 - TeaCache and Cache-DiT cannot be enabled on the same server.
 - Image+audio Ref2VA accepts exactly one image and one audio reference.
 - Video Ref2VA accepts one or more video files, but not an additional standalone
@@ -1,173 +0,0 @@
-# MiniMax Music 3
-
-> Text-to-music: lyrics plus a style caption in, a 32 kHz stereo song out
-
-## Summary
-
- Vendor: MiniMax
- Model: `MiniMaxAI/MiniMax-Music3`
- Task: Text-to-music generation (vocals and instrumental together)
- Mode: Online serving through the OpenAI-compatible `/v1/audio/speech` API
- Maintainer: @linyueqian
-
-## When to use this recipe
-
-Use it to serve `MiniMaxAI/MiniMax-Music3` for full-song generation up to six
-minutes. This is not a TTS model with a musical voice: there is no reference
-speaker, no `voice`, and no `temperature`. What you control is the lyrics, the
-caption, the seed, and the length.
-
-## References
-
- Model card: [MiniMaxAI/MiniMax-Music3](https://huggingface.co/MiniMaxAI/MiniMax-Music3)
- Demo samples: [MiniMax Music 3 demo](https://minimax-ai.github.io/music3-demo/)
- Deploy config: `vllm_omni/deploy/minimax_music3.yaml` (single GPU),
-  `vllm_omni/deploy/minimax_music3_2gpu.yaml` (split across two)
-
-## Architecture
-
-| Component | Spec |
-|---|---|
-| Backbone | Qwen3 decoder, 36 layers, hidden 4096, GQA 32/8, vocab 200k |
-| Depth decoder | 4 layers, hidden 4096, 16 heads; 7 residual codebooks of 1024 |
-| Frame | 8 codebooks; `c0` in the backbone vocabulary, `c1..c7` in the depth decoder |
-| AR guidance | Classifier-free, scale 1.5, `c0` masked to the conditioned branch's top 50 |
-| Acoustic stage | Flow-matching transformer (36 layers, dim 2048) plus a DAC-style decoder, 512x upsampling |
-| Solver | Euler, 30 steps, acoustic guidance scale 1.7 |
-| Window | 200 frames per acoustic chunk, 100-frame hop |
-| Frame rate | 25 frames per second |
-| Context length | 10,240 tokens |
-| Output | 32 kHz stereo WAV |
-
-Generation is two-stage. The backbone predicts one audio frame per decode step,
-each frame eight RVQ codebooks deep. Every 200 frames are handed to the
-flow-matching transformer, which solves a latent that the decoder turns into
-waveform.
-
-**Guidance doubles the rows.** Every request decodes twice: one row sees the
-real prompt, the other sees the same prompt with the caption-and-lyrics span
-replaced by `<|audio_cfg|>`. Both rows hold their own KV cache for the whole
-song, so a request costs twice the KV its length suggests. Size the pool for
-rows, not requests.
-
-## Hardware Support
-
-## GPU
-
-### 1x H200 141GB
-
-#### Environment
-
- OS: Linux
- Python: 3.12
- CUDA: 12.9
- vLLM version: 0.27.0
- vLLM-Omni version or commit: branch `feat/minimax-music3`
-
-#### Command
-
-```bash
-vllm serve MiniMaxAI/MiniMax-Music3 \
-    --host 0.0.0.0 --port 8000 \
-    --trust-remote-code --omni
-```
-
-The default deploy config `vllm_omni/deploy/minimax_music3.yaml` is discovered
-automatically from the checkpoint's `model_type=minimax_music3`. It colocates
-both stages on device 0.
-
-#### Verification
-
-```bash
-curl -X POST http://localhost:8000/v1/audio/speech \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "MiniMaxAI/MiniMax-Music3",
-    "input": "[Verse]\nWalking down the empty street at midnight\nStreetlights flicker like a broken dream\n[Chorus]\nAnd I keep on walking\nTill the morning finds me",
-    "instructions": "A melancholic lo-fi hip-hop track at 85 BPM in F minor: mellow Rhodes piano riff, soft vinyl crackle, dusty boom-bap drums with a laid-back swing, warm upright bass. Intimate bedroom production, gentle tape saturation, no bright cymbals.",
-    "seed": 1,
-    "max_new_tokens": 750
-  }' \
-  --output song.wav
-```
-
-Expected: a 30 second 32 kHz stereo WAV. `max_new_tokens` counts audio frames
-at 25 per second, so 750 frames is at most 30 seconds. It is a cap, not a
-target: the model ends the song itself when it emits the audio-end token, and a
-response shorter than the cap is the model finishing rather than a truncation.
-
-#### Notes
-
- **Memory:** stage 0 takes `gpu_memory_utilization: 0.6` for the bf16 backbone
-  and its KV pool; stage 1 takes 0.2 for the transformer and decoder weights,
-  which sit outside any KV budget.
- **Key flags:** none required. Guidance, the solver step count and the window
-  geometry are fixed by the checkpoint and are not request parameters.
- **Known limitations:**
-  - The external API is non-streaming; `stream: true` is rejected.
-  - `max_model_len` is 10,240, so a maximum-length prompt (5,000 tokens) and a
-    maximum-length song (9,000 frames) cannot both fit. Long captions shorten
-    the maximum song. This is a property of the checkpoint.
-  - The acoustic stage runs in float32. bfloat16 is accepted but measurably
-    degrades the solver.
-
-### 2x H200 141GB
-
-Use `minimax_music3_2gpu.yaml` to put the acoustic stage on the second device:
-
-```bash
-vllm serve MiniMaxAI/MiniMax-Music3 \
-    --host 0.0.0.0 --port 8000 \
-    --trust-remote-code --omni \
-    --deploy-config minimax_music3_2gpu.yaml
-```
-
-Only the placement differs; both layouts run the acoustic stage in float32.
-
-## Writing the request
-
-Two fields carry the request, and both are required.
-
- `input` is the **lyrics**. Structure tags (`[Verse]`, `[Chorus]`, `[Bridge]`,
-  `[Outro]`) steer the arrangement and are part of the prompt contract.
-  **Put a tag on its own line.** Normalization keeps only the tags on a line
-  that starts with one and drops the rest of that line, so lyrics written next
-  to a tag are silently lost:
-
-  ```text
-  "[Verse]\nWalking down the street"   ->   [start] [verse] Walking down the street
-  "[Verse] Walking down the street"    ->   [start] [verse]
-  ```
-
- `instructions` is the **caption** describing genre, instrumentation, tempo,
-  mood and production. It is the strongest control available: vague captions
-  give generic arrangements. Long structured captions work well, since the
-  model was trained on descriptions covering global attributes, emotional
-  progression, vocal detail and arrangement.
-
-A request is deterministic in its seed: the same lyrics, caption, seed and
-length return the same audio. Changing only the seed gives another take of the
-same song.
-
-## Request parameters
-
-| Parameter | Type | Default | Description |
-|---|---|---|---|
-| `input` | string | required | Lyrics, non-empty. Tags on their own lines |
-| `instructions` | string | required | Caption, non-empty |
-| `seed` | int | `0` | Non-negative. Fixes the output for a given request |
-| `max_new_tokens` | int | `9000` | Cap on audio frames, 25 per second. Maximum 9,000 |
-| `response_format` | string | `"wav"` | Output container |
-
-## Parameters the model rejects
-
-The request is refused rather than silently ignored, so a mistake is visible
-immediately.
-
-| Parameter | Reason |
-|---|---|
-| `temperature`, `top_p`, `top_k`, `repetition_penalty` | Sampling is fixed: guidance at 1.5, then a seeded top-k 50 draw |
-| `voice` | There is no speaker to select; the vocal comes from the caption |
-| `ref_audio`, `ref_text`, `language`, `task_type` | No reference-audio conditioning and no language tag |
-| `speed` | Only `1.0`. Tempo belongs in the caption, e.g. "at 92 BPM" |
-| `stream: true` | The external API is non-streaming |
@@ -51,7 +51,7 @@ NemotronH (~3B active parameters). Pick the pipeline by task:
 `model_type` auto-resolves `audex_tts.yaml`):

 ```bash
-vllm serve nvidia/Nemotron-Labs-Audex-2B \
+vllm-omni serve nvidia/Nemotron-Labs-Audex-2B \
    --host 0.0.0.0 --port 8097 \
    --trust-remote-code --omni
 # other modes: add --stage-configs-path vllm_omni/deploy/audex_{tta,thinker_only,s2s}.yaml
@@ -109,7 +109,7 @@ The 30B REQUIRES its explicit deploy yaml (the shared HF `model_type`
 auto-resolves to the 2B-tuned config otherwise):

 ```bash
-vllm serve nvidia/Nemotron-Labs-Audex-30B-A3B \
+vllm-omni serve nvidia/Nemotron-Labs-Audex-30B-A3B \
    --host 0.0.0.0 --port 8097 \
    --trust-remote-code --omni \
    --stage-configs-path vllm_omni/deploy/audex_tts_30b.yaml
@@ -1,193 +0,0 @@
-# SANA-WM
-
-> Camera-controllable first-frame image-to-video world model.
-
-## Summary
-
- Vendor: Efficient-Large-Model / NVlabs SANA
- Model: `BBBBruce/SANA-WM_bidirectional-stage1-diffusers` (standard diffusers layout, converted offline from the NVlabs release; Stage-1 transformer + VAE only)
- Task: First-frame image-to-video generation with camera control
- Mode: Online serving with the OpenAI-compatible video API
- Model weights: about 13 GB for the Stage-1 transformer (10 GB) and VAE (2.3 GB)
- Local disk: reserve about 40 GB for the Hugging Face cache and runtime artifacts
- Recommended GPU: 24 GB or larger CUDA GPU
- Maintainer: Community
-
-## When to use this recipe
-
-Use this recipe when you want to serve SANA-WM through `/v1/videos` or
-`/v1/videos/sync`. The model takes a text prompt, a first-frame image, and
-either an action DSL string or explicit camera poses. vLLM-Omni serves the
-SANA-WM Stage-1 DiT, decoded through the SANA VAE. The optional LTX-2 refiner
-stage is not supported by this integration; it is a planned follow-up.
-
-## References
-
- Upstream model card: <https://huggingface.co/Efficient-Large-Model/SANA-WM_bidirectional>
- Video API: [`docs/serving/videos_api.md`](../../docs/serving/videos_api.md)
-
-## Hardware Support
-
-## GPU
-
-### 1x NVIDIA RTX PRO 6000 Blackwell 96GB
-
-#### Capacity
-
- Model storage: the Stage-1 transformer is about 10 GB and the VAE about
-  2.3 GB. The Gemma text encoder is a separate 4.9 GB repo. The engine prefetches
-  the whole model repo at startup (`allow_patterns=["*"]`), which is why the
-  Stage-1 weights live in their own repo — the two-stage one carries an
-  additional 84 GB `refiner/` that this path never loads.
- Text encoder: the pipeline tries `google/gemma-2-2b-it` first, then falls back
-  to the ungated mirror `Efficient-Large-Model/gemma-2-2b-it`. The first repo is
-  gated, so without an accepted licence and `hf auth login` you will see one
-  failed load before the fallback succeeds — that is expected, not an error to
-  chase. Set `VLLM_OMNI_SANA_WM_STAGE1_TEXT_ENCODER` to pin a specific repo or a
-  local path and skip both.
- Disk sizing: provision about 40 GB of local disk or Hugging Face cache volume
-  so the model, temporary downloads, and generated artifacts fit without cache
-  eviction.
- GPU sizing: the default 1280x704, 161-frame, 60-step serving profile peaks at
-  22.6 GB of device memory and takes about 133 s to generate on one RTX PRO
-  6000 Blackwell. The peak lands in the VAE decode, which is why the pipeline
-  forces VAE tiling on regardless of `vae_use_tiling` — without it the same
-  request costs about 9 GB more, and a 321-frame one OOMs outright. On smaller
-  GPUs, lower `width`, `height`, or `num_frames` before serving production
-  requests.
-
-#### Environment
-
- OS: Linux
- Python: 3.10+
- Driver / runtime: NVIDIA driver with CUDA runtime supported by your PyTorch
-  build
- Recommended operator library: Triton, installed through the vLLM/vLLM-Omni
-  Python environment
- vLLM version: Match the repository requirements for your checkout
- vLLM-Omni version or commit: Use the commit you are deploying from
-
-#### Command
-
-The repo ships the standard Diffusers layout (`model_index.json` +
-`transformer/`, `vae/`), and its `model_index.json` names `SanaWmPipeline`, so
-the pipeline class resolves on its own.
-
-```bash
-CUDA_VISIBLE_DEVICES=0 \
-vllm serve BBBBruce/SANA-WM_bidirectional-stage1-diffusers \
-  --omni \
-  --host 0.0.0.0 \
-  --port 8091
-```
-
-No deploy config: single-stage diffusion models are deliberately absent from
-`OMNI_PIPELINES` (`vllm_omni/config/pipeline_registry.py`), so stage resolution
-falls back to the default stage config and a YAML's stage settings — including
-`default_sampling_params` — would not be applied. The production generation
-settings therefore live in the model (`num_inference_steps=60`,
-`guidance_scale=5.0`); a request that omits a field gets them. The examples
-below still pass every field explicitly so the numbers are visible.
-
-If you point this at the older two-stage repo
-(`BBBBruce/SANA-WM_bidirectional-diffusers`), startup fails with `Model class
-SanaWmTwoStagesPipeline not found in diffusion model registry`, because that
-repo's `model_index.json` names a class this build does not register. Add
-`--model-class-name SanaWmPipeline` to override it.
-
-#### Verification
-
-Use a short smoke request first:
-
-```bash
-curl -sS -X POST http://localhost:8091/v1/videos/sync \
-  -H "Accept: video/mp4" \
-  -F "prompt=A slow forward camera move through a quiet city street." \
-  -F "negative_prompt=blurry, low quality, distorted, watermark" \
-  -F "input_reference=@/path/to/first_frame.png;type=image/png" \
-  -F "width=1280" \
-  -F "height=704" \
-  -F "num_frames=9" \
-  -F "fps=16" \
-  -F "num_inference_steps=2" \
-  -F "guidance_scale=5.0" \
-  -F "seed=42" \
-  --form-string 'extra_params={"sana_wm":{"action":"w-8","translation_speed":0.055,"rotation_speed_deg":1.2,"intrinsics":{"fx":640,"fy":640,"cx":640,"cy":352}}}' \
-  -o sana_wm_smoke.mp4
-```
-
-For a production-length request, note that the action durations must sum to
-`num_frames - 1` — the rollout includes the identity start pose — and a
-mismatch is rejected rather than padded or truncated:
-
-```bash
-curl -sS -X POST http://localhost:8091/v1/videos/sync \
-  -H "Accept: video/mp4" \
-  -F "prompt=A slow forward camera move through a quiet city street." \
-  -F "negative_prompt=blurry, low quality, distorted, watermark" \
-  -F "input_reference=@/path/to/first_frame.png;type=image/png" \
-  -F "width=1280" \
-  -F "height=704" \
-  -F "num_frames=161" \
-  -F "fps=16" \
-  -F "num_inference_steps=60" \
-  -F "guidance_scale=5.0" \
-  -F "seed=42" \
-  --form-string 'extra_params={"sana_wm":{"action":"w-160","translation_speed":0.055,"rotation_speed_deg":1.2,"intrinsics":{"fx":640,"fy":640,"cx":640,"cy":352}}}' \
-  -o sana_wm_output.mp4
-```
-
-Use `POST /v1/videos` instead when you want job storage and polling rather than
-inline MP4 bytes. It accepts the same form fields as `/v1/videos/sync`.
-
-```bash
-create_response=$(curl -sS -X POST http://localhost:8091/v1/videos \
-  -H "Accept: application/json" \
-  -F "prompt=A slow forward camera move through a quiet city street." \
-  -F "negative_prompt=blurry, low quality, distorted, watermark" \
-  -F "input_reference=@/path/to/first_frame.png;type=image/png" \
-  -F "width=1280" \
-  -F "height=704" \
-  -F "num_frames=161" \
-  -F "fps=16" \
-  -F "num_inference_steps=60" \
-  -F "guidance_scale=5.0" \
-  -F "seed=42" \
-  --form-string 'extra_params={"sana_wm":{"action":"w-160","translation_speed":0.055,"rotation_speed_deg":1.2,"intrinsics":{"fx":640,"fy":640,"cx":640,"cy":352}}}')
-
-video_id=$(echo "$create_response" | jq -r '.id')
-curl -sS "http://localhost:8091/v1/videos/${video_id}" | jq .
-curl -L "http://localhost:8091/v1/videos/${video_id}/content" -o sana_wm_output.mp4
-```
-
-#### Notes
-
- Sequence parallelism is not supported. The bidirectional gated delta
-  recurrence carries state across frames, so a rank cannot denoise a slice
-  of the token sequence in isolation; supporting it needs a distributed scan
-  or an all-gather before the GDN blocks.
-
- `input_reference` is required for the first frame. Use `image_reference` only
-  when you need a JSON-safe image URL or data URL instead of a multipart upload.
- `sana_wm` must provide exactly one of `action` or `camera`.
- Action strings use comma-separated `<keys>-<duration>` segments. Supported
-  keys are `w`, `a`, `s`, `d` for translation and `i`, `j`, `k`, `l` for
-  pitch/yaw rotation. The durations must sum to `num_frames - 1`.
- Explicit camera control (alternative to `action`): pass
-  `"camera": {"poses": [...]}` where `poses` is a list of `num_frames`
-  camera-to-world 4x4 matrices (row-major, OpenCV `+X right, +Y down, +Z forward`
-  convention), e.g.
-  `extra_params={"sana_wm":{"camera":{"poses":[[[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]], ...]},"intrinsics":{...}}}`.
-  Most callers should prefer `action`; explicit poses exist for callers that
-  already have a per-frame trajectory.
- Explicit `intrinsics` are recommended and take the mapping form
-  `{"fx":640,"fy":640,"cx":640,"cy":352}` (for 1280x704). This `{fx,fy,cx,cy}`
-  mapping is the only accepted intrinsics form; omit `intrinsics` to derive them
-  from the output resolution. All four values must be finite, and `fx`/`fy`
-  must be positive — the ray map divides by them.
- The video API returns decoded MP4 bytes and has no `output_type` field, so
-  raw Stage-1 latents are reachable only from the offline API
-  (`OmniDiffusionSamplingParams(output_type="latent")`); see
-  [`tests/e2e/offline_inference/test_sana_wm.py`](../../tests/e2e/offline_inference/test_sana_wm.py).
-  Putting `output_type` in `extra_params` does not work: it lands in
-  `sampling_params.extra_args`, while the pipeline reads the top-level field.
@@ -71,35 +71,6 @@ single-GPU entry point; `minicpmo_4_5_2gpu.yaml` is the recommended
 two-GPU profile. The removed fused two-stage implementation is not retained as
 a fallback because it would duplicate state machines and correctness paths.

-### Graph execution
-
-Graph boundaries follow each stage's state model:
-
-| Stage | Graph mode on Ascend | Eager boundary |
-| --- | --- | --- |
-| 0 Thinker | vLLM `PIECEWISE` | multimodal preprocessing and output routing |
-| 1 Talker | vLLM `PIECEWISE` | conditioning preprocessing, codec sampling, request-state updates |
-| 2 Code2Wav | inner exact-shape NPUGraph for the CFM DiT estimator | encoder, timestep embedding, HiFT/RNG, request parsing, stream-state commit |
-
-Stage 2 keeps `enforce_eager: true` for the generation runner because its
-Python request metadata and per-request cache dictionaries are not valid
-outer-graph inputs. The backend captures only the deterministic CFM DiT
-estimator, with an exact graph key for each tensor shape. Timestep embedding
-stays eager because the upstream implementation creates a CPU frequency tensor;
-HiFT stays eager because it generates random phase. The graph cache stores up
-to 32 entries by default, after which unseen shapes run eagerly.
-
-An ACL graph capture failure is fatal to that Stage-2 process because older
-torch-npu releases can leave allocator and RNG capture state invalid. Restart
-the service after a capture failure. To run without capture, set
-`code2wav_enable_npu_graph: false` under the Stage-2
-`platforms.npu.stages[].additional_config` block before startup. Tune the cache
-limit there with `code2wav_max_npu_graphs`. Graph mode also requires
-`ASCEND_LAUNCH_BLOCKING` to be unset or set to `0`.
-
-The inner NPUGraph is independent of the outer runner setting, so do not use a
-global `--enforce-eager` override when Stage 0/1 `PIECEWISE` replay is desired.
-
 ## GPU

 ### 1 x GPU (default — single command)
@@ -240,9 +211,9 @@ speech output (TTS)"** checkbox on / off.
  model processes still share one CUDA device.
 - `--trust-remote-code` is required — the HF repo ships a custom
  `MiniCPMO` config / model class.
- Stage 0 Thinker and Stage 1 Talker enable vLLM CUDA Graphs. Stage 2 keeps its
-  request orchestration eager while using inner CFM and HiFT CUDA Graphs. On
-  Ascend, the exact-shape CFM DiT estimator instead uses inner NPUGraph replay.
+- Stage 0 Thinker and Stage 1 Talker enable vLLM CUDA Graphs. Stage 2 remains
+  eager because its request-owned Flow/HiFT caches and variable chunk/cache
+  shapes are not yet exposed through a static exact-shape graph wrapper.
 - All default stages use `max_num_seqs: 4` to reduce cross-process GPU
  contention. Talker AR
  state and Code2Wav caches are request-owned; Code2Wav batches only
@@ -253,8 +224,8 @@ speech output (TTS)"** checkbox on / off.
 - `StageRequestStats.batch_size` is a request-scoped placeholder, not the
  scheduler's execution batch.
 - Single-GPU co-location trades throughput for hardware density: Stage 0/1
-  CUDA Graph replay and Stage 2 vocoder kernels compete across three CUDA
-  contexts. Use the 8x4090 config or a custom multi-GPU mapping for
+  CUDA Graph replay and eager Stage 2 vocoder kernels compete across three
+  CUDA contexts. Use the 8x4090 config or a custom multi-GPU mapping for
  throughput-sensitive serving.

 ### 8 x RTX 4090 24GB (consumer-GPU layout)
@@ -252,53 +252,3 @@ curl -X POST http://localhost:8091/v1/audio/speech \
    - stage_id: 1
      gpu_memory_utilization: 0.15
  ```
-
-### 1x AMD MI300X, 1.7B checkpoints
-
-#### Environment
-
- OS: Linux 6.8.0-134-generic, x86_64
- Container: official ROCm image built from `docker/Dockerfile.rocm`
- Python: 3.12.13
- PyTorch: 2.11.0+gitd0c8b1f
- Driver / runtime: AMD 6.19.14.31400000 / ROCm 7.2.53211
- GPU: one AMD Instinct MI300X, `gfx942:sramecc+:xnack-`, 191.69 GiB visible HBM
- vLLM version: 0.27.0+rocm723
- vLLM Omni version or commit: `3fecb6953ca8dc51210cc0421ef24552267a41ef`
- Installed vLLM Omni package metadata: `0.27.0rc2.dev44+g55abdade9.rocm`
- ONNX Runtime: onnxruntime-rocm 1.22.2.post3 with `ROCMExecutionProvider`
- transformers: 5.15.0
-
-#### Command
-
-The MI300X tests also worked without `enforce_eager`. To use that setup, remove `enforce_eager: true` from stage 1 under `platforms.rocm` in `vllm_omni/deploy/qwen3_tts.yaml`.
-
-Set this environment variable before you start Qwen3-TTS:
-
-```bash
-export MIOPEN_FIND_MODE=FAST
-```
-
-#### Verification
-
-Each row records one offline request with one prompt and batch size one. Request time is the logged `e2e_wall_time_ms`, and stage 1 time is the logged `e2e_stage_1_wall_time_ms`.
-
-| Checkpoint | Code2Wav configuration | `MIOPEN_FIND_MODE` | Request time | Stage 1 time | Output duration | Real time factor | Maximum sampled device memory |
-| --- | --- | --- | ---: | ---: | ---: | ---: | ---: |
-| CustomVoice | Eager | Unset | 1.683 s | 1.613 s | 5.68 s | 0.30 | 60.91 GiB |
-| CustomVoice | Graphs allowed | `FAST` | 1.326 s | 1.259 s | 5.68 s | 0.23 | 62.19 GiB |
-| VoiceDesign | Eager | Unset | 1.195 s | 1.180 s | 4.40 s | 0.27 | 60.91 GiB |
-| VoiceDesign | Graphs allowed | `FAST` | 1.127 s | 1.054 s | 4.40 s | 0.26 | 62.21 GiB |
-| Base | Eager | Unset | 13.330 s | 13.263 s | 4.32 s | 3.09 | 61.74 GiB |
-| Base | Graphs allowed | `FAST` | 14.314 s | 14.245 s | 4.80 s | 2.98 | 62.84 GiB |
-
-All six processes finished successfully, and each output passed the 24 kHz mono WAV checks.
-
-#### Notes
-
- The current ROCm config keeps Code2Wav in eager mode.
- The tested alternative allows graphs and sets [`MIOPEN_FIND_MODE=FAST`](https://rocm.docs.amd.com/projects/MIOpen/en/develop/reference/env_variables.html). MIOpen uses a saved kernel choice when available and uses its immediate fallback otherwise.
- CustomVoice used a graph for one of four logged Code2Wav batches. VoiceDesign used a graph for two of four batches. Base used no graphs for its four logged batches.
- The recorded request time with graphs allowed was lower for CustomVoice and VoiceDesign and higher for Base. The Base runs generated different amounts of audio, so their request times are not a direct comparison.
- Each setup was tested with one request, so the timing is not a performance benchmark.
- GPU memory was sampled once per second with `rocm-smi`.
@@ -49,19 +49,16 @@ recipes/
 | [`Helios/Helios.md`](./Helios/Helios.md) | Text-to-video, image-to-video, and video-to-video generation | 1x NVIDIA H20 |
 | [`inclusionAI/Ming-flash-omni-2.0.md`](./inclusionAI/Ming-flash-omni-2.0.md) | Online serving for multimodal chat + standalone TTS | 4x H100 / 1x H100 80GB |
 | [`inclusionAI/Ming-omni-tts.md`](./inclusionAI/Ming-omni-tts.md) | Offline + online dense Ming TTS/audio generation | 1x H100 80GB / 1x AMD MI300X (ROCm 7.2) |
-| [`k2-fsa/OmniVoice.md`](./k2-fsa/OmniVoice.md) | Offline and online multilingual text-to-speech | 1x AMD MI300X (ROCm 7.2) |
 | [`IndexTeam/IndexTTS-2.md`](./IndexTeam/IndexTTS-2.md) | Online serving for voice-cloned TTS with optional emotion control | 1x L4 24GB or larger CUDA GPU |
 | [`IndexTeam/IndexTTS-2_5.md`](./IndexTeam/IndexTTS-2_5.md) | Online + offline multilingual voice-cloned TTS with native speed and emotion control | 1x NVIDIA H20 96GB |
 | [`MiniMaxAI/MiniMax-H3.md`](./MiniMaxAI/MiniMax-H3.md) | T2VA, FL2VA, image+audio Ref2VA, and multi-video Ref2VA serving | 1x GPU with CPU offload / 4x B300 |
 | [`MiniMaxAI/MiniMax-H3-MUSA.md`](./MiniMaxAI/MiniMax-H3-MUSA.md) | MiniMax H3 T2VA, FL2VA, and Ref2VA on MUSA | 4x MTT S5000 (TP4 Ref2VA profile) |
 | [`krea/Krea-2.md`](./krea/Krea-2.md) | Text-to-image (Turbo + Raw), offline + online, with LoRA | 1x H100 80GB |
 | [`LTX/LTX-2.md`](./LTX/LTX-2.md) | LTX-2/LTX-2.3 text-to-video and image-to-video with synchronized audio | H200 141GB / 96GB-class GPU |
-| [`LTX/LTX-2.5.md`](./LTX/LTX-2.5.md) | LTX-2.5-Diffusers: Full/SFT one-stage and distilled two-stage T2V/I2V with synchronized audio | NVIDIA B300; cuDNN-qualified |
 | [`MammothModa2/MammothModa2.md`](./MammothModa2/MammothModa2.md) | Preview and Dev text-to-image (AR → DiT); Dev text/image understanding | Preview: 1x L40S 48GB / 1x ≥40GB GPU; Dev: 1x NVIDIA GPU with sufficient cache headroom |
 | [`meituan-longcat/LongCat-Video-Avatar-1.5.md`](./meituan-longcat/LongCat-Video-Avatar-1.5.md) | Audio-driven avatar video generation (AT2V / AI2V, single- and multi-speaker, AVC continuation) | 1x H100 80GB |
 | [`mistralai/Voxtral-TTS.md`](./mistralai/Voxtral-TTS.md) | Online serving for TTS | 1x RTX 4090 24GB |
 | [`Robbyant/LingBot-Video.md`](./Robbyant/LingBot-Video.md) | Native dense and MoE text-to-video serving | Dense: 1x L20X; MoE: 1x L20X (~67.7 GiB peak) |
-| [`Robbyant/LingBot-World-2.0.md`](./Robbyant/LingBot-World-2.0.md) | Offline and experimental realtime interactive world generation | 1x H200/B200 or 2x B200 for TP=2 |
 | [`cosmos3/Cosmos3-Nano.md`](./cosmos3/Cosmos3-Nano.md) | Text-to-image, text-to-video, image-to-video, video-to-video generation, text to video with sound, action policy | 1x H200 141GB / B300 |
 | [`cosmos3/Cosmos3-Edge.md`](./cosmos3/Cosmos3-Edge.md) | T2I / T2V / I2V + action (Physical-AI) generation on the Nemotron-based Edge transformer | 1x GPU (~8 GiB) |
 | [`cosmos3/Cosmos3-Super.md`](./cosmos3/Cosmos3-Super.md) | 64B T2I / T2V / I2V / V2V generation (+ optional audio) / Action policy | 8x H200/H100/A100 / 2x H200 / B300 |
@@ -70,14 +67,13 @@ recipes/
 | [`OpenMOSS/MOSS-TTS.md`](./OpenMOSS/MOSS-TTS.md) | Online + offline multilingual TTS (MOSS-TTS family, 8B) | 1x H100 80GB |
 | [`NVIDIA/Nemotron-Labs-Audex.md`](./NVIDIA/Nemotron-Labs-Audex.md) | TTS / text-to-audio / audio understanding / cascaded S2S (Audex 2B + 30B-A3B) | 1x H100 80GB |
 | [`NVIDIA/NemotronLabs-VoiceChat.md`](./NVIDIA/NemotronLabs-VoiceChat.md) | Offline speech-to-speech voice chat (11B, frame-locked 12.5 Hz, 3-stage thinker/talker/code2wav) | 1x H100 80GB |
-| [`NVIDIA/SANA-WM.md`](./NVIDIA/SANA-WM.md) | First-frame image-to-video serving with camera control | 1x 24GB+ CUDA GPU; ~40GB disk |
 | [`Qwen/Qwen-Image.md`](./Qwen/Qwen-Image.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt mixed FP8/NVFP4 | 1x A100 80GB / 2x B200 |
 | [`Qwen/Qwen-Image-2512.md`](./Qwen/Qwen-Image-2512.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt FP8 / mixed FP8/NVFP4 | 1x A800 80GB / 2x B200 |
 | [`Qwen/Qwen-Image-Edit.md`](./Qwen/Qwen-Image-Edit.md) | Text-guided single-image editing | 1x or 2x H200 141GB |
 | [`Qwen/Qwen3-Omni.md`](./Qwen/Qwen3-Omni.md) | Online serving for multimodal chat | 1x A100 80GB |
-| [`Qwen/Qwen3-TTS.md`](./Qwen/Qwen3-TTS.md) | Text-to-speech serving (CustomVoice / VoiceDesign / Base) | 1x H100/A100 80GB / 1x AMD MI300X |
-| [`SenseNova/SenseNova-U1.md`](./SenseNova/SenseNova-U1.md) | Unified image generation and understanding | 1x or 2x H200 / 1x AMD MI300X |
-| [`StabilityAI/Stable-Audio-Open.md`](./StabilityAI/Stable-Audio-Open.md) | Offline + online text-to-audio generation (Stable Audio Open) | 1x RTX 4090 24GB / 1x AMD MI300X |
+| [`Qwen/Qwen3-TTS.md`](./Qwen/Qwen3-TTS.md) | Text-to-speech serving (CustomVoice / VoiceDesign / Base) | 1x H100/A100 80GB |
+| [`SenseNova/SenseNova-U1.md`](./SenseNova/SenseNova-U1.md) | Unified image generation and understanding | 1x H200 (144GB) |
+| [`StabilityAI/Stable-Audio-Open.md`](./StabilityAI/Stable-Audio-Open.md) | Offline + online text-to-audio generation (Stable Audio Open) | 1x RTX 4090 24GB |
 | [`Tencent/Covo-Audio-Chat.md`](./Tencent/Covo-Audio-Chat.md) | Online serving for audio chat | 1x A100 80GB |
 | [`Tencent/HunyuanImage-3.0-Instruct.md`](./Tencent/HunyuanImage-3.0-Instruct.md) | DiT-only text-to-image serving and benchmark, including ModelOpt mixed FP8/NVFP4 | 4x H100/H800 80GB / 2x B200 |
 | [`Wan-AI/Wan2.2-T2V.md`](./Wan-AI/Wan2.2-T2V.md) | Text-to-video serving (Wan2.2 14B) with Skip-Softmax sparse attention | 1x datacenter Blackwell (B200/B300) |
@@ -1,122 +0,0 @@
-# LingBot-World 2.0
-
-> Offline and experimental realtime interactive world generation
-
-## Summary
-
- Vendor: Robbyant
- Model: `robbyant/lingbot-world-v2-14b-causal-fast-diffusers`
- Task: image-conditioned interactive world generation
- Modes: offline trajectory replay and in-process realtime AR-Diffusion ticks
- Hardware validated: NVIDIA H200 and B200
- Maintainer: Community
-
-The checkpoint is separately licensed under CC BY-NC-SA and restricted to
-non-commercial use. The vLLM-Omni integration code remains Apache-2.0.
-
-## Offline generation
-
-The offline path consumes one source image and an action directory containing
-`poses.npy` with shape `[frames, 4, 4]` and `intrinsics.npy` with shape
-`[frames, 4]`.
-
-```bash
-python examples/offline_inference/diffusion/lingbot_world_v2.py \
-  --prompt "The camera moves slowly forward through the scene." \
-  --image /path/to/first_frame.png \
-  --action-dir /path/to/actions/forward \
-  --num-frames 81 \
-  --output lingbot_world_v2.mp4
-```
-
-The current causal-fast checkpoint uses four DMD sampling steps. Raw frame
-counts must be `9 + 12k`, up to 117 frames. Camera files may contain a
-longer bounded source trajectory (the official example contains 269 frames);
-the runtime consumes only the prefix needed by the request.
-
-## Realtime in-process generation
-
-The realtime example keeps AR-Diffusion KV and model-owned state across
-requests. Each JSONL line describes the prompt and/or three latent-frame camera
-actions applied at the next chunk boundary:
-
-```json
-{"event_id":1,"prompt":"A road through a forest","frames":[["j"],[],[]]}
-{"event_id":2,"frames":[["w"],["w"],["w"]]}
-{"event_id":3,"prompt":"The road enters a snowy valley","frames":[[],[],[]]}
-```
-
-Run:
-
-```bash
-python examples/offline_inference/diffusion/lingbot_world_v2_realtime.py \
-  --image /path/to/first_frame.png \
-  --events /path/to/events.jsonl \
-  --output-dir /tmp/lingbot-realtime \
-  --gpu-memory-fraction 0.6
-```
-
-The example writes one latent tensor and one metadata JSON file per chunk. It
-exercises the same `ARDiffusionSessionManager -> ARDiffusionOmniTickConsumer
-> AsyncOmni -> ARDiffusionEngine` path used by a future HTTP or WebSocket
-transport.
-
-This PR intentionally does not define a public realtime HTTP/WebSocket schema.
-An online serving client should be added together with the public transport
-API rather than exposing LingBot-specific event fields from the generic model
-runtime. The structured camera-interaction frontend and transport work is
-tracked separately in
-[vllm-project/vllm-omni#5527](https://github.com/vllm-project/vllm-omni/pull/5527).
-
-## Realtime identity and controls
-
- `session_id` identifies the persistent world and its worker-owned state.
- `event_id` identifies a prompt/control update and remains monotonic across reset.
- `chunk_index` is contiguous from zero and restarts from zero after reset.
- `request_id` correlates one chunk snapshot with its output metadata.
- AsyncOmni uses a separate UUID-suffixed internal engine routing ID.
-
-The generic runtime transports controls as opaque snapshots. LingBot's adapter
-accepts:
-
- `lingbot.camera_actions.v1` for per-latent-frame key states such as `w`, `a`,
-  `s`, `d`, `i`, `j`, `k`, and `l`;
- `lingbot.camera_trajectory.v1` for explicit pose/intrinsics trajectories.
-
-## Validation
-
-Real-checkpoint validation uses 480x832 output, four DMD steps, and seed 42.
-The exercised matrix includes:
-
- TP=1 and TP=2 execution;
- two interleaved resident sessions;
- action input and prompt switching;
- seven contiguous chunks crossing the sink plus recent rolling window;
- direct versus paged replay;
- CUDA Graph execution;
- reset, close, failure cleanup, and exact metadata matching; and
- nine-frame VAE decode.
-
-Official `generate.py` accuracy and performance numbers, together with
-generation artifacts, are recorded in the PR validation section for the exact
-tested commit.
-
-## Current limitations
-
- Only the 14B causal-fast checkpoint is supported.
- The realtime control plane is internal; there is no public server transport yet.
- AR-Diffusion stages currently require one replica because session-affine
-  routing across replicas is not implemented.
- One AR block is generated per request and `max_num_seqs` must be one.
- Stateful streaming VAE decode is not implemented; the realtime example emits
-  latent chunks.
- SP/USP, pipeline/CFG parallelism, HSDP, VAE parallelism, quantization,
-  Cache-DiT, TeaCache, causal-pretrain, and the 1.3B checkpoint are not claimed.
- No AMD GPU, Ascend NPU, or Intel GPU support is claimed.
-
-## References
-
- Checkpoint: <https://huggingface.co/robbyant/lingbot-world-v2-14b-causal-fast-diffusers>
- Official implementation: <https://github.com/robbyant/lingbot-world-v2>
- Offline example: [`examples/offline_inference/diffusion/lingbot_world_v2.py`](../../examples/offline_inference/diffusion/lingbot_world_v2.py)
- Realtime design: [`docs/design/feature/realtime_ar_diffusion.md`](../../docs/design/feature/realtime_ar_diffusion.md)
@@ -178,48 +178,6 @@ with the expected 1536×2720 resolution.
 - The LLM transformer uses `QKVParallelLinear` and `MergedColumnParallelLinear`
  for fused QKV and gate/up projections with TP support.

-### 1x AMD MI300X 192GB
-
-#### Environment
-
- OS: Linux 6.8.0-134-generic, x86_64
- Container: official ROCm image built from `docker/Dockerfile.rocm`
- Python: 3.12.13
- PyTorch: 2.11.0+gitd0c8b1f
- Driver / runtime: AMD 6.19.14.31400000 / ROCm 7.2.53211
- GPU: one AMD Instinct MI300X, `gfx942:sramecc+:xnack-`, 191.69 GiB visible HBM
- vLLM version: 0.27.0+rocm723
- vLLM Omni version or commit: `a704c8759c96e123c0d7c89b11f120b1c0f120cf`
- Installed vLLM Omni package metadata: `0.27.0rc2.dev44+g55abdade9.rocm`
-
-#### Command
-
-```bash
-python3 examples/offline_inference/text_to_image/text_to_image.py \
-    --model SenseNova/SenseNova-U1-8B-MoT \
-    --prompt "Close portrait of an elderly woman by a farmhouse window, textured skin, gentle smile, warm natural light, emotional documentary look." \
-    --width 1536 \
-    --height 2720 \
-    --seed 42 \
-    --num-inference-steps 50 \
-    --cfg-scale 4.0 \
-    --extra-body '{"think": true, "cfg_norm": "none", "timestep_shift": 3.0, "t_eps": 0.02}' \
-    --enable-diffusion-pipeline-profiler \
-    --log-stats \
-    --output sensenova_u1_mi300x.png
-```
-
-#### Verification
-
-The command completed and wrote a valid 1536 by 2720 RGB PNG.
-
-#### Notes
-
- Generation with 50 inference steps took 34.011 seconds.
- Model loading used 32.774 GiB and took 13.987 seconds.
- The internal profiler recorded 35.67 GB reserved and 35.10 GB allocated for the request.
- The highest one second whole device memory sample was 37.93 GiB.
-
 ## Online Serving

 SenseNova-U1 supports all four modalities via the OpenAI-compatible
@@ -78,7 +78,7 @@ python examples/offline_inference/text_to_audio/text_to_audio.py \
 Start the online serving endpoint:

 ```bash
-vllm serve /path/to/stable-audio-open-1.0 \
+vllm-omni serve /path/to/stable-audio-open-1.0 \
  --host 0.0.0.0 \
  --port 8091 \
  --gpu-memory-utilization 0.9 \
@@ -164,50 +164,7 @@ PY
  `torchaudio==2.11.0+cu130`.
 - The `NIXL is not available`, `GLOO_SOCKET_IFNAME`, and `torchsde` boundary
  warnings observed during validation did not prevent successful generation.
- The RTX 4090 entry was validated on one 24 GB GPU. The MI300X entry below covers one 192 GB GPU.
+- This recipe was validated on one RTX 4090 24GB GPU only. Other GPU counts,
+  ROCm, XPU, and NPU setups are not covered here.
 - Long generations, higher inference-step counts, and non-WAV response formats
  were not benchmarked in this recipe.
-
-#### 1x AMD MI300X 192GB
-
-##### Environment
-
- OS: Linux 6.8.0-134-generic, x86_64
- Container: official ROCm image built from `docker/Dockerfile.rocm`
- Python: 3.12.13
- PyTorch: 2.11.0+gitd0c8b1f
- Driver / runtime: AMD 6.19.14.31400000 / ROCm 7.2.53211
- GPU: one AMD Instinct MI300X, `gfx942:sramecc+:xnack-`, 191.69 GiB visible HBM
- vLLM version: 0.27.0+rocm723
- vLLM Omni version or commit: `73e1368c7bb940efe1a025859c9d6c8eeeb2e3f0`
- Installed vLLM Omni package metadata: `0.27.0rc2.dev44+g55abdade9.rocm`
-
-##### Commands
-
-```bash
-python3 examples/offline_inference/text_to_audio/text_to_audio.py \
-    --model stabilityai/stable-audio-open-1.0 \
-    --prompt "A gentle piano melody with soft room ambience" \
-    --negative-prompt "Low quality, distorted, noisy" \
-    --seed 42 \
-    --guidance-scale 7.0 \
-    --audio-length 10.0 \
-    --num-inference-steps 50 \
-    --cache-backend tea_cache \
-    --enable-diffusion-pipeline-profiler \
-    --output stable_audio_10s.wav
-```
-
-##### Verification
-
-The command completed and wrote a valid 44.1 kHz stereo WAV with 10.00 seconds of audio.
-
-##### Notes
-
- TeaCache ran with `rel_l1_thresh=0.2`.
- Model loading used 2.7891 GiB and took 3.706 seconds.
- Generation took 4.750 seconds, which gives a real time factor of 0.475 for the 10.00 second output.
- The internal profiler recorded 15.65 GB reserved and 9.68 GB allocated for the request.
- The highest one second whole device memory sample was 19.61 GiB.
- The output RMS was 0.0887, and its peak absolute amplitude was 0.5761.
- The full process took 384 seconds, including startup and compilation.
@@ -39,9 +39,6 @@ platform section, document one or more tested hardware configurations.

 #### Command

-Serve commands use `vllm serve <model> --omni`. The `--omni` flag is what
-selects the Omni pipeline, so it is required and cannot be dropped.
-
 ```bash
 # Add the exact command(s) here
 ```
@@ -189,10 +189,8 @@ wait

 - `--dlo-use-allgather` (default) requires all concurrent requests to have the same
  `num_inference_steps` (AllGather is a collective that needs all ranks synchronized)
- Online per-tensor FP8 linears with DLO AllGather use the ordinary loader
-  instead of direct checkpoint mmap, then shard the finalized FP8 weights and
-  scales. Size host memory for a transient complete FP8 model on every rank at
-  startup
+- Online quantization (FP8) is incompatible with the DLO+AllGather mmap path;
+  use `--dlo-no-use-allgather` or disable online quantization
 - Tensor Parallel > 1 is rejected in the DLO+AllGather mmap path. TP with
  `--dlo-no-use-allgather` retains the standard TP loader and should be treated
  as an experimental compatibility path
@@ -5,21 +5,20 @@
 ## Summary

 - Vendor: inclusionAI
- Model: `inclusionAI/Ming-omni-tts-16.8B-A3B`, `inclusionAI/Ming-omni-tts-0.5B`
+- Model: `inclusionAI/Ming-omni-tts-0.5B`
 - Task: Text-to-speech with style, dialect, cloning, and multi-speaker controls
 - Mode: Online serving via the OpenAI-compatible `/v1/audio/speech` API
 - Maintainer: Community

 ## References

- [Huggingface: inclusionAI/Ming-omni-tts-16.8B-A3B](https://huggingface.co/inclusionAI/Ming-omni-tts-16.8B-A3B)
- [Huggingface: inclusionAI/Ming-omni-tts-0.5B](https://huggingface.co/inclusionAI/Ming-omni-tts-0.5B)
+- [Huggingface Model card](https://huggingface.co/inclusionAI/Ming-omni-tts-0.5B)
 - Upstream repository [inclusionAI/Ming-omni-tts](https://github.com/inclusionAI/Ming-omni-tts)


 ## Hardware Support

-This recipe documents a validated ROCm configuration and a CUDA configuration for the dense 0.5B and the MoE 16.8B two-stage TTS pipeline deployment.
+This recipe documents a validated ROCm configuration and a CUDA configuration for the dense 0.5B two-stage TTS pipeline deployment.
 Other hardware is welcome as community validation lands.

 ## CUDA
@@ -40,7 +39,11 @@ Other hardware is welcome as community validation lands.
 Launch the two-stage talker:

 ```bash
-vllm serve inclusionAI/Ming-omni-tts-16.8B-A3B --omni --port 8091
+vllm-omni serve inclusionAI/Ming-omni-tts-0.5B \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --omni \
+    --port 8091 \
+    --enforce-eager
 ```

 #### Verification
@@ -51,7 +54,7 @@ Basic synthesis (save the WAV bytes):
 curl -X POST http://localhost:8091/v1/audio/speech \
    -H "Content-Type: application/json" \
    -d '{
-      "model": "inclusionAI/Ming-omni-tts-16.8B-A3B",
+      "model": "inclusionAI/Ming-omni-tts-0.5B",
      "input": "你好，这是 Ming 在线语音合成测试。",
      "response_format": "wav"
    }' --output ming_tts_basic.wav
@@ -71,7 +74,7 @@ Reference-audio zero-shot cloning:
 REF_AUDIO="$BASE/10002287-00000094.wav"
 jq -n --arg ref_audio "$REF_AUDIO" \
    '{
-       model: "inclusionAI/Ming-omni-tts-16.8B-A3B",
+       model: "inclusionAI/Ming-omni-tts-0.5B",
       input: "我们的愿景是构建未来服务业的数字化基础设施，为世界带来更多微小而美好的改变。",
       ref_audio: $ref_audio,
       ref_text: "在此奉劝大家别乱打美白针。",
@@ -104,7 +107,7 @@ jq -n \
    --arg a "$REF_A" \
    --arg b "$REF_B" \
    '{
-       model: "inclusionAI/Ming-omni-tts-16.8B-A3B",
+       model: "inclusionAI/Ming-omni-tts-0.5B",
       input: $input,
       ref_audio: [$a, $b],
       ref_text: $ref_text,
@@ -167,35 +170,6 @@ python examples/online_serving/text_to_speech/ming_tts/openai_speech_client.py \
 `--ref-audio` matches upstream `use_spk_emb=True`; do not add `--ref-text`
 for the dialect case.

-**Offline smoke result on ROCm 7.2:**
-
-A later offline smoke run used the same MI300X model with the following software:
-
- GPU: one AMD Instinct MI300X with 191.69 GiB visible HBM and gfx942
- Kernel: Linux 6.8.0-134-generic, x86_64
- Python: 3.12.13
- PyTorch: 2.11.0+gitd0c8b1f
- ROCm or HIP: 7.2.53211
- vLLM: 0.27.0+rocm723
- vLLM Omni checkout: `73e1368c7bb940efe1a025859c9d6c8eeeb2e3f0`
-
-```bash
-python3 examples/offline_inference/text_to_speech/ming_tts/end2end.py \
-    --model inclusionAI/Ming-omni-tts-0.5B \
-    --deploy-config vllm_omni/deploy/ming_tts.yaml \
-    --case basic \
-    --text "你好，这是 AMD MI300X ROCm 配方验证。" \
-    --output-dir ./ming_tts_output \
-    --output-name ming_omni_tts_mi300x.wav \
-    --enforce-eager \
-    --log-stats \
-    --metadata-json ./ming_tts_manifest.json
-```
-
-The checked run produced a valid 44.1 kHz mono WAV with 4.80 seconds of audio, RMS 0.1449, and peak absolute amplitude 0.8621. The first request took 20.406 seconds, which gives a real time factor of 4.25. The stage weight loads used 1.31 GiB and 1.47 GiB and took 1.756 seconds and 1.264 seconds. The largest one second whole device memory sample was 91.05 GiB, including the reserved KV cache. Both stages used eager mode, and the AR stage selected `TRITON_ATTN`.
-
-The run used the official ROCm image built from `docker/Dockerfile.rocm`.
-
 ## Notes

 - The official ROCm image includes the platform dependencies.
@@ -1,98 +0,0 @@
-# OmniVoice
-
-> Offline and online text to speech with automatic voice, voice design, and reference voice cloning
-
-## Summary
-
- Vendor: k2-fsa
- Model: `k2-fsa/OmniVoice`
- Task: Multilingual text to speech
- Mode: Offline inference for all modes and online serving for automatic voice
- Maintainer: Community
-
-## References
-
- [Hugging Face model](https://huggingface.co/k2-fsa/OmniVoice)
- [Offline text to speech examples](../../docs/user_guide/examples/offline_inference/text_to_speech.md#omnivoice)
- [Online text to speech examples](../../docs/user_guide/examples/online_serving/text_to_speech.md#omnivoice)
- [Deploy config](../../vllm_omni/deploy/omnivoice.yaml)
-
-## Requirements
-
-Download the checkpoint before the first run:
-
-```bash
-huggingface-cli download k2-fsa/OmniVoice
-```
-
-Automatic voice and voice design require `transformers>=4.57.0`. Reference voice cloning requires `transformers>=5.3.0`.
-
-The vLLM Omni deploy config runs the OmniVoice generator and decoder inside one float32 diffusion stage.
-
-## ROCm
-
-### 1x AMD MI300X
-
-#### Environment
-
- GPU: one AMD Instinct MI300X with 191.69 GiB visible HBM and gfx942
- Kernel: Linux 6.8.0-134-generic, x86_64
- Python: 3.12.13
- PyTorch: 2.11.0+gitd0c8b1f
- ROCm or HIP: 7.2.53211
- vLLM: 0.27.0+rocm723
- vLLM Omni checkout: `73e1368c7bb940efe1a025859c9d6c8eeeb2e3f0`
- transformers: 5.15.0
-
-The run used the official ROCm image built from `docker/Dockerfile.rocm`.
-
-#### Automatic voice
-
-Run the offline example from the repository root:
-
-```bash
-python3 examples/offline_inference/text_to_speech/omnivoice/end2end.py \
-    --model k2-fsa/OmniVoice \
-    --deploy-config vllm_omni/deploy/omnivoice.yaml \
-    --text "Hello, this is OmniVoice running on one AMD MI300X." \
-    --seed 42 \
-    --output omnivoice_mi300x.wav
-```
-
-The checked run produced a valid 24 kHz mono WAV with 3.60 seconds of audio, RMS 0.1194, and peak absolute amplitude 0.9129. The first request took 0.685 seconds, which gives a real time factor of 0.19. Model loading used 3.5739 GiB and took 9.564 seconds. The request used 4.56 GB reserved and 3.64 GB allocated according to the internal profiler, while the largest one second whole device sample was 8.33 GiB. The log states that the OmniVoice Triton kernels loaded.
-
-#### Reference voice cloning
-
-The offline interface also accepts a reference WAV and its transcript:
-
-```bash
-python3 examples/offline_inference/text_to_speech/omnivoice/end2end.py \
-    --model k2-fsa/OmniVoice \
-    --text "Hello, this is a cloned voice." \
-    --ref-audio ref.wav \
-    --ref-text "This is the reference transcription." \
-    --output cloned.wav
-```
-
-#### Voice design
-
-```bash
-python3 examples/offline_inference/text_to_speech/omnivoice/end2end.py \
-    --model k2-fsa/OmniVoice \
-    --text "Hello, this is a designed voice." \
-    --instruct "female, low pitch, British accent" \
-    --output designed.wav
-```
-
-## Online serving
-
-Online serving currently supports automatic voice:
-
-```bash
-vllm serve k2-fsa/OmniVoice --omni --port 8091 --trust-remote-code
-```
-
-```bash
-cd examples/online_serving/text_to_speech/omnivoice
-python speech_client.py --text "Hello, how are you?"
-```
@@ -1,108 +0,0 @@
-# Pi0 (π0, Pi-Zero) VLA
-
-> Vision-Language-Action policy serving over the OpenPI realtime websocket
-
-## Summary
-
- Vendor: Physical Intelligence (checkpoint redistributed by LeRobot)
- Model: `lerobot/pi0_base`
- Task: Vision-Language-Action — multi-camera images + a language instruction +
-  robot proprioceptive state → a continuous action chunk
-  `[action_horizon, action_dim] = [50, 32]` via flow-matching denoising. π0 does
-  **not** emit text tokens.
- Mode: Online serving via the OpenPI realtime robot API
-  (`/v1/realtime/robot/openpi` websocket)
- Maintainer: Community
-
-## When to use this recipe
-
-Use this recipe to deploy `lerobot/pi0_base` as a robot policy server: a robot
-client connects over the OpenPI websocket and, per control step, sends an
-observation (camera frames + state + instruction) and receives an action chunk.
-
-A single pipeline class (`Pi0Pipeline`) serves the policy. π0 is stateless across
-calls (no KV reuse), so the protocol's `session_id` / `reset` are accepted but
-ignored.
-
-## Weights
-
-π0 uses the LeRobot `lerobot/pi0_base` checkpoint (HF, ~13 GB). Either let the
-server download it (`MODEL=lerobot/pi0_base`) or point at a local copy.
-
-## Hardware Support
-
-### 1x H200 141GB (Online serving)
-
-#### Environment
-
- OS: Ubuntu 22.04+
- Python: 3.12+
- Driver / runtime: NVIDIA CUDA environment
- vLLM / vLLM-Omni: use the commit you are deploying from
- OpenPI client extras (for the example client): `openpi-client`
-  (`pip install -e packages/openpi-client` from the
-  [openpi](https://github.com/Physical-Intelligence/openpi) repo), `websockets`,
-  `msgpack`, `msgpack-numpy`
-
-#### Command
-
-```bash
-vllm serve lerobot/pi0_base \
-  --omni \
-  --host 0.0.0.0 --port 8000 \
-  --served-model-name pi0 \
-  --deploy-config vllm_omni/deploy/pi0.yaml \
-  --enforce-eager --disable-log-stats
-```
-
-The deploy config (`vllm_omni/deploy/pi0.yaml`) declares a single diffusion stage
-(`Pi0Pipeline`), `float32`, `max_num_seqs: 1`, and the `policy_server_config`
-handshake metadata (3 cameras, `joint_position`, action horizon 50, action dim
-32) that enables the OpenPI realtime serving layer.
-
-#### Verification
-
-Run the example client (sends a synthetic observation and prints the returned
-action chunk):
-
-```bash
-python examples/online_serving/pi0/openpi_client.py \
-  --host 127.0.0.1 --port 8000 \
-  --prompt "pick up the red block and place it in the bin"
-```
-
-It connects, prints the server metadata, sends robot observations, and prints the
-returned `[action_horizon, action_dim] = [50, 32]` action chunks. The observation
-is a flat dict per inference:
-
-```python
-{
-    "observation.images.base_0_rgb":        np.uint8[H, W, 3],
-    "observation.images.left_wrist_0_rgb":  np.uint8[H, W, 3],
-    "observation.images.right_wrist_0_rgb": np.uint8[H, W, 3],
-    "state":   np.float32[state_dim],   # zero-padded to max_state_dim=32 server-side
-    "prompt":  "pick up the red block",
-    "session_id": "<uuid>",             # accepted but ignored (π0 is stateless)
-}
-```
-
-Camera keys must match the server's `image_feature_keys` (the checkpoint's
-`input_features` order). If your robot uses different camera names, set
-`model_config.image_key_map` in `pi0.yaml` to map raw obs keys → feature keys.
-
-## Correctness
-
-π0's flow-matching kernel is bit-for-bit matched to the LeRobot `PI0Policy`
-reference (`max|Δ| = 7.15e-07`, CPU/float32, fixed noise; see
-`tests/diffusion/models/pi0/test_pi0_parity.py::test_pi0_vllm_omni_vs_lerobot`).
-
-## References
-
- Model blog: <https://www.physicalintelligence.company/blog/pi0>
- Checkpoint: <https://huggingface.co/lerobot/pi0_base>
- Pipeline: [`vllm_omni/diffusion/models/pi0/pipeline_pi0.py`](../../vllm_omni/diffusion/models/pi0/pipeline_pi0.py)
- Deploy config: [`vllm_omni/deploy/pi0.yaml`](../../vllm_omni/deploy/pi0.yaml)
- Example client: [`examples/online_serving/pi0/`](../../examples/online_serving/pi0/)
- Tests: CPU units + LeRobot parity in [`tests/diffusion/models/pi0/`](../../tests/diffusion/models/pi0/),
-  OpenPI websocket e2e in
-  [`tests/e2e/online_serving/test_pi0_expansion.py`](../../tests/e2e/online_serving/test_pi0_expansion.py)
@@ -44,20 +44,20 @@ conditioned on reference audio and its transcript.
 Start the server from the repository root:

 ```bash
-vllm serve zai-org/GLM-TTS --omni --trust-remote-code --port 8091
+vllm-omni serve zai-org/GLM-TTS --omni --trust-remote-code --port 8091
 ```

 Async chunking is enabled by default in the bundled deployment config. For
 the sync (non-streaming) path:

 ```bash
-vllm serve zai-org/GLM-TTS --omni --trust-remote-code --port 8091 --no-async-chunk
+vllm-omni serve zai-org/GLM-TTS --omni --trust-remote-code --port 8091 --no-async-chunk
 ```

 Use a custom deploy config for advanced cases:

 ```bash
-vllm serve zai-org/GLM-TTS --omni --trust-remote-code --port 8091 \
+vllm-omni serve zai-org/GLM-TTS --omni --trust-remote-code --port 8091 \
  --deploy-config /path/to/your_glm_tts_overrides.yaml
 ```

@@ -1,15 +1,15 @@
 # Common dependencies for all platforms
-# transformers: required unified multimodal model classes are unavailable
-# before 5.10.1; 5.10.0 is yanked, and 5.15 has a known construction
-# regression. transformers 5.9 also changed
-# _adjust_missing_and_unexpected_keys from
+# transformers: kept exactly aligned with the upstream vLLM constraint.
+# vLLM pins ">= 4.56.0, != 5.0.*, != 5.1.*, != 5.2.*, != 5.3.*, != 5.4.*, != 5.5.0".
+# The previous extra "< 5.9.0" exclusion was removed when re-aligning with
+# upstream: transformers 5.9 changed _adjust_missing_and_unexpected_keys from
 # list+list to set|set, breaking models that declare
 # _keys_to_ignore_on_load_unexpected as a list (e.g. OpenMOSS-Team/MOSS-TTS-Nano)
 # with "TypeError: unsupported operand type(s) for |: 'list' and 'set'".
 # That breakage is now handled in code instead of by pinning:
 # MossTTSNanoForGeneration wraps weight loading in
 # transformers_keys_to_ignore_compat (vllm_omni/model_executor/models/utils.py).
-transformers >= 5.10.1, < 5.15
+transformers >= 5.5.3
 av>=14.0.0
 omegaconf>=2.3.0
 diffusers==0.38.0
@@ -9,17 +9,13 @@ from pathlib import Path
 import pytest

 from vllm_omni.benchmarks.data_modules.daily_omni_dataset import (
-    DailyOmniDataset,
    daily_omni_local_qa_json,
    daily_omni_local_videos_dir,
-    ensure_daily_omni_hub_root,
    resolve_daily_omni_local_root,
 )

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

-_HUB_ID = "liarliar/Daily-Omni"
-

 def _write_qa_json(root: Path) -> Path:
    qa = root / "qa.json"
@@ -27,28 +23,6 @@ def _write_qa_json(root: Path) -> Path:
    return qa


-def _stage_offline_hub_cache(tmp_path: Path, monkeypatch: pytest.MonkeyPatch, *, with_qa: bool = True) -> Path:
-    """Build a hub cache holding ``liarliar/Daily-Omni`` and cut off all network access.
-
-    Reproduces the air-gapped NPU runner: the dataset is pre-staged with ``hf download``, so it
-    only exists in the *hub* cache -- never in the ``datasets`` cache that ``load_dataset`` reads.
-    """
-    constants = pytest.importorskip("huggingface_hub").constants
-
-    cache = tmp_path / "hub"
-    repo_dir = cache / "datasets--liarliar--Daily-Omni"
-    snapshot = repo_dir / "snapshots" / "deadbeef"
-    snapshot.mkdir(parents=True)
-    if with_qa:
-        _write_qa_json(snapshot)
-    (repo_dir / "refs").mkdir()
-    (repo_dir / "refs" / "main").write_text("deadbeef", encoding="utf-8")
-
-    monkeypatch.setattr(constants, "HF_HUB_CACHE", str(cache))
-    monkeypatch.setattr(constants, "HF_HUB_OFFLINE", True)
-    return snapshot
-
-
 def test_resolve_local_root_returns_none_for_hub_id() -> None:
    assert resolve_daily_omni_local_root("liarliar/Daily-Omni") is None
    assert resolve_daily_omni_local_root("") is None
@@ -116,37 +90,3 @@ def test_missing_qa_json_reports_none(tmp_path: Path) -> None:
    root = resolve_daily_omni_local_root(str(tmp_path))
    assert daily_omni_local_qa_json(root) is None
    assert daily_omni_local_videos_dir(root) is None
-
-
-def test_hub_id_resolves_to_offline_cache(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
-    snapshot = _stage_offline_hub_cache(tmp_path, monkeypatch)
-
-    root = ensure_daily_omni_hub_root(_HUB_ID)
-
-    assert root == snapshot.resolve()
-    assert daily_omni_local_qa_json(root) == (snapshot / "qa.json").resolve()
-
-
-def test_dataset_loads_hub_id_without_network(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
-    """The regression this file exists for: a Hub id must not require ``load_dataset``."""
-    snapshot = _stage_offline_hub_cache(tmp_path, monkeypatch)
-    # Any use of `datasets` here means we went back to the path that cannot read the hub cache.
-    monkeypatch.setattr("vllm_omni.benchmarks.data_modules.daily_omni_dataset.load_dataset", None)
-
-    dataset = DailyOmniDataset(dataset_path=_HUB_ID, random_seed=0)
-
-    assert dataset.qa_json_path == (snapshot / "qa.json").resolve()
-    assert [row["video_id"] for row in dataset.data] == ["vid0"]
-
-
-def test_uncached_hub_id_reports_both_attempts(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
-    _stage_offline_hub_cache(tmp_path, monkeypatch, with_qa=False)
-    monkeypatch.setattr("vllm_omni.benchmarks.data_modules.daily_omni_dataset.load_dataset", None)
-
-    with pytest.raises(RuntimeError) as excinfo:
-        DailyOmniDataset(dataset_path="someone-else/Daily-Omni", random_seed=0)
-
-    message = str(excinfo.value)
-    assert "someone-else/Daily-Omni" in message
-    assert "snapshot_download failed" in message
-    assert "--daily-omni-qa-json" in message
@@ -39,6 +39,8 @@ def _render(changed_files: list[str]) -> str:


 def test_bootstrap_if_injected_by_step_key() -> None:
+    import yaml
+
    rendered = _render_bootstrap_pipeline(
        BOOTSTRAP_STEPS_TEMPLATE,
        decision=resolve_ci_decision([]),
@@ -140,7 +142,12 @@ def test_mirror_hardwares_a2b3_npu_4_expands_agents_image_and_plugins() -> None:
    ]


-def _surviving_labels(doc: dict, changed_files: list[str]) -> set[str]:
+COVERAGE_PILOT_LABELS = ("Diffusion · Bagel Test", "TTS · Qwen3-TTS Base Test")
+MERGE_PIPELINE = Path(".buildkite/cuda/test-merge.yml")
+
+
+def _surviving_labels(changed_files: list[str]) -> set[str]:
+    doc = yaml.safe_load(MERGE_PIPELINE.read_text(encoding="utf-8"))
    rendered = _render_test_pipeline(doc, changed_files=changed_files)
    labels: set[str] = set()

@@ -156,29 +163,6 @@ def _surviving_labels(doc: dict, changed_files: list[str]) -> set[str]:
    return labels


-# Synthetic coverage-style job: shared inputs that change what the split measures.
-_COVERAGE_SHARED_INPUTS_DOC = {
-    "steps": [
-        {
-            "label": "Coverage Pilot",
-            "source_file_dependencies": [
-                "tests/e2e/online_serving/test_example.py",
-                ".buildkite/common/scripts/run_cov_split.sh",
-                "pyproject.toml",
-            ],
-            "commands": [".buildkite/common/scripts/run_cov_split.sh --model-id example"],
-        },
-        {
-            "label": "Unrelated Model Test",
-            "source_file_dependencies": [
-                "tests/e2e/online_serving/test_other.py",
-            ],
-            "commands": ["pytest -sv tests/e2e/online_serving/test_other.py"],
-        },
-    ],
-}
-
-
@pytest.mark.parametrize(
    "changed_file",
    [
@@ -186,17 +170,18 @@ _COVERAGE_SHARED_INPUTS_DOC = {
        "pyproject.toml",
    ],
 )
-def test_coverage_shared_inputs_select_dependent_job(changed_file: str) -> None:
-    """Jobs that list coverage shared inputs must stay selected when those files change."""
-    labels = _surviving_labels(_COVERAGE_SHARED_INPUTS_DOC, [changed_file])
-    assert "Coverage Pilot" in labels
-    assert "Unrelated Model Test" not in labels
+def test_coverage_pilots_selected_by_their_shared_inputs(changed_file: str) -> None:
+    """The split helper and the coverage config both change what these jobs measure.
+
+    Without them in source_file_dependencies the jobs are filtered out of normal
+    PR builds, so a regression would only surface in a later nightly.
+    """
+    labels = _surviving_labels([changed_file])
+    for pilot in COVERAGE_PILOT_LABELS:
+        assert pilot in labels, f"{pilot} dropped when {changed_file} changed"


-def test_coverage_shared_inputs_ignored_for_unrelated_change() -> None:
-    labels = _surviving_labels(
-        _COVERAGE_SHARED_INPUTS_DOC,
-        ["vllm_omni/entrypoints/openai/serving_chat.py"],
-    )
-    assert "Coverage Pilot" not in labels
-    assert "Unrelated Model Test" not in labels
+def test_coverage_pilots_not_selected_by_unrelated_change() -> None:
+    labels = _surviving_labels(["vllm_omni/entrypoints/openai/serving_chat.py"])
+    for pilot in COVERAGE_PILOT_LABELS:
+        assert pilot not in labels
@@ -14,8 +14,7 @@ import pytest
 from transformers import PretrainedConfig, Qwen3OmniMoeConfig

 from tests.helpers.stage_config import get_deploy_config_path, get_deploy_config_stage
-from vllm_omni.config import config_factory as config_factory_module
-from vllm_omni.config.config_factory import StageConfigFactory, _materialize_object_storage_configs
+from vllm_omni.config.config_factory import StageConfigFactory
 from vllm_omni.config.endpoint_policy import EndpointRestriction, OmniServingCapability
 from vllm_omni.config.omni_config import VllmOmniConfig
 from vllm_omni.config.pipeline_registry import OMNI_PIPELINES, register_pipeline, resolve_pipeline_config
@@ -56,7 +55,6 @@ def clear_config_factory_caches():
    yield
    StageConfigFactory.get_hf_config.cache_clear()
    StageConfigFactory.try_infer_model_type.cache_clear()
-    _materialize_object_storage_configs.cache_clear()


 Q3_OMNI_ALL_STAGES_HF_CONFIG = Qwen3OmniMoeConfig(enable_audio_output=True)
@@ -1102,13 +1100,6 @@ class TestResolveScheduler:


 class TestDeployConfigLoading:
-    def test_rejects_legacy_stage_args_schema(self, tmp_path):
-        deploy_path = tmp_path / "legacy.yaml"
-        deploy_path.write_text("stage_args:\n  - stage_id: 0\n", encoding="utf-8")
-
-        with pytest.raises(ValueError, match=r"stage_args.*PipelineConfig.*stages"):
-            load_deploy_config(deploy_path)
-
    def test_load_minicpmo_duplex_deploy_config(self):
        deploy_path = Path(get_deploy_config_path("minicpmo_4_5_duplex.yaml"))

@@ -1384,8 +1375,7 @@ stages:
        assert async_stages[1].custom_process_input_func is None

    def test_no_bundled_legacy_stage_config_yamls(self):
-        repo_root = Path(__file__).resolve().parents[2]
-        stage_config_dir = repo_root / "vllm_omni" / "model_executor" / "stage_configs"
+        stage_config_dir = Path(__file__).parent.parent / "vllm_omni" / "model_executor" / "stage_configs"
        assert not list(stage_config_dir.glob("*.yaml"))

    def test_merge_pipeline_deploy(self):
@@ -2775,89 +2765,3 @@ class TestPipelineConfigResolvers:
            pass

        assert resolver(NotTheRightHfConfig()) is None
-
-
-class TestObjectStorageConfigResolution:
-    """Object-storage URIs must be materialized locally before any HF-style reads.
-
-    Regression coverage for review on the Run:AI PR: ``get_config`` and the
-    config.json/model_index.json fallbacks crashed with ``HFValidationError``
-    for ``s3://`` URIs, and the name-match fallback scanned the whole URI, so
-    a bucket named after another pipeline could hijack pipeline selection.
-    """
-
-    @pytest.fixture
-    def fake_object_storage(self, monkeypatch, tmp_path):
-        """Replace ObjectStorageModel with a fake that "pulls" into a tmp dir.
-
-        Returns a recorder: calls to the returned callable write files into
-        the materialized directory, and ``recorder.pulls`` records every
-        ``pull_files`` invocation.
-        """
-        pulls: list[tuple[str, list[str] | None, list[str] | None]] = []
-        materialized_files: list[tuple[str, str]] = []
-
-        class FakeObjectStorageModel:
-            def __init__(self, url: str):
-                self.dir = str(tmp_path)
-
-            def pull_files(self, model_path, allow_pattern=None, ignore_pattern=None):
-                pulls.append((model_path, allow_pattern, ignore_pattern))
-                for name, content in materialized_files:
-                    (tmp_path / name).write_text(content)
-
-        monkeypatch.setattr(config_factory_module, "ObjectStorageModel", FakeObjectStorageModel)
-
-        class Recorder:
-            def set_files(self, files: list[tuple[str, str]]) -> None:
-                materialized_files.clear()
-                materialized_files.extend(files)
-
-        recorder = Recorder()
-        recorder.pulls = pulls
-        return recorder
-
-    def test_passthrough_for_non_uri(self, fake_object_storage):
-        assert _materialize_object_storage_configs("org/model") == "org/model"
-        assert _materialize_object_storage_configs("/local/model") == "/local/model"
-        assert fake_object_storage.pulls == []
-
-    def test_materialize_pulls_configs_once_per_uri(self, fake_object_storage):
-        uri = "s3://bucket/model"
-
-        first = _materialize_object_storage_configs(uri)
-        second = _materialize_object_storage_configs(uri)
-
-        assert first == second
-        assert len(fake_object_storage.pulls) == 1
-        pulled_uri, allow_pattern, ignore_pattern = fake_object_storage.pulls[0]
-        assert pulled_uri == uri
-        assert "*.json" in allow_pattern
-        assert ignore_pattern is None
-
-    def test_try_infer_model_type_reads_materialized_config(self, fake_object_storage):
-        """A misleading bucket name must not beat the real config.json content."""
-        fake_object_storage.set_files(
-            [
-                (
-                    "config.json",
-                    '{"model_type": "qwen3_omni_moe", "architectures": ["Qwen3OmniMoeForConditionalGeneration"]}',
-                )
-            ]
-        )
-        uri = "s3://qwen3-tts-models/Qwen3-Omni-30B-A3B-Instruct"
-
-        model = StageConfigFactory.try_infer_model_type(model=uri, trust_remote_code=False)
-
-        assert model == "qwen3_omni_moe"
-        assert fake_object_storage.pulls  # materialization happened
-
-    def test_name_match_fallback_scans_basename_only(self, fake_object_storage):
-        # Empty config.json (CosyVoice3 style) forces the name-match fallback.
-        fake_object_storage.set_files([("config.json", "{}")])
-
-        deceptive = "s3://qwen3-tts-models/plain-checkpoint"
-        assert StageConfigFactory.try_infer_model_type(model=deceptive, trust_remote_code=False) is None
-
-        matching = "s3://any-bucket/my-cosyvoice3-model"
-        assert StageConfigFactory.try_infer_model_type(model=matching, trust_remote_code=False) == "cosyvoice3"
@@ -1,119 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-"""Guard the NPU pytest circular import through DiffusionOutput."""
-
-from __future__ import annotations
-
-import ast
-import os
-import subprocess
-import sys
-from pathlib import Path
-
-import pytest
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-REPO_ROOT = Path(__file__).resolve().parents[2]
-
-
-def _top_level_import_modules(path: Path) -> set[str]:
-    tree = ast.parse(path.read_text(encoding="utf-8"))
-    modules: set[str] = set()
-    for node in tree.body:
-        if isinstance(node, ast.ImportFrom) and node.module:
-            modules.add(node.module)
-        elif isinstance(node, ast.Import):
-            modules.update(alias.name for alias in node.names)
-    return modules
-
-
-def _assert_isolated_import_succeeds(script: str) -> None:
-    env = os.environ.copy()
-    env.pop("CUDA_VISIBLE_DEVICES", None)
-    env.pop("HIP_VISIBLE_DEVICES", None)
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        cwd=REPO_ROOT,
-        env=env,
-        capture_output=True,
-        text=True,
-        timeout=120,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_config_package_does_not_eagerly_import_pipeline_registry() -> None:
-    imported = _top_level_import_modules(REPO_ROOT / "vllm_omni" / "config" / "__init__.py")
-    assert "vllm_omni.config.config_factory" not in imported
-    assert "vllm_omni.config.pipeline_registry" not in imported
-
-
-def test_config_package_lazy_exports_still_resolve() -> None:
-    from vllm_omni.config import StageConfigFactory, register_pipeline
-    from vllm_omni.config.config_factory import StageConfigFactory as DirectStageConfigFactory
-    from vllm_omni.config.pipeline_registry import register_pipeline as direct_register_pipeline
-
-    assert StageConfigFactory is DirectStageConfigFactory
-    assert register_pipeline is direct_register_pipeline
-
-
-def test_npu_platform_defers_minimax_h3_encoder_patch() -> None:
-    src = (REPO_ROOT / "vllm_omni" / "platforms" / "npu" / "platform.py").read_text(encoding="utf-8")
-    init_start = src.index("def __init__(self) -> None:")
-    runtime_start = src.index("def init_diffusion_model_runner_runtime")
-    init_src = src[init_start:runtime_start]
-    runtime_src = src[runtime_start:]
-    assert "apply_minimax_h3_qwen3vl_patch" not in init_src
-    assert "apply_minimax_h3_qwen3vl_patch" in runtime_src
-    assert "apply_minimax_h3_qwen3vl_swiglu_patch" in runtime_src
-
-
-def test_lora_config_import_does_not_load_pipeline_registry() -> None:
-    """Import LoRAConfig and diffusion.data in a fresh interpreter.
-
-    The NPU failure was ``data.py`` importing LoRAConfig while still loading,
-    then ``vllm_omni.config`` re-entering ``pipeline_registry`` → PI0 →
-    DiffusionOutput. This child process must not preload those exports.
-    """
-    _assert_isolated_import_succeeds(
-        """
-import sys
-
-from vllm_omni.config.lora import LoRAConfig
-
-if "vllm_omni.config.pipeline_registry" in sys.modules:
-    raise SystemExit("LoRAConfig import loaded pipeline_registry")
-if "vllm_omni.diffusion.models.pi0.pipeline_pi0" in sys.modules:
-    raise SystemExit("LoRAConfig import loaded PI0_PIPELINE")
-
-from vllm_omni.diffusion.data import DiffusionOutput
-
-if DiffusionOutput is None or LoRAConfig is None:
-    raise SystemExit("expected LoRAConfig and DiffusionOutput")
-"""
-    )
-
-
-def test_pipeline_registry_does_not_import_pi0_runtime() -> None:
-    """Resolving the pi0 topology must not import its runtime pipeline.
-
-    ``pipeline_pi0`` imports ``diffusion.data``. Importing it while
-    ``diffusion.data`` is only partially initialized closes the Ascend plugin's
-    circular-import loop, so the registry must depend only on a lightweight
-    pipeline-config module.
-    """
-    _assert_isolated_import_succeeds(
-        """
-import sys
-
-from vllm_omni.config.pipeline_registry import resolve_pipeline_config
-
-if "vllm_omni.diffusion.models.pi0.pipeline_pi0" in sys.modules:
-    raise SystemExit("pipeline_registry imported the pi0 runtime pipeline")
-
-pipeline = resolve_pipeline_config("pi0")
-if pipeline is None or pipeline.model_type != "pi0":
-    raise SystemExit("expected the registered pi0 pipeline config")
-"""
-    )
@@ -5,20 +5,11 @@
 from __future__ import annotations

 from dataclasses import fields
-from inspect import Parameter, signature
 from pathlib import Path

-import msgspec
 import pytest
 from pydantic import ValidationError
-from pydantic.fields import FieldInfo
 from transformers import Qwen3OmniMoeConfig
-from vllm.config import CacheConfig as VllmCacheConfig
-from vllm.config import CompilationConfig as VllmCompilationConfig
-from vllm.config import LoadConfig as VllmLoadConfig
-from vllm.config import ParallelConfig as VllmParallelConfig
-from vllm.config import ProfilerConfig as VllmProfilerConfig
-from vllm.config import SchedulerConfig as VllmSchedulerConfig

 from tests.helpers.stage_config import get_deploy_config_path
 from vllm_omni.config import omni_config as omni_config_module
@@ -49,7 +40,6 @@ from vllm_omni.config.stage_config import (
    merge_pipeline_deploy,
 )
 from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
-from vllm_omni.engine.stage_engine_startup import _serialize_stage_config
 from vllm_omni.engine.stage_init_utils import build_legacy_engine_args_dict

 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
@@ -253,29 +243,9 @@ def test_from_pipeline_config_rejects_unowned_deploy_engine_extras(engine_extras
    ],
    ids=["global", "ar-stage", "generation-stage"],
 )
-def test_from_pipeline_config_accepts_upstream_cache_dtype_for_llm_stages(cli_overrides, stage_id):
-    stage = _from_pipeline_key("qwen3_tts", cli_overrides=cli_overrides).stage_by_id(stage_id)
-
-    assert stage.cache_config.cache_dtype == "fp8"
-    assert "cache_dtype" in stage.cache_config._omni_explicit_fields
-
-
-@pytest.mark.parametrize("field_name", ["data_parallel_master_ip", "data_parallel_address"])
-def test_from_pipeline_config_normalizes_nested_parallel_config_aliases(field_name):
-    pipeline = _resolve_pipeline_or_skip("qwen3_tts")
-    deploy = DeployConfig(
-        stages=[
-            StageDeployConfig(
-                stage_id=0,
-                engine_extras={"parallel_config": {field_name: "10.0.0.1"}},
-            )
-        ]
-    )
-
-    stage = VllmOmniConfig.from_pipeline_config(pipeline, user_deploy_config=deploy).stage_by_id(0)
-
-    assert stage.parallel_config.data_parallel_master_ip == "10.0.0.1"
-    assert "data_parallel_master_ip" in stage.parallel_config._omni_explicit_fields
+def test_from_pipeline_config_rejects_diffusion_only_cli_fields_for_llm_stages(cli_overrides, stage_id):
+    with pytest.raises(ValueError, match=rf"Stage {stage_id} .*no structured config owner: kv_cache_dtype"):
+        _from_pipeline_key("qwen3_tts", cli_overrides=cli_overrides)


 def test_from_pipeline_config_accepts_diffusion_only_cli_fields_for_diffusion_stage():
@@ -467,8 +437,6 @@ def test_vllm_omni_stage_config_public_fields_use_typed_stage_realizations():
        "connector_config",
        "runtime_config",
        "parallel_config",
-        "compilation_config",
-        "profiler_config",
        "quantization_config",
    }
    assert "diffusion_config" not in public_fields
@@ -487,47 +455,10 @@ def test_runtime_config_fields_match_structured_runtime_scope():
        "num_gpus",
        "log_level",
        "log_stats",
+        "profiler_config",
    }


-def test_upstream_config_compatible_fields_materialize_mapping_inputs():
-    compilation_config = {"backend": "eager"}
-    profiler_config = {"profiler": "cuda"}
-
-    stage_config = VllmOmniARStageConfig(
-        stage_pipeline_config=_resolve_pipeline_or_skip("qwen3_tts").stages[0],
-        compilation_config=compilation_config,
-        profiler_config=profiler_config,
-    )
-
-    assert isinstance(stage_config.compilation_config, VllmCompilationConfig)
-    assert stage_config.compilation_config.backend == "eager"
-    assert isinstance(stage_config.profiler_config, VllmProfilerConfig)
-    assert stage_config.profiler_config.profiler == "cuda"
-
-
-def test_upstream_config_compatible_fields_validate_mapping_inputs():
-    with pytest.raises(ValidationError):
-        VllmOmniARStageConfig(
-            stage_pipeline_config=_resolve_pipeline_or_skip("qwen3_tts").stages[0],
-            profiler_config={"delay_iterations": -1},
-        )
-
-
-def test_upstream_config_compatible_fields_keep_prebuilt_config_types():
-    compilation_config = VllmCompilationConfig(backend="eager")
-    profiler_config = VllmProfilerConfig(profiler="cuda")
-
-    stage_config = VllmOmniARStageConfig(
-        stage_pipeline_config=_resolve_pipeline_or_skip("qwen3_tts").stages[0],
-        compilation_config=compilation_config,
-        profiler_config=profiler_config,
-    )
-
-    assert stage_config.compilation_config is compilation_config
-    assert stage_config.profiler_config is profiler_config
-
-
 def test_sub_config_fields_match_structured_scopes():
    assert {f.name for f in fields(OmniStageModelConfig)} == {
        "model",
@@ -558,6 +489,7 @@ def test_sub_config_fields_match_structured_scopes():
        "enforce_eager",
        "max_cudagraph_capture_size",
        "enable_flashinfer_autotune",
+        "compilation_config",
        "enable_multithread_weight_load",
        "num_weight_load_threads",
        "disable_autocast",
@@ -567,25 +499,29 @@ def test_sub_config_fields_match_structured_scopes():
        "model_subdir",
        "tokenizer_subdir",
    }
-    vllm_load_fields = {f.name for f in fields(VllmLoadConfig)}
-    assert issubclass(OmniStageLoadConfig, VllmLoadConfig)
-    assert {f.name for f in fields(OmniStageLoadConfig)} == vllm_load_fields | {
+    assert {f.name for f in fields(OmniStageLoadConfig)} == {
        "tokenizer",
+        "download_dir",
        "skip_tokenizer_init",
+        "load_format",
        "tokenizer_mode",
        "config_format",
        "skip_mm_profiling",
    }
-    assert OmniStageLoadConfig(load_format="PT").load_format == "pt"
-    assert issubclass(OmniStageCacheConfig, VllmCacheConfig)
-    assert {f.name for f in fields(OmniStageCacheConfig)} == {f.name for f in fields(VllmCacheConfig)} | {
+    assert {f.name for f in fields(OmniStageCacheConfig)} == {
+        "kv_cache_memory_bytes",
+        "gpu_memory_utilization",
+        "enable_prefix_caching",
        "disable_hybrid_kv_cache_manager",
        "mm_processor_cache_gb",
        "mamba_ssm_cache_dtype",
    }
-    assert issubclass(OmniStageSchedulerConfig, VllmSchedulerConfig)
-    assert {f.name for f in fields(OmniStageSchedulerConfig)} == {f.name for f in fields(VllmSchedulerConfig)} | {
+    assert {f.name for f in fields(OmniStageSchedulerConfig)} == {
+        "max_num_seqs",
+        "max_num_batched_tokens",
        "max_model_len",
+        "enable_chunked_prefill",
+        "async_scheduling",
    }
    assert {f.name for f in fields(OmniStageConnectorConfig)} == {
        "async_chunk",
@@ -594,10 +530,17 @@ def test_sub_config_fields_match_structured_scopes():
        "output_connectors",
        "input_connectors",
    }
-    vllm_parallel_fields = {f.name for f in fields(VllmParallelConfig)}
-    assert issubclass(OmniStageParallelConfig, VllmParallelConfig)
-    assert {f.name for f in fields(OmniStageParallelConfig)} == vllm_parallel_fields
-    assert {f.name for f in fields(OmniStageDiffusionParallelConfig)} == vllm_parallel_fields | {
+    assert {f.name for f in fields(OmniStageParallelConfig)} == {
+        "pipeline_parallel_size",
+        "data_parallel_size",
+        "tensor_parallel_size",
+        "enable_expert_parallel",
+        "world_size",
+    }
+    assert {f.name for f in fields(OmniStageDiffusionParallelConfig)} == {
+        "pipeline_parallel_size",
+        "data_parallel_size",
+        "tensor_parallel_size",
        "sequence_parallel_size",
        "ulysses_degree",
        "ring_degree",
@@ -611,86 +554,20 @@ def test_sub_config_fields_match_structured_scopes():
        "mask_sp_padding",
        "hsdp_shard_size",
        "hsdp_replicate_size",
+        "enable_expert_parallel",
+        "world_size",
    }


-def test_inherited_sub_configs_initialize_transport_safe_derived_fields():
-    scheduler_config = OmniStageSchedulerConfig(max_num_batched_tokens=4096)
-    assert scheduler_config.max_num_encoder_input_tokens == 4096
-    assert scheduler_config.encoder_cache_size == 4096
-
-    unresolved_scheduler_config = OmniStageSchedulerConfig()
-    assert unresolved_scheduler_config.max_num_encoder_input_tokens is None
-    assert unresolved_scheduler_config.encoder_cache_size is None
-
-    parallel_config = OmniStageParallelConfig(data_parallel_size=3, data_parallel_rank=2)
-    assert parallel_config.data_parallel_index == 2
-
-    diffusion_parallel_config = OmniStageDiffusionParallelConfig(data_parallel_size=3, data_parallel_rank=2)
-    assert diffusion_parallel_config.data_parallel_index == 2
-
-
-@pytest.mark.parametrize(
-    "config_cls",
-    [
-        OmniStageLoadConfig,
-        OmniStageCacheConfig,
-        OmniStageSchedulerConfig,
-        OmniStageParallelConfig,
-        OmniStageDiffusionParallelConfig,
-    ],
-)
-def test_inherited_sub_configs_are_keyword_only(config_cls):
-    assert all(parameter.kind is Parameter.KEYWORD_ONLY for parameter in signature(config_cls).parameters.values())
-
-    with pytest.raises(TypeError):
-        config_cls(1)
-
-
-@pytest.mark.parametrize(
-    "config_cls",
-    [
-        OmniStageLoadConfig,
-        OmniStageCacheConfig,
-        OmniStageSchedulerConfig,
-        OmniStageParallelConfig,
-        OmniStageDiffusionParallelConfig,
-    ],
-)
-def test_inherited_sub_configs_remain_msgpack_transport_safe(config_cls):
-    config = config_cls()
-
-    uninitialized_fields = [
-        config_field.name
-        for config_field in fields(config)
-        if isinstance(getattr(config, config_field.name), FieldInfo)
-    ]
-    assert uninitialized_fields == []
-
-    msgspec.msgpack.encode(_serialize_stage_config(config))
-
-
-def test_structured_llm_stage_registration_payloads_remain_msgpack_transport_safe():
-    omni_config = _from_pipeline_key("qwen3_tts")
-
-    for stage_config in omni_config.stage_configs:
-        payload = {
-            "stage_id": stage_config.stage_id,
-            "stage_config": _serialize_stage_config(stage_config),
-        }
-        msgspec.msgpack.encode(payload)
-
-
 def test_diffusion_parallel_config_fields_cover_legacy_surface():
    from vllm_omni.diffusion.data import DiffusionParallelConfig

    legacy_fields = {f.name for f in fields(DiffusionParallelConfig)}
    structured_fields = {f.name for f in fields(OmniStageDiffusionParallelConfig)}
    expected_upstream_fields = {"mask_sp_padding"}
-    vllm_parallel_fields = {f.name for f in fields(VllmParallelConfig)}

    assert legacy_fields | expected_upstream_fields <= structured_fields
-    assert structured_fields - legacy_fields - expected_upstream_fields - vllm_parallel_fields == set()
+    assert structured_fields - legacy_fields - expected_upstream_fields == {"world_size"}


 def test_diffusion_parallel_config_keeps_current_diffusion_parallel_surface():
@@ -733,7 +610,8 @@ def test_diffusion_parallel_config_matches_diffusion_parallel_world_size_for_vae

 def test_diffusion_parallel_config_supports_diffusion_hsdp_auto_sharding():
    cfg = OmniStageDiffusionParallelConfig(
-        ulysses_degree=4,
+        pipeline_parallel_size=2,
+        ulysses_degree=2,
        use_hsdp=True,
        hsdp_shard_size=-1,
        hsdp_replicate_size=2,
@@ -744,10 +622,10 @@ def test_diffusion_parallel_config_supports_diffusion_hsdp_auto_sharding():


 def test_diffusion_parallel_config_rejects_hsdp_with_tp_or_dp():
-    with pytest.raises(ValueError, match="not compatible with TP"):
+    with pytest.raises(ValueError, match="cannot be used with TP or DP"):
        OmniStageDiffusionParallelConfig(tensor_parallel_size=2, use_hsdp=True, hsdp_shard_size=2)

-    with pytest.raises(ValueError, match="not compatible with DP"):
+    with pytest.raises(ValueError, match="cannot be used with TP or DP"):
        OmniStageDiffusionParallelConfig(data_parallel_size=2, use_hsdp=True, hsdp_shard_size=2)


@@ -1200,21 +1078,3 @@ def test_diffusion_config_projection_keeps_mapping_quantization_config_serializa
    cfg = omni_config_module._DiffusionConfigProjection.from_kwargs(quantization_config=quantization_config)

    assert cfg.quantization_config == quantization_config
-
-
-def test_diffusion_quantization_mapping_reaches_terminal_config(monkeypatch):
-    from vllm_omni.diffusion.data import OmniDiffusionConfig
-
-    quantization_config = {"method": "int8", "activation_scheme": "dynamic"}
-    cfg = omni_config_module._DiffusionConfigProjection.from_kwargs(
-        quantization_config=quantization_config,
-    )
-
-    # Exercise the terminal construction performed by the future typed startup
-    # path without probing ports or loading remote model metadata.
-    monkeypatch.setattr(OmniDiffusionConfig, "_resolve_master_port", lambda _self: 29500)
-    monkeypatch.setattr(OmniDiffusionConfig, "enrich_config", lambda _self: None)
-    cfg.enrich_config()
-
-    assert cfg.quantization_config is not None
-    assert cfg.quantization_config.get_name() == "int8"
@@ -4,10 +4,7 @@ from types import SimpleNamespace
 import pytest
 from vllm.v1.engine import FinishReason

-from vllm_omni.core.sched.omni_scheduler_mixin import (
-    DEFAULT_INPUT_WAIT_TIMEOUT_S,
-    OmniSchedulerMixin,
-)
+from vllm_omni.core.sched.omni_scheduler_mixin import OmniSchedulerMixin
 from vllm_omni.core.sched.output import OmniChunkRecvHandle

 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
@@ -25,25 +22,13 @@ def test_schedule_lifecycle_helpers_process_and_restore_both_input_paths():
    scheduler.requests = {"request": object()}
    scheduler._consume_pending_connector_output = lambda mode: calls.append(("consume", mode))
    scheduler._process_pending_input_timeouts = lambda: calls.append(("timeouts",))
-
-    def _collect_timed_out(timeout_s):
-        calls.append(("chunk-timeouts", timeout_s))
-        return set()
-
-    def _collect_failed_sends():
-        calls.append(("failed-sends",))
-        return {}
-
    scheduler.chunk_transfer_adapter = SimpleNamespace(
-        receives_chunks=True,
        process_pending_chunks=lambda waiting, running, scheduler_requests: calls.append(
            ("process", waiting, running, scheduler_requests)
        ),
        restore_queues=lambda waiting, running, scheduler_requests: calls.append(
            ("restore-chunks", waiting, running, scheduler_requests)
        ),
-        collect_timed_out_request_ids=_collect_timed_out,
-        collect_failed_send_request_ids=_collect_failed_sends,
    )
    scheduler.input_coordinator = SimpleNamespace(
        restore_queues=lambda waiting: calls.append(("restore-full", waiting))
@@ -56,10 +41,6 @@ def test_schedule_lifecycle_helpers_process_and_restore_both_input_paths():
        ("consume", "ar"),
        ("timeouts",),
        ("process", scheduler.waiting, scheduler.running, scheduler.requests),
-        # The chunk deadline runs after chunks are applied, so a chunk that
-        # arrived this cycle resets the clock before it is measured (R1.1).
-        ("chunk-timeouts", DEFAULT_INPUT_WAIT_TIMEOUT_S),
-        ("failed-sends",),
        ("restore-chunks", scheduler.waiting, scheduler.running, scheduler.requests),
        ("restore-full", scheduler.waiting),
    ]
@@ -1,10 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Unit coverage for the two stage-input timeout hooks.
-
-``_process_pending_input_timeouts`` covers the full-payload transport;
-``_process_pending_chunk_timeouts`` covers async-chunk. Both share
-``VLLM_OMNI_INPUT_WAIT_TIMEOUT_S``.
+"""Unit coverage for _process_pending_input_timeouts.

 Verifies that the mixin correctly *delegates* timed-out requests to the
 base scheduler's ``finish_requests`` API with ``RequestStatus.FINISHED_ERROR``.
@@ -16,10 +12,6 @@ this file only asserts the wiring from the mixin to that API.

 from __future__ import annotations

-import importlib
-import logging
-import math
-import os
 from types import SimpleNamespace

 import pytest
@@ -110,248 +102,5 @@ def test_process_pending_input_timeouts_disabled_when_timeout_zero(monkeypatch):
    assert scheduler.finish_calls == []


-class _FakeChunkAdapter:
-    """Stands in for OmniChunkTransferAdapter's timeout-collection surface."""
-
-    def __init__(self, timed_out_ids, *, receives_chunks=True):
-        self._timed_out_ids = set(timed_out_ids)
-        self.receives_chunks = receives_chunks
-        self.calls = []
-
-    def collect_timed_out_request_ids(self, timeout_s):
-        self.calls.append(timeout_s)
-        return set(self._timed_out_ids)
-
-
-class _FakeChunkScheduler(OmniSchedulerMixin):
-    def __init__(self, requests, adapter):
-        self.requests = requests
-        self.chunk_transfer_adapter = adapter
-        self.finish_calls = []
-
-    def finish_requests(self, req_ids, status):
-        self.finish_calls.append((set(req_ids), status))
-
-
-def test_process_pending_chunk_timeouts_delegates_to_finish_requests():
-    """A request stalled in WAITING_FOR_CHUNK is failed, not left parked.
-
-    This is the async-chunk half of the safety net (RFC #4855 R1.1); before it
-    existed, the chunk path had no deadline at all and issue #3833 could park a
-    request forever.
-    """
-    req_id = "stalled-stream"
-    adapter = _FakeChunkAdapter(timed_out_ids={req_id})
-    scheduler = _FakeChunkScheduler({req_id: SimpleNamespace(request_id=req_id)}, adapter)
-
-    scheduler._process_pending_chunk_timeouts()
-
-    assert len(adapter.calls) == 1
-    assert adapter.calls[0] > 0
-    assert len(scheduler.finish_calls) == 1
-    finished_ids, status = scheduler.finish_calls[0]
-    assert finished_ids == {req_id}
-    assert getattr(status, "name", str(status)).endswith("FINISHED_ERROR")
-
-
-def test_process_pending_chunk_timeouts_skips_already_freed_request():
-    adapter = _FakeChunkAdapter(timed_out_ids={"already-freed"})
-    scheduler = _FakeChunkScheduler(requests={}, adapter=adapter)
-
-    scheduler._process_pending_chunk_timeouts()
-
-    assert scheduler.finish_calls == []
-
-
-def test_process_pending_chunk_timeouts_noop_on_a_producer_stage():
-    """Stage 0 does not receive chunks, so it has nothing to expire."""
-    adapter = _FakeChunkAdapter(timed_out_ids={"r1"}, receives_chunks=False)
-    scheduler = _FakeChunkScheduler({"r1": SimpleNamespace(request_id="r1")}, adapter)
-
-    scheduler._process_pending_chunk_timeouts()
-
-    assert adapter.calls == []
-    assert scheduler.finish_calls == []
-
-
-def test_process_pending_chunk_timeouts_noop_without_adapter():
-    class _NoAdapter(OmniSchedulerMixin):
-        def __init__(self):
-            self.requests = {}
-            self.chunk_transfer_adapter = None
-            self.finish_calls = []
-
-        def finish_requests(self, req_ids, status):
-            self.finish_calls.append((set(req_ids), status))
-
-    scheduler = _NoAdapter()
-    scheduler._process_pending_chunk_timeouts()
-    assert scheduler.finish_calls == []
-
-
-def test_process_pending_chunk_timeouts_disabled_when_timeout_zero(monkeypatch):
-    """The shared knob disables both transports' nets together."""
-    from vllm_omni.core.sched import omni_scheduler_mixin
-
-    monkeypatch.setattr(omni_scheduler_mixin, "DEFAULT_INPUT_WAIT_TIMEOUT_S", 0.0)
-
-    adapter = _FakeChunkAdapter(timed_out_ids={"r1"})
-    scheduler = _FakeChunkScheduler({"r1": SimpleNamespace(request_id="r1")}, adapter)
-    scheduler._process_pending_chunk_timeouts()
-
-    assert adapter.calls == []
-    assert scheduler.finish_calls == []
-
-
-class _FakeSendFailureAdapter:
-    """Stands in for the adapter's drained send-failure map (R1.2)."""
-
-    def __init__(self, failures):
-        self._failures = dict(failures)
-        self.collect_calls = 0
-
-    def collect_failed_send_request_ids(self):
-        self.collect_calls += 1
-        failures, self._failures = self._failures, {}
-        return failures
-
-
-class _FakeSendFailureScheduler(OmniSchedulerMixin):
-    def __init__(self, requests, adapter):
-        self.requests = requests
-        self.chunk_transfer_adapter = adapter
-
-
-def test_log_failed_chunk_sends_reports_a_live_request(caplog):
-    adapter = _FakeSendFailureAdapter({"req-live": "OSError: No space left on device"})
-    scheduler = _FakeSendFailureScheduler({"req-live": SimpleNamespace(request_id="req-live")}, adapter)
-
-    with caplog.at_level(logging.ERROR):
-        scheduler._log_failed_chunk_sends()
-
-    assert "req-live" in caplog.text
-    assert "No space left on device" in caplog.text
-
-
-def test_log_failed_chunk_sends_still_reports_a_departed_request(caplog):
-    """``collect_*`` drains the map, so a failure for a request that already left
-    this scheduler is the one place a drop could still go unrecorded."""
-    adapter = _FakeSendFailureAdapter({"req-gone": "OSError: No space left on device"})
-    scheduler = _FakeSendFailureScheduler({}, adapter)
-
-    with caplog.at_level(logging.WARNING):
-        scheduler._log_failed_chunk_sends()
-
-    assert adapter.collect_calls == 1
-    assert "req-gone" in caplog.text
-    assert "No space left on device" in caplog.text
-    assert "finished or aborted" in caplog.text
-
-
-def test_log_failed_chunk_sends_noop_without_adapter():
-    scheduler = _FakeSendFailureScheduler({}, None)
-    scheduler._log_failed_chunk_sends()
-
-
-# --- R1.4: VLLM_OMNI_INPUT_WAIT_TIMEOUT_S is parsed at import time, so each of
-# these reloads the module under a different environment. One knob now gates the
-# deadline on both transports, which is why a silent 0 matters more than it did
-# when only the full-payload path used it.
-
-
-@pytest.fixture
-def reload_with(monkeypatch):
-    """Reload the mixin module under a given env value, then put it back.
-
-    ``importlib.reload`` re-executes into the live module dict, so whatever the
-    last test parsed is what every later test in the session sees -- ``-k
-    zero_warns`` alone would leave ``DEFAULT_INPUT_WAIT_TIMEOUT_S = 0.0`` and
-    silently disable both deadlines. The rejection cases are worse: the
-    ValueError fires *after* the rebind, so ``nan`` lingers in the module.
-    ``monkeypatch`` restores the environment but not the module, so reload once
-    more on the way out.
-    """
-    import vllm_omni.core.sched.omni_scheduler_mixin as mod
-
-    def _reload(raw):
-        if raw is None:
-            monkeypatch.delenv("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", raising=False)
-        else:
-            monkeypatch.setenv("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", raw)
-        return importlib.reload(mod)
-
-    yield _reload
-
-    monkeypatch.undo()
-    importlib.reload(mod)
-
-
-@pytest.mark.parametrize(
-    "raw,expected",
-    [
-        (None, 600.0),
-        ("30", 30.0),
-        ("0.5", 0.5),
-        ("0", 0.0),  # explicit opt-out is still honoured
-    ],
-)
-def test_timeout_accepts_valid_values(reload_with, raw, expected):
-    mod = reload_with(raw)
-    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == expected
-
-
-@pytest.mark.parametrize("raw", ["-1", "-600", "-0.5"])
-def test_negative_timeout_is_rejected(reload_with, raw):
-    """`-1` is a common "no limit" idiom; substituting 600 would give an operator
-    who meant "never time out" mysterious failures ten minutes in. Fail loudly."""
-    with pytest.raises(ValueError, match="negative"):
-        reload_with(raw)
-
-
-@pytest.mark.parametrize("raw", ["nan", "inf", "-inf", "1e999"])
-def test_non_finite_timeout_is_rejected(reload_with, raw):
-    """nan compares false against every deadline check and inf is unreachable;
-    both disable the net while looking like a number."""
-    with pytest.raises(ValueError, match="not finite"):
-        reload_with(raw)
-
-
-def test_zero_warns_and_names_both_transports(reload_with, caplog):
-    with caplog.at_level(logging.WARNING):
-        mod = reload_with("0")
-    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == 0.0
-    text = caplog.text
-    assert "DISABLED" in text
-    assert "WAITING_FOR_INPUT" in text and "WAITING_FOR_CHUNK" in text
-
-
-def test_unparsable_timeout_falls_back(reload_with):
-    mod = reload_with("not-a-number")
-    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == 600.0
-
-
-def test_a_rejected_value_does_not_outlive_its_test(monkeypatch):
-    """The reason ``reload_with`` reloads on the way out, done here by hand.
-
-    ``nan`` is bound before the ValueError fires, so the reload that rejects it
-    still leaves it in the module -- and nan compares false against every
-    deadline check, which is exactly the silent-disable this item exists to
-    stop. Restoring the environment is not enough; the module has to be reloaded
-    too.
-    """
-    import vllm_omni.core.sched.omni_scheduler_mixin as mod
-
-    ambient = float(os.environ.get("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", "600"))
-
-    monkeypatch.setenv("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", "nan")
-    with pytest.raises(ValueError, match="not finite"):
-        importlib.reload(mod)
-    assert math.isnan(mod.DEFAULT_INPUT_WAIT_TIMEOUT_S)
-
-    monkeypatch.undo()
-    importlib.reload(mod)
-    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == ambient
-
-
 if __name__ == "__main__":
    pytest.main([__file__, "-v"])
@@ -653,7 +653,6 @@ def run_benchmark(
    random_input_len: Any | None = None,
    random_output_len: Any | None = None,
    resource_label: str | None = None,
-    num_warmups: int = 2,
 ) -> dict[str, Any]:
    """Run one ``vllm bench serve --omni`` iteration and return parsed metrics.

@@ -678,7 +677,7 @@ def run_benchmark(
        + args
        + [
            "--num-warmups",
-            str(num_warmups),
+            "2",
            "--save-result",
            "--result-dir",
            os.environ.get("BENCHMARK_DIR", "tests"),
@@ -279,6 +279,5 @@ def test_performance_benchmark(omni_server, benchmark_params):
            random_input_len=params.get("random_input_len"),
            random_output_len=params.get("random_output_len"),
            resource_label=resource_label,
-            num_warmups=max(2, int(concurrency)),
        )
        assert_result(result, params, num_prompt)
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-stage BAGEL (TP=1, CFG=1), t2i Task",
@@ -61,7 +61,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-stage BAGEL (TP=1, CFG=1), i2i Task",
@@ -113,7 +113,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Two-stage BAGEL (bagel.yaml), TP=1, CFG=1, t2i Task",
@@ -164,7 +164,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Two-stage BAGEL (bagel.yaml), TP=1, CFG=1, i2i Task",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "tts"
    ],
    "server_params": {
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-device baseline with enforce-eager (no torch.compile)",
@@ -64,7 +64,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-device with torch.compile (default, no enforce-eager)",
@@ -117,7 +117,7 @@
          "num_cards": 2
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "CFG-parallel=2 with enforce-eager",
@@ -172,7 +172,7 @@
          "num_cards": 2
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "CFG-parallel=2 with torch.compile",
@@ -226,7 +226,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "CacheDiT with enforce-eager",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-device baseline (two input images)",
@@ -76,7 +76,7 @@
          "num_cards": 4
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + VAE patch parallel=4",
@@ -125,7 +125,7 @@
          "num_cards": 4
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + CacheDiT",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-device baseline",
@@ -73,7 +73,7 @@
          "num_cards": 4
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + VAE patch parallel=4",
@@ -121,7 +121,7 @@
          "num_cards": 4
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + CacheDiT",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-device baseline",
@@ -424,10 +424,10 @@
        "baseline": {
          "H100": {
            "median_audio_ttfp_ms": [
-              82.6344
+              82.1886
            ],
            "median_audio_rtf": [
-              0.1277
+              0.1241
            ]
          }
        }
@@ -453,13 +453,13 @@
        "baseline": {
          "H100": {
            "median_audio_ttfp_ms": [
-              170.3737
+              160.9168
            ],
            "median_audio_rtf": [
-              0.2076
+              0.1907
            ],
            "audio_throughput": [
-              37.0834
+              39.2332
            ]
          }
        }
@@ -485,13 +485,13 @@
        "baseline": {
          "H100": {
            "median_audio_ttfp_ms": [
-              101.0293
+              100.1265
            ],
            "median_audio_rtf": [
-              0.1498
+              0.1437
            ],
            "audio_throughput": [
-              10.1186
+              11.7605
            ]
          }
        }
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "tts"
    ],
    "server_params": {
Author	SHA1	Message	Date
Hongsheng Liu	7ec78360bb	Merge branch 'main' into codex-split-attention-offload-guides	2026-08-15 07:00:22 +08:00
Hongsheng Liu	5569abf0a2	Update docs/design/feature/offloader/README.md Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 23:37:08 +08:00
Hongsheng Liu	10853fb4f3	Update docs/design/feature/offloader/README.md Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 23:37:01 +08:00
Hongsheng Liu	a52c19ea8b	docs: organize offloader feature designs Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 11:35:07 +08:00
Hongsheng Liu	8b61499662	docs: rename offloader guides Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 09:30:34 +08:00
Hongsheng Liu	7872663bf0	docs: split diffusion feature guides Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 08:55:41 +08:00