Address video-audio example review feedback

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
Harden the shared video-audio example
2026-08-17 16:45:55 +08:00 · 2026-08-17 13:48:27 +08:00 · 2026-08-16 21:14:26 -07:00 · 2026-08-17 11:21:28 +08:00 · 2026-08-17 10:51:17 +08:00 · 2026-08-16 11:22:28 +08:00
165 changed files with 14958 additions and 1828 deletions
@@ -159,19 +159,7 @@ 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 --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'
-            "
+          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
        mirror_hardwares: l4_1

      - label: ":full_moon: TTS · Perf Test"
@@ -188,35 +176,6 @@ 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
@@ -246,8 +205,7 @@ 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,
-      # and Bagel feature coverage is not duplicated via a broad tests/e2e sweep.
+      # Use explicit file shards so single-card cases do not reserve 2+ GPUs.

      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Single-GPU"
        timeout_in_minutes: 120
@@ -255,15 +213,6 @@ 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
@@ -274,47 +223,14 @@ 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 test_bagel_expansion and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
+          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
        mirror_hardwares: l4_4

      - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
@@ -327,7 +243,7 @@ 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

@@ -355,70 +271,6 @@ 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
@@ -440,12 +292,6 @@ 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:
@@ -456,15 +302,8 @@ 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 · LTX-2.5 Online Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_ltx25.py -m "full_model and cuda" --run-level "full_model"
-        mirror_hardwares: h100_1
-
      - label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
        timeout_in_minutes: 180
        commands:
@@ -483,23 +322,8 @@ 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 · LTX-2.5 Official Parity Test"
-        timeout_in_minutes: 180
-        commands:
-          - pytest -s -v tests/e2e/accuracy/ltx/test_ltx25_official_similarity.py::test_ltx25_distilled_two_stage_matches_official -m full_model --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/ltx/test_ltx25_official_similarity.py::test_ltx25_full_matches_official -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
@@ -575,16 +399,6 @@ 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"
@@ -595,32 +409,20 @@ 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"
@@ -149,4 +149,4 @@ steps:
            pytest -sv
            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
            --run-level "full_model"
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_4
@@ -96,33 +96,6 @@ 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
@@ -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|tts_adapters/__init__\.py)|tools/pre_commit/check_tts_adapter\.py)$
+      files: ^(vllm_omni/entrypoints/openai/serving_speech\.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
@@ -116,6 +116,7 @@ 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
@@ -4,6 +4,9 @@
 > `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
@@ -51,14 +54,13 @@ 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 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.
+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.

 Scratch storage is also capability-driven. `frames_per_block` reserves space for
 an uncommitted current video block, while `max_scratch_tokens_per_branch`
@@ -4,7 +4,7 @@ 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
-the standard loader's rank-local weights and avoid an additional collective.
+complete rank-local block sources and avoid an additional collective.

 For user-facing commands, see the
 [distributed layerwise offloading guide](../../../user_guide/diffusion/offloader/distributed_layerwise_offload.md)
@@ -14,10 +14,20 @@ and the [Cosmos3 recipe](https://github.com/vllm-project/vllm-omni/blob/main/rec

 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 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.
+`--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.

 The compatibility matrix below describes the current implementation. The
 unit-level guards are covered, but not every parallelism combination has a
@@ -41,6 +51,36 @@ 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
@@ -61,6 +101,15 @@ 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.
@@ -68,9 +117,20 @@ 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. 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.
+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.

 This mode means:

@@ -80,21 +140,18 @@ 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 keep a full host-side model copy per rank, subject to
-  shared OS page-cache behavior.
+- 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.
 - 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. DP replicas remain independent; no DLO weight sharding or cross-DP DLO collective. |
+| **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. |
 | **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** | 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. |
+| **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. |
 | **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
@@ -122,11 +179,15 @@ AllGather DP multi-concurrency requires:
 The no-AllGather path does not impose these DLO-specific synchronized-wave
 requirements.

-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.
+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. Online quantization remains incompatible with DLO AllGather;
+use `--dlo-no-use-allgather` or disable online quantization.
+
+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).

 ## Validation coverage

@@ -134,15 +195,75 @@ Current source-level validation includes:

 - HSDP + DLO + AllGather rejection;
 - HSDP + DLO without AllGather acceptance at configuration level;
- TP rejection in the DLO+AllGather mmap path;
+- loader preflight fallback for TP, HSDP, online quantization, unknown custom
+  loaders, missing keys, and shape/dtype mismatches;
+- 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;
 - resident-layer requests requiring no-AllGather;
 - DP request-wave validation for denoising-step compatibility;
 - sharding, double-buffer, AllGather-size, and heterogeneous-block regression
  tests.

-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.
+### 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.

 ## Recommendations

@@ -150,7 +271,9 @@ on the target CUDA/NCCL or CANN/HCCL hardware.
  scaling path.
 - Use **SP + DLO AllGather** for long-sequence workloads when DP concurrency is
  not the goal.
- Use **no-AllGather** to bring up TP or workstation PCIe configurations, with
-  the expectation of higher host-memory use and lower validation confidence.
+- 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.
 - Prefer **HSDP alone** for production HSDP deployments until the combined
  HSDP + DLO no-AllGather path has broader end-to-end coverage.
@@ -0,0 +1,213 @@
+# 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.
@@ -22,6 +22,7 @@ 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

@@ -37,6 +37,7 @@ 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` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `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` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
@@ -117,6 +118,10 @@ 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
@@ -3,7 +3,9 @@
 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 its standard-loader rank-local weights independently.
+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.

 See the [DLO feature design](../../../design/feature/offloader/distributed_layerwise_offload.md)
 for the implementation contract and compatibility matrix.
@@ -59,21 +61,48 @@ 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` | Keep standard-loader rank-local weights | `false` |
+| `--dlo-no-use-allgather` | Stream complete rank-local blocks without a DLO weight collective | `false` |
 | `--dlo-resident-layers N` | Keep N leading main-DiT blocks on device; requires no-AllGather and model-declared resident paths | `0` |

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

-The DLO plus AllGather path:
+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:

 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. calls model-specific `post_load_weights()` conversions; and
-5. restores the saved non-persistent buffers.
+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.

-This avoids one full checkpoint RSS copy per rank and does not require
-model-specific loading code.
+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.

 ## Declarative topology

@@ -103,16 +132,22 @@ must enter each collective.

 ## Limitations

- 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.
+- 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.
 - HSDP plus AllGather is rejected to avoid double sharding. HSDP without
  AllGather has limited end-to-end validation.
+- Online quantization uses the ordinary loader with no-AllGather. It remains
+  incompatible with DLO AllGather.
 - 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.
@@ -3,7 +3,26 @@
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/x_to_video_audio>.


-The `DreamID-Omni` pipeline generates short videos from text, image and video.
+The shared runner generates video with optional audio from text, image, and audio
+inputs. It can be extended through `vllm_omni.model_extras` without adding
+another model-specific runner.
+
+## Shared request and output contract
+
+The example builds one canonical request envelope: `prompt` is text,
+`modalities` is `["video"]`, and optional `multi_modal_data` contains RGB PIL
+images plus audio `(waveform, sample_rate)` tuples. Audio files are loaded in
+full by design; the runner does not apply a time window. Prepare a trimmed input
+file explicitly when only part of a recording should condition the model.
+DreamID-Omni's audio conditioning sequence grows with the complete input
+duration, so long recordings increase denoising memory and step time.
+
+Model-specific request translation (for example, DreamID-Omni's two negative
+prompts and its 16 kHz input requirement) is declared in `model_extras`. Model
+outputs must provide `fps` and, when audio is present, `audio_sample_rate`.
+`--fps` and `--audio-sample-rate` are available as explicit metadata overrides.
+The shared encoder normalizes supported video tensor layouts, so this runner
+does not branch on a model name or assume a model-specific frame layout.

 ## Local CLI Usage
 ### Download the Model locally
@@ -91,7 +110,8 @@ python x_to_video_audio.py \
 Key arguments:
 - `--prompt`: text description (string).
 - `--model`: path to the model local directory.
- `--height/--width`: output resolution (defaults 704 * 1024).
+- `--model-class-name`: optional diffusion pipeline class override.
+- `--height/--width`: output resolution (defaults 704 * 1280).
 - `--image-path`: path to the input image list.
 - `--audio-path`: path to the input audio list, indicate the timbre of the output video.
 - `--cfg-parallel-size`: number of parallel cfg parallel (defaults 1).
@@ -101,6 +121,9 @@ Key arguments:
 - `--num-inference-steps`: number of denoising steps (defaults 45).
 - `--video-negative-prompt`: negative prompt for video generation.
 - `--audio-negative-prompt`: negative prompt for audio generation.
+- `--extra-body`: JSON object containing parameters declared by the selected model.
+- `--fps`: explicit output FPS override when a model does not return that metadata.
+- `--audio-sample-rate`: explicit output audio sample-rate override.
 - `--enable-cpu-offload`: enable CPU offload (defaults False).

 ## Example materials
@@ -24,7 +24,6 @@ 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,
@@ -228,7 +227,6 @@ 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()


@@ -0,0 +1,288 @@
+# 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,
+    *,
+    parallel_config: Any,
+) -> 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,
+        "parallel_config": parallel_config,
+        "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.diffusion.data import DiffusionParallelConfig
+    from vllm_omni.entrypoints.omni import Omni
+    from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+    parallel_config = DiffusionParallelConfig(tensor_parallel_size=args.tensor_parallel_size)
+    omni_kwargs = build_omni_kwargs(args, paths, parallel_config=parallel_config)
+    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()
@@ -0,0 +1,239 @@
+# 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.data import DiffusionParallelConfig
+    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,
+        parallel_config=DiffusionParallelConfig(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()
@@ -82,9 +82,6 @@ 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()


@@ -1,21 +1,32 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Reproduce MiniMax-H3 request-mode and DLO/DP2 lifecycle behavior.
+"""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.

 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_dp2_lifecycle.py \
-        --model /path/to/MiniMax-H3/FL2VA --mode dlo-dp2
+    python examples/offline_inference/minimax_h3/dlo_lifecycle.py \
+        --model /path/to/MiniMax-H3/FL2VA --mode dlo-no-allgather --dp-size 2

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

 from __future__ import annotations
@@ -40,14 +51,33 @@ 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-dp2", "request"),
+        choices=("dlo", "dlo-no-allgather", "request"),
        required=True,
-        help="DLO AllGather DP2 or ordinary non-DLO TP2 request mode",
+        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)",
    )
    parser.add_argument("--steps", type=int, default=2)
    parser.add_argument("--duration", type=float, default=5.0)
@@ -60,10 +90,11 @@ 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": 2,
+        "num_gpus": args.dp_size if is_dlo else args.tp_size,
        "usp": 1,
        "ring": 1,
        "vae_parallel_mode": "tile",
@@ -74,22 +105,22 @@ def engine_kwargs(args: argparse.Namespace) -> dict[str, Any]:
        "stage_init_timeout": args.init_timeout,
        "init_timeout": args.init_timeout,
    }
-    if args.mode == "dlo-dp2":
+    if is_dlo:
        common.update(
            tensor_parallel_size=1,
-            data_parallel_size=2,
+            data_parallel_size=args.dp_size,
            text_encoder_tp_size=1,
            vae_patch_parallel_size=1,
            enable_distributed_layerwise_offload=True,
-            dlo_use_allgather=True,
+            dlo_use_allgather=args.mode == "dlo",
            dlo_resident_layers=0,
        )
    else:
        common.update(
-            tensor_parallel_size=2,
+            tensor_parallel_size=args.tp_size,
            data_parallel_size=1,
-            text_encoder_tp_size=2,
-            vae_patch_parallel_size=2,
+            text_encoder_tp_size=args.tp_size,
+            vae_patch_parallel_size=args.tp_size,
            enable_distributed_layerwise_offload=False,
        )
    return common
@@ -172,7 +203,8 @@ async def run(args: argparse.Namespace) -> dict[str, Any]:
        "engine_kwargs": engine_kwargs(args),
    }
    try:
-        if args.mode == "dlo-dp2":
+        uses_dp_wave = args.mode == "dlo" and args.dp_size > 1
+        if uses_dp_wave:
            started = time.perf_counter()
            asymmetric = await asyncio.gather(
                generate_one(
@@ -182,12 +214,15 @@ async def run(args: argparse.Namespace) -> dict[str, Any]:
                    prompt="",
                    seed=1001,
                ),
-                generate_one(
-                    engine,
-                    args,
-                    request_id="invalid-peer",
-                    prompt=DEFAULT_PROMPTS[0],
-                    seed=1002,
+                *(
+                    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)
                ),
                return_exceptions=True,
            )
@@ -195,18 +230,18 @@ 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"]) != 2:
-                raise RuntimeError("Both requests in the asymmetric DP wave must fail before dispatch")
+            if len(summary["asymmetric_errors"]) != args.dp_size:
+                raise RuntimeError("Every request in the asymmetric DP wave must fail before dispatch")

        started = time.perf_counter()
-        request_count = 2 if args.mode == "dlo-dp2" else 1
+        request_count = args.dp_size if uses_dp_wave else 1
        outputs = await asyncio.gather(
            *(
                generate_one(
                    engine,
                    args,
                    request_id=f"recovery-{index}",
-                    prompt=DEFAULT_PROMPTS[index],
+                    prompt=DEFAULT_PROMPTS[index % len(DEFAULT_PROMPTS)],
                    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(
-        "--stage-configs-path",
+        "--deploy-config",
        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.stage_configs_path:
-        omni_kwargs["stage_configs_path"] = args.stage_configs_path
+    if args.deploy_config:
+        omni_kwargs["deploy_config"] = args.deploy_config
    omni = Omni(model=str(ckpt), trust_remote_code=True, **omni_kwargs)
    outputs = omni.generate(inputs, sampling_params_list)

@@ -38,17 +38,6 @@ 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(
@@ -152,7 +141,8 @@ def _build_omni_kwargs(args, model_name: str) -> dict:
        log_stats=args.log_stats,
        stage_init_timeout=args.stage_init_timeout,
    )
-    omni_kwargs["deploy_config"] = args.deploy_config or DEFAULT_DEPLOY_CONFIG
+    if args.deploy_config:
+        omni_kwargs["deploy_config"] = args.deploy_config
    return omni_kwargs


@@ -32,11 +32,10 @@ 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_stage_config() -> str:
+def _default_deploy_config() -> str:
    repo_root = Path(__file__).resolve().parents[4]
    return str(repo_root / "vllm_omni" / "deploy" / "qwen3_tts.yaml")

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

@@ -156,7 +155,7 @@ def parse_args() -> Any:
        default=None,
        help="Optional YAML file for forced aligner settings (incl. gpu_memory_utilization)",
    )
-    parser.add_argument("--stage-configs-path", default=None, help="Qwen3-TTS deploy YAML")
+    parser.add_argument("--deploy-config", 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")
@@ -164,7 +163,6 @@ 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()


@@ -1,6 +1,25 @@
 # X-To-Video-Audio

-The `DreamID-Omni` pipeline generates short videos from text, image and video.
+The shared runner generates video with optional audio from text, image, and audio
+inputs. It currently documents DreamID-Omni and MagiHuman, and can be extended
+through `vllm_omni.model_extras` without adding another model-specific runner.
+
+## Shared request and output contract
+
+The example builds one canonical request envelope: `prompt` is text,
+`modalities` is `["video"]`, and optional `multi_modal_data` contains RGB PIL
+images plus audio `(waveform, sample_rate)` tuples. Audio files are loaded in
+full by design; the runner does not apply a time window. Prepare a trimmed input
+file explicitly when only part of a recording should condition the model.
+DreamID-Omni's audio conditioning sequence grows with the complete input
+duration, so long recordings increase denoising memory and step time.
+
+Model-specific request translation (for example, DreamID-Omni's two negative
+prompts and its 16 kHz input requirement) is declared in `model_extras`. Model
+outputs must provide `fps` and, when audio is present, `audio_sample_rate`.
+`--fps` and `--audio-sample-rate` are available as explicit metadata overrides.
+The shared encoder normalizes supported video tensor layouts, so this runner
+does not branch on a model name or assume a model-specific frame layout.

 ## Local CLI Usage
 ### Download the Model locally
@@ -147,7 +166,8 @@ in `vllm_omni/model_extras/magi_human.py`, routed via `extra_args`):
 Key arguments:
 - `--prompt`: text description (string).
 - `--model`: path to the model local directory.
- `--height/--width`: output resolution (defaults 704 * 1024).
+- `--model-class-name`: optional diffusion pipeline class override.
+- `--height/--width`: output resolution (defaults 704 * 1280).
 - `--image-path`: path to the input image list.
 - `--audio-path`: path to the input audio list, indicate the timbre of the output video.
 - `--cfg-parallel-size`: number of parallel cfg parallel (defaults 1).
@@ -157,6 +177,9 @@ Key arguments:
 - `--num-inference-steps`: number of denoising steps (defaults 45).
 - `--video-negative-prompt`: negative prompt for video generation.
 - `--audio-negative-prompt`: negative prompt for audio generation.
+- `--extra-body`: JSON object containing parameters declared by the selected model.
+- `--fps`: explicit output FPS override when a model does not return that metadata.
+- `--audio-sample-rate`: explicit output audio sample-rate override.
 - `--enable-cpu-offload`: enable CPU offload (defaults False).
 - `--cache-backend`: enable `cache_dit` for acceleration.
 - `--quantization`: online (dynamic) quantization method — `fp8` or `int8`.
@@ -3,32 +3,97 @@

 import argparse
 import json
+import math
 import re
 import time
+from collections.abc import Mapping
+from dataclasses import dataclass
+from pathlib import Path
 from typing import Any

 import numpy as np
+import torch
 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.diffusion.utils.param_utils import apply_declared_extra_args
 from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.entrypoints.openai.video_api_utils import encode_video_bytes
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+from vllm_omni.model_extras import (
+    build_x_to_video_audio_prompt as build_model_x_to_video_audio_prompt,
+)
+from vllm_omni.model_extras import (
+    get_extra_body_params,
+    get_input_audio_sample_rate,
+    get_model_class_name,
+    get_output_tensor_range,
+)

 IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".bmp", ".webp", ".gif"}
 AUDIO_EXTENSIONS = {".wav", ".mp3", ".flac", ".m4a", ".aac", ".ogg"}


+@dataclass(frozen=True)
+class XToVideoAudioOutput:
+    """Canonical generated video+audio payload and its required metadata."""
+
+    video: Any
+    audio: Any | None
+    fps: float
+    audio_sample_rate: int | None
+    output_tensor_range: str = "negative_one_to_one"
+
+
+def parse_json_object(value: str) -> dict[str, Any]:
+    try:
+        parsed = json.loads(value)
+    except json.JSONDecodeError as exc:
+        raise argparse.ArgumentTypeError(f"--extra-body must be valid JSON: {exc}") from exc
+    if not isinstance(parsed, dict):
+        raise argparse.ArgumentTypeError("--extra-body must be a JSON object")
+    return parsed
+
+
+def build_x_to_video_audio_prompt(
+    prompt: str,
+    media_inputs: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """Build the canonical request envelope for image/audio-to-video models."""
+    result: dict[str, Any] = {"prompt": prompt, "modalities": ["video"]}
+    if media_inputs:
+        result["multi_modal_data"] = media_inputs
+    return result
+
+
+def _clean_official_prompt(prompt: str) -> str:
+    """Remove metadata tags used by the official DreamID prompt fixtures."""
+    prompt = re.sub(
+        r"\[SPEAKER_TIMESTAMPS_START\].*?\[SPEAKER_TIMESTAMPS_END\]",
+        "",
+        prompt,
+        flags=re.DOTALL,
+    ).strip()
+    prompt = re.sub(
+        r"\[AUDIO_DESCRIPTION_START].*?\[AUDIO_DESCRIPTION_END]",
+        "",
+        prompt,
+        flags=re.DOTALL,
+    ).strip()
+    prompt = re.sub(r"\[[A-Z_]+\]", "", prompt)
+    return re.sub(r"\n\s*\n", "\n", prompt).strip()
+
+
 def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Offline inference for X -> video+audio models.")
    parser.add_argument("--model", required=True, help="Model ckpt root directory.")
    parser.add_argument(
        "--model-type",
-        default="dreamid-omni",
-        choices=["dreamid-omni", "magi-human"],
-        help="Model type.",
+        default=None,
+        help="Optional legacy model type override (for example, magi-human).",
    )
+    parser.add_argument("--model-class-name", default=None, help="Optional diffusion pipeline class override.")
    parser.add_argument("--prompt", default=None, help="Text prompt.")

    parser.add_argument("--image-path", type=str, nargs="+", help="list of image-path")
@@ -36,9 +101,9 @@ def parse_args() -> argparse.Namespace:
    parser.add_argument("--prompt-file", type=str, default=None, help="Text prompt in json format.")
    parser.add_argument(
        "--extra-body",
-        type=str,
+        type=parse_json_object,
        default=None,
-        help="[magi-human] JSON dict of model-specific extra params (declared in vllm_omni/model_extras/), "
+        help="JSON dict of model-specific extra params (declared in vllm_omni/model_extras/), "
        'merged into sampling extra_args. Example: \'{"image_path": "/path/to/img.jpg", "seconds": 5}\'.',
    )

@@ -71,13 +136,20 @@ def parse_args() -> argparse.Namespace:
        default="robotic, muffled, echo, distorted",
        help="Negative prompt for audio.",
    )
+    parser.add_argument("--fps", type=float, default=None, help="Override output FPS metadata.")
+    parser.add_argument(
+        "--audio-sample-rate",
+        type=int,
+        default=None,
+        help="Override output audio sample-rate metadata.",
+    )
    parser.add_argument("--output", default="dreamid_output.mp4", help="Output video path.")
    parser.add_argument(
        "--quantization",
        type=str,
        default=None,
        choices=["fp8", "int8"],
-        help=("Online (dynamic) quantization method for the DreamID-Omni transformer. "),
+        help="Online (dynamic) quantization method for the model transformer.",
    )
    parser.add_argument(
        "--enable-cpu-offload",
@@ -94,7 +166,7 @@ def parse_args() -> argparse.Namespace:
    parser.add_argument(
        "--use-hsdp",
        action="store_true",
-        help="Enable HSDP for DreamID-Omni fused transformer blocks.",
+        help="Enable HSDP for supported transformer blocks.",
    )
    parser.add_argument(
        "--hsdp-shard-size",
@@ -111,20 +183,105 @@ def parse_args() -> argparse.Namespace:
    return parser.parse_args()


-def load_image_and_audio(image_paths, audio_paths):
-    image = []
-    audio = []
+def load_image_and_audio(
+    image_paths: list[str] | None,
+    audio_paths: list[str] | None,
+    *,
+    audio_sample_rate: int | None = None,
+) -> tuple[list[Image.Image], list[tuple[Any, int]]]:
+    """Load complete media inputs into the canonical multimodal representation."""
+    images = []
+    audios = []

-    for path in image_paths:
+    for path in image_paths or []:
        with Image.open(path) as img:
-            img = img.convert("RGB")
-            image.append(img)
+            images.append(img.convert("RGB"))

-    for path in audio_paths:
-        audio_array, sr = load_audio(path, sr=16000)
-        audio_array = audio_array[int(sr * 1) : int(sr * 3)]
-        audio.append(audio_array)
-    return image, audio
+    for path in audio_paths or []:
+        waveform, sample_rate = load_audio(path, sr=audio_sample_rate)
+        audios.append((waveform, sample_rate))
+    return images, audios
+
+
+def extract_x_to_video_audio_output(
+    outputs: Any,
+    *,
+    fps: float | None = None,
+    audio_sample_rate: int | None = None,
+    output_tensor_range: str = "negative_one_to_one",
+) -> XToVideoAudioOutput:
+    """Normalize engine output without depending on a model's tensor layout."""
+    if isinstance(outputs, list):
+        if not outputs:
+            raise RuntimeError("No output returned from the model.")
+        result = outputs[0]
+    else:
+        result = outputs
+    if result is None:
+        raise RuntimeError("No output returned from the model.")
+
+    multimodal_output = getattr(result, "multimodal_output", None) or {}
+    if not isinstance(multimodal_output, Mapping):
+        raise RuntimeError("Model multimodal output must be a mapping.")
+    images = getattr(result, "images", None)
+    video = images[0] if images else multimodal_output.get("video")
+    if video is None:
+        raise RuntimeError("No video payload found in model output.")
+
+    resolved_fps = fps if fps is not None else multimodal_output.get("fps")
+    try:
+        resolved_fps = float(resolved_fps)
+    except (TypeError, ValueError) as exc:
+        raise RuntimeError("Model output must declare a valid 'fps', or pass --fps.") from exc
+    if not math.isfinite(resolved_fps) or resolved_fps <= 0:
+        raise RuntimeError("Output FPS must be finite and positive.")
+
+    audio = multimodal_output.get("audio")
+    resolved_sample_rate = audio_sample_rate
+    if resolved_sample_rate is None:
+        resolved_sample_rate = multimodal_output.get("audio_sample_rate")
+    if audio is not None:
+        try:
+            resolved_sample_rate = int(resolved_sample_rate)
+        except (TypeError, ValueError) as exc:
+            raise RuntimeError(
+                "Model output with audio must declare a valid 'audio_sample_rate', or pass --audio-sample-rate."
+            ) from exc
+        if resolved_sample_rate <= 0:
+            raise RuntimeError("Output audio sample rate must be positive.")
+    elif resolved_sample_rate is not None:
+        resolved_sample_rate = int(resolved_sample_rate)
+
+    return XToVideoAudioOutput(video, audio, resolved_fps, resolved_sample_rate, output_tensor_range)
+
+
+def _normalize_output_tensor_range(video: Any, source_range: str) -> Any:
+    """Normalize floating-point output according to the pipeline contract."""
+    if isinstance(video, torch.Tensor):
+        if not video.is_floating_point():
+            return video
+        video = video.detach().cpu().float().numpy()
+    if isinstance(video, np.ndarray):
+        if not np.issubdtype(video.dtype, np.floating):
+            return video
+        if source_range == "negative_one_to_one":
+            return np.clip(video, -1.0, 1.0) * 0.5 + 0.5
+        if source_range == "zero_to_one":
+            return np.clip(video, 0.0, 1.0)
+        raise ValueError(f"Unsupported floating-point tensor range: {source_range!r}")
+    if isinstance(video, list):
+        return [_normalize_output_tensor_range(frame, source_range) for frame in video]
+    return video
+
+
+def encode_x_to_video_audio_output(output: XToVideoAudioOutput) -> bytes:
+    """Encode any video layout supported by the shared video API utility."""
+    return encode_video_bytes(
+        _normalize_output_tensor_range(output.video, output.output_tensor_range),
+        output.fps,
+        output.audio,
+        output.audio_sample_rate,
+    )


 def _extract_peak_memory_mb(result: Any) -> float:
@@ -157,45 +314,10 @@ def main() -> None:
    if args.prompt_file:
        with open(args.prompt_file) as f:
            text_prompt = json.load(f)
-            text_prompt = re.sub(
-                r"\[SPEAKER_TIMESTAMPS_START\].*?\[SPEAKER_TIMESTAMPS_END\]", "", text_prompt, flags=re.DOTALL
-            ).strip()
-            text_prompt = re.sub(
-                r"\[AUDIO_DESCRIPTION_START].*?\[AUDIO_DESCRIPTION_END]", "", text_prompt, flags=re.DOTALL
-            ).strip()
-            text_prompt = re.sub(r"\[[A-Z_]+\]", "", text_prompt)
-            text_prompt = re.sub(r"\n\s*\n", "\n", text_prompt).strip()
-
-    if args.model_type == "magi-human":
-        prompt = text_prompt
-        extra_args = json.loads(args.extra_body) if args.extra_body else {}
-        sampling_params = OmniDiffusionSamplingParams(
-            height=args.height,
-            width=args.width,
-            num_inference_steps=args.num_inference_steps,
-            seed=args.seed,
-            extra_args=extra_args,
-        )
-    else:
-        image, audio = load_image_and_audio(args.image_path, args.audio_path)
-
-        prompt = {
-            "prompt": text_prompt,
-            "video_negative_prompt": args.video_negative_prompt,
-            "audio_negative_prompt": args.audio_negative_prompt,
-            "multi_modal_data": {"image": image, "audio": audio},
-        }
-
-        sampling_params = OmniDiffusionSamplingParams(
-            height=args.height,
-            width=args.width,
-            num_inference_steps=args.num_inference_steps,
-            seed=args.seed,
-            extra_args={
-                "solver_name": args.solver_name,
-                "shift": args.shift,
-            },
-        )
+            if isinstance(text_prompt, str):
+                text_prompt = _clean_official_prompt(text_prompt)
+    if not isinstance(text_prompt, str):
+        raise ValueError("Prompt content must be a JSON string or --prompt text.")

    parallel_config = DiffusionParallelConfig(
        cfg_parallel_size=args.cfg_parallel_size,
@@ -220,64 +342,83 @@ def main() -> None:
            "scm_steps_policy": "dynamic",
        }

-    omni_kwargs = dict(
+    omni_kwargs: dict[str, Any] = dict(
        model=args.model,
        parallel_config=parallel_config,
-        model_type=args.model_type,
        enable_cpu_offload=args.enable_cpu_offload,
        enable_layerwise_offload=args.enable_layerwise_offload,
        cache_backend=args.cache_backend,
        cache_config=cache_config,
    )
+    if args.model_type is not None:
+        omni_kwargs["model_type"] = args.model_type
+    if args.model_class_name is not None:
+        omni_kwargs["model_class_name"] = args.model_class_name
    if args.quantization is not None:
        omni_kwargs["quantization"] = args.quantization
    omni = Omni(**omni_kwargs)
-    start = time.perf_counter()
-    outputs = omni.generate(prompt, sampling_params)
-    elapsed = time.perf_counter() - start
+    try:
+        model_class_name = args.model_class_name or get_model_class_name(omni)
+        input_sample_rate = get_input_audio_sample_rate(model_class_name)
+        images, audios = load_image_and_audio(
+            args.image_path,
+            args.audio_path,
+            audio_sample_rate=input_sample_rate,
+        )
+        media_inputs: dict[str, Any] = {}
+        if images:
+            media_inputs["image"] = images
+        if audios:
+            media_inputs["audio"] = audios

-    if not outputs:
-        raise RuntimeError("No output returned from the model.")
-    result = outputs[0]
-    if not result.images:
-        raise RuntimeError("No video frames found in model output.")
-    generated_video = result.images[0]
-    mm = result.multimodal_output or {}
-    generated_audio = mm.get("audio")
+        canonical_prompt = build_x_to_video_audio_prompt(text_prompt, media_inputs)
+        prompt = build_model_x_to_video_audio_prompt(
+            model_class_name,
+            canonical_prompt,
+            {
+                "video_negative_prompt": args.video_negative_prompt,
+                "audio_negative_prompt": args.audio_negative_prompt,
+            },
+        )

-    if args.model_type == "magi-human":
-        # MagiHuman returns frames already as (F, H, W, C) uint8, ready to mux.
-        fps = float(mm.get("fps", 25))
-        sample_rate = int(mm.get("audio_sample_rate", 24000))
-        frames = generated_video
-    else:
-        fps = float(mm.get("fps", 24))
-        sample_rate = int(mm.get("audio_sample_rate", 16000))
-        # DreamID-Omni returns video as (C, F, H, W) float32 in [-1, 1].
-        # mux_video_audio_bytes expects (F, H, W, C) uint8.
-        if not isinstance(generated_video, np.ndarray) or generated_video.ndim != 4:
-            raise RuntimeError(f"Unexpected video shape: {getattr(generated_video, 'shape', None)}")
-        frames = generated_video.transpose(1, 2, 3, 0)
-        frames = (np.clip((frames + 1.0) / 2.0, 0.0, 1.0) * 255.0).round().astype(np.uint8)
+        sampling_params = OmniDiffusionSamplingParams(
+            height=args.height,
+            width=args.width,
+            num_inference_steps=args.num_inference_steps,
+            seed=args.seed,
+            extra_args={},
+        )
+        declared_extra_body_params = get_extra_body_params(model_class_name)
+        extra_body = dict(args.extra_body or {})
+        legacy_extra_body = {"solver_name": args.solver_name, "shift": args.shift}
+        for key, value in legacy_extra_body.items():
+            if key in declared_extra_body_params:
+                extra_body.setdefault(key, value)
+        if declared_extra_body_params:
+            apply_declared_extra_args(sampling_params, declared_extra_body_params, extra_body)
+        elif extra_body:
+            sampling_params.extra_args.update({key: value for key, value in extra_body.items() if value is not None})

-    audio_np = None
-    if generated_audio is not None:
-        audio_np = np.squeeze(np.asarray(generated_audio)).astype(np.float32)
+        start = time.perf_counter()
+        outputs = omni.generate(prompt, sampling_params)
+        elapsed = time.perf_counter() - start
+        output = extract_x_to_video_audio_output(
+            outputs,
+            fps=args.fps,
+            audio_sample_rate=args.audio_sample_rate,
+            output_tensor_range=get_output_tensor_range(model_class_name),
+        )

-    output_path = args.output
-    video_bytes = mux_video_audio_bytes(
-        frames,
-        audio_np,
-        fps=float(fps),
-        audio_sample_rate=sample_rate,
-    )
-    with open(output_path, "wb") as f:
-        f.write(video_bytes)
-    print(f"Saved generated video to {output_path}")
-    print(f"Total time: {elapsed:.2f}s")
-    peak_mb = _extract_peak_memory_mb(outputs)
-    if peak_mb:
-        print(f"Worker peak GPU memory (reserved): {peak_mb:.2f} MiB ({peak_mb / 1024:.2f} GiB)")
+        output_path = Path(args.output)
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        output_path.write_bytes(encode_x_to_video_audio_output(output))
+        print(f"Saved generated video to {output_path}")
+        print(f"Total time: {elapsed:.2f}s")
+        peak_mb = _extract_peak_memory_mb(outputs)
+        if peak_mb:
+            print(f"Worker peak GPU memory (reserved): {peak_mb:.2f} MiB ({peak_mb / 1024:.2f} GiB)")
+    finally:
+        omni.close()


 if __name__ == "__main__":
@@ -45,7 +45,7 @@ MusicGen-style delay pattern).
 **Online serving:**

 ```bash
-vllm-omni serve bosonai/higgs-audio-v3-tts-4b \
+vllm serve bosonai/higgs-audio-v3-tts-4b \
    --host 0.0.0.0 --port 8095 \
    --trust-remote-code --omni
 ```
@@ -51,7 +51,7 @@ NemotronH (~3B active parameters). Pick the pipeline by task:
 `model_type` auto-resolves `audex_tts.yaml`):

 ```bash
-vllm-omni serve nvidia/Nemotron-Labs-Audex-2B \
+vllm 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-omni serve nvidia/Nemotron-Labs-Audex-30B-A3B \
+vllm 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
@@ -60,6 +60,7 @@ recipes/
 | [`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 |
@@ -0,0 +1,122 @@
+# 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)
@@ -78,7 +78,7 @@ python examples/offline_inference/text_to_audio/text_to_audio.py \
 Start the online serving endpoint:

 ```bash
-vllm-omni serve /path/to/stable-audio-open-1.0 \
+vllm serve /path/to/stable-audio-open-1.0 \
  --host 0.0.0.0 \
  --port 8091 \
  --gpu-memory-utilization 0.9 \
@@ -39,6 +39,9 @@ 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
 ```
@@ -39,7 +39,7 @@ Other hardware is welcome as community validation lands.
 Launch the two-stage talker:

 ```bash
-vllm-omni serve inclusionAI/Ming-omni-tts-0.5B \
+vllm serve inclusionAI/Ming-omni-tts-0.5B \
    --deploy-config vllm_omni/deploy/ming_tts.yaml \
    --omni \
    --port 8091 \
@@ -44,20 +44,20 @@ conditioned on reference audio and its transcript.
 Start the server from the repository root:

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

 Use a custom deploy config for advanced cases:

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

@@ -1102,6 +1102,13 @@ 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"))

@@ -1377,7 +1384,8 @@ stages:
        assert async_stages[1].custom_process_input_func is None

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

    def test_merge_pipeline_deploy(self):
@@ -4,7 +4,10 @@ from types import SimpleNamespace
 import pytest
 from vllm.v1.engine import FinishReason

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

 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
@@ -22,13 +25,25 @@ 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))
@@ -41,6 +56,10 @@ 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),
    ]
@@ -1,6 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Unit coverage for _process_pending_input_timeouts.
+"""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``.

 Verifies that the mixin correctly *delegates* timed-out requests to the
 base scheduler's ``finish_requests`` API with ``RequestStatus.FINISHED_ERROR``.
@@ -12,6 +16,10 @@ 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
@@ -102,5 +110,248 @@ 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"])
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Single-stage BAGEL (TP=1, CFG=1), t2i Task",
@@ -61,7 +61,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Single-stage BAGEL (TP=1, CFG=1), i2i Task",
@@ -113,7 +113,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Two-stage BAGEL (bagel.yaml), TP=1, CFG=1, t2i Task",
@@ -164,7 +164,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Two-stage BAGEL (bagel.yaml), TP=1, CFG=1, i2i Task",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "tts"
    ],
    "server_params": {
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Single-device baseline with enforce-eager (no torch.compile)",
@@ -64,7 +64,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Single-device with torch.compile (default, no enforce-eager)",
@@ -117,7 +117,7 @@
          "num_cards": 2
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "CFG-parallel=2 with enforce-eager",
@@ -172,7 +172,7 @@
          "num_cards": 2
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "CFG-parallel=2 with torch.compile",
@@ -226,7 +226,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "CacheDiT with enforce-eager",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Single-device baseline (two input images)",
@@ -76,7 +76,7 @@
          "num_cards": 4
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + VAE patch parallel=4",
@@ -125,7 +125,7 @@
          "num_cards": 4
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + CacheDiT",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Single-device baseline",
@@ -73,7 +73,7 @@
          "num_cards": 4
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + VAE patch parallel=4",
@@ -121,7 +121,7 @@
          "num_cards": 4
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + CacheDiT",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "diffusion"
    ],
    "description": "Single-device baseline",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "full_model",
+      "local_model",
      "tts"
    ],
    "server_params": {
@@ -18,6 +18,7 @@ from vllm_omni.experimental.ar_diffusion.capability import (
 )
 from vllm_omni.experimental.ar_diffusion.kv_cache import ARDiffusionKVConfig
 from vllm_omni.experimental.ar_diffusion.runner import ARDiffusionModelRunner
+from vllm_omni.experimental.ar_diffusion.tick_protocol import ARDiffusionTickRequest

 BLOCK = 16
 POS = "positive"
@@ -56,6 +57,21 @@ def dreamzero_like_spec(*, capacity: int = 2) -> ARDiffusionKVCacheSpec:
    )


+def tiny_spec(*, capacity: int = 2) -> ARDiffusionKVCacheSpec:
+    return ARDiffusionKVCacheSpec(
+        num_layers=1,
+        num_kv_heads=1,
+        head_size=1,
+        tokens_per_frame=1,
+        frames_per_block=3,
+        window_frames=3,
+        sink_frames=3,
+        kv_branches=(ARDiffusionKVBranchSpec("main", 0),),
+        session_capacity=capacity,
+        cross_attention=(ARDiffusionCrossAttentionKVSpec("text", 2),),
+    )
+
+
 class CapablePipeline:
    def __init__(self, spec: ARDiffusionKVCacheSpec) -> None:
        self.spec = spec
@@ -103,6 +119,7 @@ def make_runner(
    *,
    available_bytes: int = 1 << 28,
    step_execution: bool = False,
+    gpu_memory_fraction: float = 0.1,
 ) -> ARDiffusionModelRunner:
    runner = object.__new__(ARDiffusionModelRunner)
    runner.od_config = SimpleNamespace(
@@ -113,7 +130,10 @@ def make_runner(
    )
    runner.device = torch.device("cpu")
    runner.pipeline = pipeline
-    runner.ar_diffusion_kv_config = ARDiffusionKVConfig(enable=True)
+    runner.ar_diffusion_kv_config = ARDiffusionKVConfig(
+        enable=True,
+        gpu_memory_fraction=gpu_memory_fraction,
+    )
    runner.kv_cache = None
    runner._ar_diffusion_capability = None
    runner._ar_diffusion_kv_cache_spec = None
@@ -132,6 +152,16 @@ def commit_one_frame(runner: ARDiffusionModelRunner, session_id: str, kv_branch:
    return state


+def commit_one_block(runner: ARDiffusionModelRunner, session_id: str, kv_branch: str):
+    state = runner._get_or_create_session(session_id)
+    assert runner.kv_cache is not None
+    seq_len = BLOCK * runner.kv_cache.frames_per_block
+    ctx = state.get_kv_caches(kv_branch, seq_len=seq_len, commit_current=True)[0].forward_ctx
+    ctx.ensure_video_slots(torch.device("cpu"))
+    state.commit_paged_context(kv_branch)
+    return state
+
+
 def test_ar_runner_rejects_pipeline_without_capability():
    runner = object.__new__(ARDiffusionModelRunner)
    runner.od_config = SimpleNamespace(max_num_seqs=1)
@@ -140,6 +170,43 @@ def test_ar_runner_rejects_pipeline_without_capability():
        runner._preallocate_kv_cache(available_bytes=1 << 20)


+def test_runner_uses_typed_tick_as_authoritative_session_contract():
+    tick = ARDiffusionTickRequest(
+        session_id="world-7",
+        request_id="request-3",
+        chunk_index=3,
+        reset=True,
+    )
+    req = SimpleNamespace(
+        request_id="request-3",
+        sampling_params=SimpleNamespace(extra_args=tick.to_extra_args()),
+    )
+
+    session_id, extra_args, parsed = ARDiffusionModelRunner._request_session(req)
+
+    assert session_id == "world-7"
+    assert extra_args == tick.to_extra_args()
+    assert parsed == tick
+
+
+def test_runner_keeps_engine_request_id_separate_from_tick_correlation_id():
+    tick = ARDiffusionTickRequest(
+        session_id="world-7",
+        request_id="client-request-3",
+        chunk_index=3,
+    )
+    req = SimpleNamespace(
+        request_id="engine-request-uuid",
+        sampling_params=SimpleNamespace(extra_args=tick.to_extra_args()),
+    )
+
+    session_id, _, parsed = ARDiffusionModelRunner._request_session(req)
+
+    assert session_id == "world-7"
+    assert req.request_id == "engine-request-uuid"
+    assert parsed.request_id == "client-request-3"
+
+
 def test_lingbot_like_single_branch_session_reuse_reset_and_close():
    pipeline = CapablePipeline(lingbot_like_spec())
    runner = make_runner(pipeline)
@@ -170,6 +237,31 @@ def test_lingbot_like_single_branch_session_reuse_reset_and_close():
    assert pipeline.closes == ["s1"]


+def test_lingbot_like_interleaved_sessions_keep_independent_kv_partitions():
+    runner = make_runner(CapablePipeline(lingbot_like_spec(capacity=2)))
+    kv = runner.kv_cache
+    assert kv is not None
+
+    session_a = commit_one_block(runner, "world-a", "main")
+    a0_blocks = kv.window_block_ids(session_a.adapter("main"))
+    session_b = commit_one_block(runner, "world-b", "main")
+    b0_blocks = kv.window_block_ids(session_b.adapter("main"))
+    session_a_again = commit_one_block(runner, "world-a", "main")
+    a1_blocks = kv.window_block_ids(session_a_again.adapter("main"))
+
+    assert session_a_again is session_a
+    assert session_a.adapter("main").request_id == "ar::world-a::main"
+    assert session_b.adapter("main").request_id == "ar::world-b::main"
+    assert session_a.adapter("main").completed_chunks == 6
+    assert session_b.adapter("main").completed_chunks == 3
+    assert len(a0_blocks) == 3
+    assert len(b0_blocks) == 3
+    assert len(a1_blocks) == 6
+    assert set(a0_blocks) <= set(a1_blocks)
+    assert set(a1_blocks).isdisjoint(b0_blocks)
+    assert tuple(runner._sessions) == ("world-b", "world-a")
+
+
 def test_lingbot_like_sink_survives_sliding_window_eviction():
    runner = make_runner(CapablePipeline(lingbot_like_spec()))
    kv = runner.kv_cache
@@ -198,12 +290,12 @@ def test_dreamzero_like_two_branches_are_independent():


 def test_lru_eviction_releases_blocks_and_notifies_pipeline():
-    pipeline = CapablePipeline(lingbot_like_spec(capacity=8))
+    pipeline = CapablePipeline(lingbot_like_spec(capacity=2))
    runner = make_runner(pipeline)
    kv = runner.kv_cache
    assert kv is not None
-    assert runner._session_capacity == 1
-    assert kv.session_capacity == 1
+    assert runner._session_capacity == 2
+    assert kv.session_capacity == 2
    free_total = kv.manager.block_pool.get_num_free_blocks()
    old = commit_one_frame(runner, "old", "main")
    k = torch.randn(1, 8, 4, 64)
@@ -211,13 +303,56 @@ def test_lru_eviction_releases_blocks_and_notifies_pipeline():
    assert kv.manager.block_pool.get_num_free_blocks() < free_total

    runner._get_or_create_session("new")
+    assert tuple(runner._sessions) == ("old", "new")
+    runner._get_or_create_session("newest")

-    assert tuple(runner._sessions) == ("new",)
+    assert tuple(runner._sessions) == ("new", "newest")
    assert pipeline.closes == ["old"]
    assert "old" not in kv._cross_sessions
    assert kv.manager.block_pool.get_num_free_blocks() == free_total


+def test_budget_reduced_capacity_drives_runner_lru():
+    pipeline = CapablePipeline(tiny_spec(capacity=2))
+    # One tiny LingBot-like session requires 128 bytes; two require 192.
+    runner = make_runner(
+        pipeline,
+        available_bytes=128,
+        gpu_memory_fraction=1.0,
+    )
+    kv = runner.kv_cache
+    assert kv is not None
+    assert kv.requested_session_capacity == 2
+    assert kv.session_capacity == 1
+    assert runner._session_capacity == 1
+
+    runner._get_or_create_session("old")
+    runner._get_or_create_session("new")
+
+    assert tuple(runner._sessions) == ("new",)
+    assert pipeline.closes == ["old"]
+
+
+def test_dreamzero_like_requested_capacity_is_capped_by_budget():
+    spec = dreamzero_like_spec(capacity=64)
+    # Per all-layer self-KV page: 65,536 bytes. Two resident sessions need:
+    # managed=(2 * (2 * 6 + 4) + 2)=34 pages, scratch=8 pages,
+    # cross-attention=1 page/session, for 44 pages total.
+    page_bytes = 65_536
+    pipeline = CapablePipeline(spec)
+    runner = make_runner(
+        pipeline,
+        available_bytes=44 * page_bytes,
+        gpu_memory_fraction=1.0,
+    )
+    kv = runner.kv_cache
+    assert kv is not None
+    assert kv.requested_session_capacity == 64
+    assert kv.session_capacity == 2
+    assert runner._session_capacity == 2
+    assert kv.cross_attention_reserved_bytes == 2 * page_bytes
+
+
 def test_forward_exception_releases_pending_allocation_and_model_state(monkeypatch):
    pipeline = CapablePipeline(lingbot_like_spec())
    runner = make_runner(pipeline)
@@ -232,7 +367,10 @@ def test_forward_exception_releases_pending_allocation_and_model_state(monkeypat
        raise RuntimeError("layer exploded")

    monkeypatch.setattr(DiffusionModelRunner, "execute_model", boom)
-    request = SimpleNamespace(sampling_params=SimpleNamespace(extra_args={"session_id": "broken"}))
+    request = SimpleNamespace(
+        request_id="broken-request",
+        sampling_params=SimpleNamespace(extra_args={"session_id": "broken"}),
+    )

    with pytest.raises(RuntimeError, match="layer exploded"):
        runner.execute_model(request)
@@ -263,7 +401,10 @@ def test_synchronize_exception_uses_forward_cleanup_path(monkeypatch):
    monkeypatch.setattr(DiffusionModelRunner, "execute_model", return_after_allocation)
    monkeypatch.setattr(torch.accelerator, "synchronize", synchronize_boom)
    runner.device = torch.device("cuda")
-    request = SimpleNamespace(sampling_params=SimpleNamespace(extra_args={"session_id": "broken"}))
+    request = SimpleNamespace(
+        request_id="broken-request",
+        sampling_params=SimpleNamespace(extra_args={"session_id": "broken"}),
+    )

    with pytest.raises(RuntimeError, match="asynchronous kernel failed"):
        runner.execute_model(request)
@@ -0,0 +1,231 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Sequence
+from typing import Any
+
+import pytest
+
+from vllm_omni.experimental.ar_diffusion.consumer import ARDiffusionOmniTickConsumer
+from vllm_omni.experimental.ar_diffusion.session import (
+    ARDiffusionSession,
+    ARDiffusionSessionEvent,
+)
+from vllm_omni.experimental.ar_diffusion.tick_protocol import (
+    ARDiffusionChunkMetadata,
+    ARDiffusionControlInput,
+    ARDiffusionTickRequest,
+)
+from vllm_omni.inputs.data import (
+    OmniDiffusionSamplingParams,
+    OmniPromptType,
+    OmniSamplingParams,
+)
+from vllm_omni.outputs import OmniRequestOutput
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+class FakeStagePool:
+    def __init__(self, num_replicas: int = 1) -> None:
+        self.num_replicas = num_replicas
+
+
+class FakeEngine:
+    def __init__(self, num_replicas: int = 1) -> None:
+        self.stage_pools = [FakeStagePool(num_replicas)]
+
+
+class FakeGenerateClient:
+    def __init__(
+        self,
+        *,
+        include_metadata: bool = True,
+        applied_event_ids: list[int] | None = None,
+        output_error: str | None = None,
+        num_replicas: int = 1,
+    ) -> None:
+        self.engine = FakeEngine(num_replicas)
+        self.include_metadata = include_metadata
+        self.applied_event_ids = applied_event_ids
+        self.output_error = output_error
+        self.calls: list[dict[str, Any]] = []
+
+    async def generate(
+        self,
+        prompt: OmniPromptType,
+        *,
+        request_id: str,
+        sampling_params_list: Sequence[OmniSamplingParams],
+        output_modalities: list[str] | None = None,
+    ) -> AsyncIterator[OmniRequestOutput]:
+        self.calls.append(
+            {
+                "prompt": prompt,
+                "request_id": request_id,
+                "sampling_params_list": sampling_params_list,
+                "output_modalities": output_modalities,
+            }
+        )
+        params = sampling_params_list[0]
+        assert isinstance(params, OmniDiffusionSamplingParams)
+        tick = ARDiffusionTickRequest.from_extra_args(params.extra_args)
+        assert tick is not None
+        chunk_metadata = ARDiffusionChunkMetadata.from_tick(tick).to_dict()
+        if self.applied_event_ids is not None:
+            chunk_metadata["applied_event_ids"] = self.applied_event_ids
+        metadata = {"metadata": {"ar_diffusion": chunk_metadata}} if self.include_metadata else {}
+        yield OmniRequestOutput(
+            request_id=request_id,
+            stage_id=0,
+            final_output_type="video",
+            images=["chunk"],  # type: ignore[list-item]
+            _multimodal_output=metadata,
+            finished=True,
+            error=self.output_error,
+        )
+
+
+class FakeLifecycle:
+    async def reset_session(self, session_id: str) -> None:
+        pass
+
+    async def close_session(self, session_id: str) -> None:
+        pass
+
+
+def make_tick() -> ARDiffusionTickRequest:
+    return ARDiffusionTickRequest(
+        session_id="world-1",
+        request_id="world-1-chunk-3",
+        chunk_index=3,
+        applied_event_ids=(7, 8),
+        prompt="turn left",
+        controls=(
+            ARDiffusionControlInput(
+                track="camera",
+                schema="example.camera.v1",
+                data={"samples": [{"x": 1.0}]},
+            ),
+        ),
+    )
+
+
+def test_consumer_rejects_replicated_ar_diffusion_stage() -> None:
+    client = FakeGenerateClient(num_replicas=2)
+
+    with pytest.raises(
+        ValueError,
+        match=r"require exactly one replica.*num_replicas=2",
+    ):
+        ARDiffusionOmniTickConsumer(
+            client,
+            prompt_provider=lambda tick: tick.prompt or "",
+            sampling_params_list=[OmniDiffusionSamplingParams()],
+            diffusion_stage_id=0,
+        )
+
+    assert client.calls == []
+
+
+@pytest.mark.asyncio
+async def test_consumer_submits_typed_tick_and_returns_standard_output() -> None:
+    client = FakeGenerateClient()
+    template = OmniDiffusionSamplingParams(
+        output_type="latent",
+        extra_args={"deployment_option": "kept"},
+    )
+    consumer = ARDiffusionOmniTickConsumer(
+        client,
+        prompt_provider=lambda tick: {
+            "prompt": tick.prompt,
+            "multi_modal_data": {"image": "initial-image"},
+        },
+        sampling_params_list=[template],
+        diffusion_stage_id=0,
+        output_modalities=["video"],
+    )
+    tick = make_tick()
+
+    output = await consumer.execute_tick(tick)
+
+    assert output.images == ["chunk"]
+    assert consumer.chunk_metadata(output) == ARDiffusionChunkMetadata.from_tick(tick)
+    assert len(client.calls) == 1
+    call = client.calls[0]
+    assert call["request_id"] == tick.request_id
+    assert call["output_modalities"] == ["video"]
+    assert call["prompt"] == {
+        "prompt": "turn left",
+        "multi_modal_data": {"image": "initial-image"},
+    }
+    submitted_params = call["sampling_params_list"][0]
+    assert submitted_params.extra_args["deployment_option"] == "kept"
+    assert ARDiffusionTickRequest.from_extra_args(submitted_params.extra_args) == tick
+    assert "ar_diffusion_tick" not in template.extra_args
+
+
+@pytest.mark.asyncio
+async def test_consumer_rejects_missing_chunk_metadata() -> None:
+    consumer = ARDiffusionOmniTickConsumer(
+        FakeGenerateClient(include_metadata=False),
+        prompt_provider=lambda tick: tick.prompt or "",
+        sampling_params_list=[OmniDiffusionSamplingParams()],
+        diffusion_stage_id=0,
+    )
+
+    with pytest.raises(ValueError, match="multimodal_output.metadata"):
+        await consumer.execute_tick(make_tick())
+
+
+@pytest.mark.asyncio
+async def test_consumer_rejects_non_monotonic_applied_event_metadata() -> None:
+    consumer = ARDiffusionOmniTickConsumer(
+        FakeGenerateClient(applied_event_ids=[8, 7]),
+        prompt_provider=lambda tick: tick.prompt or "",
+        sampling_params_list=[OmniDiffusionSamplingParams()],
+        diffusion_stage_id=0,
+    )
+
+    with pytest.raises(ValueError, match="unique and strictly increasing"):
+        await consumer.execute_tick(make_tick())
+
+
+@pytest.mark.asyncio
+async def test_consumer_preserves_engine_failure_before_metadata_validation() -> None:
+    consumer = ARDiffusionOmniTickConsumer(
+        FakeGenerateClient(include_metadata=False, output_error="model failed"),
+        prompt_provider=lambda tick: tick.prompt or "",
+        sampling_params_list=[OmniDiffusionSamplingParams()],
+        diffusion_stage_id=0,
+    )
+
+    with pytest.raises(RuntimeError, match="model failed"):
+        await consumer.execute_tick(make_tick())
+
+
+@pytest.mark.asyncio
+async def test_session_commits_only_after_standard_engine_output_metadata() -> None:
+    client = FakeGenerateClient()
+    consumer = ARDiffusionOmniTickConsumer(
+        client,
+        prompt_provider=lambda tick: tick.prompt or "",
+        sampling_params_list=[OmniDiffusionSamplingParams()],
+        diffusion_stage_id=0,
+    )
+    session = ARDiffusionSession(
+        "world-1",
+        tick_consumer=consumer,
+        lifecycle=FakeLifecycle(),
+        max_pending_events=4,
+        request_id_factory=lambda session_id, chunk_index: f"{session_id}-chunk-{chunk_index}",
+    )
+    await session.accept_event(ARDiffusionSessionEvent(event_id=7, prompt="turn left"))
+
+    output = await session.next_chunk()
+
+    assert output.request_id == "world-1-chunk-0"
+    assert session.chunk_index == 1
+    assert session.pending_event_count == 0
+    assert client.calls[0]["request_id"] == "world-1-chunk-0"
@@ -30,6 +30,8 @@ pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]

 BLOCK = 16

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

 def make_spec(*, chunk_size=BLOCK, window_chunks=2, sink_chunks=0, reset_at_boundary=False):
    return ChunkWindowSpec(
@@ -95,7 +97,6 @@ def test_sliding_replace_snaps_to_chunk_boundary():
 def test_sink_chunks_protected():
    # sink = 1 chunk (16 tokens) is never skipped.
    skip = chunk_window_skipped_tokens(80, chunk_size=16, sliding_window=32, sink_chunks=1, reset_at_boundary=False)
-    # raw overflow = 80 - 32 - 16 = 32 -> snapped 32
    assert skip == 32


@@ -201,12 +202,149 @@ def test_cross_attn_pool_deducted_from_self_attn_budget():
        kv_branches=(ARDiffusionKVBranchSpec("positive", 0), ARDiffusionKVBranchSpec("negative", 0)),
        session_capacity=1,
        cross_attention_lengths={"text": L},
-        device=torch.device("cpu"),
    )
    cross_bytes = 2 * 2 * L * 4 * 64 * torch.float16.itemsize * 2  # K+V, pos+neg, layers
-    expected = compute_num_blocks(avail - cross_bytes, 0.5, kv.spec.page_size_bytes * 2)
+    page_bytes = kv.spec.page_size_bytes * 2
+    scratch_bytes = kv.scratch_num_blocks * page_bytes
+    expected = (int(avail * 0.5) - cross_bytes - scratch_bytes) // page_bytes
    assert kv.num_blocks == expected
-    assert expected > 1 * (2 + 1) + 2  # above the local-slot floor, so the cross deduction is what's tested
+    assert kv.cross_attention_reserved_bytes == cross_bytes
+    assert (kv.num_blocks_total * page_bytes) + cross_bytes <= int(avail * 0.5)
+    assert expected > 1 * (2 + 1) + 2  # above the local-slot minimum, so the cross deduction is what's tested
+
+
+def _make_tiny_capacity_kv(
+    *,
+    requested_capacity: int,
+    available_bytes: int,
+    gpu_memory_fraction: float = 1.0,
+    model_owned_state_bytes_per_session: int = 0,
+) -> ARDiffusionKVCache:
+    return ARDiffusionKVCache(
+        ARDiffusionKVConfig(
+            enable=True,
+            chunk_size=1,
+            window_chunks=3,
+            sink_chunks=3,
+            gpu_memory_fraction=gpu_memory_fraction,
+        ),
+        num_layers=1,
+        num_kv_heads=1,
+        head_size=1,
+        dtype=torch.float32,
+        block_size=1,
+        max_model_len=128,
+        available_bytes=available_bytes,
+        kv_branches=(ARDiffusionKVBranchSpec("main", 0),),
+        session_capacity=requested_capacity,
+        cross_attention_lengths={"text": 2},
+        frames_per_block=3,
+        model_owned_state_bytes_per_session=model_owned_state_bytes_per_session,
+        device=torch.device("cpu"),
+    )
+
+
+def test_capacity_two_retains_both_windows_and_allocates_next_block():
+    # LingBot-like single-branch geometry: page=8 bytes; capacity=2 requires
+    # 17 managed + 3 scratch blocks, plus two 16-byte cross-attention
+    # reservations: 192 bytes exactly.
+    kv = _make_tiny_capacity_kv(requested_capacity=2, available_bytes=192)
+    assert kv.requested_session_capacity == 2
+    assert kv.session_capacity == 2
+    assert kv.managed_num_blocks == 17
+
+    adapters = [kv.begin_request(session_id) for session_id in ("first", "second")]
+    for adapter in adapters:
+        for _ in range(6):  # three sink + three window blocks
+            kv.allocate_token_slots(adapter, 1)
+            kv.commit_chunk(adapter)
+        assert len(kv.window_block_ids(adapter)) == 6
+
+    # Only one request is in flight, so its three-frame block is counted once.
+    kv.allocate_token_slots(adapters[0], 3)
+
+
+def test_requested_capacity_is_capped_and_cross_reservation_uses_effective_capacity():
+    kv = _make_tiny_capacity_kv(requested_capacity=64, available_bytes=192)
+
+    assert kv.requested_session_capacity == 64
+    assert kv.session_capacity == 2
+    assert kv.cross_attention_bytes_per_session == 16
+    assert kv.cross_attention_reserved_bytes == 32
+    assert kv.num_blocks_total * 8 + kv.cross_attention_reserved_bytes == kv.memory_budget_bytes
+
+
+def test_capacity_rejects_budget_that_cannot_fit_one_session():
+    # One session needs 11 managed + 3 scratch blocks and 16 cross bytes:
+    # 14 * 8 + 16 = 128 bytes.
+    with pytest.raises(ValueError, match="cannot fit one session.*available=127 bytes.*required=128 bytes"):
+        _make_tiny_capacity_kv(requested_capacity=1, available_bytes=127)
+
+
+def test_fraction_is_soft_floor_when_one_session_fits_actual_memory():
+    kv = _make_tiny_capacity_kv(
+        requested_capacity=2,
+        available_bytes=1280,
+        gpu_memory_fraction=0.05,
+    )
+
+    assert kv.configured_memory_budget_bytes == 64
+    assert kv.memory_budget_bytes == 128
+    assert kv.session_capacity == 1
+
+
+def test_model_owned_state_reduces_effective_session_capacity():
+    kv = _make_tiny_capacity_kv(
+        requested_capacity=2,
+        available_bytes=200,
+        model_owned_state_bytes_per_session=16,
+    )
+
+    assert kv.session_capacity == 1
+    assert kv.model_owned_state_reserved_bytes == 16
+    assert (
+        kv.num_blocks_total * 8 + kv.cross_attention_reserved_bytes + kv.model_owned_state_reserved_bytes
+        <= kv.memory_budget_bytes
+    )
+
+
+def _make_shipped_lingbot_geometry(*, gpu_memory_fraction: float) -> ARDiffusionKVCache:
+    return ARDiffusionKVCache(
+        ARDiffusionKVConfig(
+            enable=True,
+            chunk_size=1560,
+            window_chunks=9,
+            sink_chunks=9,
+            gpu_memory_fraction=gpu_memory_fraction,
+        ),
+        num_layers=40,
+        num_kv_heads=40,
+        head_size=128,
+        dtype=torch.bfloat16,
+        block_size=1560,
+        max_model_len=1 << 20,
+        available_bytes=100 * (1 << 30),
+        kv_branches=(ARDiffusionKVBranchSpec("main", 0),),
+        session_capacity=2,
+        cross_attention_lengths={"text": 512},
+        frames_per_block=3,
+        model_owned_state_bytes_per_session=7_488_000,
+    )
+
+
+def test_shipped_lingbot_geometry_default_fraction_admits_one_session():
+    kv = _make_shipped_lingbot_geometry(gpu_memory_fraction=0.1)
+
+    assert kv.configured_memory_budget_bytes == 10 * (1 << 30)
+    assert kv.memory_budget_bytes == 33_653_670_400
+    assert kv.session_capacity == 1
+
+
+def test_shipped_lingbot_geometry_tuned_fraction_admits_two_sessions():
+    kv = _make_shipped_lingbot_geometry(gpu_memory_fraction=0.6)
+
+    assert kv.memory_budget_bytes == 60 * (1 << 30)
+    assert kv.session_capacity == 2


 def _make_kv(
@@ -221,15 +359,24 @@ def _make_kv(
        if local_branches == 1
        else (ARDiffusionKVBranchSpec("positive", 0), ARDiffusionKVBranchSpec("negative", 1))
    )
+    page_bytes = 2 * BLOCK * 4 * 64 * torch.float32.itemsize
+    declared_scratch_blocks = (max_scratch_tokens_per_branch + BLOCK - 1) // BLOCK
+    scratch_blocks = local_branches * (num_frame_per_block + declared_scratch_blocks)
+    managed_blocks = local_branches * (window_chunks + num_frame_per_block) + 2
    return ARDiffusionKVCache(
-        ARDiffusionKVConfig(enable=True, chunk_size=BLOCK, window_chunks=window_chunks),
+        ARDiffusionKVConfig(
+            enable=True,
+            chunk_size=BLOCK,
+            window_chunks=window_chunks,
+            gpu_memory_fraction=1.0,
+        ),
        num_layers=1,
        num_kv_heads=4,
        head_size=64,
        dtype=torch.float32,
        block_size=BLOCK,
        max_model_len=4096,
-        available_bytes=1 << 16,  # tiny -> the floor binds
+        available_bytes=(managed_blocks + scratch_blocks) * page_bytes,
        device=torch.device("cpu"),
        kv_branches=kv_branches,
        session_capacity=1,
@@ -54,13 +54,15 @@ def test_full_request_lifecycle_and_eviction():
        used = {int(s) // kv.block_size for s in slots}
        assert kv.null_block_id not in used
        assert used <= set(block_table)
-        # Resident window stays bounded by window + the in-flight chunk.
+        # Allocation may transiently add one in-flight chunk.
        assert len(kv.window_block_ids(adapter)) <= kv.spec.window_chunks + 1
-        free_after.append(kv.manager.block_pool.get_num_free_blocks())
        kv.commit_chunk(adapter)
+        # A successful commit immediately prunes physical ownership.
+        assert len(kv.window_block_ids(adapter)) <= kv.spec.window_chunks
+        free_after.append(kv.manager.block_pool.get_num_free_blocks())

    # Pool memory plateaus once the window is full (eviction recycles blocks).
-    assert free_after[-1] == free_after[kv.spec.window_chunks]
+    assert free_after[-1] == free_after[kv.spec.window_chunks - 1]

    kv.end_request(adapter)
    assert kv.manager.block_pool.get_num_free_blocks() == free_total
@@ -332,7 +332,7 @@ def test_block_table_padded_to_fixed_width():

 def test_prepare_is_idempotent_and_layers_share_metadata():
    device = torch.device("cpu")
-    _, st = make_state(num_layers=2)
+    kv, st = make_state(num_layers=2)
    contexts = st.get_kv_caches(POS, seq_len=BLOCK, commit_current=False)
    fctx = contexts[0].forward_ctx
    fctx.prepare(device=device, action_len=0, query_len=BLOCK)
@@ -349,6 +349,10 @@ def test_prepare_is_idempotent_and_layers_share_metadata():
    assert i0.block_table is i1.block_table
    assert i0.seq_lens is i1.seq_lens
    assert i0.video_slots is i1.video_slots
+    assert i0.key_pool is kv._k_pools[0]
+    assert i0.value_pool is kv._v_pools[0]
+    assert i1.key_pool is kv._k_pools[1]
+    assert i1.value_pool is kv._v_pools[1]


 def test_layer_inputs_before_prepare_raises():
@@ -174,6 +174,19 @@ def test_window_reset_can_keep_named_cross_attention_cache():
    assert not st.is_cross_attention_populated(POS, "text")


+def test_clear_cross_attention_preserves_self_attention_history():
+    kv, st = make_state(num_layers=1, cross_attn_length=8)
+    _prepare_and_commit(st, POS, 2)
+    k = torch.randn(1, 8, N_HEADS, HEAD_DIM)
+    st.populate_cross_attention(POS, "text", [(k, k)])
+    resident_before = kv.window_block_ids(st.adapter(POS))
+
+    st.clear_cross_attention()
+
+    assert kv.window_block_ids(st.adapter(POS)) == resident_before
+    assert not st.is_cross_attention_populated(POS, "text")
+
+
 def test_cross_attention_uses_logical_branch_when_local_slot_is_shared():
    _, st = make_state(num_layers=2, cross_attn_length=8, shared_local_index=True)
    assert st.kv_cache.num_local_kv_branches == 1
@@ -0,0 +1,505 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import asyncio
+from typing import Any
+
+import pytest
+
+from vllm_omni.experimental.ar_diffusion.session import (
+    ARDiffusionChunkMetadataError,
+    ARDiffusionEventAcceptanceStatus,
+    ARDiffusionEventQueueFullError,
+    ARDiffusionPreparedControls,
+    ARDiffusionSession,
+    ARDiffusionSessionError,
+    ARDiffusionSessionEvent,
+    ARDiffusionSessionManager,
+    ARDiffusionSessionStateError,
+    ARDiffusionSessionStatus,
+    ARDiffusionStaleEventError,
+    ARDiffusionWorkerLifecycle,
+)
+from vllm_omni.experimental.ar_diffusion.tick_protocol import (
+    ARDiffusionChunkMetadata,
+    ARDiffusionControlInput,
+    ARDiffusionTickRequest,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+class FakeTickConsumer:
+    def __init__(self) -> None:
+        self.ticks: list[ARDiffusionTickRequest] = []
+        self.failures_remaining = 0
+        self.started: asyncio.Event | None = None
+        self.release: asyncio.Event | None = None
+        self.metadata_override: ARDiffusionChunkMetadata | None = None
+
+    async def execute_tick(self, tick: ARDiffusionTickRequest) -> ARDiffusionChunkMetadata:
+        self.ticks.append(tick)
+        if self.started is not None:
+            self.started.set()
+        if self.release is not None:
+            await self.release.wait()
+        if self.failures_remaining:
+            self.failures_remaining -= 1
+            raise RuntimeError("fake tick failed")
+        return self.metadata_override or ARDiffusionChunkMetadata.from_tick(tick)
+
+    def chunk_metadata(self, output: ARDiffusionChunkMetadata) -> ARDiffusionChunkMetadata:
+        return output
+
+
+class FakeLifecycle:
+    def __init__(self) -> None:
+        self.resets: list[str] = []
+        self.closes: list[str] = []
+        self.close_failures_remaining = 0
+
+    async def reset_session(self, session_id: str) -> None:
+        self.resets.append(session_id)
+
+    async def close_session(self, session_id: str) -> None:
+        self.closes.append(session_id)
+        if self.close_failures_remaining:
+            self.close_failures_remaining -= 1
+            raise RuntimeError("fake close failed")
+
+
+class FailingCommitReducer:
+    def __init__(self) -> None:
+        self.commit_calls = 0
+        self.reset_calls = 0
+
+    def prepare(
+        self,
+        *,
+        current_controls: dict[str, ARDiffusionControlInput],
+        events: tuple[ARDiffusionSessionEvent, ...],
+        chunk_index: int,
+    ) -> ARDiffusionPreparedControls:
+        del current_controls, events, chunk_index
+        return ARDiffusionPreparedControls(controls=())
+
+    def commit(self, prepared: ARDiffusionPreparedControls) -> None:
+        del prepared
+        self.commit_calls += 1
+        raise RuntimeError("fake reducer commit failed")
+
+    def reset(self) -> None:
+        self.reset_calls += 1
+
+
+def make_session(
+    *,
+    max_pending_events: int = 8,
+    consumer: FakeTickConsumer | None = None,
+    lifecycle: FakeLifecycle | None = None,
+) -> tuple[ARDiffusionSession, FakeTickConsumer, FakeLifecycle]:
+    resolved_consumer = consumer or FakeTickConsumer()
+    resolved_lifecycle = lifecycle or FakeLifecycle()
+    session = ARDiffusionSession(
+        "world-1",
+        tick_consumer=resolved_consumer,
+        lifecycle=resolved_lifecycle,
+        max_pending_events=max_pending_events,
+        request_id_factory=lambda session_id, chunk_index: f"{session_id}-request-{chunk_index}",
+    )
+    return session, resolved_consumer, resolved_lifecycle
+
+
+@pytest.mark.asyncio
+async def test_events_are_accepted_then_sorted_and_applied_at_chunk_boundary() -> None:
+    session, consumer, _ = make_session()
+    event_two = ARDiffusionSessionEvent(event_id=2, prompt="turn")
+    event_one = ARDiffusionSessionEvent(
+        event_id=1,
+        controls=(
+            ARDiffusionControlInput(
+                track="camera",
+                schema="example.camera.v1",
+                data={"samples": [{"x": 1.0}]},
+            ),
+        ),
+    )
+
+    accepted_two = await session.accept_event(event_two)
+    accepted_one = await session.accept_event(event_one)
+    duplicate = await session.accept_event(event_two)
+
+    assert accepted_two.status is ARDiffusionEventAcceptanceStatus.ACCEPTED
+    assert accepted_one.status is ARDiffusionEventAcceptanceStatus.ACCEPTED
+    assert duplicate.status is ARDiffusionEventAcceptanceStatus.DUPLICATE
+    assert consumer.ticks == []
+
+    metadata = await session.next_chunk()
+
+    assert metadata.applied_event_ids == (1, 2)
+    assert consumer.ticks[0] == ARDiffusionTickRequest(
+        session_id="world-1",
+        request_id="world-1-request-0",
+        chunk_index=0,
+        applied_event_ids=(1, 2),
+        prompt="turn",
+        controls=(
+            ARDiffusionControlInput(
+                track="camera",
+                schema="example.camera.v1",
+                data={"samples": [{"x": 1.0}]},
+            ),
+        ),
+    )
+    assert session.pending_event_count == 0
+
+    applied_duplicate = await session.accept_event(event_two)
+    assert applied_duplicate.status is ARDiffusionEventAcceptanceStatus.DUPLICATE
+
+    next_metadata = await session.next_chunk()
+    assert next_metadata.chunk_index == 1
+    assert next_metadata.applied_event_ids == ()
+    assert consumer.ticks[1].prompt == "turn"
+    assert consumer.ticks[1].controls == consumer.ticks[0].controls
+
+
+@pytest.mark.asyncio
+async def test_composite_event_is_isolated_and_queue_admission_is_atomic() -> None:
+    session, consumer, _ = make_session(max_pending_events=1)
+    payload = {"samples": [{"x": 1.0}]}
+    composite = ARDiffusionSessionEvent(
+        event_id=5,
+        prompt="new prompt",
+        controls=(
+            ARDiffusionControlInput(
+                track="camera",
+                schema="example.camera.v1",
+                data=payload,
+            ),
+            ARDiffusionControlInput(
+                track="action",
+                schema="example.action.v1",
+                data={"keys": ["left"]},
+            ),
+        ),
+    )
+    await session.accept_event(composite)
+    payload["samples"][0]["x"] = 99.0
+
+    duplicate = await session.accept_event(composite)
+    assert duplicate.status is ARDiffusionEventAcceptanceStatus.DUPLICATE
+    with pytest.raises(ARDiffusionEventQueueFullError, match="queue is full"):
+        await session.accept_event(ARDiffusionSessionEvent(event_id=6, prompt="rejected"))
+
+    await session.next_chunk()
+    assert consumer.ticks[0].applied_event_ids == (5,)
+    assert consumer.ticks[0].prompt == "new prompt"
+    assert consumer.ticks[0].controls[0].data["keys"] == ("left",)
+    assert consumer.ticks[0].controls[1].data["samples"] == ({"x": 1.0},)
+    assert consumer.ticks[0].controls[0].to_dict()["data"] == {"keys": ["left"]}
+    assert consumer.ticks[0].controls[1].to_dict()["data"] == {"samples": [{"x": 1.0}]}
+
+
+def test_event_rejects_duplicate_tracks_and_non_transport_payloads() -> None:
+    with pytest.raises(ValueError, match="at most once"):
+        ARDiffusionSessionEvent(
+            event_id=1,
+            controls=(
+                ARDiffusionControlInput(track="camera", schema="one", data={}),
+                ARDiffusionControlInput(track="camera", schema="two", data={}),
+            ),
+        )
+
+    with pytest.raises(ValueError, match="transport-safe"):
+        ARDiffusionControlInput(
+            track="camera",
+            schema="example.camera.v1",
+            data={"object": object()},
+        )
+
+
+@pytest.mark.asyncio
+async def test_failed_tick_closes_worker_and_requires_reset() -> None:
+    consumer = FakeTickConsumer()
+    consumer.failures_remaining = 1
+    lifecycle = FakeLifecycle()
+    session, _, _ = make_session(consumer=consumer, lifecycle=lifecycle)
+    await session.accept_event(ARDiffusionSessionEvent(event_id=3, prompt="retry me"))
+
+    with pytest.raises(RuntimeError, match="fake tick failed"):
+        await session.next_chunk()
+
+    assert session.status is ARDiffusionSessionStatus.FAILED
+    assert session.chunk_index == 0
+    assert session.pending_event_count == 1
+    assert lifecycle.closes == ["world-1"]
+    with pytest.raises(ARDiffusionSessionStateError, match="failed"):
+        await session.next_chunk()
+
+    await session.reset()
+    assert session.status is ARDiffusionSessionStatus.ACTIVE
+    assert session.pending_event_count == 0
+    assert lifecycle.resets == ["world-1"]
+    await session.accept_event(ARDiffusionSessionEvent(event_id=4, prompt="after reset"))
+    metadata = await session.next_chunk()
+    assert metadata.chunk_index == 0
+    assert metadata.applied_event_ids == (4,)
+    assert [tick.applied_event_ids for tick in consumer.ticks] == [(3,), (4,)]
+
+
+@pytest.mark.asyncio
+async def test_inflight_snapshot_is_immutable_and_new_event_waits_for_next_chunk() -> None:
+    consumer = FakeTickConsumer()
+    consumer.started = asyncio.Event()
+    consumer.release = asyncio.Event()
+    session, _, _ = make_session(consumer=consumer)
+    await session.accept_event(ARDiffusionSessionEvent(event_id=10, prompt="first"))
+
+    first_chunk = asyncio.create_task(session.next_chunk())
+    await consumer.started.wait()
+    accepted = await session.accept_event(ARDiffusionSessionEvent(event_id=11, prompt="second"))
+    assert accepted.status is ARDiffusionEventAcceptanceStatus.ACCEPTED
+    assert consumer.ticks[0].prompt == "first"
+    assert consumer.ticks[0].applied_event_ids == (10,)
+
+    with pytest.raises(ARDiffusionStaleEventError, match="event floor is 10"):
+        await session.accept_event(ARDiffusionSessionEvent(event_id=9, prompt="stale"))
+
+    consumer.release.set()
+    first_metadata = await first_chunk
+    second_metadata = await session.next_chunk()
+    assert first_metadata.applied_event_ids == (10,)
+    assert second_metadata.applied_event_ids == (11,)
+    assert consumer.ticks[1].prompt == "second"
+
+
+@pytest.mark.asyncio
+async def test_metadata_mismatch_does_not_commit_snapshot() -> None:
+    consumer = FakeTickConsumer()
+    consumer.metadata_override = ARDiffusionChunkMetadata(
+        session_id="another-world",
+        request_id="wrong",
+        chunk_index=0,
+    )
+    lifecycle = FakeLifecycle()
+    session, _, _ = make_session(consumer=consumer, lifecycle=lifecycle)
+    await session.accept_event(ARDiffusionSessionEvent(event_id=1, prompt="pending"))
+
+    with pytest.raises(ARDiffusionChunkMetadataError, match="does not match"):
+        await session.next_chunk()
+
+    assert session.chunk_index == 0
+    assert session.pending_event_count == 1
+    assert session.status is ARDiffusionSessionStatus.FAILED
+    assert lifecycle.closes == ["world-1"]
+
+
+@pytest.mark.asyncio
+async def test_reducer_commit_failure_is_fail_closed_without_partial_session_commit() -> None:
+    consumer = FakeTickConsumer()
+    lifecycle = FakeLifecycle()
+    reducer = FailingCommitReducer()
+    session = ARDiffusionSession(
+        "world-1",
+        tick_consumer=consumer,
+        lifecycle=lifecycle,
+        max_pending_events=4,
+        request_id_factory=lambda session_id, chunk_index: f"{session_id}-request-{chunk_index}",
+        control_reducer=reducer,
+    )
+    await session.accept_event(ARDiffusionSessionEvent(event_id=1, prompt="pending"))
+
+    with pytest.raises(RuntimeError, match="fake reducer commit failed"):
+        await session.next_chunk()
+
+    assert reducer.commit_calls == 1
+    assert session.status is ARDiffusionSessionStatus.FAILED
+    assert session.chunk_index == 0
+    assert session.pending_event_count == 1
+    assert lifecycle.closes == ["world-1"]
+    with pytest.raises(ARDiffusionSessionStateError, match="failed"):
+        await session.next_chunk()
+
+    await session.reset()
+    assert reducer.reset_calls == 1
+    assert session.status is ARDiffusionSessionStatus.ACTIVE
+    assert session.chunk_index == 0
+    assert session.pending_event_count == 0
+
+
+@pytest.mark.asyncio
+async def test_reset_close_and_disconnect_reach_lifecycle_boundary() -> None:
+    consumer = FakeTickConsumer()
+    lifecycle = FakeLifecycle()
+    manager = ARDiffusionSessionManager(
+        tick_consumer=consumer,
+        lifecycle=lifecycle,
+        max_pending_events=4,
+        request_id_factory=lambda session_id, chunk_index: f"{session_id}-{chunk_index}",
+    )
+    session = await manager.create_session("world-reset")
+    await session.accept_event(ARDiffusionSessionEvent(event_id=4, prompt="before reset"))
+    await session.next_chunk()
+    await manager.reset_session("world-reset")
+
+    assert lifecycle.resets == ["world-reset"]
+    assert session.status is ARDiffusionSessionStatus.ACTIVE
+    assert session.chunk_index == 0
+    duplicate = await session.accept_event(ARDiffusionSessionEvent(event_id=4, prompt="duplicate"))
+    assert duplicate.status is ARDiffusionEventAcceptanceStatus.DUPLICATE
+
+    reset_tick = await session.next_chunk()
+    assert reset_tick.chunk_index == 0
+    assert consumer.ticks[-1].prompt is None
+    assert consumer.ticks[-1].controls == ()
+
+    await manager.close_session("world-reset")
+    assert lifecycle.closes == ["world-reset"]
+    assert session.status is ARDiffusionSessionStatus.CLOSED
+    with pytest.raises(ARDiffusionSessionStateError, match="does not exist"):
+        await manager.get_session("world-reset")
+
+    disconnected = await manager.create_session("world-disconnect")
+    await manager.disconnect("world-disconnect")
+    assert disconnected.status is ARDiffusionSessionStatus.CLOSED
+    assert lifecycle.closes[-1] == "world-disconnect"
+
+
+@pytest.mark.asyncio
+async def test_manager_keeps_interleaved_sessions_independent() -> None:
+    consumer = FakeTickConsumer()
+    manager = ARDiffusionSessionManager(
+        tick_consumer=consumer,
+        lifecycle=FakeLifecycle(),
+        max_pending_events=4,
+        request_id_factory=lambda session_id, chunk_index: f"{session_id}-request-{chunk_index}",
+    )
+    session_a = await manager.create_session("world-a")
+    session_b = await manager.create_session("world-b")
+
+    await session_a.accept_event(ARDiffusionSessionEvent(event_id=10, prompt="a0"))
+    await session_a.next_chunk()
+    await session_b.accept_event(ARDiffusionSessionEvent(event_id=1, prompt="b0"))
+    await session_b.next_chunk()
+    await session_a.accept_event(ARDiffusionSessionEvent(event_id=11, prompt="a1"))
+    await session_a.next_chunk()
+
+    assert [
+        (
+            tick.session_id,
+            tick.request_id,
+            tick.chunk_index,
+            tick.applied_event_ids,
+        )
+        for tick in consumer.ticks
+    ] == [
+        ("world-a", "world-a-request-0", 0, (10,)),
+        ("world-b", "world-b-request-0", 0, (1,)),
+        ("world-a", "world-a-request-1", 1, (11,)),
+    ]
+    assert session_a.chunk_index == 2
+    assert session_b.chunk_index == 1
+    with pytest.raises(ARDiffusionStaleEventError, match="event floor is 11"):
+        await session_a.accept_event(ARDiffusionSessionEvent(event_id=2, prompt="stale only for a"))
+    assert (
+        await session_b.accept_event(ARDiffusionSessionEvent(event_id=2, prompt="b1"))
+    ).status is ARDiffusionEventAcceptanceStatus.ACCEPTED
+
+
+@pytest.mark.asyncio
+async def test_failed_close_retains_tombstone_until_cleanup_retry_succeeds() -> None:
+    consumer = FakeTickConsumer()
+    lifecycle = FakeLifecycle()
+    lifecycle.close_failures_remaining = 1
+    manager = ARDiffusionSessionManager(
+        tick_consumer=consumer,
+        lifecycle=lifecycle,
+        max_pending_events=4,
+    )
+    session = await manager.create_session("world-close-retry")
+
+    with pytest.raises(RuntimeError, match="fake close failed"):
+        await manager.close_session("world-close-retry")
+
+    assert session.status is ARDiffusionSessionStatus.CLEANUP_FAILED
+    assert await manager.get_session("world-close-retry") is session
+    with pytest.raises(ARDiffusionSessionStateError, match="already exists"):
+        await manager.create_session("world-close-retry")
+    with pytest.raises(ARDiffusionSessionStateError, match="cleanup_failed"):
+        await manager.reset_session("world-close-retry")
+
+    await manager.close_session("world-close-retry")
+
+    assert lifecycle.closes == ["world-close-retry", "world-close-retry"]
+    assert session.status is ARDiffusionSessionStatus.CLOSED
+    with pytest.raises(ARDiffusionSessionStateError, match="does not exist"):
+        await manager.get_session("world-close-retry")
+    recreated = await manager.create_session("world-close-retry")
+    assert recreated is not session
+
+
+@pytest.mark.asyncio
+async def test_failed_disconnect_retains_session_for_explicit_cleanup_retry() -> None:
+    lifecycle = FakeLifecycle()
+    lifecycle.close_failures_remaining = 1
+    manager = ARDiffusionSessionManager(
+        tick_consumer=FakeTickConsumer(),
+        lifecycle=lifecycle,
+        max_pending_events=4,
+    )
+    session = await manager.create_session("world-disconnect-retry")
+
+    with pytest.raises(RuntimeError, match="fake close failed"):
+        await manager.disconnect("world-disconnect-retry")
+
+    assert session.status is ARDiffusionSessionStatus.CLEANUP_FAILED
+    assert await manager.get_session("world-disconnect-retry") is session
+
+    await manager.close_session("world-disconnect-retry")
+    assert session.status is ARDiffusionSessionStatus.CLOSED
+
+
+class FakeRPCClient:
+    def __init__(self, result: list[Any]) -> None:
+        self.result = result
+        self.calls: list[dict[str, Any]] = []
+
+    async def collective_rpc(self, **kwargs: Any) -> list[Any]:
+        self.calls.append(kwargs)
+        return self.result
+
+
+@pytest.mark.asyncio
+async def test_worker_lifecycle_routes_to_selected_stage_rpc() -> None:
+    client = FakeRPCClient([[True]])
+    lifecycle = ARDiffusionWorkerLifecycle(client, stage_ids=[2], timeout=3.0)
+
+    await lifecycle.reset_session("world-1")
+    await lifecycle.close_session("world-1")
+
+    assert client.calls == [
+        {
+            "method": "reset_ar_diffusion_session",
+            "timeout": 3.0,
+            "args": ("world-1",),
+            "stage_ids": [2],
+        },
+        {
+            "method": "close_ar_diffusion_session",
+            "timeout": 3.0,
+            "args": ("world-1",),
+            "stage_ids": [2],
+        },
+    ]
+
+
+@pytest.mark.asyncio
+async def test_worker_lifecycle_rejects_unsupported_stage_result() -> None:
+    lifecycle = ARDiffusionWorkerLifecycle(
+        FakeRPCClient([{"supported": False, "error": "not an AR stage"}]),
+        stage_ids=[1],
+    )
+
+    with pytest.raises(ARDiffusionSessionError, match="not an AR stage"):
+        await lifecycle.close_session("world-1")
@@ -0,0 +1,108 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from vllm_omni.experimental.ar_diffusion.tick_protocol import (
+    ARDiffusionChunkMetadata,
+    ARDiffusionControlInput,
+    ARDiffusionTickRequest,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def test_tick_round_trip_preserves_identity_and_control_snapshot() -> None:
+    tick = ARDiffusionTickRequest(
+        session_id="world-1",
+        request_id="chunk-request-7",
+        chunk_index=7,
+        applied_event_ids=(10, 11),
+        prompt="turn left",
+        controls=(
+            ARDiffusionControlInput(
+                track="camera",
+                schema="lingbot.camera_trajectory.v1",
+                data={"poses": [[1.0, 0.0]], "intrinsics": [[2.0]]},
+            ),
+        ),
+    )
+
+    restored = ARDiffusionTickRequest.from_extra_args(tick.to_extra_args())
+
+    assert restored == tick
+    assert ARDiffusionChunkMetadata.from_tick(tick).to_dict() == {
+        "session_id": "world-1",
+        "request_id": "chunk-request-7",
+        "chunk_index": 7,
+        "applied_event_ids": [10, 11],
+    }
+
+
+@pytest.mark.parametrize(
+    ("field", "value", "message"),
+    [
+        ("session_id", "", "session_id"),
+        ("request_id", "", "request_id"),
+        ("chunk_index", -1, "chunk_index"),
+        ("applied_event_ids", (2, 1), "strictly increasing"),
+        ("applied_event_ids", (1, 1), "strictly increasing"),
+    ],
+)
+def test_tick_rejects_invalid_identity(
+    field: str,
+    value: object,
+    message: str,
+) -> None:
+    values = {
+        "session_id": "world-1",
+        "request_id": "request-1",
+        "chunk_index": 0,
+        "applied_event_ids": (),
+    }
+    values[field] = value
+
+    with pytest.raises(ValueError, match=message):
+        ARDiffusionTickRequest(**values)
+
+
+def test_tick_snapshot_is_detached_and_deeply_immutable() -> None:
+    payload = {"samples": [{"keys": ["w"]}]}
+    event_ids = [1, 2]
+    controls = [
+        ARDiffusionControlInput(
+            track="camera",
+            schema="example.camera.v1",
+            data=payload,
+        )
+    ]
+    tick = ARDiffusionTickRequest(
+        session_id="world-1",
+        request_id="request-a",
+        chunk_index=0,
+        applied_event_ids=event_ids,  # type: ignore[arg-type]
+        controls=controls,  # type: ignore[arg-type]
+    )
+    serialized = tick.to_extra_args()
+
+    payload["samples"][0]["keys"].append("j")
+    event_ids.append(3)
+    controls.clear()
+    serialized["ar_diffusion_tick"]["controls"][0]["data"]["samples"][0]["keys"].append("k")
+
+    assert tick.applied_event_ids == (1, 2)
+    assert isinstance(tick.controls, tuple)
+    assert tick.to_extra_args()["ar_diffusion_tick"]["controls"] == [
+        {
+            "track": "camera",
+            "schema": "example.camera.v1",
+            "data": {"samples": [{"keys": ["w"]}]},
+        }
+    ]
+    with pytest.raises(TypeError):
+        tick.controls[0].data["samples"] = ()  # type: ignore[index]
+    with pytest.raises(AttributeError):
+        tick.controls[0].data["samples"][0]["keys"].append("j")
+
+
+def test_legacy_request_has_no_typed_tick() -> None:
+    assert ARDiffusionTickRequest.from_extra_args({"session_id": "legacy"}) is None
@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from vllm_omni.diffusion.worker.diffusion_worker import DiffusionWorker
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+class FakeARDiffusionRunner:
+    def __init__(self) -> None:
+        self.resets: list[str] = []
+        self.closes: list[str] = []
+
+    def reset_session(self, session_id: str) -> None:
+        self.resets.append(session_id)
+
+    def close_session(self, session_id: str) -> None:
+        self.closes.append(session_id)
+
+
+class FakeRegularRunner:
+    pass
+
+
+def make_worker(runner: object) -> DiffusionWorker:
+    worker = object.__new__(DiffusionWorker)
+    worker.model_runner = runner
+    return worker
+
+
+def test_worker_delegates_ar_session_lifecycle_to_runner() -> None:
+    runner = FakeARDiffusionRunner()
+    worker = make_worker(runner)
+
+    assert worker.reset_ar_diffusion_session("world-1") is True
+    assert worker.close_ar_diffusion_session("world-1") is True
+    assert runner.resets == ["world-1"]
+    assert runner.closes == ["world-1"]
+
+
+def test_worker_reports_unsupported_runner_without_model_assumptions() -> None:
+    worker = make_worker(FakeRegularRunner())
+
+    assert worker.reset_ar_diffusion_session("world-1") is False
+    assert worker.close_ar_diffusion_session("world-1") is False
+
+
+def test_worker_rejects_empty_session_id() -> None:
+    worker = make_worker(FakeARDiffusionRunner())
+
+    with pytest.raises(ValueError, match="session_id"):
+        worker.close_ar_diffusion_session("")
@@ -16,6 +16,11 @@ from vllm.config.load import LoadConfig
 from vllm_omni.diffusion.config import get_current_diffusion_config, get_current_diffusion_config_or_none
 from vllm_omni.diffusion.data import OmniDiffusionConfig
 from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
+from vllm_omni.diffusion.model_loader.host_weight_plan import (
+    HostWeightPlan,
+    HostWeightPlanResult,
+    TensorBinding,
+)
 from vllm_omni.diffusion.models.helios import HeliosPipeline
 from vllm_omni.diffusion.registry import initialize_model

@@ -258,6 +263,137 @@ def test_load_model_custom_pipeline_sets_current_diffusion_config(monkeypatch):
    assert get_current_diffusion_config_or_none() is None


+def test_dlo_transfers_loader_plan_and_skips_ordinary_weight_loading(monkeypatch):
+    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
+
+    od_config = SimpleNamespace(
+        dtype=torch.float32,
+        parallel_config=SimpleNamespace(use_hsdp=False, tensor_parallel_size=1),
+        quantization_config=None,
+        enable_distributed_layerwise_offload=True,
+        dlo_use_allgather=False,
+        model="unused",
+    )
+    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
+    model = nn.Module()
+    model.transformer = nn.Linear(2, 2, bias=False)
+    plan = HostWeightPlan(
+        backing_kind="checkpoint_mmap",
+        bindings={},
+    )
+    loaded_ordinary_weights = False
+
+    def load_weights(_model):
+        nonlocal loaded_ordinary_weights
+        loaded_ordinary_weights = True
+
+    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
+    loader.load_weights = load_weights  # type: ignore[method-assign]
+    loader._apply_skip_softmax_calibration = lambda _model: None  # type: ignore[method-assign]
+    monkeypatch.setattr(
+        loader_mod,
+        "build_checkpoint_mmap_plan",
+        lambda *_args, **_kwargs: HostWeightPlanResult(plan),
+    )
+
+    assert loader.load_model(load_device="cpu") is model
+    assert not loaded_ordinary_weights
+    assert loader.take_host_weight_plan() is plan
+    assert loader.take_host_weight_plan() is None
+
+
+def test_dlo_plan_loads_component_sources_outside_planned_dit(monkeypatch):
+    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
+
+    od_config = SimpleNamespace(
+        dtype=torch.float32,
+        parallel_config=SimpleNamespace(use_hsdp=False, tensor_parallel_size=1),
+        quantization_config=None,
+        enable_distributed_layerwise_offload=True,
+        dlo_use_allgather=False,
+        model="unused",
+    )
+    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
+
+    class MixedSourceModel(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.transformer = nn.Linear(2, 2, bias=False)
+            self.text_encoder = nn.Linear(2, 2, bias=False)
+            self.weights_sources = (
+                DiffusersPipelineLoader.ComponentSource("unused", None, None, prefix="transformer."),
+                DiffusersPipelineLoader.ComponentSource("unused", None, None, prefix="text_encoder."),
+            )
+            self.loaded_weight_names: list[str] = []
+
+        def load_weights(self, weights):
+            self.loaded_weight_names = [name for name, _ in weights]
+            return set(self.loaded_weight_names)
+
+    model = MixedSourceModel()
+    plan = HostWeightPlan(
+        backing_kind="checkpoint_mmap",
+        bindings={
+            "transformer.weight": TensorBinding(
+                checkpoint_key="weight",
+                file_path="unused",
+            )
+        },
+        planned_source_prefixes=frozenset({"transformer."}),
+    )
+    requested_prefixes: list[str] = []
+
+    def get_weights(source, model=None):
+        del model
+        requested_prefixes.append(source.prefix)
+        yield source.prefix + "weight", torch.ones(2, 2)
+
+    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
+    loader._get_weights_iterator = get_weights  # type: ignore[method-assign]
+    loader._apply_skip_softmax_calibration = lambda _model: None  # type: ignore[method-assign]
+    monkeypatch.setattr(
+        loader_mod,
+        "build_checkpoint_mmap_plan",
+        lambda *_args, **_kwargs: HostWeightPlanResult(plan),
+    )
+
+    assert loader.load_model(load_device="cpu") is model
+    assert requested_prefixes == ["text_encoder."]
+    assert model.loaded_weight_names == ["text_encoder.weight"]
+    assert loader.take_host_weight_plan() is plan
+
+
+def test_dlo_plan_fallback_runs_ordinary_loader(monkeypatch):
+    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
+
+    od_config = SimpleNamespace(
+        dtype=torch.float32,
+        parallel_config=SimpleNamespace(use_hsdp=False, tensor_parallel_size=1),
+        quantization_config=None,
+        enable_distributed_layerwise_offload=True,
+        dlo_use_allgather=False,
+        model="unused",
+    )
+    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
+    model = nn.Module()
+    model.transformer = nn.Linear(2, 2, bias=False)
+    calls: list[str] = []
+
+    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
+    loader.load_weights = lambda _model: calls.append("load")  # type: ignore[method-assign]
+    loader._process_weights_after_loading = lambda *_args: calls.append("process")  # type: ignore[method-assign]
+    loader._apply_skip_softmax_calibration = lambda _model: None  # type: ignore[method-assign]
+    monkeypatch.setattr(
+        loader_mod,
+        "build_checkpoint_mmap_plan",
+        lambda *_args, **_kwargs: HostWeightPlanResult(None, "not direct-compatible"),
+    )
+
+    assert loader.load_model(load_device="cpu") is model
+    assert calls == ["load", "process"]
+    assert loader.take_host_weight_plan() is None
+
+
 def test_hsdp_processes_quantized_weights_before_sharding(mocker):
    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
    from vllm_omni.diffusion.offloader.module_collector import PipelineModules
@@ -6,7 +6,10 @@ from types import SimpleNamespace

 import pytest

-from vllm_omni.diffusion.models.dreamzero.pipeline_dreamzero import DreamZeroPipeline
+from vllm_omni.diffusion.models.dreamzero.pipeline_dreamzero import (
+    DREAMZERO_MODEL_OWNED_STATE_BYTES_PER_SESSION,
+    DreamZeroPipeline,
+)
 from vllm_omni.diffusion.models.dreamzero.state_dreamzero import DreamZeroState

 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
@@ -32,6 +35,37 @@ def test_dreamzero_pipeline_state_is_session_keyed() -> None:
    assert session_b.call_count == 3


+def test_dreamzero_kv_spec_accounts_for_model_owned_cuda_state(monkeypatch) -> None:
+    pipeline = DreamZeroPipeline.__new__(DreamZeroPipeline)
+    pipeline.transformer = SimpleNamespace(
+        frame_seqlen=16,
+        blocks=[
+            SimpleNamespace(
+                self_attn=SimpleNamespace(
+                    max_attention_size=96,
+                    tp_num_heads=4,
+                )
+            )
+        ],
+        text_len=8,
+        model_type="t2v",
+        num_layers=2,
+        num_heads=4,
+        dim=256,
+        num_frame_per_block=4,
+        num_action_per_block=2,
+        num_state_per_block=1,
+    )
+    monkeypatch.setattr(
+        "vllm_omni.diffusion.models.dreamzero.pipeline_dreamzero.get_classifier_free_guidance_world_size",
+        lambda: 1,
+    )
+
+    spec = pipeline.ar_diffusion_kv_cache_spec()
+
+    assert spec.model_owned_state_bytes_per_session == DREAMZERO_MODEL_OWNED_STATE_BYTES_PER_SESSION
+
+
 def test_dreamzero_pipeline_state_follows_runner_lifecycle_notifications() -> None:
    pipeline = _empty_pipeline()

@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0
@@ -0,0 +1,13 @@
+{
+  "_class_name": "LingBotWorldCausalDMDPipeline",
+  "_diffusers_version": "0.35.0.dev0",
+  "boundary_ratio": 0.9,
+  "image_encoder": [null, null],
+  "image_processor": [null, null],
+  "scheduler": ["diffusers", "UniPCMultistepScheduler"],
+  "text_encoder": ["transformers", "UMT5EncoderModel"],
+  "tokenizer": ["transformers", "T5TokenizerFast"],
+  "transformer": ["diffusers", "CausalLingBotWorldTransformer3DModel"],
+  "transformer_2": [null, null],
+  "vae": ["diffusers", "AutoencoderKLWan"]
+}
@@ -0,0 +1,42 @@
+{
+  "sources": {
+    "config": "https://huggingface.co/robbyant/lingbot-world-v2-14b-causal-fast-diffusers/resolve/main/transformer/config.json",
+    "shard_1_header": "https://huggingface.co/robbyant/lingbot-world-v2-14b-causal-fast-diffusers/resolve/main/transformer/diffusion_pytorch_model-00001-of-00008.safetensors",
+    "shard_8_header": "https://huggingface.co/robbyant/lingbot-world-v2-14b-causal-fast-diffusers/resolve/main/transformer/diffusion_pytorch_model-00008-of-00008.safetensors"
+  },
+  "dtype": "F32",
+  "shard_representatives": {
+    "diffusion_pytorch_model-00001-of-00008.safetensors": "blocks.0.cam_injector_layer1.bias",
+    "diffusion_pytorch_model-00002-of-00008.safetensors": "blocks.4.cam_injector_layer1.bias",
+    "diffusion_pytorch_model-00003-of-00008.safetensors": "blocks.10.cam_injector_layer1.bias",
+    "diffusion_pytorch_model-00004-of-00008.safetensors": "blocks.14.cam_scale_layer.bias",
+    "diffusion_pytorch_model-00005-of-00008.safetensors": "blocks.20.cam_injector_layer1.bias",
+    "diffusion_pytorch_model-00006-of-00008.safetensors": "blocks.25.cam_injector_layer1.bias",
+    "diffusion_pytorch_model-00007-of-00008.safetensors": "blocks.30.cam_injector_layer1.bias",
+    "diffusion_pytorch_model-00008-of-00008.safetensors": "blocks.35.cam_injector_layer1.bias"
+  },
+  "parameter_shapes": {
+    "blocks.0.cam_injector_layer1.bias": [5120],
+    "blocks.4.cam_injector_layer1.bias": [5120],
+    "blocks.10.cam_injector_layer1.bias": [5120],
+    "blocks.14.cam_scale_layer.bias": [5120],
+    "blocks.20.cam_injector_layer1.bias": [5120],
+    "blocks.25.cam_injector_layer1.bias": [5120],
+    "blocks.30.cam_injector_layer1.bias": [5120],
+    "blocks.35.cam_injector_layer1.bias": [5120],
+    "patch_embedding.weight": [5120, 36, 1, 2, 2],
+    "patch_embedding_wancamctrl.weight": [5120, 1536],
+    "c2ws_hidden_states_layer1.weight": [5120, 5120],
+    "c2ws_hidden_states_layer2.weight": [5120, 5120],
+    "blocks.0.modulation": [1, 6, 5120],
+    "blocks.0.self_attn.q.weight": [5120, 5120],
+    "blocks.0.ffn.0.weight": [13824, 5120],
+    "blocks.0.ffn.2.weight": [5120, 13824],
+    "blocks.0.cam_injector_layer1.weight": [5120, 5120],
+    "blocks.0.cam_injector_layer2.weight": [5120, 5120],
+    "blocks.0.cam_scale_layer.weight": [5120, 5120],
+    "blocks.0.cam_shift_layer.weight": [5120, 5120],
+    "head.modulation": [1, 2, 5120],
+    "head.head.weight": [64, 5120]
+  }
+}
@@ -0,0 +1,30 @@
+{
+  "_class_name": "UniPCMultistepScheduler",
+  "_diffusers_version": "0.35.0.dev0",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "disable_corrector": [],
+  "dynamic_thresholding_ratio": 0.995,
+  "final_sigmas_type": "zero",
+  "flow_shift": 5.0,
+  "lower_order_final": true,
+  "num_train_timesteps": 1000,
+  "predict_x0": true,
+  "prediction_type": "flow_prediction",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "solver_order": 2,
+  "solver_p": null,
+  "solver_type": "bh2",
+  "steps_offset": 0,
+  "thresholding": false,
+  "time_shift_type": "exponential",
+  "timestep_spacing": "linspace",
+  "trained_betas": null,
+  "use_beta_sigmas": false,
+  "use_dynamic_shifting": false,
+  "use_exponential_sigmas": false,
+  "use_flow_sigmas": true,
+  "use_karras_sigmas": false
+}
@@ -0,0 +1,112 @@
+{
+  "source": "https://huggingface.co/robbyant/lingbot-world-v2-14b-causal-fast-diffusers/resolve/main/transformer/diffusion_pytorch_model.safetensors.index.json",
+  "index_parameter_count": 1421,
+  "canonical_name_set_sha256": "5d31b7f64fd5e5e271b789b2dbe50fb7bbf146bd8b750646bdc9abb4e5b8139d",
+  "canonical_name_to_shard_sha256": "e1addc27d2b1fad6f10226a23a265ee3e3c61e74dce4d5859727bdf180a67992",
+  "canonical_serialization": "sorted lines formatted as parameter_name=shard_filename with LF separators and no trailing LF",
+  "shards": {
+    "diffusion_pytorch_model-00001-of-00008.safetensors": {
+      "parameter_count": 180,
+      "parameter_names_sha256": "81a3d4778de604e75609aae7dac3d8dcbc081811e7748267fe953bbeb375e79d"
+    },
+    "diffusion_pytorch_model-00002-of-00008.safetensors": {
+      "parameter_count": 180,
+      "parameter_names_sha256": "4e36e2596a6468f29179fa5795396f2e9a7f7d8debc912b34a6f6c2c7c7c610d"
+    },
+    "diffusion_pytorch_model-00003-of-00008.safetensors": {
+      "parameter_count": 180,
+      "parameter_names_sha256": "6f74a7b2cf7444c2daa2b3fc32bd3787589b35543c230c5c13b89f0f0f2a0998"
+    },
+    "diffusion_pytorch_model-00004-of-00008.safetensors": {
+      "parameter_count": 180,
+      "parameter_names_sha256": "ec5c967ed5271ba7834337c4ee93a73f8ed0eead4492d4920624251c65c058ac"
+    },
+    "diffusion_pytorch_model-00005-of-00008.safetensors": {
+      "parameter_count": 180,
+      "parameter_names_sha256": "ad1fdab5830dd9ce43ea007f112b4be223327b7c8197c766d9cf9219e74d619b"
+    },
+    "diffusion_pytorch_model-00006-of-00008.safetensors": {
+      "parameter_count": 180,
+      "parameter_names_sha256": "fd58f0d86be42d8afcf55d3e07d8a22f7f6ef5064da931a600c890b71b6dfbae"
+    },
+    "diffusion_pytorch_model-00007-of-00008.safetensors": {
+      "parameter_count": 180,
+      "parameter_names_sha256": "305a1714b5f4fb54d5877c593b451273ab1490d90fd0896a299d96e140295047"
+    },
+    "diffusion_pytorch_model-00008-of-00008.safetensors": {
+      "parameter_count": 161,
+      "parameter_names_sha256": "4c18d94d96168682eb212f93e3cdf7313928776d8d9e6e4de0255fe899decb25"
+    }
+  },
+  "top_level_modules": [
+    "blocks",
+    "c2ws_hidden_states_layer1",
+    "c2ws_hidden_states_layer2",
+    "head",
+    "patch_embedding",
+    "patch_embedding_wancamctrl",
+    "text_embedding",
+    "time_embedding",
+    "time_projection"
+  ],
+  "non_block_parameters": [
+    "c2ws_hidden_states_layer1.bias",
+    "c2ws_hidden_states_layer1.weight",
+    "c2ws_hidden_states_layer2.bias",
+    "c2ws_hidden_states_layer2.weight",
+    "head.head.bias",
+    "head.head.weight",
+    "head.modulation",
+    "patch_embedding.bias",
+    "patch_embedding.weight",
+    "patch_embedding_wancamctrl.bias",
+    "patch_embedding_wancamctrl.weight",
+    "text_embedding.0.bias",
+    "text_embedding.0.weight",
+    "text_embedding.2.bias",
+    "text_embedding.2.weight",
+    "time_embedding.0.bias",
+    "time_embedding.0.weight",
+    "time_embedding.2.bias",
+    "time_embedding.2.weight",
+    "time_projection.1.bias",
+    "time_projection.1.weight"
+  ],
+  "block_parameter_suffixes": [
+    "cam_injector_layer1.bias",
+    "cam_injector_layer1.weight",
+    "cam_injector_layer2.bias",
+    "cam_injector_layer2.weight",
+    "cam_scale_layer.bias",
+    "cam_scale_layer.weight",
+    "cam_shift_layer.bias",
+    "cam_shift_layer.weight",
+    "cross_attn.k.bias",
+    "cross_attn.k.weight",
+    "cross_attn.norm_k.weight",
+    "cross_attn.norm_q.weight",
+    "cross_attn.o.bias",
+    "cross_attn.o.weight",
+    "cross_attn.q.bias",
+    "cross_attn.q.weight",
+    "cross_attn.v.bias",
+    "cross_attn.v.weight",
+    "ffn.0.bias",
+    "ffn.0.weight",
+    "ffn.2.bias",
+    "ffn.2.weight",
+    "modulation",
+    "norm3.bias",
+    "norm3.weight",
+    "self_attn.k.bias",
+    "self_attn.k.weight",
+    "self_attn.norm_k.weight",
+    "self_attn.norm_q.weight",
+    "self_attn.o.bias",
+    "self_attn.o.weight",
+    "self_attn.q.bias",
+    "self_attn.q.weight",
+    "self_attn.v.bias",
+    "self_attn.v.weight"
+  ]
+}
@@ -0,0 +1,294 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import asyncio
+
+import pytest
+import torch
+
+from vllm_omni.diffusion.models.lingbot_world.actions import (
+    LINGBOT_CAMERA_ACTION_SCHEMA,
+    LINGBOT_CAMERA_TRAJECTORY_SCHEMA,
+    LingBotCameraControlReducer,
+    integrate_lingbot_camera_actions,
+    parse_lingbot_camera_action_frames,
+)
+from vllm_omni.experimental.ar_diffusion.session import (
+    ARDiffusionSession,
+    ARDiffusionSessionEvent,
+)
+from vllm_omni.experimental.ar_diffusion.tick_protocol import (
+    ARDiffusionChunkMetadata,
+    ARDiffusionControlInput,
+    ARDiffusionTickRequest,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
+
+
+def _action_control(mode: str, **data) -> ARDiffusionControlInput:
+    return ARDiffusionControlInput(
+        track="camera",
+        schema=LINGBOT_CAMERA_ACTION_SCHEMA,
+        data={"mode": mode, **data},
+    )
+
+
+def _prepared_frames(prepared) -> tuple[tuple[str, ...], ...]:
+    assert len(prepared.controls) == 1
+    return prepared.controls[0].data["frames"]
+
+
+def test_state_controls_hold_release_and_preserve_short_pulse() -> None:
+    reducer = LingBotCameraControlReducer()
+    pressed = reducer.prepare(
+        current_controls={},
+        events=(
+            ARDiffusionSessionEvent(
+                event_id=1,
+                controls=(
+                    _action_control(
+                        "state",
+                        transitions=[{"actions": ["W"], "client_ts_ms": 10}],
+                    ),
+                ),
+            ),
+        ),
+        chunk_index=0,
+    )
+    assert _prepared_frames(pressed) == (("w",), ("w",), ("w",))
+    assert pressed.controls[0].to_dict()["data"]["frames"] == [["w"], ["w"], ["w"]]
+    reducer.commit(pressed)
+
+    held = reducer.prepare(
+        current_controls={"camera": pressed.controls[0]},
+        events=(),
+        chunk_index=1,
+    )
+    assert _prepared_frames(held) == (("w",), ("w",), ("w",))
+    reducer.commit(held)
+
+    pulse = reducer.prepare(
+        current_controls={"camera": held.controls[0]},
+        events=(
+            ARDiffusionSessionEvent(
+                event_id=2,
+                controls=(
+                    _action_control(
+                        "state",
+                        transitions=[
+                            {"actions": ["j"], "client_ts_ms": 20},
+                            {"actions": [], "client_ts_ms": 21},
+                        ],
+                    ),
+                ),
+            ),
+        ),
+        chunk_index=2,
+    )
+    assert _prepared_frames(pulse) == (("j",), (), ())
+    reducer.commit(pulse)
+
+    released = reducer.prepare(
+        current_controls={"camera": pulse.controls[0]},
+        events=(),
+        chunk_index=3,
+    )
+    assert _prepared_frames(released) == ((), (), ())
+
+
+def test_script_is_transactional_consumed_once_and_neutral_padded() -> None:
+    reducer = LingBotCameraControlReducer()
+    event = ARDiffusionSessionEvent(
+        event_id=1,
+        controls=(
+            _action_control(
+                "script",
+                frames=[["w"], ["d"], ["l"], ["i"]],
+            ),
+        ),
+    )
+    first_attempt = reducer.prepare(
+        current_controls={},
+        events=(event,),
+        chunk_index=0,
+    )
+    assert _prepared_frames(first_attempt) == (("w",), ("d",), ("l",))
+
+    # An uncommitted failed attempt must not consume the reducer's script.
+    second_attempt = reducer.prepare(
+        current_controls={},
+        events=(event,),
+        chunk_index=0,
+    )
+    assert _prepared_frames(second_attempt) == (("w",), ("d",), ("l",))
+    reducer.commit(second_attempt)
+
+    tail = reducer.prepare(
+        current_controls={"camera": second_attempt.controls[0]},
+        events=(),
+        chunk_index=1,
+    )
+    assert _prepared_frames(tail) == (("i",), (), ())
+    reducer.commit(tail)
+    exhausted = reducer.prepare(
+        current_controls={"camera": tail.controls[0]},
+        events=(),
+        chunk_index=2,
+    )
+    assert _prepared_frames(exhausted) == ((), (), ())
+
+
+def test_trajectory_control_remains_an_expert_override() -> None:
+    reducer = LingBotCameraControlReducer()
+    trajectory = ARDiffusionControlInput(
+        track="camera",
+        schema=LINGBOT_CAMERA_TRAJECTORY_SCHEMA,
+        data={"poses": [[[1.0]]], "intrinsics": [[1.0]]},
+    )
+    prepared = reducer.prepare(
+        current_controls={},
+        events=(
+            ARDiffusionSessionEvent(
+                event_id=1,
+                controls=(trajectory,),
+            ),
+        ),
+        chunk_index=0,
+    )
+    assert prepared.controls == (trajectory,)
+
+
+def test_action_integrator_matches_lingbot_motion_and_calibration() -> None:
+    trajectory, pitch = integrate_lingbot_camera_actions(
+        [["w"], ["d"], ["l"]],
+        width=832,
+        height=480,
+    )
+
+    assert pitch == 0.0
+    torch.testing.assert_close(
+        trajectory.poses[0, :3, 3],
+        torch.tensor([0.0, 0.0, 0.05], dtype=torch.float64),
+        atol=1e-6,
+        rtol=0,
+    )
+    torch.testing.assert_close(
+        trajectory.poses[1, :3, 3],
+        torch.tensor([0.05, 0.0, 0.05], dtype=torch.float64),
+        atol=1e-6,
+        rtol=0,
+    )
+    expected_yaw = torch.tensor(
+        [
+            [0.9945219, 0.0, 0.10452846],
+            [0.0, 1.0, 0.0],
+            [-0.10452846, 0.0, 0.9945219],
+        ],
+        dtype=torch.float64,
+    )
+    torch.testing.assert_close(
+        trajectory.poses[2, :3, :3],
+        expected_yaw,
+        atol=1e-6,
+        rtol=0,
+    )
+    torch.testing.assert_close(
+        trajectory.intrinsics[0],
+        torch.tensor([500.0, 500.0, 416.0, 240.0]),
+    )
+
+    resized, _ = integrate_lingbot_camera_actions(
+        [[]],
+        width=416,
+        height=240,
+    )
+    scaled = resized.intrinsics[0] * torch.tensor([416 / 832, 240 / 480, 416 / 832, 240 / 480])
+    torch.testing.assert_close(
+        scaled,
+        torch.tensor([500.0, 500.0, 208.0, 120.0]),
+    )
+
+
+@pytest.mark.parametrize(
+    ("data", "message"),
+    [
+        ({"mode": "frames", "frames": [["x"], [], []]}, "W/A/S/D/I/J/K/L"),
+        ({"mode": "frames", "frames": [["w"]]}, "exactly one action list"),
+        ({"mode": "state", "frames": [[], [], []]}, "mode='frames'"),
+    ],
+)
+def test_model_tick_action_payload_validation(data, message: str) -> None:
+    with pytest.raises(ValueError, match=message):
+        parse_lingbot_camera_action_frames(data, expected_frames=3)
+
+
+class _TickConsumer:
+    def __init__(self) -> None:
+        self.ticks: list[ARDiffusionTickRequest] = []
+
+    async def execute_tick(self, tick: ARDiffusionTickRequest) -> ARDiffusionChunkMetadata:
+        self.ticks.append(tick)
+        return ARDiffusionChunkMetadata.from_tick(tick)
+
+    def chunk_metadata(self, output: ARDiffusionChunkMetadata) -> ARDiffusionChunkMetadata:
+        return output
+
+
+class _Lifecycle:
+    async def reset_session(self, session_id: str) -> None:
+        del session_id
+
+    async def close_session(self, session_id: str) -> None:
+        del session_id
+
+
+def test_session_reducer_keeps_prompt_and_actions_atomic_and_resets() -> None:
+    async def exercise() -> None:
+        consumer = _TickConsumer()
+        reducer = LingBotCameraControlReducer()
+        session = ARDiffusionSession(
+            "world-actions",
+            tick_consumer=consumer,
+            lifecycle=_Lifecycle(),
+            max_pending_events=8,
+            request_id_factory=lambda session_id, chunk_index: f"{session_id}-{chunk_index}",
+            control_reducer=reducer,
+        )
+        await session.accept_event(
+            ARDiffusionSessionEvent(
+                event_id=1,
+                prompt="turn toward the window",
+                controls=(
+                    _action_control(
+                        "state",
+                        transitions=[{"actions": ["d"]}],
+                    ),
+                ),
+            )
+        )
+        await session.next_chunk()
+
+        tick = consumer.ticks[0]
+        assert tick.prompt == "turn toward the window"
+        assert tick.applied_event_ids == (1,)
+        assert tick.controls[0].data["frames"] == (("d",), ("d",), ("d",))
+
+        await session.reset()
+        await session.accept_event(
+            ARDiffusionSessionEvent(
+                event_id=2,
+                controls=(
+                    _action_control(
+                        "state",
+                        transitions=[{"actions": []}],
+                    ),
+                ),
+            )
+        )
+        await session.next_chunk()
+        assert consumer.ticks[1].chunk_index == 0
+        assert consumer.ticks[1].controls[0].data["frames"] == ((), (), ())
+
+    asyncio.run(exercise())
@@ -0,0 +1,543 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import importlib.util
+import math
+import sys
+from pathlib import Path
+from types import ModuleType
+
+import pytest
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
+
+_MODULE_PATH = Path(__file__).parents[4] / "vllm_omni/diffusion/models/lingbot_world/transformer.py"
+_MODULE_NAME = "_lingbot_world_attention_under_test"
+_MISSING_MODULE = object()
+_STUBBED_MODULE_NAMES = (
+    "vllm",
+    "vllm.distributed",
+    "vllm.model_executor",
+    "vllm.model_executor.layers",
+    "vllm.model_executor.layers.conv",
+    "vllm.model_executor.layers.linear",
+    "vllm.model_executor.model_loader",
+    "vllm.model_executor.model_loader.weight_utils",
+    "vllm.model_executor.utils",
+    "vllm_omni",
+    "vllm_omni.diffusion",
+    "vllm_omni.diffusion.attention",
+    "vllm_omni.diffusion.attention.layer",
+    "vllm_omni.diffusion.layers",
+    "vllm_omni.diffusion.layers.norm",
+    "vllm_omni.diffusion.layers.rope",
+    "vllm_omni.experimental",
+    "vllm_omni.experimental.ar_diffusion",
+    "vllm_omni.experimental.ar_diffusion.kv_cache",
+    "vllm_omni.experimental.ar_diffusion.kv_cache.paged_attention",
+)
+
+
+@pytest.fixture(autouse=True)
+def _inference_context():
+    with torch.inference_mode():
+        yield
+
+
+def _install_vllm_stubs() -> None:
+    for name in _STUBBED_MODULE_NAMES:
+        sys.modules[name] = ModuleType(name)
+
+    distributed = sys.modules["vllm.distributed"]
+    distributed.get_tensor_model_parallel_rank = lambda: 0
+    distributed.get_tensor_model_parallel_world_size = lambda: 1
+    distributed.tensor_model_parallel_all_reduce = lambda value: value
+
+    def set_weight_attrs(weight: torch.Tensor, attrs: dict) -> None:
+        for name, value in attrs.items():
+            setattr(weight, name, value)
+
+    sys.modules["vllm.model_executor.utils"].set_weight_attrs = set_weight_attrs
+    sys.modules["vllm.model_executor.model_loader.weight_utils"].default_weight_loader = lambda param, loaded_weight: (
+        param.data.copy_(loaded_weight)
+    )
+
+    class _Linear(nn.Module):
+        def __init__(
+            self,
+            input_size: int,
+            output_size: int,
+            *,
+            bias: bool = True,
+            return_bias: bool = False,
+            **kwargs,
+        ) -> None:
+            super().__init__()
+            del kwargs
+            self.return_bias = return_bias
+            self.weight = nn.Parameter(torch.empty(output_size, input_size))
+            self.bias = nn.Parameter(torch.empty(output_size)) if bias else None
+            nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+            if self.bias is not None:
+                nn.init.zeros_(self.bias)
+            self.calls = 0
+
+        def forward(self, value: torch.Tensor):
+            self.calls += 1
+            output = F.linear(value, self.weight, self.bias)
+            return (output, self.bias) if self.return_bias else output
+
+    linear = sys.modules["vllm.model_executor.layers.linear"]
+    linear.ColumnParallelLinear = _Linear
+    linear.RowParallelLinear = _Linear
+
+    class _QKVLinear(nn.Module):
+        def __init__(
+            self,
+            hidden_size: int,
+            head_size: int,
+            total_num_heads: int,
+            total_num_kv_heads: int | None = None,
+            *,
+            bias: bool = True,
+            **kwargs,
+        ) -> None:
+            super().__init__()
+            del kwargs
+            self.num_heads = total_num_heads
+            self.num_kv_heads = total_num_kv_heads or total_num_heads
+            output_size = (self.num_heads + 2 * self.num_kv_heads) * head_size
+            self.weight = nn.Parameter(torch.empty(output_size, hidden_size))
+            self.bias = nn.Parameter(torch.empty(output_size)) if bias else None
+            nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+            if self.bias is not None:
+                nn.init.zeros_(self.bias)
+            self.calls = 0
+
+            def load_shard(param: torch.Tensor, loaded_weight: torch.Tensor, shard_id: str) -> None:
+                shard_index = {"q": 0, "k": 1, "v": 2}[shard_id]
+                shard_size = param.shape[0] // 3
+                param.data.narrow(0, shard_index * shard_size, shard_size).copy_(loaded_weight)
+
+            self.weight.weight_loader = load_shard
+            if self.bias is not None:
+                self.bias.weight_loader = load_shard
+
+        def forward(self, value: torch.Tensor):
+            self.calls += 1
+            return F.linear(value, self.weight, self.bias), None
+
+    linear.QKVParallelLinear = _QKVLinear
+
+    class _Conv3dLayer(nn.Conv3d):
+        def __init__(self, in_channels: int, out_channels: int, kernel_size, stride, **kwargs) -> None:
+            super().__init__(in_channels, out_channels, kernel_size=kernel_size, stride=stride, **kwargs)
+
+    sys.modules["vllm.model_executor.layers.conv"].Conv3dLayer = _Conv3dLayer
+
+    class _LayerNorm(nn.LayerNorm):
+        def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine: bool = True) -> None:
+            super().__init__(dim, eps=eps, elementwise_affine=elementwise_affine)
+
+    sys.modules["vllm_omni.diffusion.layers.norm"].LayerNorm = _LayerNorm
+
+    class _Attention(nn.Module):
+        def __init__(self, *args, softmax_scale: float, **kwargs) -> None:
+            super().__init__()
+            del args
+            self.softmax_scale = softmax_scale
+            self.causal = kwargs["causal"]
+            self.calls: list[tuple[torch.Tensor, torch.Tensor, torch.Tensor]] = []
+
+        def forward(
+            self,
+            query: torch.Tensor,
+            key: torch.Tensor,
+            value: torch.Tensor,
+            attn_metadata=None,
+        ) -> torch.Tensor:
+            del attn_metadata
+            self.calls.append((query.detach().clone(), key.detach().clone(), value.detach().clone()))
+            scores = torch.einsum("bqhd,bkhd->bhqk", query, key) * self.softmax_scale
+            weights = scores.softmax(dim=-1)
+            return torch.einsum("bhqk,bkhd->bqhd", weights, value)
+
+    sys.modules["vllm_omni.diffusion.attention.layer"].Attention = _Attention
+
+    class _RotaryEmbeddingWan(nn.Module):
+        def __init__(self, *args, **kwargs) -> None:
+            super().__init__()
+            del args, kwargs
+            self.calls: list[tuple[torch.Tensor, torch.Tensor]] = []
+
+        def forward(self, value: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
+            self.calls.append((cos.detach().clone(), sin.detach().clone()))
+            return value
+
+    sys.modules["vllm_omni.diffusion.layers.rope"].RotaryEmbeddingWan = _RotaryEmbeddingWan
+
+    paged_attention = sys.modules["vllm_omni.experimental.ar_diffusion.kv_cache.paged_attention"]
+
+    class _PagedLayerContext:
+        pass
+
+    class _PagedLayerInputs:
+        pass
+
+    paged_attention.ARDiffusionPagedLayerContext = _PagedLayerContext
+    paged_attention.ARDiffusionPagedLayerInputs = _PagedLayerInputs
+    paged_attention.ar_diffusion_paged_attention = lambda *args, **kwargs: (_ for _ in ()).throw(
+        AssertionError("CUDA paged attention was not expected in this CPU test")
+    )
+    paged_attention.paged_write_attn = lambda *args, **kwargs: (_ for _ in ()).throw(
+        AssertionError("paged path was not expected in this direct-cache test")
+    )
+
+
+def _load_module():
+    assert _MODULE_PATH.exists(), "LingBot attention module has not been implemented"
+    module_names = (*_STUBBED_MODULE_NAMES, _MODULE_NAME)
+    previous = {name: sys.modules.get(name, _MISSING_MODULE) for name in module_names}
+    try:
+        _install_vllm_stubs()
+        spec = importlib.util.spec_from_file_location(_MODULE_NAME, _MODULE_PATH)
+        assert spec is not None and spec.loader is not None
+        module = importlib.util.module_from_spec(spec)
+        sys.modules[spec.name] = module
+        spec.loader.exec_module(module)
+        return module
+    finally:
+        for name, original in previous.items():
+            if original is _MISSING_MODULE:
+                sys.modules.pop(name, None)
+            else:
+                sys.modules[name] = original
+
+
+def test_load_module_restores_process_module_state() -> None:
+    module_names = (*_STUBBED_MODULE_NAMES, _MODULE_NAME)
+    previous = {name: sys.modules.get(name, _MISSING_MODULE) for name in module_names}
+
+    _load_module()
+
+    for name, original in previous.items():
+        assert sys.modules.get(name, _MISSING_MODULE) is original
+
+
+def test_allocate_lingbot_cache_creates_request_local_layer_storage() -> None:
+    module = _load_module()
+
+    cache = module.allocate_lingbot_cache(
+        batch_size=2,
+        num_layers=3,
+        max_tokens=7,
+        num_local_heads=2,
+        head_dim=4,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    assert len(cache.self_attention) == 3
+    assert len(cache.cross_attention) == 3
+    assert cache.cross_attention == [None, None, None]
+    assert cache.self_attention[0].key.shape == (2, 7, 2, 4)
+    assert cache.self_attention[0].value.shape == (2, 7, 2, 4)
+    assert cache.self_attention[0].key.dtype == torch.float32
+    assert cache.self_attention[0].end == 0
+    assert cache.self_attention[0].key.data_ptr() != cache.self_attention[1].key.data_ptr()
+
+
+def _allocate_single_layer(module, *, max_tokens: int):
+    return module.allocate_lingbot_cache(
+        batch_size=1,
+        num_layers=1,
+        max_tokens=max_tokens,
+        num_local_heads=1,
+        head_dim=2,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+
+def _set_identity_attention(attention: nn.Module) -> None:
+    qkv = getattr(attention, "qkv", None)
+    if qkv is not None:
+        projections = (qkv, attention.o)
+    else:
+        projections = (attention.q, attention.k, attention.v, attention.o)
+    for projection in projections:
+        with torch.no_grad():
+            identity = torch.eye(2)
+            projection.weight.copy_(torch.cat((identity, identity, identity)) if projection is qkv else identity)
+            if projection.bias is not None:
+                projection.bias.zero_()
+    attention.norm_q = nn.Identity()
+    attention.norm_k = nn.Identity()
+
+
+def _tokens(*values: float) -> torch.Tensor:
+    return torch.tensor([[[value, 0.0] for value in values]])
+
+
+def _record_inputs(module: nn.Module) -> list[tuple]:
+    records: list[tuple] = []
+    module.register_forward_pre_hook(lambda _module, args: records.append(args))
+    return records
+
+
+def _record_outputs(module: nn.Module) -> list[torch.Tensor]:
+    records: list[torch.Tensor] = []
+
+    def record(_module, _args, output) -> None:
+        records.append(output[0] if isinstance(output, tuple) else output)
+
+    module.register_forward_hook(record)
+    return records
+
+
+def _cache_snapshot(cache) -> tuple[torch.Tensor, torch.Tensor, tuple[int, int, int | None, int]]:
+    return (
+        cache.key.detach().view(torch.uint8).clone(),
+        cache.value.detach().view(torch.uint8).clone(),
+        (cache.end, cache.absolute_end, cache.last_start, cache.sink_end),
+    )
+
+
+def _assert_cache_unchanged(cache, snapshot) -> None:
+    key, value, metadata = snapshot
+    assert torch.equal(cache.key.detach().view(torch.uint8), key)
+    assert torch.equal(cache.value.detach().view(torch.uint8), value)
+    assert (cache.end, cache.absolute_end, cache.last_start, cache.sink_end) == metadata
+
+
+def test_self_attention_repeated_offset_overwrites_then_later_offset_appends() -> None:
+    module = _load_module()
+    attention = module.LingBotSelfAttention(dim=2, num_heads=1)
+    _set_identity_attention(attention)
+    cache = _allocate_single_layer(module, max_tokens=6).self_attention[0]
+
+    attention(_tokens(1, 2), cache=cache, current_start=0, sink_tokens=0)
+    attention(_tokens(10, 20), cache=cache, current_start=0, sink_tokens=0)
+
+    assert cache.end == 2
+    assert cache.absolute_end == 2
+    torch.testing.assert_close(cache.key[0, : cache.end, 0, 0], torch.tensor([10.0, 20.0]))
+
+    attention(_tokens(3, 4), cache=cache, current_start=2, sink_tokens=0)
+
+    assert cache.end == 4
+    assert cache.absolute_end == 4
+    torch.testing.assert_close(cache.key[0, : cache.end, 0, 0], torch.tensor([10.0, 20.0, 3.0, 4.0]))
+
+
+def test_self_attention_uses_one_fused_qkv_projection() -> None:
+    module = _load_module()
+    attention = module.LingBotSelfAttention(dim=2, num_heads=1)
+    cache = _allocate_single_layer(module, max_tokens=2).self_attention[0]
+
+    attention(_tokens(1, 2), cache=cache, current_start=0, sink_tokens=0)
+
+    assert attention.qkv.calls == 1
+
+
+def test_self_attention_routes_paged_inputs_through_engine_owned_cache() -> None:
+    module = _load_module()
+    attention = module.LingBotSelfAttention(dim=2, num_heads=1)
+    _set_identity_attention(attention)
+    paged_inputs = module.ARDiffusionPagedLayerInputs()
+    calls = []
+
+    def paged_write(inputs, query, key, value, k_act, v_act, scale):
+        calls.append((inputs, query.clone(), key.clone(), value.clone(), k_act, v_act, scale))
+        return query
+
+    module.paged_write_attn = paged_write
+    output = attention(
+        _tokens(1, 2),
+        cache=paged_inputs,
+        current_start=0,
+        sink_tokens=0,
+        update_cache=False,
+    )
+
+    assert output.shape == (1, 2, 2)
+    assert len(calls) == 1
+    assert calls[0][0] is paged_inputs
+    assert calls[0][4:6] == (None, None)
+    assert attention.attn.calls == []
+    assert not any(hasattr(attention, name) for name in ("q", "k", "v"))
+
+
+def test_self_attention_is_chunk_causal_without_masking_inside_current_chunk() -> None:
+    module = _load_module()
+    attention = module.LingBotSelfAttention(dim=2, num_heads=1)
+    _set_identity_attention(attention)
+    cache = _allocate_single_layer(module, max_tokens=8).self_attention[0]
+    attention_inputs = _record_inputs(attention.attn)
+
+    attention(_tokens(1, 2), cache=cache, current_start=0, sink_tokens=0)
+    first_keys = attention_inputs[-1][1].detach().clone()
+    attention(_tokens(3, 4), cache=cache, current_start=2, sink_tokens=0)
+    second_keys = attention_inputs[-1][1].detach().clone()
+
+    assert attention.attn.causal is False
+    torch.testing.assert_close(first_keys[0, :, 0, 0], torch.tensor([1.0, 2.0]))
+    torch.testing.assert_close(second_keys[0, :, 0, 0], torch.tensor([1.0, 2.0, 3.0, 4.0]))
+
+
+def test_self_attention_retains_sink_and_latest_local_history_after_eviction() -> None:
+    module = _load_module()
+    attention = module.LingBotSelfAttention(dim=2, num_heads=1)
+    _set_identity_attention(attention)
+    cache = _allocate_single_layer(module, max_tokens=4).self_attention[0]
+    attention_inputs = _record_inputs(attention.attn)
+
+    attention(_tokens(1, 2), cache=cache, current_start=0, sink_tokens=1)
+    attention(_tokens(3, 4), cache=cache, current_start=2, sink_tokens=1)
+    attention(_tokens(5, 6), cache=cache, current_start=4, sink_tokens=1)
+
+    visible_keys = attention_inputs[-1][1].detach().clone()
+    assert cache.end == 4
+    assert cache.absolute_end == 6
+    torch.testing.assert_close(visible_keys[0, :, 0, 0], torch.tensor([1.0, 4.0, 5.0, 6.0]))
+    torch.testing.assert_close(cache.key[0, : cache.end, 0, 0], torch.tensor([1.0, 4.0, 5.0, 6.0]))
+
+
+def test_cross_attention_projects_encoder_kv_once_per_request() -> None:
+    module = _load_module()
+    attention = module.LingBotCrossAttention(dim=2, num_heads=1)
+    _set_identity_attention(attention)
+    key_outputs = _record_outputs(attention.k)
+    value_outputs = _record_outputs(attention.v)
+    attention_inputs = _record_inputs(attention.attn)
+
+    output, cache = attention(_tokens(1), _tokens(2, 3), cache=None)
+    second_output, reused_cache = attention(_tokens(4), _tokens(20, 30), cache=cache)
+
+    assert output.shape == second_output.shape == (1, 1, 2)
+    assert reused_cache is cache
+    assert len(key_outputs) == 1
+    assert len(value_outputs) == 1
+    assert cache.end == 2
+    torch.testing.assert_close(cache.key[0, :, 0, 0], torch.tensor([2.0, 3.0]))
+    torch.testing.assert_close(attention_inputs[-1][1], cache.key)
+
+
+def test_self_attention_cache_is_isolated_between_requests() -> None:
+    module = _load_module()
+    attention = module.LingBotSelfAttention(dim=2, num_heads=1)
+    _set_identity_attention(attention)
+    first = _allocate_single_layer(module, max_tokens=4).self_attention[0]
+    second = _allocate_single_layer(module, max_tokens=4).self_attention[0]
+
+    attention(_tokens(1, 2), cache=first, current_start=0, sink_tokens=0)
+
+    assert first.end == 2
+    assert second.end == 0
+    assert torch.count_nonzero(second.key) == 0
+    attention(_tokens(8), cache=second, current_start=0, sink_tokens=0)
+    torch.testing.assert_close(first.key[0, : first.end, 0, 0], torch.tensor([1.0, 2.0]))
+    torch.testing.assert_close(second.key[0, : second.end, 0, 0], torch.tensor([8.0]))
+
+
+def test_cross_attention_keeps_checkpoint_qkvo_and_norm_parameter_names() -> None:
+    module = _load_module()
+    attention = module.LingBotCrossAttention(dim=4, num_heads=2)
+
+    names = set(attention.state_dict())
+
+    assert {
+        "q.weight",
+        "k.weight",
+        "v.weight",
+        "o.weight",
+        "norm_q.weight",
+        "norm_k.weight",
+    } <= names
+    assert callable(attention.norm_q.weight.weight_loader)
+
+
+def test_tp_world_size_one_attention_output_shapes() -> None:
+    module = _load_module()
+    self_attention = module.LingBotSelfAttention(dim=4, num_heads=2)
+    cross_attention = module.LingBotCrossAttention(dim=4, num_heads=2)
+    cache = module.allocate_lingbot_cache(
+        batch_size=2,
+        num_layers=1,
+        max_tokens=5,
+        num_local_heads=2,
+        head_dim=2,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+    hidden_states = torch.randn(2, 3, 4)
+    encoder_hidden_states = torch.randn(2, 5, 4)
+
+    self_output = self_attention(hidden_states, cache=cache.self_attention[0], current_start=0, sink_tokens=0)
+    cross_output, cache.cross_attention[0] = cross_attention(
+        hidden_states,
+        encoder_hidden_states,
+        cache=cache.cross_attention[0],
+    )
+
+    assert self_output.shape == hidden_states.shape
+    assert cross_output.shape == hidden_states.shape
+    assert cache.cross_attention[0] is not None
+
+
+def test_self_attention_applies_rotary_embedding_to_current_query_and_key() -> None:
+    module = _load_module()
+    attention = module.LingBotSelfAttention(dim=2, num_heads=1)
+    cache = _allocate_single_layer(module, max_tokens=2).self_attention[0]
+    cos = torch.ones(2, 1)
+    sin = torch.zeros(2, 1)
+    rotary_inputs = _record_inputs(attention.rotary_embedding)
+
+    attention(_tokens(1, 2), cache=cache, current_start=0, rotary_emb=(cos, sin), sink_tokens=0)
+
+    assert len(rotary_inputs) == 2
+    torch.testing.assert_close(rotary_inputs[0][1], cos)
+    torch.testing.assert_close(rotary_inputs[1][2], sin)
+
+
+def test_attention_block_owns_checkpoint_parent_namespaces() -> None:
+    module = _load_module()
+    block = module.LingBotAttentionBlock(dim=4, num_heads=2, prefix="blocks.0")
+
+    names = set(block.state_dict())
+    expected = {
+        *(f"self_attn.{child}.weight" for child in ("qkv", "o", "norm_q", "norm_k")),
+        *(f"cross_attn.{child}.weight" for child in ("q", "k", "v", "o", "norm_q", "norm_k")),
+    }
+
+    assert expected <= names
+
+
+def test_cross_attention_caches_are_isolated_between_requests() -> None:
+    module = _load_module()
+    attention = module.LingBotCrossAttention(dim=2, num_heads=1)
+
+    _, first = attention(_tokens(1), _tokens(2, 3), cache=None)
+    _, second = attention(_tokens(1), _tokens(8, 9), cache=None)
+
+    assert first.key.untyped_storage().data_ptr() != second.key.untyped_storage().data_ptr()
+    assert first.value.untyped_storage().data_ptr() != second.value.untyped_storage().data_ptr()
+    second_key = second.key.clone()
+    first.key.zero_()
+    torch.testing.assert_close(second.key, second_key)
+
+
+def test_tp_rmsnorm_weight_loader_selects_rank_shard(monkeypatch: pytest.MonkeyPatch) -> None:
+    module = _load_module()
+    monkeypatch.setattr(module, "get_tensor_model_parallel_world_size", lambda: 2)
+    monkeypatch.setattr(module, "get_tensor_model_parallel_rank", lambda: 1)
+    norm = module._LingBotRMSNorm(2, eps=1e-6)
+
+    norm.weight.weight_loader(norm.weight, torch.tensor([10.0, 20.0, 30.0, 40.0]))
+
+    torch.testing.assert_close(norm.weight, torch.tensor([30.0, 40.0]))
@@ -0,0 +1,525 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import importlib.util
+import math
+import subprocess
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+
+_MODULE_PATH = Path(__file__).parents[4] / "vllm_omni/diffusion/models/lingbot_world/camera.py"
+_SPEC = importlib.util.spec_from_file_location("_lingbot_world_camera_under_test", _MODULE_PATH)
+assert _SPEC is not None and _SPEC.loader is not None
+_CAMERA_MODULE = importlib.util.module_from_spec(_SPEC)
+sys.modules[_SPEC.name] = _CAMERA_MODULE
+_SPEC.loader.exec_module(_CAMERA_MODULE)
+
+CameraTrajectory = _CAMERA_MODULE.CameraTrajectory
+build_plucker_embedding = _CAMERA_MODULE.build_plucker_embedding
+interpolate_camera_trajectory = _CAMERA_MODULE.interpolate_camera_trajectory
+load_camera_trajectory = _CAMERA_MODULE.load_camera_trajectory
+resolve_trusted_action_directory = _CAMERA_MODULE.resolve_trusted_action_directory
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
+
+
+def _identity_poses(num_frames: int) -> np.ndarray:
+    return np.repeat(np.eye(4, dtype=np.float64)[None], num_frames, axis=0)
+
+
+def _write_trajectory(
+    directory: Path,
+    poses: np.ndarray,
+    intrinsics: np.ndarray,
+) -> None:
+    np.save(directory / "poses.npy", poses)
+    np.save(directory / "intrinsics.npy", intrinsics)
+
+
+def _trusted(directory: Path):
+    return resolve_trusted_action_directory(".", directory)
+
+
+def _trajectory(
+    poses: torch.Tensor,
+    intrinsics: torch.Tensor | None = None,
+) -> CameraTrajectory:
+    if intrinsics is None:
+        intrinsics = torch.tensor([[832.0 / 3.0, 160.0, 416.0, 240.0]]).repeat(poses.shape[0], 1)
+    return CameraTrajectory(poses=poses, intrinsics=intrinsics)
+
+
+@pytest.mark.parametrize("present_file", [None, "poses.npy"])
+def test_load_camera_trajectory_rejects_missing_files(tmp_path: Path, present_file: str | None) -> None:
+    if present_file == "poses.npy":
+        np.save(tmp_path / present_file, _identity_poses(1))
+
+    missing_file = "poses.npy" if present_file is None else "intrinsics.npy"
+    with pytest.raises(FileNotFoundError, match=missing_file):
+        load_camera_trajectory(_trusted(tmp_path))
+
+
+@pytest.mark.parametrize(
+    ("poses", "intrinsics", "message"),
+    [
+        (np.zeros((2, 3, 4)), np.zeros((2, 4)), "poses.npy"),
+        (_identity_poses(2), np.zeros((2, 3)), "intrinsics.npy"),
+        (_identity_poses(0), np.zeros((0, 4)), "1 and 4096 source frames"),
+        (_identity_poses(2), np.zeros((1, 4)), "same number of frames"),
+    ],
+)
+def test_load_camera_trajectory_rejects_invalid_shapes(
+    tmp_path: Path,
+    poses: np.ndarray,
+    intrinsics: np.ndarray,
+    message: str,
+) -> None:
+    _write_trajectory(tmp_path, poses, intrinsics)
+
+    with pytest.raises(ValueError, match=message):
+        load_camera_trajectory(_trusted(tmp_path))
+
+
+@pytest.mark.parametrize("array_name", ["poses", "intrinsics"])
+def test_load_camera_trajectory_rejects_non_finite_values(tmp_path: Path, array_name: str) -> None:
+    poses = _identity_poses(1)
+    intrinsics = np.ones((1, 4), dtype=np.float64)
+    if array_name == "poses":
+        poses[0, 0, 0] = np.nan
+    else:
+        intrinsics[0, 0] = np.inf
+    _write_trajectory(tmp_path, poses, intrinsics)
+
+    with pytest.raises(ValueError, match="finite"):
+        load_camera_trajectory(_trusted(tmp_path))
+
+
+def test_load_camera_trajectory_preserves_raw_c2w_poses(tmp_path: Path) -> None:
+    first_c2w = np.array(
+        [
+            [0.0, -1.0, 0.0, 2.0],
+            [1.0, 0.0, 0.0, 3.0],
+            [0.0, 0.0, 1.0, 4.0],
+            [0.0, 0.0, 0.0, 1.0],
+        ]
+    )
+    second_c2w = np.array(
+        [
+            [0.0, 0.0, 1.0, 5.0],
+            [0.0, 1.0, 0.0, 4.0],
+            [-1.0, 0.0, 0.0, 6.0],
+            [0.0, 0.0, 0.0, 1.0],
+        ]
+    )
+    poses = np.stack((first_c2w, second_c2w))
+    intrinsics = np.array([[100.0, 110.0, 120.0, 130.0]] * 2)
+    _write_trajectory(tmp_path, poses, intrinsics)
+
+    trajectory = load_camera_trajectory(_trusted(tmp_path))
+
+    assert trajectory.poses.dtype == torch.float32
+    assert trajectory.intrinsics.dtype == torch.float32
+    torch.testing.assert_close(trajectory.poses, torch.from_numpy(poses).float())
+    torch.testing.assert_close(trajectory.intrinsics, torch.from_numpy(intrinsics).float())
+
+
+def test_load_camera_trajectory_rejects_object_dtype_before_materializing(tmp_path: Path) -> None:
+    poses = _identity_poses(1).astype(object)
+    intrinsics = np.ones((1, 4), dtype=np.float32)
+    _write_trajectory(tmp_path, poses, intrinsics)
+
+    with pytest.raises(ValueError, match="poses.npy.*real numeric"):
+        load_camera_trajectory(_trusted(tmp_path))
+
+
+def test_load_camera_trajectory_accepts_official_length_and_materializes_request_prefix(tmp_path: Path) -> None:
+    _write_trajectory(
+        tmp_path,
+        _identity_poses(269),
+        np.arange(269 * 4, dtype=np.float32).reshape(269, 4),
+    )
+
+    trajectory = load_camera_trajectory(_trusted(tmp_path))
+
+    assert trajectory.poses.shape == (117, 4, 4)
+    assert trajectory.intrinsics.shape == (117, 4)
+    np.testing.assert_array_equal(
+        trajectory.intrinsics.numpy(),
+        np.arange(269 * 4, dtype=np.float32).reshape(269, 4)[:117],
+    )
+
+
+def test_load_camera_trajectory_rejects_unbounded_source_frame_count(tmp_path: Path) -> None:
+    _write_trajectory(
+        tmp_path,
+        _identity_poses(4097),
+        np.ones((4097, 4), dtype=np.float32),
+    )
+
+    with pytest.raises(ValueError, match="poses.npy.*bounded camera-array limit|4096 source frames"):
+        load_camera_trajectory(_trusted(tmp_path))
+
+
+def test_load_camera_trajectory_rejects_truncated_payload_before_loading(tmp_path: Path) -> None:
+    _write_trajectory(tmp_path, _identity_poses(1), np.ones((1, 4), dtype=np.float32))
+    poses_path = tmp_path / "poses.npy"
+    poses_path.write_bytes(poses_path.read_bytes()[:-1])
+
+    with pytest.raises(ValueError, match="poses.npy byte size"):
+        load_camera_trajectory(_trusted(tmp_path))
+
+
+def test_load_camera_trajectory_rejects_oversized_npy_header(tmp_path: Path) -> None:
+    header_size = 20_000
+    (tmp_path / "poses.npy").write_bytes(
+        np.lib.format.magic(2, 0) + header_size.to_bytes(4, "little") + b" " * (header_size - 1) + b"\n"
+    )
+    np.save(tmp_path / "intrinsics.npy", np.ones((1, 4), dtype=np.float32))
+
+    with pytest.raises(ValueError, match="poses.npy.*oversized NPY header"):
+        load_camera_trajectory(_trusted(tmp_path))
+
+
+def test_load_camera_trajectory_rejects_symlink_swap_after_resolution(tmp_path: Path) -> None:
+    trusted_root = tmp_path / "trusted"
+    action_directory = trusted_root / "action"
+    outside = tmp_path / "outside"
+    action_directory.mkdir(parents=True)
+    outside.mkdir()
+    _write_trajectory(action_directory, _identity_poses(1), np.ones((1, 4), dtype=np.float32))
+    _write_trajectory(outside, _identity_poses(1), np.ones((1, 4), dtype=np.float32))
+    resolved = resolve_trusted_action_directory("action", trusted_root)
+    action_directory.rename(trusted_root / "original-action")
+    action_directory.symlink_to(outside, target_is_directory=True)
+
+    with pytest.raises(ValueError, match="securely open.*action directory"):
+        load_camera_trajectory(resolved)
+
+
+def test_load_camera_trajectory_rejects_symlinked_camera_file(tmp_path: Path) -> None:
+    trusted_root = tmp_path / "trusted"
+    action_directory = trusted_root / "action"
+    outside = tmp_path / "outside"
+    action_directory.mkdir(parents=True)
+    outside.mkdir()
+    _write_trajectory(action_directory, _identity_poses(1), np.ones((1, 4), dtype=np.float32))
+    np.save(outside / "poses.npy", _identity_poses(1))
+    resolved = resolve_trusted_action_directory("action", trusted_root)
+    (action_directory / "poses.npy").unlink()
+    (action_directory / "poses.npy").symlink_to(outside / "poses.npy")
+
+    with pytest.raises(ValueError, match="poses.npy.*opened securely"):
+        load_camera_trajectory(resolved)
+
+
+@pytest.mark.skipif(not hasattr(__import__("os"), "mkfifo"), reason="requires POSIX FIFO support")
+def test_load_camera_trajectory_rejects_fifo_without_blocking(tmp_path: Path) -> None:
+    import os
+
+    os.mkfifo(tmp_path / "poses.npy")
+    np.save(tmp_path / "intrinsics.npy", np.ones((1, 4), dtype=np.float32))
+    script = """
+import importlib.util
+import sys
+
+spec = importlib.util.spec_from_file_location("_lingbot_camera_fifo_test", sys.argv[1])
+module = importlib.util.module_from_spec(spec)
+sys.modules[spec.name] = module
+spec.loader.exec_module(module)
+action = module.resolve_trusted_action_directory(".", sys.argv[2])
+try:
+    module.load_camera_trajectory(action)
+except ValueError as error:
+    print(error)
+else:
+    raise AssertionError("FIFO camera input was accepted")
+"""
+
+    completed = subprocess.run(
+        [sys.executable, "-c", script, str(_MODULE_PATH), str(tmp_path)],
+        capture_output=True,
+        text=True,
+        timeout=3,
+        check=False,
+    )
+
+    assert completed.returncode == 0, completed.stderr
+    assert "poses.npy must be a regular NPY file" in completed.stdout
+
+
+def test_load_camera_trajectory_decodes_the_validated_file_snapshot(tmp_path: Path, monkeypatch) -> None:
+    original_poses = _identity_poses(1)
+    replacement_poses = original_poses.copy()
+    replacement_poses[0, 0, 3] = 123.0
+    _write_trajectory(tmp_path, original_poses, np.ones((1, 4), dtype=np.float32))
+    poses_path = tmp_path / "poses.npy"
+    original_load = _CAMERA_MODULE.np.load
+    mutation_count = 0
+
+    def mutate_source_then_decode(source, *args, **kwargs):
+        nonlocal mutation_count
+        if mutation_count == 0:
+            mutation_count += 1
+            np.save(poses_path, replacement_poses)
+        return original_load(source, *args, **kwargs)
+
+    monkeypatch.setattr(_CAMERA_MODULE.np, "load", mutate_source_then_decode)
+
+    trajectory = load_camera_trajectory(_trusted(tmp_path))
+
+    assert mutation_count == 1
+    torch.testing.assert_close(trajectory.poses, torch.from_numpy(original_poses).float())
+
+
+def test_load_camera_trajectory_ignores_non_camera_event_files(tmp_path: Path) -> None:
+    poses = _identity_poses(1)
+    intrinsics = np.ones((1, 4), dtype=np.float32)
+    _write_trajectory(tmp_path, poses, intrinsics)
+    np.save(tmp_path / "wasd_events.npy", np.array([{"key": "w"}], dtype=object))
+
+    trajectory = load_camera_trajectory(_trusted(tmp_path))
+
+    torch.testing.assert_close(trajectory.poses, torch.from_numpy(poses).float())
+
+
+def test_interpolate_camera_trajectory_uses_linear_and_spherical_interpolation() -> None:
+    poses = torch.eye(4).repeat(2, 1, 1)
+    poses[1, :3, :3] = torch.tensor(
+        [
+            [0.0, -1.0, 0.0],
+            [1.0, 0.0, 0.0],
+            [0.0, 0.0, 1.0],
+        ]
+    )
+    poses[1, :3, 3] = torch.tensor([2.0, 4.0, 6.0])
+    intrinsics = torch.tensor([[10.0, 20.0, 30.0, 40.0], [20.0, 40.0, 60.0, 80.0]])
+    trajectory = CameraTrajectory(poses=poses, intrinsics=intrinsics)
+
+    result = interpolate_camera_trajectory(trajectory, 3)
+
+    torch.testing.assert_close(result.poses[0], poses[0])
+    torch.testing.assert_close(result.poses[-1], poses[-1])
+    torch.testing.assert_close(result.poses[1, :3, 3], torch.tensor([1.0, 2.0, 3.0]))
+    expected_mid_rotation = torch.tensor(
+        [
+            [math.sqrt(0.5), -math.sqrt(0.5), 0.0],
+            [math.sqrt(0.5), math.sqrt(0.5), 0.0],
+            [0.0, 0.0, 1.0],
+        ]
+    )
+    torch.testing.assert_close(result.poses[1, :3, :3], expected_mid_rotation)
+    torch.testing.assert_close(result.intrinsics[1], torch.tensor([15.0, 30.0, 45.0, 60.0]))
+
+
+def test_build_plucker_embedding_has_forward_center_ray() -> None:
+    trajectory = _trajectory(torch.eye(4).unsqueeze(0))
+
+    result = build_plucker_embedding(
+        trajectory,
+        height=3,
+        width=3,
+        target_height=3,
+        target_width=3,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    torch.testing.assert_close(result[0, :3, 1, 1], torch.zeros(3))
+    torch.testing.assert_close(result[0, 3:, 1, 1], torch.tensor([0.0, 0.0, 1.0]))
+
+
+def test_build_plucker_embedding_scales_reference_intrinsics() -> None:
+    trajectory = _trajectory(
+        torch.eye(4).unsqueeze(0),
+        torch.tensor([[416.0, 240.0, 416.0, 240.0]]),
+    )
+
+    result = build_plucker_embedding(
+        trajectory,
+        height=4,
+        width=8,
+        target_height=4,
+        target_width=8,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    expected_corner_direction = torch.tensor([-0.875, -0.75, 1.0])
+    expected_corner_direction /= torch.linalg.vector_norm(expected_corner_direction)
+    torch.testing.assert_close(result[0, 3:, 0, 0], expected_corner_direction)
+
+
+def test_build_plucker_embedding_samples_target_pixel_centers() -> None:
+    trajectory = _trajectory(
+        torch.eye(4).unsqueeze(0),
+        torch.tensor([[416.0, 240.0, 416.0, 240.0]]),
+    )
+
+    result = build_plucker_embedding(
+        trajectory,
+        height=6,
+        width=10,
+        target_height=2,
+        target_width=5,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    # Target centers map through the source scale: (0.5 * 2, 0.5 * 3).
+    expected_direction = torch.tensor([-0.8, -0.5, 1.0])
+    expected_direction /= torch.linalg.vector_norm(expected_direction)
+    torch.testing.assert_close(result[0, 3:, 0, 0], expected_direction)
+
+
+def test_build_plucker_embedding_uses_framewise_deltas_for_rotations_and_translations() -> None:
+    poses = torch.eye(4).repeat(3, 1, 1)
+    poses[0, :3, 3] = torch.tensor([5.0, 7.0, 11.0])
+    poses[1, :3, :3] = torch.tensor(
+        [
+            [0.0, -1.0, 0.0],
+            [1.0, 0.0, 0.0],
+            [0.0, 0.0, 1.0],
+        ]
+    )
+    poses[1, :3, 3] = torch.tensor([5.0, 9.0, 11.0])
+    poses[2, :3, :3] = torch.tensor(
+        [
+            [-1.0, 0.0, 0.0],
+            [0.0, -1.0, 0.0],
+            [0.0, 0.0, 1.0],
+        ]
+    )
+    poses[2, :3, 3] = torch.tensor([3.0, 9.0, 11.0])
+    trajectory = _trajectory(
+        poses,
+        torch.tensor([[416.0, 480.0, 208.0, 240.0]]).repeat(3, 1),
+    )
+
+    result = build_plucker_embedding(
+        trajectory,
+        height=1,
+        width=2,
+        target_height=1,
+        target_width=2,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    expected_direction = torch.tensor([0.0, 1.0, 1.0]) / math.sqrt(2.0)
+    torch.testing.assert_close(result[0, :3, 0, 1], torch.zeros(3))
+    torch.testing.assert_close(result[1, :3, 0, 1], torch.tensor([0.0, 1.0, 0.0]))
+    torch.testing.assert_close(result[2, :3, 0, 1], torch.tensor([0.0, 1.0, 0.0]))
+    torch.testing.assert_close(result[1, 3:, 0, 1], expected_direction)
+    torch.testing.assert_close(result[2, 3:, 0, 1], expected_direction)
+
+
+def test_build_plucker_embedding_normalizes_framewise_translation_by_max_norm() -> None:
+    poses = torch.eye(4).repeat(3, 1, 1)
+    poses[:, :3, 3] = torch.tensor(
+        [
+            [10.0, -2.0, 5.0],
+            [13.0, 2.0, 5.0],
+            [19.0, 10.0, 5.0],
+        ]
+    )
+
+    result = build_plucker_embedding(
+        _trajectory(poses),
+        height=1,
+        width=1,
+        target_height=1,
+        target_width=1,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    expected_origins = torch.tensor([[0.0, 0.0, 0.0], [0.3, 0.4, 0.0], [0.6, 0.8, 0.0]])
+    torch.testing.assert_close(result[:, :3, 0, 0], expected_origins)
+
+
+def test_build_plucker_embedding_interpolates_raw_poses_before_framewise_conversion() -> None:
+    poses = torch.eye(4).repeat(2, 1, 1)
+    poses[0, :3, 3] = torch.tensor([20.0, 4.0, -2.0])
+    poses[1, :3, 3] = torch.tensor([26.0, 12.0, -2.0])
+    raw = _trajectory(poses)
+
+    interpolated = interpolate_camera_trajectory(raw, 3)
+    result = build_plucker_embedding(
+        interpolated,
+        height=1,
+        width=1,
+        target_height=1,
+        target_width=1,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    expected_delta = torch.tensor([0.6, 0.8, 0.0])
+    torch.testing.assert_close(result[0, :3, 0, 0], torch.zeros(3))
+    torch.testing.assert_close(result[1, :3, 0, 0], expected_delta)
+    torch.testing.assert_close(result[2, :3, 0, 0], expected_delta)
+
+
+def test_build_plucker_embedding_uses_half_pixel_centers_and_origin_direction_channel_order() -> None:
+    poses = torch.eye(4).repeat(2, 1, 1)
+    poses[1, :3, 3] = torch.tensor([2.0, 0.0, 0.0])
+    trajectory = _trajectory(
+        poses,
+        torch.tensor([[832.0, 480.0, 0.0, 0.0]]).repeat(2, 1),
+    )
+
+    result = build_plucker_embedding(
+        trajectory,
+        height=1,
+        width=2,
+        target_height=1,
+        target_width=2,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    expected_direction = torch.tensor([0.25, 0.5, 1.0])
+    expected_direction /= torch.linalg.vector_norm(expected_direction)
+    torch.testing.assert_close(result[1, :3, 0, 0], torch.tensor([1.0, 0.0, 0.0]))
+    torch.testing.assert_close(result[1, 3:, 0, 0], expected_direction)
+
+
+def test_build_plucker_embedding_honors_shape_dtype_and_device() -> None:
+    trajectory = _trajectory(torch.eye(4).repeat(2, 1, 1))
+
+    result = build_plucker_embedding(
+        trajectory,
+        height=12,
+        width=20,
+        target_height=3,
+        target_width=5,
+        device=torch.device("cpu"),
+        dtype=torch.float64,
+    )
+
+    assert result.shape == (2, 6, 3, 5)
+    assert result.dtype == torch.float64
+    assert result.device == torch.device("cpu")
+
+
+def test_build_plucker_embedding_is_deterministic() -> None:
+    trajectory = _trajectory(torch.eye(4).repeat(2, 1, 1))
+    kwargs = {
+        "height": 12,
+        "width": 20,
+        "target_height": 3,
+        "target_width": 5,
+        "device": torch.device("cpu"),
+        "dtype": torch.float32,
+    }
+
+    first = build_plucker_embedding(trajectory, **kwargs)
+    second = build_plucker_embedding(trajectory, **kwargs)
+
+    torch.testing.assert_close(first, second, rtol=0.0, atol=0.0)
@@ -0,0 +1,685 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import hashlib
+import inspect
+import json
+import math
+from pathlib import Path
+
+import pytest
+import torch
+
+from tests.diffusion.models.lingbot_world import test_lingbot_world_attention as attention_tests
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
+
+_FIXTURE_PATH = Path(__file__).with_name("fixtures") / "lingbot_world_weight_index_names.json.fixture"
+_SHAPE_FIXTURE_PATH = Path(__file__).with_name("fixtures") / "lingbot_world_official_shapes.json.fixture"
+
+
+@pytest.fixture(autouse=True)
+def _inference_context():
+    with torch.inference_mode():
+        yield
+
+
+def _tiny_model(
+    module,
+    *,
+    num_layers: int = 2,
+    num_frames_per_block: int = 1,
+    sliding_window_num_frames: int = 3,
+):
+    return module.CausalLingBotWorldTransformer3DModel(
+        patch_size=(1, 2, 2),
+        num_attention_heads=2,
+        attention_head_dim=2,
+        in_channels=36,
+        out_channels=2,
+        text_dim=6,
+        freq_dim=4,
+        ffn_dim=8,
+        num_layers=num_layers,
+        cross_attn_norm=True,
+        eps=1e-6,
+        rope_max_seq_len=16,
+        sink_size=1,
+        num_frames_per_block=num_frames_per_block,
+        sliding_window_num_frames=sliding_window_num_frames,
+        local_attn_size=-1,
+    )
+
+
+def _cache(module, model, *, max_tokens: int | None = None):
+    if max_tokens is None:
+        window_frames = (
+            model.config.local_attn_size
+            if model.config.local_attn_size != -1
+            else model.config.sliding_window_num_frames
+        )
+        max_tokens = window_frames * 2 * 2
+    return module.allocate_lingbot_cache(
+        batch_size=1,
+        num_layers=len(model.blocks),
+        max_tokens=max_tokens,
+        num_local_heads=2,
+        head_dim=2,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+
+def _checkpoint_parameter_specs(model) -> dict[str, tuple[torch.Size, torch.dtype]]:
+    specs: dict[str, tuple[torch.Size, torch.dtype]] = {}
+    for name, parameter in model.named_parameters():
+        marker = ".self_attn.qkv."
+        if marker not in name:
+            specs[name] = (parameter.shape, parameter.dtype)
+            continue
+        shard_shape = torch.Size((parameter.shape[0] // 3, *parameter.shape[1:]))
+        for projection in ("q", "k", "v"):
+            specs[name.replace(marker, f".self_attn.{projection}.")] = (shard_shape, parameter.dtype)
+    return specs
+
+
+def _checkpoint_weights_for_model(model) -> tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
+    checkpoint_weights: dict[str, torch.Tensor] = {}
+    expected_parameters: dict[str, torch.Tensor] = {}
+    next_value = 1
+    for name, parameter in model.named_parameters():
+        marker = ".self_attn.qkv."
+        if marker not in name:
+            value = torch.full_like(parameter, next_value)
+            checkpoint_weights[name] = value
+            expected_parameters[name] = value
+            next_value += 1
+            continue
+        shards = []
+        shard_shape = (parameter.shape[0] // 3, *parameter.shape[1:])
+        for projection in ("q", "k", "v"):
+            value = torch.full(shard_shape, next_value, dtype=parameter.dtype, device=parameter.device)
+            checkpoint_weights[name.replace(marker, f".self_attn.{projection}.")] = value
+            shards.append(value)
+            next_value += 1
+        expected_parameters[name] = torch.cat(shards)
+    return checkpoint_weights, expected_parameters
+
+
+def _self_cache_snapshot(cache) -> list[tuple[torch.Tensor, torch.Tensor, tuple[int, int, int | None, int]]]:
+    return [attention_tests._cache_snapshot(layer_cache) for layer_cache in cache.self_attention]
+
+
+def _assert_self_cache_unchanged(cache, snapshot) -> None:
+    for layer_cache, layer_snapshot in zip(cache.self_attention, snapshot, strict=True):
+        attention_tests._assert_cache_unchanged(layer_cache, layer_snapshot)
+
+
+def test_tiny_transformer_runs_four_chunks_with_explicit_cache_commit_and_camera_path() -> None:
+    torch.manual_seed(7)
+    module = attention_tests._load_module()
+    model = _tiny_model(module).eval()
+    cache = _cache(module, model)
+    encoder_hidden_states = torch.randn(1, 3, 6)
+    camera_hidden_states = torch.randn(1, 6 * 8 * 8, 1, 4, 4)
+    cross_key_outputs = [attention_tests._record_outputs(block.cross_attn.k) for block in model.blocks]
+    text_projection_outputs = attention_tests._record_outputs(model.text_embedding)
+    outputs = []
+
+    assert model.patch_embedding.in_channels == 36
+    assert model.patch_embedding_wancamctrl.in_features == 6 * 8 * 8 * 1 * 2 * 2
+
+    for start_frame in range(4):
+        hidden_states = torch.randn(1, 36, 1, 4, 4)
+        timestep = torch.tensor([float(start_frame + 1)])
+        snapshot = _self_cache_snapshot(cache)
+
+        transient_output = model(
+            hidden_states,
+            timestep,
+            encoder_hidden_states,
+            camera_hidden_states,
+            cache=cache,
+            start_frame=start_frame,
+            update_cache=False,
+        )
+
+        assert transient_output.shape == (1, 2, 1, 4, 4)
+        _assert_self_cache_unchanged(cache, snapshot)
+        assert all(layer_cache is not None for layer_cache in cache.cross_attention)
+
+        committed_output = model(
+            hidden_states,
+            timestep,
+            encoder_hidden_states,
+            camera_hidden_states,
+            cache=cache,
+            start_frame=start_frame,
+            update_cache=True,
+        )
+        torch.testing.assert_close(committed_output, transient_output)
+        outputs.append(committed_output)
+
+        expected_token_end = (start_frame + 1) * 2 * 2
+        assert all(layer_cache.absolute_end == expected_token_end for layer_cache in cache.self_attention)
+
+    assert len(outputs) == 4
+    assert all(len(outputs) == 1 for outputs in cross_key_outputs)
+    assert len(text_projection_outputs) == 1
+
+    alternate_cache = _cache(module, model)
+    alternate_output = model(
+        torch.zeros(1, 36, 1, 4, 4),
+        torch.tensor([1.0]),
+        encoder_hidden_states,
+        torch.zeros_like(camera_hidden_states),
+        cache=alternate_cache,
+        start_frame=0,
+        update_cache=False,
+    )
+    camera_output = model(
+        torch.zeros(1, 36, 1, 4, 4),
+        torch.tensor([1.0]),
+        encoder_hidden_states,
+        torch.ones_like(camera_hidden_states),
+        cache=_cache(module, model),
+        start_frame=0,
+        update_cache=False,
+    )
+    assert not torch.equal(camera_output, alternate_output)
+
+
+def test_partial_cross_attention_cache_projects_text_only_for_missing_layers() -> None:
+    module = attention_tests._load_module()
+    model = _tiny_model(module, num_layers=2).eval()
+    cache = _cache(module, model)
+    hidden_states = torch.randn(1, 36, 1, 4, 4)
+    timestep = torch.tensor([1.0])
+    encoder_hidden_states = torch.randn(1, 3, 6)
+    camera_hidden_states = torch.randn(1, 6 * 8 * 8, 1, 4, 4)
+
+    model(
+        hidden_states,
+        timestep,
+        encoder_hidden_states,
+        camera_hidden_states,
+        cache=cache,
+        start_frame=0,
+        update_cache=False,
+    )
+    cache.cross_attention[1] = None
+    text_projection_outputs = attention_tests._record_outputs(model.text_embedding)
+    cross_key_outputs = [attention_tests._record_outputs(block.cross_attn.k) for block in model.blocks]
+
+    model(
+        hidden_states,
+        timestep,
+        encoder_hidden_states,
+        camera_hidden_states,
+        cache=cache,
+        start_frame=0,
+        update_cache=False,
+    )
+
+    assert len(text_projection_outputs) == 1
+    assert [len(outputs) for outputs in cross_key_outputs] == [0, 1]
+    assert all(layer_cache is not None for layer_cache in cache.cross_attention)
+
+
+@pytest.mark.parametrize("frames", [1, 6], ids=("partial", "multiple_blocks"))
+def test_forward_rejects_chunks_that_do_not_equal_configured_block_size(frames: int) -> None:
+    module = attention_tests._load_module()
+    model = _tiny_model(
+        module,
+        num_layers=1,
+        num_frames_per_block=3,
+        sliding_window_num_frames=6,
+    )
+    cache = _cache(module, model)
+    snapshot = _self_cache_snapshot(cache)
+    patch_inputs = attention_tests._record_inputs(model.patch_embedding)
+
+    with pytest.raises(ValueError, match="exactly 3 post-patch frames"):
+        model(
+            torch.randn(1, 36, frames, 4, 4),
+            torch.tensor([1.0]),
+            torch.randn(1, 3, 6),
+            torch.randn(1, 6 * 8 * 8, frames, 4, 4),
+            cache=cache,
+            start_frame=0,
+            update_cache=True,
+        )
+
+    assert patch_inputs == []
+    _assert_self_cache_unchanged(cache, snapshot)
+    assert cache.cross_attention == [None]
+
+
+def test_forward_accepts_exactly_one_configured_frame_block() -> None:
+    module = attention_tests._load_module()
+    model = _tiny_model(
+        module,
+        num_layers=1,
+        num_frames_per_block=3,
+        sliding_window_num_frames=6,
+    )
+
+    output = model(
+        torch.randn(1, 36, 3, 4, 4),
+        torch.tensor([1.0]),
+        torch.randn(1, 3, 6),
+        torch.randn(1, 6 * 8 * 8, 3, 4, 4),
+        cache=_cache(module, model),
+        start_frame=0,
+        update_cache=False,
+    )
+
+    assert output.shape == (1, 2, 3, 4, 4)
+
+
+def test_transformer_allocates_request_cache_from_its_configured_geometry() -> None:
+    module = attention_tests._load_module()
+    model = _tiny_model(
+        module,
+        num_layers=2,
+        num_frames_per_block=3,
+        sliding_window_num_frames=6,
+    )
+
+    cache = model.allocate_cache(
+        batch_size=2,
+        latent_height=8,
+        latent_width=12,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    assert len(cache.self_attention) == 2
+    assert len(cache.cross_attention) == 2
+    assert cache.cross_attention == [None, None]
+    assert all(layer.key.shape == (2, 6 * 4 * 6, 2, 2) for layer in cache.self_attention)
+    assert all(layer.value.shape == (2, 6 * 4 * 6, 2, 2) for layer in cache.self_attention)
+
+
+def test_video_patch_embedding_uses_temporal_height_width_token_order() -> None:
+    module = attention_tests._load_module()
+    model = _tiny_model(module, num_layers=1, num_frames_per_block=2)
+    hidden_states = torch.zeros(1, 36, 2, 2, 4)
+    hidden_states[0, 0] = torch.tensor(
+        [
+            [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]],
+            [[9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]],
+        ]
+    )
+    with torch.no_grad():
+        model.patch_embedding.weight.zero_()
+        model.patch_embedding.bias.zero_()
+        model.patch_embedding.weight[0, 0, 0, 0, 0] = 1
+
+    tokens = model.patch_embedding(hidden_states).flatten(2).transpose(1, 2)
+
+    torch.testing.assert_close(tokens[0, :, 0], torch.tensor([1.0, 3.0, 9.0, 11.0]))
+
+
+def test_unpatchify_restores_two_frame_channel_and_spatial_order() -> None:
+    module = attention_tests._load_module()
+    model = _tiny_model(module, num_layers=1, num_frames_per_block=2)
+    hidden_states = (
+        torch.stack(
+            (
+                torch.arange(0, 8),
+                torch.arange(10, 18),
+                torch.arange(20, 28),
+                torch.arange(30, 38),
+            )
+        )
+        .unsqueeze(0)
+        .float()
+    )
+    expected = torch.tensor(
+        [
+            [
+                [[0, 2, 10, 12], [4, 6, 14, 16]],
+                [[20, 22, 30, 32], [24, 26, 34, 36]],
+            ],
+            [
+                [[1, 3, 11, 13], [5, 7, 15, 17]],
+                [[21, 23, 31, 33], [25, 27, 35, 37]],
+            ],
+        ],
+        dtype=torch.float32,
+    ).unsqueeze(0)
+
+    output = model._unpatchify(
+        hidden_states,
+        batch_size=1,
+        frames=2,
+        height=1,
+        width=2,
+    )
+
+    torch.testing.assert_close(output, expected)
+
+
+def test_head_modulation_broadcasts_distinct_condition_per_frame() -> None:
+    module = attention_tests._load_module()
+    model = _tiny_model(module, num_layers=1, num_frames_per_block=2)
+    model.head.norm = torch.nn.Identity()
+    with torch.no_grad():
+        model.head.modulation.zero_()
+        model.head.head.weight.zero_()
+        model.head.head.bias.zero_()
+        model.head.head.weight[:, 0] = 1
+    hidden_states = torch.ones(1, 4, model.dim)
+    timestep_embedding = torch.tensor([[[10.0, 0.0, 0.0, 0.0], [20.0, 0.0, 0.0, 0.0]]])
+    expected = torch.tensor([21.0, 21.0, 41.0, 41.0]).view(1, 4, 1).expand(1, 4, 8)
+
+    output = model.head(hidden_states, timestep_embedding)
+
+    torch.testing.assert_close(output, expected)
+
+
+def test_constructor_defaults_match_official_checkpoint_config() -> None:
+    module = attention_tests._load_module()
+    parameters = inspect.signature(module.CausalLingBotWorldTransformer3DModel.__init__).parameters
+    expected = {
+        "in_channels": 36,
+        "out_channels": 16,
+        "num_layers": 40,
+        "num_attention_heads": 40,
+        "attention_head_dim": 128,
+        "patch_size": (1, 2, 2),
+        "text_dim": 4096,
+        "ffn_dim": 13824,
+        "freq_dim": 256,
+        "rope_max_seq_len": 1024,
+        "eps": 1e-6,
+        "cross_attn_norm": True,
+        "sink_size": 9,
+        "num_frames_per_block": 3,
+        "sliding_window_num_frames": 18,
+        "local_attn_size": -1,
+    }
+
+    assert {name: parameters[name].default for name in expected} == expected
+
+
+def test_transformer_exposes_parameter_dtype_for_pipeline_runtime() -> None:
+    module = attention_tests._load_module()
+    model = _tiny_model(module)
+
+    assert model.dtype == next(model.parameters()).dtype
+
+
+def test_transformer_declares_regional_compile_block() -> None:
+    module = attention_tests._load_module()
+
+    assert module.CausalLingBotWorldTransformer3DModel._repeated_blocks == ["LingBotAttentionBlock"]
+
+
+def test_lingbot_rms_norm_uses_global_tp_square_mean(monkeypatch) -> None:
+    module = attention_tests._load_module()
+    reduced_values: list[torch.Tensor] = []
+
+    monkeypatch.setattr(module, "get_tensor_model_parallel_world_size", lambda: 2)
+
+    def all_reduce(value: torch.Tensor) -> torch.Tensor:
+        reduced_values.append(value.detach().clone())
+        return value * 2
+
+    monkeypatch.setattr(module, "tensor_model_parallel_all_reduce", all_reduce)
+    norm = module._LingBotRMSNorm(hidden_size=2, eps=0.0)
+    value = torch.tensor([[3.0, 4.0]])
+
+    output = norm(value)
+
+    torch.testing.assert_close(output, value / math.sqrt(12.5))
+    assert len(reduced_values) == 1
+    torch.testing.assert_close(reduced_values[0], torch.tensor([[25.0]]))
+
+
+def test_attention_rejects_heads_not_divisible_by_tp_size(monkeypatch) -> None:
+    module = attention_tests._load_module()
+    monkeypatch.setattr(module, "get_tensor_model_parallel_world_size", lambda: 3)
+
+    with pytest.raises(ValueError, match="num_heads=2.*tp_size=3"):
+        module.LingBotSelfAttention(dim=4, num_heads=2)
+
+
+def test_constructor_rejects_unsupported_qk_norm_with_config_error() -> None:
+    module = attention_tests._load_module()
+
+    with pytest.raises(ValueError, match="qk_norm.*rms_norm_across_heads"):
+        module.CausalLingBotWorldTransformer3DModel(qk_norm="layer_norm")
+
+
+@pytest.mark.parametrize("field", ["image_dim", "added_kv_proj_dim", "pos_embed_seq_len"])
+def test_constructor_rejects_non_null_image_embedding_fields(field: str) -> None:
+    module = attention_tests._load_module()
+
+    with pytest.raises(ValueError, match=field):
+        module.CausalLingBotWorldTransformer3DModel(**{field: 4})
+
+
+def test_constructor_rejects_unsupported_quantization_with_runtime_error() -> None:
+    module = attention_tests._load_module()
+
+    with pytest.raises(RuntimeError, match="quant_config.*not supported"):
+        module.CausalLingBotWorldTransformer3DModel(quant_config=object())
+
+
+def test_from_config_accepts_diffusers_metadata_and_normalizes_patch_size() -> None:
+    module = attention_tests._load_module()
+
+    class ConfigProbe(module.CausalLingBotWorldTransformer3DModel):
+        def __init__(self, **kwargs) -> None:
+            self.received_kwargs = kwargs
+
+    config = {
+        "_class_name": "CausalLingBotWorldTransformer3DModel",
+        "_diffusers_version": "0.35.0.dev0",
+        "patch_size": [1, 2, 2],
+        "num_attention_heads": 40,
+        "attention_head_dim": 128,
+        "in_channels": 36,
+        "out_channels": 16,
+        "text_dim": 4096,
+        "freq_dim": 256,
+        "ffn_dim": 13824,
+        "num_layers": 40,
+        "cross_attn_norm": True,
+        "eps": 1e-6,
+        "image_dim": None,
+        "added_kv_proj_dim": None,
+        "rope_max_seq_len": 1024,
+        "pos_embed_seq_len": None,
+        "qk_norm": "rms_norm_across_heads",
+        "sink_size": 9,
+        "num_frames_per_block": 3,
+        "sliding_window_num_frames": 18,
+        "local_attn_size": -1,
+    }
+
+    model = ConfigProbe.from_config(config, prefix="transformer")
+
+    assert model.received_kwargs["patch_size"] == (1, 2, 2)
+    assert model.received_kwargs["num_layers"] == 40
+    assert model.received_kwargs["prefix"] == "transformer"
+
+
+@pytest.mark.parametrize(
+    ("field", "value"),
+    [
+        ("in_channels", 35),
+        ("num_layers", 39),
+        ("num_attention_heads", 32),
+        ("attention_head_dim", 64),
+        ("ffn_dim", 10240),
+        ("num_frames_per_block", 4),
+        ("sliding_window_num_frames", 24),
+        ("sink_size", 0),
+    ],
+)
+def test_from_config_rejects_checkpoint_topology_drift(field: str, value: object) -> None:
+    module = attention_tests._load_module()
+    config = {
+        "_class_name": "CausalLingBotWorldTransformer3DModel",
+        "_diffusers_version": "0.35.0.dev0",
+        "patch_size": [1, 2, 2],
+        "num_attention_heads": 40,
+        "attention_head_dim": 128,
+        "in_channels": 36,
+        "out_channels": 16,
+        "text_dim": 4096,
+        "freq_dim": 256,
+        "ffn_dim": 13824,
+        "num_layers": 40,
+        "cross_attn_norm": True,
+        "eps": 1e-6,
+        "image_dim": None,
+        "added_kv_proj_dim": None,
+        "rope_max_seq_len": 1024,
+        "pos_embed_seq_len": None,
+        "qk_norm": "rms_norm_across_heads",
+        "sink_size": 9,
+        "num_frames_per_block": 3,
+        "sliding_window_num_frames": 18,
+        "local_attn_size": -1,
+    }
+    config[field] = value
+
+    with pytest.raises(ValueError, match=field):
+        module.CausalLingBotWorldTransformer3DModel.from_config(config)
+
+
+def test_from_config_ignores_non_semantic_checkpoint_metadata() -> None:
+    module = attention_tests._load_module()
+    config = {
+        "_class_name": "CausalLingBotWorldTransformer3DModel",
+        "architectures": ["CausalLingBotWorldTransformer3DModel"],
+        "future_diffusers_metadata": {"version": 1},
+    }
+
+    with torch.device("meta"):
+        model = module.CausalLingBotWorldTransformer3DModel.from_config(config)
+
+    assert model.config.in_channels == 36
+    assert model.config.num_layers == 40
+
+
+def test_load_weights_uses_parameter_loaders_and_rejects_unknown_model_keys() -> None:
+    module = attention_tests._load_module()
+    model = _tiny_model(module, num_layers=1)
+    checkpoint_weights, expected_parameters = _checkpoint_weights_for_model(model)
+    loader_calls: list[str] = []
+    qkv_weight = model.blocks[0].self_attn.qkv.weight
+
+    def record_loader(param: torch.Tensor, loaded_weight: torch.Tensor, shard_id: str) -> None:
+        loader_calls.append(shard_id)
+        shard_index = {"q": 0, "k": 1, "v": 2}[shard_id]
+        shard_size = param.shape[0] // 3
+        param.data.narrow(0, shard_index * shard_size, shard_size).copy_(loaded_weight)
+
+    qkv_weight.weight_loader = record_loader
+    loaded = model.load_weights(iter(checkpoint_weights.items()))
+
+    assert set(checkpoint_weights) <= loaded
+    assert set(dict(model.named_parameters())) <= loaded
+    assert loader_calls == ["q", "k", "v"]
+    for name, param in model.named_parameters():
+        torch.testing.assert_close(param, expected_parameters[name])
+
+    first_name = next(iter(checkpoint_weights))
+    partial = _tiny_model(module, num_layers=1)
+    partial_loaded = partial.load_weights([(first_name, checkpoint_weights[first_name])])
+    expected_partial = {first_name}
+    for projection in ("q", "k", "v"):
+        marker = f".self_attn.{projection}."
+        if marker in first_name:
+            expected_partial.add(first_name.replace(marker, ".self_attn.qkv."))
+            break
+    assert partial_loaded == expected_partial
+
+    with pytest.raises(KeyError, match="unexpected_model.weight"):
+        model.load_weights([("unexpected_model.weight", torch.ones(1))])
+
+
+def test_load_weights_consumes_checkpoint_iterator_incrementally() -> None:
+    module = attention_tests._load_module()
+    model = _tiny_model(module, num_layers=1)
+    first_name, first_param = next(iter(model.named_parameters()))
+    loaded_weight = torch.full_like(first_param, 17)
+
+    def weights():
+        yield first_name, loaded_weight
+        torch.testing.assert_close(first_param, loaded_weight)
+
+    assert model.load_weights(weights()) == {first_name}
+
+
+def test_real_auto_weights_loader_covers_fused_model_parameter_namespace() -> None:
+    utils = pytest.importorskip("vllm.model_executor.models.utils")
+    module = attention_tests._load_module()
+    transformer = _tiny_model(module, num_layers=1)
+    checkpoint_weights, _ = _checkpoint_weights_for_model(transformer)
+
+    class Pipeline(torch.nn.Module):
+        def __init__(self) -> None:
+            super().__init__()
+            self.transformer = transformer
+
+    pipeline = Pipeline()
+    loaded = utils.AutoWeightsLoader(pipeline).load_weights(
+        (f"transformer.{name}", value) for name, value in checkpoint_weights.items()
+    )
+
+    assert set(dict(pipeline.named_parameters())) <= loaded
+
+
+def test_checkpoint_weight_index_fixture_matches_model_namespaces() -> None:
+    module = attention_tests._load_module()
+    fixture = json.loads(_FIXTURE_PATH.read_text())
+    model = _tiny_model(module, num_layers=1)
+    parameter_names = set(_checkpoint_parameter_specs(model))
+
+    assert {name.split(".", 1)[0] for name in parameter_names} == set(fixture["top_level_modules"])
+    assert {name for name in parameter_names if not name.startswith("blocks.")} == set(fixture["non_block_parameters"])
+    assert {name.removeprefix("blocks.0.") for name in parameter_names if name.startswith("blocks.0.")} == set(
+        fixture["block_parameter_suffixes"]
+    )
+    expanded_names = set(fixture["non_block_parameters"])
+    expanded_names.update(
+        f"blocks.{layer}.{suffix}" for layer in range(40) for suffix in fixture["block_parameter_suffixes"]
+    )
+    canonical_names = "\n".join(sorted(expanded_names))
+
+    assert len(expanded_names) == fixture["index_parameter_count"] == 1421
+    assert hashlib.sha256(canonical_names.encode()).hexdigest() == fixture["canonical_name_set_sha256"]
+    assert fixture["canonical_name_to_shard_sha256"] == (
+        "e1addc27d2b1fad6f10226a23a265ee3e3c61e74dce4d5859727bdf180a67992"
+    )
+    assert list(fixture["shards"]) == [
+        f"diffusion_pytorch_model-{index:05}-of-00008.safetensors" for index in range(1, 9)
+    ]
+    assert sum(shard["parameter_count"] for shard in fixture["shards"].values()) == 1421
+    assert all(len(shard["parameter_names_sha256"]) == 64 for shard in fixture["shards"].values())
+
+
+def test_official_default_shapes_match_public_safetensors_header_fixture() -> None:
+    module = attention_tests._load_module()
+    fixture = json.loads(_SHAPE_FIXTURE_PATH.read_text())
+
+    with torch.device("meta"):
+        model = module.CausalLingBotWorldTransformer3DModel()
+    parameter_specs = _checkpoint_parameter_specs(model)
+    expected_dtype = {"F32": torch.float32}[fixture["dtype"]]
+
+    assert len(fixture["shard_representatives"]) == 8
+    assert set(fixture["shard_representatives"].values()) <= set(fixture["parameter_shapes"])
+
+    for name, expected_shape in fixture["parameter_shapes"].items():
+        shape, dtype = parameter_specs[name]
+        assert tuple(shape) == tuple(expected_shape), name
+        assert dtype == expected_dtype
@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+import pytest
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
+
+_ROOT = Path(__file__).parents[4]
+_PRODUCTION_FILES = (
+    _ROOT / "vllm_omni/diffusion/models/lingbot_world/camera.py",
+    _ROOT / "vllm_omni/diffusion/models/lingbot_world/transformer.py",
+    _ROOT / "vllm_omni/diffusion/models/lingbot_world/pipeline.py",
+)
+
+
+def test_all_lingbot_production_functions_have_complete_annotations() -> None:
+    missing: list[str] = []
+    for path in _PRODUCTION_FILES:
+        tree = ast.parse(path.read_text())
+        for node in ast.walk(tree):
+            if not isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
+                continue
+            arguments = (*node.args.posonlyargs, *node.args.args, *node.args.kwonlyargs)
+            for argument in arguments:
+                if argument.arg not in {"self", "cls"} and argument.annotation is None:
+                    missing.append(f"{path.name}:{node.lineno}:{node.name}:{argument.arg}")
+            if node.args.vararg is not None and node.args.vararg.annotation is None:
+                missing.append(f"{path.name}:{node.lineno}:{node.name}:*{node.args.vararg.arg}")
+            if node.args.kwarg is not None and node.args.kwarg.annotation is None:
+                missing.append(f"{path.name}:{node.lineno}:{node.name}:**{node.args.kwarg.arg}")
+            if node.returns is None:
+                missing.append(f"{path.name}:{node.lineno}:{node.name}:return")
+
+    assert missing == []
@@ -191,6 +191,49 @@ def test_model_load_weights_transforms_before_calling_vllm_loader():
    ]


+def test_loader_adapter_declares_equivalent_direct_mmap_transform(monkeypatch):
+    from vllm_omni.diffusion.model_loader.checkpoint_adapters import (
+        get_direct_mmap_adapter,
+    )
+    from vllm_omni.diffusion.models.minimax_h3 import minimax_h3_transformer as h3
+
+    monkeypatch.setattr(h3, "QKVParallelLinear", _FakeLinear)
+    monkeypatch.setattr(h3, "RowParallelLinear", _FakeLinear)
+    monkeypatch.setattr(h3, "Attention", _FakeAttention)
+
+    arch = h3.MiniMaxH3DiTArchConfig(
+        hidden_size=1,
+        num_attention_heads=2,
+        attention_head_dim=1,
+        rope_inv_freq_len=1,
+    )
+    attention = h3.MiniMaxH3Attention(
+        arch,
+        quant_config=None,
+        prefix="blocks.0.attn",
+    )
+    transformer = object.__new__(h3.MiniMaxH3DiTModel)
+    nn.Module.__init__(transformer)
+    transformer.blocks = nn.ModuleList([nn.Module()])
+    transformer.blocks[0].attn = attention
+    pipeline = nn.Module()
+    pipeline.transformer = transformer
+
+    adapter = get_direct_mmap_adapter(pipeline)
+    assert adapter is not None
+    policy = adapter.policy_for(
+        "transformer.blocks.0.attn.qkv_proj.weight",
+        attention.qkv_proj.weight,
+    )
+    assert policy is not None
+    assert policy.allow_custom_loader
+    assert policy.transform is not None
+    checkpoint_weight = torch.arange(6, dtype=torch.float32).reshape(6, 1)
+
+    assert policy.transform(checkpoint_weight)[:, 0].tolist() == [0, 3, 1, 4, 2, 5]
+    assert not hasattr(attention.qkv_proj.weight, "mmap_weight_transform")
+
+
 def test_pipeline_resolves_transformer_component_quant_config():
    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
        _resolve_component_quant_config,
@@ -18,6 +18,11 @@ from torch import nn
 from torch.distributed.tensor import DeviceMesh, DTensor, Replicate

 import vllm_omni.diffusion.offloader.distributed_layerwise_backend as dist_backend_module
+from vllm_omni.diffusion.model_loader.host_weight_plan import (
+    HostWeightPlan,
+    TensorBinding,
+    build_checkpoint_mmap_plan,
+)
 from vllm_omni.diffusion.offloader.base import OffloadConfig, OffloadStrategy
 from vllm_omni.diffusion.offloader.block_discovery import (
    get_blocks_attr_names,
@@ -30,7 +35,10 @@ from vllm_omni.diffusion.offloader.distributed_layerwise_backend import (
    PinnedResidentLayerGroup,
 )
 from vllm_omni.diffusion.offloader.module_residency import PinnedModuleStager
-from vllm_omni.diffusion.offloader.offload_plan import OffloadPlan, get_offload_plan
+from vllm_omni.diffusion.offloader.offload_plan import (
+    OffloadPlan,
+    get_offload_plan,
+)
 from vllm_omni.platforms import current_omni_platform

 pytestmark = [pytest.mark.diffusion, pytest.mark.cpu, pytest.mark.core_model]
@@ -48,6 +56,9 @@ class DummyEvent:
    def record(self, _stream) -> None:
        return None

+    def synchronize(self) -> None:
+        return None
+

@contextmanager
 def dummy_stream(_stream):
@@ -351,8 +362,9 @@ class TestDistributedLayerwiseOffloadHook:
        block.weight = nn.Parameter(torch.zeros(4, dtype=torch.float32))
        next_block = nn.Module()
        next_block.weight = nn.Parameter(torch.arange(4, dtype=torch.float32))
-        next_block.weight.mmap_weight_transform = lambda tensor: tensor.flip(0)
-        next_block.weight.mmap_weight_transform_pending = True
+
+        def transform(tensor):
+            return tensor.flip(0)

        hook = DistributedLayerwiseOffloadHook(
            next_block=next_block,
@@ -361,17 +373,17 @@ class TestDistributedLayerwiseOffloadHook:
            dp_size=2,
            rank=0,
            pin_memory=False,
+            tensor_transforms={id(next_block.weight): transform},
        )
        hook.initialize_hook(block)

        assert torch.equal(hook.cpu_shards[torch.float32], torch.tensor([3.0, 2.0]))

-    def test_regular_loader_weight_does_not_reapply_mmap_transform(self, dist_group, patched_offload_runtime):
+    def test_transform_is_not_applied_without_a_loader_plan(self, dist_group, patched_offload_runtime):
        block = nn.Module()
        block.weight = nn.Parameter(torch.zeros(4, dtype=torch.float32))
        next_block = nn.Module()
        next_block.weight = nn.Parameter(torch.arange(4, dtype=torch.float32))
-        next_block.weight.mmap_weight_transform = lambda tensor: tensor.flip(0)

        hook = DistributedLayerwiseOffloadHook(
            next_block=next_block,
@@ -385,6 +397,82 @@ class TestDistributedLayerwiseOffloadHook:

        assert torch.equal(hook.cpu_shards[torch.float32], torch.tensor([0.0, 1.0]))

+    def test_rank_local_mmap_retains_source_and_uses_staging(self, dist_group, patched_offload_runtime):
+        current_block = nn.Linear(2, 2, bias=False)
+        next_block = nn.Linear(2, 2, bias=False)
+        next_block.weight.data.copy_(torch.arange(4, dtype=torch.float32).view(2, 2))
+        source_storage = next_block.weight.untyped_storage().data_ptr()
+
+        def transform(tensor):
+            return tensor.t()
+
+        hook = DistributedLayerwiseOffloadHook(
+            next_block=next_block,
+            device=torch.device("cpu"),
+            dp_group=None,
+            dp_size=1,
+            rank=0,
+            pin_memory=False,
+            rank_local_mmap=True,
+            tensor_transforms={id(next_block.weight): transform},
+        )
+        hook.initialize_hook(current_block)
+
+        source = hook.cpu_sources[torch.float32][0]["tensor"]
+        assert source.untyped_storage().data_ptr() == source_storage
+        assert hook.cpu_shards == {}
+        assert next_block.weight.numel() == 0
+
+        hook.cpu_staging_buffers = [
+            {torch.float32: torch.empty(4)},
+            {torch.float32: torch.empty(4)},
+        ]
+        hook.prefetch_layer(slot=0, non_blocking=False)
+
+        assert torch.equal(
+            next_block.weight,
+            torch.tensor([[0.0, 2.0], [1.0, 3.0]]),
+        )
+        assert next_block.weight.stride() == (1, 2)
+        assert torch.equal(source, torch.arange(4, dtype=torch.float32).view(2, 2))
+
+    def test_rank_local_mmap_staging_is_bounded_by_largest_block(self, patched_offload_runtime):
+        hooks = []
+        for size in (4, 9):
+            current_block = nn.Linear(1, 1, bias=False)
+            next_block = nn.Module()
+            next_block.weight = nn.Parameter(torch.arange(size, dtype=torch.float32))
+            hook = DistributedLayerwiseOffloadHook(
+                next_block=next_block,
+                device=torch.device("cpu"),
+                dp_group=None,
+                dp_size=1,
+                rank=0,
+                pin_memory=False,
+                shared_buffers=[None, None],
+                rank_local_mmap=True,
+            )
+            hook.initialize_hook(current_block)
+            hooks.append(hook)
+
+        resident_block = nn.Module()
+        resident_block.weight = nn.Parameter(torch.arange(12, dtype=torch.float32))
+        resident_group = PinnedResidentLayerGroup(
+            [resident_block],
+            device=torch.device("cpu"),
+            copy_stream=DummyStream(),
+            pin_memory=False,
+            rank_local_mmap=True,
+        )
+        staging = DistributedLayerwiseOffloadBackend._allocate_shared_cpu_staging_buffers(
+            hooks,
+            resident_group,
+        )
+
+        assert len(staging) == 2
+        assert staging[0][torch.float32].numel() == 12
+        assert staging[1][torch.float32].numel() == 12
+

 class TestPinnedResidentLayerGroup:
    def test_load_offload_reuses_pinned_master_weights(self, patched_offload_runtime):
@@ -439,23 +527,41 @@ class TestPinnedResidentLayerGroup:
        assert block.weight.stride() == expected_stride
        group.offload()

-    def test_mmap_loader_attrs_survive_to_empty_parameter_replacement(self):
-        module = nn.Linear(2, 2, bias=False)
+    def test_rank_local_mmap_resident_layers_retain_sources(self, patched_offload_runtime):
+        blocks = [nn.Linear(2, 2, bias=False) for _ in range(3)]
+        expected = []
+        source_storage = []
+        for index, block in enumerate(blocks):
+            block.weight.data.fill_(index + 1)
+            expected.append(block.weight.detach().clone())
+            source_storage.append(block.weight.untyped_storage().data_ptr())

-        def transform(tensor):
-            return tensor.flip(0)
+        group = PinnedResidentLayerGroup(
+            blocks,
+            device=torch.device("cpu"),
+            copy_stream=DummyStream(),
+            pin_memory=False,
+            rank_local_mmap=True,
+        )

-        module.weight.mmap_weight_transform = transform
+        for index, state in enumerate(group._states):
+            source = state["cpu_sources"][torch.float32][0]["tensor"]
+            assert source.untyped_storage().data_ptr() == source_storage[index]
+            assert state["cpu_shards"] == {}
+        assert len(group._cpu_staging_buffers) == 2

-        backend = object.__new__(DistributedLayerwiseOffloadBackend)
-        backend._remember_mmap_param_attrs(module)
-        module.to_empty(device="meta")
+        group.load()
+        for block, weight in zip(blocks, expected):
+            assert torch.equal(block.weight, weight)

-        replacement = nn.Parameter(torch.arange(4).reshape(2, 2).float())
-        backend._attach_mmap_param_attrs("weight", replacement, module.weight)
+        blocks[0].weight.data.zero_()
+        group.offload()
+        group.load()
+        for block, weight in zip(blocks, expected):
+            assert torch.equal(block.weight, weight)

-        assert replacement.mmap_weight_transform is transform
-        assert replacement.mmap_weight_transform_pending is True
+        group.offload()
+        assert all(block.weight.numel() == 0 for block in blocks)

    def test_shared_allgather_output_is_narrowed_to_current_block(
        self, dist_group, patched_offload_runtime, monkeypatch
@@ -598,7 +704,6 @@ class TestMmapWeightLoading:
            OffloadConfig(
                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
                pin_cpu_memory=False,
-                model_path=str(tmp_path),
            ),
            torch.device("cpu"),
        )
@@ -606,13 +711,81 @@ class TestMmapWeightLoading:
            dits=[pipeline.transformer],
            dit_names=["transformer"],
        )
+        plan = HostWeightPlan(
+            backing_kind="checkpoint_mmap",
+            bindings={
+                name: TensorBinding(
+                    checkpoint_key=name,
+                    file_path=str(weight_file),
+                )
+                for name in weights
+            },
+        )

-        backend._load_weights_via_mmap(pipeline, modules)
+        backend._load_weights_via_mmap(pipeline, modules, plan)

        assert pipeline.transformer.post_load_calls == 1
        assert pipeline.transformer.time_embedder.weight.dtype == torch.float32
        assert pipeline.transformer.blocks[0].weight.dtype == torch.bfloat16

+    def test_allgather_flag_with_group_size_one_uses_rank_local_mmap(
+        self,
+        tmp_path,
+        patched_offload_runtime,
+    ):
+        class Transformer(nn.Module):
+            _layerwise_offload_blocks_attrs = ["blocks"]
+
+            def __init__(self):
+                super().__init__()
+                self.blocks = nn.ModuleList(
+                    [
+                        nn.Linear(2, 2, bias=False),
+                        nn.Linear(2, 2, bias=False),
+                    ]
+                )
+
+        class Pipeline(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.transformer = Transformer()
+
+            @staticmethod
+            def _remap_ckpt_key(key):
+                return key
+
+        pipeline = Pipeline()
+        weights = {name: torch.ones_like(param) for name, param in pipeline.named_parameters()}
+        checkpoint_file = tmp_path / "model.safetensors"
+        save_file(weights, str(checkpoint_file))
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", pipeline.transformer),),
+            sources=(),
+            model_path=str(tmp_path),
+            tensor_parallel_size=1,
+            use_hsdp=False,
+            online_quantization=False,
+        )
+        assert result.plan is not None
+        backend = DistributedLayerwiseOffloadBackend(
+            OffloadConfig(
+                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
+                pin_cpu_memory=False,
+                dp_size=1,
+                dlo_use_allgather=True,
+            ),
+            torch.device("cpu"),
+            host_weight_plan=result.plan,
+        )
+
+        backend.enable(pipeline)
+
+        assert backend._using_rank_local_mmap
+        assert backend.dp_group is None
+        assert all(hook.rank_local_mmap for group in backend._all_hook_groups for hook in group)
+        backend.disable()
+

 class TestGetBlocksFromDit:
    def test_get_blocks_from_dit_single_block_attr(self):
@@ -878,23 +1051,123 @@ class TestOffloadPlan:


 class TestMmapValidation:
-    """Tests for mmap loader validation: TP rejection, strict check, validate_loaded_weights."""
+    """Tests for loader preflight and backend plan realization."""

-    def test_tp_rejected_when_tp_world_size_gt_1(self, tmp_path, patched_offload_runtime, monkeypatch):
-        """DLO+AllGather should reject when TP world size > 1."""
-        import json
-        from types import SimpleNamespace
-
-        from safetensors.torch import save_file
-
-        from vllm_omni.diffusion.offloader.base import OffloadConfig, OffloadStrategy
-
-        # Mock TP world size = 2
-        monkeypatch.setattr(
-            "vllm.distributed.parallel_state.get_tensor_model_parallel_world_size",
-            lambda: 2,
+    def test_component_source_prefix_builds_model_namespace(self, tmp_path):
+        source_root = tmp_path / "partition"
+        checkpoint_dir = source_root / "transformer"
+        checkpoint_dir.mkdir(parents=True)
+        checkpoint_file = checkpoint_dir / "model.safetensors"
+        save_file(
+            {"blocks.0.weight": torch.arange(4, dtype=torch.float32).reshape(1, 4)},
+            str(checkpoint_file),
        )

+        class Pipeline(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.transformer = nn.Module()
+                self.transformer.blocks = nn.ModuleList([nn.Linear(4, 1, bias=False)])
+                self.weights_sources = [
+                    SimpleNamespace(
+                        model_or_path=str(source_root),
+                        subfolder="transformer",
+                        revision=None,
+                        prefix="transformer.",
+                    )
+                ]
+
+        pipeline = Pipeline()
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", pipeline.transformer),),
+            sources=tuple(pipeline.weights_sources),
+            model_path=None,
+            tensor_parallel_size=1,
+            use_hsdp=False,
+            online_quantization=False,
+        )
+
+        assert result.fallback_reason is None
+        assert result.plan is not None
+        assert result.plan.bindings["transformer.blocks.0.weight"] == TensorBinding(
+            checkpoint_key="blocks.0.weight",
+            file_path=str(checkpoint_file),
+        )
+        assert result.plan.planned_source_prefixes == frozenset({"transformer."})
+
+    def test_non_dedicated_component_source_falls_back_before_discovery(self):
+        pipeline = nn.Module()
+        pipeline.transformer = nn.Linear(2, 2, bias=False)
+        sources = (
+            SimpleNamespace(
+                model_or_path="unused",
+                subfolder=None,
+                revision=None,
+                prefix="",
+            ),
+        )
+
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", pipeline.transformer),),
+            sources=sources,
+            model_path=None,
+            tensor_parallel_size=1,
+            use_hsdp=False,
+            online_quantization=False,
+        )
+
+        assert result.plan is None
+        assert "dedicated component weight source" in result.fallback_reason
+
+    def test_preflight_falls_back_before_mutation_for_missing_model_tensors(self, tmp_path):
+        source_root = tmp_path / "partition"
+        checkpoint_dir = source_root / "transformer"
+        checkpoint_dir.mkdir(parents=True)
+        save_file(
+            {"blocks.0.weight": torch.ones((2, 2), dtype=torch.float32)},
+            str(checkpoint_dir / "model.safetensors"),
+        )
+
+        class Transformer(nn.Module):
+            _layerwise_offload_blocks_attrs = ["blocks"]
+
+            def __init__(self):
+                super().__init__()
+                self.blocks = nn.ModuleList([nn.Linear(2, 2, bias=False), nn.Linear(2, 2, bias=False)])
+
+        class Pipeline(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.transformer = Transformer()
+                self.weights_sources = [
+                    SimpleNamespace(
+                        model_or_path=str(source_root),
+                        subfolder="transformer",
+                        revision=None,
+                        prefix="transformer.",
+                    )
+                ]
+
+        pipeline = Pipeline()
+        original_weight = pipeline.transformer.blocks[0].weight
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", pipeline.transformer),),
+            sources=tuple(pipeline.weights_sources),
+            model_path=None,
+            tensor_parallel_size=1,
+            use_hsdp=False,
+            online_quantization=False,
+        )
+
+        assert result.plan is None
+        assert "1 required DiT tensors" in result.fallback_reason
+        assert pipeline.transformer.blocks[0].weight is original_weight
+        assert not original_weight.is_meta
+
+    def test_tp_falls_back_before_checkpoint_discovery(self):
        class SimpleModel(nn.Module):
            _layerwise_offload_blocks_attrs = ["blocks"]

@@ -912,41 +1185,118 @@ class TestMmapValidation:
                return key

        pipeline = SimplePipeline()
-        weights = {name: torch.ones(p.shape, dtype=p.dtype) for name, p in pipeline.named_parameters() if not p.is_meta}
-        save_file(weights, str(tmp_path / "model.safetensors"))
-        (tmp_path / "model.safetensors.index.json").write_text(
-            json.dumps({"weight_map": {k: "model.safetensors" for k in weights}})
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", pipeline.transformer),),
+            sources=(),
+            model_path="unused",
+            tensor_parallel_size=2,
+            use_hsdp=False,
+            online_quantization=False,
        )

-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=False,
-                model_path=str(tmp_path),
-            ),
-            torch.device("cpu"),
-        )
-        modules = SimpleNamespace(dits=[pipeline.transformer], dit_names=["transformer"])
+        assert result.plan is None
+        assert result.fallback_reason == "TP=2 requires the ordinary loader"

-        with pytest.raises(ValueError, match="Tensor Parallel"):
-            backend._load_weights_via_mmap(pipeline, modules)
+    @pytest.mark.parametrize(
+        ("use_hsdp", "online_quantization", "expected_reason"),
+        [
+            (True, False, "HSDP requires the ordinary loader"),
+            (False, True, "online quantization requires the ordinary loader"),
+        ],
+    )
+    def test_runtime_layouts_that_need_loader_callbacks_fall_back(
+        self,
+        use_hsdp,
+        online_quantization,
+        expected_reason,
+    ):
+        pipeline = nn.Module()
+        pipeline.transformer = nn.Linear(2, 2, bias=False)

-    def test_tp_allowed_when_tp_world_size_eq_1(self, tmp_path, patched_offload_runtime, monkeypatch):
-        """DLO+AllGather should work when TP world size = 1, even if params
-        have custom weight_loader attributes (e.g. QKVParallelLinear at TP=1)."""
-        import json
-        from types import SimpleNamespace
-
-        from safetensors.torch import save_file
-
-        from vllm_omni.diffusion.offloader.base import OffloadConfig, OffloadStrategy
-
-        # Mock TP world size = 1 (default)
-        monkeypatch.setattr(
-            "vllm.distributed.parallel_state.get_tensor_model_parallel_world_size",
-            lambda: 1,
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", pipeline.transformer),),
+            sources=(),
+            model_path="unused",
+            tensor_parallel_size=1,
+            use_hsdp=use_hsdp,
+            online_quantization=online_quantization,
        )

+        assert result.plan is None
+        assert result.fallback_reason == expected_reason
+
+    @pytest.mark.parametrize(
+        ("checkpoint_tensor", "expected_reason"),
+        [
+            (torch.ones((3, 2), dtype=torch.float32), "shape mismatch"),
+            (torch.ones((2, 2), dtype=torch.bfloat16), "dtype mismatch"),
+        ],
+    )
+    def test_source_metadata_mismatch_falls_back(self, tmp_path, checkpoint_tensor, expected_reason):
+        checkpoint_file = tmp_path / "model.safetensors"
+        save_file({"transformer.weight": checkpoint_tensor}, str(checkpoint_file))
+
+        class Pipeline(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.transformer = nn.Linear(2, 2, bias=False)
+
+            @staticmethod
+            def _remap_ckpt_key(key):
+                return key
+
+        pipeline = Pipeline()
+
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", pipeline.transformer),),
+            sources=(),
+            model_path=str(tmp_path),
+            tensor_parallel_size=1,
+            use_hsdp=False,
+            online_quantization=False,
+        )
+
+        assert result.plan is None
+        assert expected_reason in result.fallback_reason
+
+    def test_non_persistent_buffers_do_not_need_checkpoint_bindings(self, tmp_path):
+        class Transformer(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.weight = nn.Parameter(torch.ones(2, 2))
+                self.register_buffer("derived", torch.ones(2), persistent=False)
+
+        checkpoint_file = tmp_path / "model.safetensors"
+        save_file({"transformer.weight": torch.ones(2, 2)}, str(checkpoint_file))
+
+        class Pipeline(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.transformer = Transformer()
+
+            @staticmethod
+            def _remap_ckpt_key(key):
+                return key
+
+        pipeline = Pipeline()
+
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", pipeline.transformer),),
+            sources=(),
+            model_path=str(tmp_path),
+            tensor_parallel_size=1,
+            use_hsdp=False,
+            online_quantization=False,
+        )
+
+        assert result.plan is not None
+        assert set(result.plan.bindings) == {"transformer.weight"}
+
+    def test_unadapted_custom_weight_loader_falls_back_at_tp1(self, tmp_path):
        def custom_weight_loader(param, weight):
            param.data.copy_(weight)

@@ -976,27 +1326,67 @@ class TestMmapValidation:
            json.dumps({"weight_map": {k: "model.safetensors" for k in weights}})
        )

-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=False,
-                model_path=str(tmp_path),
-            ),
-            torch.device("cpu"),
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", pipeline.transformer),),
+            sources=(),
+            model_path=str(tmp_path),
+            tensor_parallel_size=1,
+            use_hsdp=False,
+            online_quantization=False,
        )
-        modules = SimpleNamespace(dits=[pipeline.transformer], dit_names=["transformer"])

-        # Should NOT raise — TP=1 is allowed even with custom weight_loader
-        backend._load_weights_via_mmap(pipeline, modules)
+        assert result.plan is None
+        assert "custom weight_loader" in result.fallback_reason
+
+    def test_cosmos3_adapter_preserves_existing_tp1_direct_mmap_contract(self, tmp_path):
+        from vllm_omni.diffusion.models.cosmos3.pipeline_cosmos3 import (
+            Cosmos3OmniDiffusersPipeline,
+        )
+        from vllm_omni.diffusion.models.cosmos3.transformer_cosmos3 import (
+            Cosmos3VFMTransformer,
+        )
+
+        transformer = object.__new__(Cosmos3VFMTransformer)
+        nn.Module.__init__(transformer)
+        transformer.proj_in = nn.Linear(2, 2, bias=False)
+        transformer.proj_in.weight.weight_loader = lambda *_args: None
+        pipeline = object.__new__(Cosmos3OmniDiffusersPipeline)
+        nn.Module.__init__(pipeline)
+        pipeline.transformer = transformer
+
+        source_root = tmp_path / "partition"
+        checkpoint_dir = source_root / "transformer"
+        checkpoint_dir.mkdir(parents=True)
+        checkpoint_file = checkpoint_dir / "model.safetensors"
+        save_file({"proj_in.weight": torch.ones(2, 2)}, str(checkpoint_file))
+        sources = (
+            SimpleNamespace(
+                model_or_path=str(source_root),
+                subfolder="transformer",
+                revision=None,
+                prefix="transformer.",
+            ),
+        )
+
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", transformer),),
+            sources=sources,
+            model_path=None,
+            tensor_parallel_size=1,
+            use_hsdp=False,
+            online_quantization=False,
+        )
+
+        assert result.plan is not None
+        assert result.plan.bindings["transformer.proj_in.weight"] == TensorBinding(
+            checkpoint_key="proj_in.weight",
+            file_path=str(checkpoint_file),
+        )

    def test_validate_loaded_weights_called(self, tmp_path, patched_offload_runtime):
        """validate_loaded_weights should be called after mmap loading."""
-        import json
-        from types import SimpleNamespace
-
-        from safetensors.torch import save_file
-
-        from vllm_omni.diffusion.offloader.base import OffloadConfig, OffloadStrategy

        class ModelWithValidate(nn.Module):
            _layerwise_offload_blocks_attrs = ["blocks"]
@@ -1038,19 +1428,64 @@ class TestMmapValidation:

        backend = DistributedLayerwiseOffloadBackend(
            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE, pin_cpu_memory=False, model_path=str(tmp_path)
+                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
+                pin_cpu_memory=False,
            ),
            torch.device("cpu"),
        )
        modules = SimpleNamespace(dits=[pipeline.transformer], dit_names=["transformer"])
+        result = build_checkpoint_mmap_plan(
+            pipeline,
+            dit_modules=(("transformer", pipeline.transformer),),
+            sources=(),
+            model_path=str(tmp_path),
+            tensor_parallel_size=1,
+            use_hsdp=False,
+            online_quantization=False,
+        )

-        backend._load_weights_via_mmap(pipeline, modules)
+        assert result.plan is not None
+        backend._load_weights_via_mmap(pipeline, modules, result.plan)
        assert pipeline.transformer.validate_called, "validate_loaded_weights should be called"


 class TestConfigValidation:
    """Tests for configuration validation in OffloadConfig / execute_request."""

+    def test_loader_plan_cannot_be_silently_dropped_on_unsupported_platform(self, monkeypatch):
+        import vllm_omni.diffusion.offloader as offloader_module
+
+        od_config = SimpleNamespace(
+            enable_cpu_offload=False,
+            enable_layerwise_offload=False,
+            enable_distributed_layerwise_offload=True,
+            dlo_use_allgather=False,
+            dlo_resident_layers=0,
+            pin_cpu_memory=False,
+            model="unused",
+            parallel_config=SimpleNamespace(
+                use_hsdp=False,
+                data_parallel_size=1,
+                sequence_parallel_size=1,
+            ),
+        )
+        plan = HostWeightPlan(
+            backing_kind="checkpoint_mmap",
+            bindings={},
+        )
+        monkeypatch.setattr(
+            offloader_module.current_omni_platform,
+            "supports_cpu_offload",
+            lambda: False,
+        )
+
+        with pytest.raises(RuntimeError, match="skipped ordinary weight materialization"):
+            offloader_module.get_offload_backend(
+                od_config,
+                device=torch.device("cpu"),
+                host_weight_plan=plan,
+            )
+
    def test_hsdp_with_allgather_rejected(self):
        """HSDP + DLO + AllGather should raise ValueError (double sharding)."""
        from vllm_omni.diffusion.offloader.base import OffloadConfig
@@ -740,6 +740,9 @@ def test_load_model_clears_cache_backend_for_unsupported_pipeline(monkeypatch):
            del kwargs
            return SimpleNamespace(transformer=torch.nn.Identity())

+        def take_host_weight_plan(self):
+            return None
+
    class _DummyMemoryProfiler:
        consumed_memory = 0

@@ -779,7 +782,11 @@ def test_load_model_clears_cache_backend_for_unsupported_pipeline(monkeypatch):
    monkeypatch.setattr(model_runner_module, "LoadConfig", lambda: object())
    monkeypatch.setattr(model_runner_module, "DiffusersPipelineLoader", _DummyLoader)
    monkeypatch.setattr(model_runner_module, "DeviceMemoryProfiler", _DummyMemoryProfiler)
-    monkeypatch.setattr(model_runner_module, "get_offload_backend", lambda od_config, device: None)
+    monkeypatch.setattr(
+        model_runner_module,
+        "get_offload_backend",
+        lambda od_config, device, host_weight_plan: None,
+    )
    monkeypatch.setattr(
        model_runner_module, "get_cache_backend", lambda cache_backend, cache_config: dummy_cache_backend
    )
@@ -418,3 +418,77 @@ class TestShutdownCleansUpFutures:

        assert len(executor._rpc_futures) == 0
        assert len(executor._output_futures) == 0
+
+
+class _RacyFuture(concurrent.futures.Future):
+    # done() always lies and reports False, to deterministically force the
+    # pump's check-then-act race window without needing real thread timing.
+    def done(self) -> bool:
+        return False
+
+
+def _racy_cancelled_future() -> concurrent.futures.Future:
+    fut = _RacyFuture()
+    fut.cancel()
+    assert fut.cancelled()
+    return fut
+
+
+class TestResultPumpCancelledFutureRace:
+    """Regression for #5793: a future cancelled concurrently with the pump's
+    resolve call (e.g. an asyncio.wait_for timeout, or a request abort) must
+    be dropped, not raise InvalidStateError and kill the pump thread.
+    """
+
+    def test_compute_done_racing_cancel_does_not_crash_pump(self):
+        executor = _make_executor()
+        fut = _racy_cancelled_future()
+        with executor._futures_lock:
+            executor._rpc_futures["1"] = fut
+
+        msg = AsyncDiffusionOutput(kind=AsyncOutputKind.COMPUTE_DONE, rpc_id="1", async_output_id="abc")
+        _feed_one_msg_to_pump(executor, msg)
+
+        assert fut.cancelled()
+
+    def test_output_ready_racing_cancel_does_not_crash_pump(self):
+        executor = _make_executor()
+        fut = _racy_cancelled_future()
+        with executor._futures_lock:
+            executor._output_futures["abc123"] = fut
+
+        msg = AsyncDiffusionOutput(
+            kind=AsyncOutputKind.OUTPUT_READY,
+            async_output_id="abc123",
+            output=DiffusionOutput(output="late"),
+        )
+        _feed_one_msg_to_pump(executor, msg)
+
+        assert fut.cancelled()
+
+    def test_batch_split_racing_cancel_does_not_crash_pump(self):
+        executor = _make_executor()
+        cancelled_fut = _racy_cancelled_future()
+        healthy_fut = concurrent.futures.Future()
+        with executor._futures_lock:
+            executor._output_futures["batch-1/r-aborted"] = cancelled_fut
+            executor._output_futures["batch-1/r-healthy"] = healthy_fut
+            executor._batch_split_map["batch-1"] = {
+                "batch-1/r-aborted": "r-aborted",
+                "batch-1/r-healthy": "r-healthy",
+            }
+
+        outputs = {
+            "r-aborted": DiffusionOutput(output="late"),
+            "r-healthy": DiffusionOutput(output="ok"),
+        }
+        msg = AsyncDiffusionOutput(
+            kind=AsyncOutputKind.OUTPUT_READY,
+            async_output_id="batch-1",
+            output=_FakeBatchOutput(outputs),
+        )
+        _feed_one_msg_to_pump(executor, msg)
+
+        assert cancelled_fut.cancelled()
+        assert healthy_fut.done()
+        assert healthy_fut.result(timeout=1.0) is outputs["r-healthy"]
@@ -1728,3 +1728,112 @@ def test_purge_is_noop_on_empty_deques(build_adapter):
    adapter.restore_queues(waiting_queue, running_queue, scheduler_requests={})
    assert running_queue == []
    assert waiting_queue == []
+
+
+# --------------------------------------------------------------------------- #
+#  Chunk-wait deadline (RFC #4855 R1.1, issue #3833)
+#
+#  Before this, the async-chunk path had no deadline of any kind, so a dropped
+#  terminal chunk or an upstream stage that died mid-stream parked the request
+#  in WAITING_FOR_CHUNK forever. None of these scenarios were covered.
+# --------------------------------------------------------------------------- #
+
+
+def _park_in_chunk_wait(adapter, request, *, waiting=True):
+    """Drive a request through one process_pending_chunks round into the wait."""
+    queue = DummyWaitingQueue([request]) if waiting else [request]
+    if waiting:
+        adapter.process_pending_chunks(queue, [])
+    else:
+        adapter.process_pending_chunks(DummyWaitingQueue(), queue)
+    return queue
+
+
+def test_chunk_wait_clock_starts_when_a_request_parks(build_adapter):
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    request = _req("r1", RequestStatus.WAITING)
+
+    _park_in_chunk_wait(adapter, request)
+
+    assert request.status == RequestStatus.WAITING_FOR_CHUNK
+    assert "r1" in adapter._waiting_since
+
+
+def test_chunk_wait_does_not_expire_before_the_deadline(build_adapter):
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    _park_in_chunk_wait(adapter, _req("r1", RequestStatus.WAITING))
+
+    assert adapter.collect_timed_out_request_ids(timeout_s=600.0) == set()
+    assert "r1" in adapter._waiting_since
+
+
+def test_dropped_terminal_chunk_expires_the_request(build_adapter):
+    """The #3833 scenario: upstream stops sending and never marks the stream done."""
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    _park_in_chunk_wait(adapter, _req("r1", RequestStatus.WAITING))
+
+    adapter._waiting_since["r1"] -= 601.0
+
+    assert adapter.collect_timed_out_request_ids(timeout_s=600.0) == {"r1"}
+    # Cleared, so a second sweep does not re-report the same request.
+    assert adapter.collect_timed_out_request_ids(timeout_s=600.0) == set()
+
+
+def test_arriving_chunk_resets_the_clock(build_adapter):
+    """A slow but healthy stream must never expire: the deadline measures stall
+    time between chunks, not the lifetime of the stream."""
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    request = _req("r1", RequestStatus.WAITING)
+    queue = _park_in_chunk_wait(adapter, request)
+    adapter.restore_queues(queue, [], scheduler_requests={"r1": request})
+
+    # Age the wait to just under the deadline, then deliver a chunk.
+    adapter._waiting_since["r1"] -= 599.0
+    adapter._finished_load_reqs.add("r1")
+    adapter.process_pending_chunks(queue, [])
+
+    assert "r1" not in adapter._waiting_since
+    assert adapter.collect_timed_out_request_ids(timeout_s=600.0) == set()
+
+
+def test_a_disabled_deadline_never_expires(build_adapter):
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    _park_in_chunk_wait(adapter, _req("r1", RequestStatus.WAITING))
+    adapter._waiting_since["r1"] -= 10_000.0
+
+    assert adapter.collect_timed_out_request_ids(timeout_s=0.0) == set()
+    assert adapter.collect_timed_out_request_ids(timeout_s=-1.0) == set()
+
+
+def test_finished_request_leaves_no_stale_timestamp(build_adapter):
+    """Otherwise a completed request would be reported as timed out later."""
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    request = _req("r1", RequestStatus.WAITING)
+    _park_in_chunk_wait(adapter, request)
+
+    adapter.finish_requests(["r1"], RequestStatus.FINISHED_STOPPED, {"r1": request})
+
+    assert "r1" not in adapter._waiting_since
+    assert adapter.collect_timed_out_request_ids(timeout_s=0.001) == set()
+
+
+def test_aborted_request_leaves_no_stale_timestamp(build_adapter):
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    _park_in_chunk_wait(adapter, _req("r1", RequestStatus.WAITING))
+
+    adapter.cleanup_receiver("r1")
+
+    assert "r1" not in adapter._waiting_since
+
+
+def test_expiry_is_per_request(build_adapter):
+    """One stalled stream must not take down its healthy neighbours."""
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar", max_num_seqs=4)
+    stalled = _req("stalled", RequestStatus.WAITING)
+    healthy = _req("healthy", RequestStatus.WAITING)
+    adapter.process_pending_chunks(DummyWaitingQueue([stalled, healthy]), [])
+
+    adapter._waiting_since["stalled"] -= 601.0
+
+    assert adapter.collect_timed_out_request_ids(timeout_s=600.0) == {"stalled"}
+    assert "healthy" in adapter._waiting_since
@@ -463,7 +463,7 @@ def _audio_metrics(reference: np.ndarray, prediction: np.ndarray) -> dict[str, f
    }


-@pytest.mark.full_model
+@pytest.mark.slow
@pytest.mark.benchmark
@pytest.mark.diffusion
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@@ -591,7 +591,7 @@ def test_ltx25_distilled_two_stage_matches_official(accuracy_artifact_root: Path
    assert audio_metrics["relative_l2"] <= LTX25_AUDIO_RELATIVE_L2_THRESHOLD


-@pytest.mark.full_model
+@pytest.mark.slow
@pytest.mark.benchmark
@pytest.mark.diffusion
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@@ -544,7 +544,7 @@ def _audio_metrics(reference: np.ndarray, prediction: np.ndarray) -> dict[str, f


@pytest.mark.parametrize("case", CASES, ids=lambda case: case.name)
-@pytest.mark.full_model
+@pytest.mark.slow
@pytest.mark.benchmark
@pytest.mark.diffusion
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@@ -17,7 +17,7 @@ from tests.helpers.mark import hardware_test
 from tests.helpers.media import get_asset_path
 from tests.helpers.runtime import OmniServer

-pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
+pytestmark = [pytest.mark.diffusion]


 SINGLE_MODEL = "Qwen/Qwen-Image-Edit"
@@ -253,6 +253,7 @@ def _diffusers_output_single_image(accuracy_artifact_root: Path, qwen_bear_image
    )


+@pytest.mark.slow
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
 def test_qwen_image_edit_single_matches_diffusers(
@@ -278,6 +279,7 @@ def test_qwen_image_edit_single_matches_diffusers(
    )


+@pytest.mark.slow
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
 def test_qwen_image_edit_2511_matches_diffusers_pixelwise(accuracy_artifact_root: Path) -> None:
@@ -17,7 +17,7 @@ from tests.helpers.env import run_post_test_cleanup, run_pre_test_cleanup
 from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniServer

-pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
+pytestmark = [pytest.mark.slow, pytest.mark.diffusion]


 MODEL_ID = "Qwen/Qwen-Image-Layered"
@@ -11,7 +11,7 @@ from tests.e2e.accuracy.helpers import assert_video_similarity_metrics, probe_vi
 from tests.helpers.mark import hardware_marks
 from tests.helpers.runtime import DiffusionResponse, OmniServer, OpenAIClientHandler

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

 MODEL = "BestWishYsh/Helios-Distilled"
 PROMPT = "A serene lakeside sunrise with mist over the water."
@@ -138,7 +138,7 @@ def _make_file_lora_request(adapter_dir: Path) -> LoRARequest:


 pytestmark = [
-    pytest.mark.full_model,
+    pytest.mark.slow,
    pytest.mark.diffusion,
    pytest.mark.parametrize("omni_runner", [_OMNI_RUNNER_PARAM], indirect=True),
 ]
@@ -109,7 +109,7 @@ def _diffusion_sampling_params(


 pytestmark = [
-    pytest.mark.full_model,
+    pytest.mark.slow,
    pytest.mark.diffusion,
 ]

@@ -3,19 +3,18 @@
 """Smoke test for HunyuanImage-3.0 Image-to-Text (I2T) pipeline."""

 from collections.abc import Generator
-from pathlib import Path

 import pytest
 import torch
 from PIL import Image

 from tests.helpers.runtime import OmniRunner
+from tests.helpers.stage_config import get_deploy_config_path
 from vllm_omni import Omni
 from vllm_omni.diffusion.models.hunyuan_image3.prompt_utils import build_prompt

 MODEL_NAME = "tencent/HunyuanImage-3.0-Instruct"
-REPO_ROOT = Path(__file__).resolve().parents[3]
-STAGE_CONFIG_PATH = REPO_ROOT / "vllm_omni" / "model_executor" / "stage_configs" / "hunyuan_image3_i2t.yaml"
+DEPLOY_CONFIG_PATH = get_deploy_config_path("hunyuan_image3_ar.yaml")

 # First 20 generated token IDs from the HF greedy reference on this input.
 # vllm-omni AR output matches this prefix bitwise; the two implementations
@@ -45,14 +44,14 @@ EXPECTED_PREFIX_TOKEN_IDS: list[int] = [
 # Decoded form, kept only for human-readable assertion messages.
 EXPECTED_PREFIX_TEXT = "The image is a solid, uniform green color with no variations, objects, or details present. It"

-pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
+pytestmark = [pytest.mark.full_model, pytest.mark.diffusion, pytest.mark.cuda]


@pytest.fixture(scope="module")
 def omni() -> Generator[Omni, None, None]:
    with OmniRunner(
        MODEL_NAME,
-        deploy_config=str(STAGE_CONFIG_PATH),
+        deploy_config=DEPLOY_CONFIG_PATH,
        trust_remote_code=True,
    ) as runner:
        yield runner.omni
@@ -0,0 +1,84 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""End-to-end smoke test for LingBot-World v2 generation."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from tests.helpers.mark import hardware_test
+from tests.helpers.runtime import OmniRunnerHandler
+from vllm_omni.diffusion.data import DiffusionParallelConfig
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+MODEL = os.environ.get(
+    "VLLM_OMNI_LINGBOT_WORLD_V2_CHECKPOINT_PATH",
+    "robbyant/lingbot-world-v2-14b-causal-fast-diffusers",
+)
+_IMAGE_PATH = os.environ.get("VLLM_OMNI_LINGBOT_WORLD_V2_IMAGE_PATH")
+_ACTION_DIR_VALUE = os.environ.get("VLLM_OMNI_LINGBOT_WORLD_V2_ACTION_DIR")
+_ACTION_DIR = Path(_ACTION_DIR_VALUE).expanduser().resolve() if _ACTION_DIR_VALUE else None
+_HAS_INPUT_ASSETS = _IMAGE_PATH is not None and _ACTION_DIR is not None
+
+_OMNI_RUNNER_PARAM = (
+    MODEL,
+    None,
+    {
+        "flow_shift": 5.0,
+        "parallel_config": DiffusionParallelConfig(tensor_parallel_size=1),
+        "model_config": {
+            "lingbot_action_root": str(_ACTION_DIR.parent) if _ACTION_DIR is not None else "",
+        },
+    },
+)
+
+pytestmark = [
+    pytest.mark.slow,
+    pytest.mark.diffusion,
+    pytest.mark.skipif(
+        not _HAS_INPUT_ASSETS,
+        reason=("VLLM_OMNI_LINGBOT_WORLD_V2_IMAGE_PATH and VLLM_OMNI_LINGBOT_WORLD_V2_ACTION_DIR are required"),
+    ),
+    pytest.mark.parametrize("omni_runner", [_OMNI_RUNNER_PARAM], indirect=True),
+]
+
+
+@hardware_test(res={"cuda": "H100"}, num_cards=1)
+def test_lingbot_world_v2_tp1_one_block(omni_runner_handler: OmniRunnerHandler) -> None:
+    """Load the real checkpoint and generate one causal block on one GPU."""
+
+    assert _IMAGE_PATH is not None
+    assert _ACTION_DIR is not None
+    response = omni_runner_handler.send_diffusion_request(
+        {
+            "model": omni_runner_handler.runner.model_name,
+            "prompt": "The camera moves slowly forward through the scene.",
+            "multi_modal_data": {"image": str(Path(_IMAGE_PATH).expanduser().resolve())},
+            "sampling_params": OmniDiffusionSamplingParams(
+                height=480,
+                width=832,
+                num_frames=9,
+                num_inference_steps=4,
+                max_sequence_length=512,
+                seed=42,
+                fps=16,
+                extra_args={
+                    "action_path": _ACTION_DIR.name,
+                    "flow_shift": 5.0,
+                },
+            ),
+        }
+    )
+
+    assert response.success
+    assert response.images is not None
+    assert len(response.images) == 1
+    video = np.asarray(response.images[0])
+    assert video.shape == (1, 9, 480, 832, 3)
+    assert np.isfinite(video).all()
+    assert float(np.ptp(video)) > 1e-6
+    assert float(np.mean(np.abs(np.diff(video, axis=1)))) > 1e-6
@@ -96,7 +96,7 @@ def _get_test_config() -> str:

 pytestmark = [
    pytest.mark.skip(reason="https://github.com/vllm-project/vllm-omni/issues/4700"),
-    pytest.mark.full_model,
+    pytest.mark.slow,
    pytest.mark.tts,
    pytest.mark.parametrize(
        "omni_runner",
@@ -271,7 +271,6 @@ def _collect_audio(omni_runner: OmniRunner, request: dict) -> tuple[torch.Tensor
 # ---------------------------------------------------------------------------


-@pytest.mark.advanced_model
@hardware_test(res={"cuda": "L4"}, num_cards=1)
 def test_moss_tts_realtime_english(omni_runner: OmniRunner, ref_audio_path: str) -> None:
    """MossTTSRealtime: English voice_clone produces non-empty 24 kHz audio."""
@@ -283,7 +282,6 @@ def test_moss_tts_realtime_english(omni_runner: OmniRunner, ref_audio_path: str)
    assert not torch.all(audio == 0), "Audio is silence"


-@pytest.mark.advanced_model
@hardware_test(res={"cuda": "L4"}, num_cards=1)
 def test_moss_tts_realtime_chinese(omni_runner: OmniRunner, ref_audio_path: str) -> None:
    """MossTTSRealtime: Chinese input produces non-empty audio."""
@@ -22,7 +22,7 @@ from tests.helpers.mark import hardware_marks
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler, dummy_messages_from_mix_data
 from tests.helpers.stage_config import get_deploy_config_path, modify_stage_config

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

 PROMPT = "A futuristic city skyline at twilight, cyberpunk style, ultra-detailed, high resolution."
 NEGATIVE_PROMPT = "low quality, blurry, distorted, deformed, watermark"
@@ -1,6 +1,8 @@
-"""Nightly-only e2e coverage for BAGEL diffusion multi-replica serving.
+"""Weekly e2e coverage for BAGEL diffusion multi-replica serving.

 This test needs 4 H100 GPUs; keep it out of generic test_bagel_* jobs.
+It is covered by the weekly Diffusion · H100 · Multi-GPU lane
+(``.buildkite/cuda/test-weekly.yml``, ``mirror_hardwares: h100_4``).
 """

 import os
@@ -17,7 +19,7 @@ MODEL = "ByteDance-Seed/BAGEL-7B-MoT"
 MULTI_REPLICA_DEPLOY = get_deploy_config_path("ci/bagel_multi_replicas_4gpu.yaml")
 ROUTE_STRESS_REQUESTS = 6

-pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
+pytestmark = [pytest.mark.slow, pytest.mark.diffusion]

 test_params = [
    OmniServerParams(
@@ -16,7 +16,7 @@ parity vs. the Diffusers baseline is covered separately (see the support plan, s
 From ``tests/``::

    pytest -s -v e2e/online_serving/test_boogu_image.py \
-        -m "full_model and diffusion and H100" --run-level=full_model
+        -m "slow and diffusion and H100"
 """

 import os
@@ -28,7 +28,7 @@ from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHand

 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

 MODEL = "Boogu/Boogu-Image-0.1-Base"
 T2I_PROMPT = "A mountain lake at sunset, photorealistic, cinematic lighting"
@@ -19,7 +19,7 @@ reference image is supported.
 From ``tests/``::

    pytest -s -v e2e/online_serving/test_boogu_image_edit.py \
-        -m "full_model and diffusion and H100" --run-level=full_model
+        -m "slow and diffusion and H100"
 """

 import base64
@@ -35,7 +35,7 @@ from tests.helpers.runtime import (
    dummy_messages_from_mix_data,
 )

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

 EDIT_PROMPT = "Change the style to a colored pencil drawing."
 SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
@@ -25,7 +25,7 @@ model and are intentionally omitted.
 From ``tests/``::

    pytest -s -v e2e/online_serving/test_boogu_image_edit_expansion.py \
-        -m "full_model and diffusion and H100" --run-level=full_model
+        -m "slow and diffusion and H100"
 """

 import os
@@ -38,7 +38,7 @@ from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHand

 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

 MODEL = "Boogu/Boogu-Image-0.1-Edit"
 EDIT_PROMPT = "Change the style to a colored pencil drawing."
@@ -30,7 +30,7 @@ omitted (add rows here when those features land).
 From ``tests/``::

    pytest -s -v e2e/online_serving/test_boogu_image_expansion.py \
-        -m "full_model and diffusion and H100" --run-level=full_model
+        -m "slow and diffusion and H100"
 """

 import os
@@ -42,7 +42,7 @@ from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHand

 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

 MODEL = "Boogu/Boogu-Image-0.1-Base"
 T2I_PROMPT = "A mountain lake at sunset, photorealistic, cinematic lighting"
@@ -30,7 +30,7 @@ from tests.helpers.runtime import (
 )
 from tests.helpers.stage_config import get_deploy_config_path, modify_stage_config

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

 MODEL = os.environ.get("GLM_IMAGE_MODEL_PATH", "zai-org/GLM-Image")

@@ -63,7 +63,7 @@ def test_text_to_image_moe(omni_server: OmniServer, openai_client: OpenAIClientH
    )


-@pytest.mark.full_model
+@pytest.mark.slow
@pytest.mark.diffusion
@pytest.mark.parametrize("omni_server", _get_server_cases(MODEL), indirect=True)
 def test_video_generation_modes_moe(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
@@ -10,7 +10,7 @@ from tests.helpers.mark import hardware_marks
 from tests.helpers.media import generate_synthetic_image
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

 DISTILLED_MODEL = os.getenv("VLLM_TEST_LTX23_DISTILLED_MODEL", "diffusers/LTX-2.3-Distilled-Diffusers")
 BASE_MODEL = os.getenv("VLLM_TEST_LTX23_MODEL", "diffusers/LTX-2.3-Diffusers")
@@ -20,7 +20,7 @@ MODEL = os.getenv("VLLM_TEST_LTX25_MODEL", DEFAULT_MODEL)
 MODEL_REVISION = os.getenv("VLLM_TEST_LTX25_MODEL_REVISION", DEFAULT_REVISION if MODEL == DEFAULT_MODEL else "")
 PROMPT = "A red fox walks through a snowy forest while the camera remains fixed."

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 SINGLE_CARD_MARKS = hardware_marks(res={"cuda": "H100"})


@@ -20,7 +20,7 @@ from tests.helpers.stage_config import get_deploy_config_path

 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

-pytestmark = [pytest.mark.omni, pytest.mark.full_model]
+pytestmark = [pytest.mark.omni, pytest.mark.slow]

 _SKIP_NEED_4_H100_NOT_CI = pytest.mark.skip(
    reason="Requires 4x H100 GPUs; skipped in CI for now.",
@@ -2,20 +2,19 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 """
-Online serving tests for the Qwen-Image-Edit family (image-to-image via chat completions).
+Online serving tests for ``Qwen/Qwen-Image-Edit-2511`` (image-to-image via chat completions).

- ``test_single_image_to_image_001``: ``Qwen/Qwen-Image-Edit`` — one reference image, fixed 512×512.
- ``test_multi_images_to_image_001``: ``Qwen/Qwen-Image-Edit-2511`` — two reference images, fixed 512×512.
- ``test_different_sizes_001``: ``Qwen/Qwen-Image-Edit-2511`` only, ``slow`` — mixed input
-  resolutions; ``extra_body`` uses per-output ``width``/``height`` lists; the test client sends one
-  scalar-size request per list index in parallel and merges images (see ``OpenAIClientHandler.send_diffusion_request``).
+- ``test_single_image_to_image_001``: one reference image, fixed 512×512.
+- ``test_multi_images_to_image_001``: two reference images, fixed 512×512.
+- ``test_different_sizes_001``: ``slow`` — mixed input resolutions; ``extra_body`` uses
+  per-output ``width``/``height`` lists; the test client sends one scalar-size request
+  per list index in parallel and merges images (see ``OpenAIClientHandler.send_diffusion_request``).

-``_get_diffusion_feature_cases`` registers a single ``default`` ``OmniServerParams`` row per model.
+``_get_diffusion_feature_cases`` registers a single ``default`` ``OmniServerParams`` row.

 From ``tests/``::

-    pytest -s -v e2e/online_serving/test_qwen_image_edit.py -m "core_model and diffusion" --run-level=core_model
-    pytest -s -v e2e/online_serving/test_qwen_image_edit.py -m "advanced_model and diffusion" --run-level=advanced_model
+    pytest -s -v e2e/online_serving/test_qwen_image_edit.py -m "slow and diffusion" --run-level=full_model
 """

 import pytest
@@ -29,6 +28,7 @@ from tests.helpers.runtime import (
    dummy_messages_from_mix_data,
 )

+MODEL = "Qwen/Qwen-Image-Edit-2511"
 EDIT_PROMPT = "Transform this modern, geometrist image into a Vincent van Gogh style impressionist painting."
 MULTI_EDIT_PROMPT = (
    "Transform the first image into a Dadaism collage art. "
@@ -39,7 +39,7 @@ NEGATIVE_PROMPT = "blurry, low quality, modern, geometrist"
 SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})


-def _get_diffusion_feature_cases(model: str):
+def _get_diffusion_feature_cases(model: str = MODEL):
    """Return one ``default`` ``OmniServerParams`` row for ``model`` (no extra ``server_args``)."""
    return [
        pytest.param(
@@ -56,11 +56,11 @@ def _get_diffusion_feature_cases(model: str):
@pytest.mark.diffusion
@pytest.mark.parametrize(
    "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit"),
+    _get_diffusion_feature_cases(),
    indirect=True,
 )
 def test_single_image_to_image_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):
-    """Single-reference edit smoke for ``Qwen/Qwen-Image-Edit`` (CFG when negative prompt + ``true_cfg_scale`` > 1)."""
+    """Single-reference edit smoke for ``Qwen/Qwen-Image-Edit-2511`` (CFG when negative prompt + ``true_cfg_scale`` > 1)."""
    image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"

    messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=EDIT_PROMPT)
@@ -86,7 +86,7 @@ def test_single_image_to_image_001(omni_server: OmniServer, openai_client: OpenA
@pytest.mark.diffusion
@pytest.mark.parametrize(
    "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit-2511"),
+    _get_diffusion_feature_cases(),
    indirect=True,
 )
 def test_multi_images_to_image_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):
@@ -117,7 +117,7 @@ def test_multi_images_to_image_001(omni_server: OmniServer, openai_client: OpenA
@pytest.mark.diffusion
@pytest.mark.parametrize(
    "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit-2511"),
+    _get_diffusion_feature_cases(),
    indirect=True,
 )
 def test_different_sizes_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):
@@ -1,8 +1,6 @@
 """
 Comprehensive tests of diffusion features that are available in online serving mode
-and are supported by the following models:
- Qwen-Image-Edit: single image input
- Qwen-Image-Edit-2511: single image input and two image inputs
+and are supported by Qwen-Image-Edit-2511 (single image input and two image inputs).
 """

 import pytest
@@ -11,10 +9,10 @@ from tests.helpers.mark import hardware_marks
 from tests.helpers.media import generate_synthetic_image
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler, dummy_messages_from_mix_data

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

-EDIT_PROMPT = "Transform this modern, geometrist image into a Vincent van Gogh style impressionist painting."
-SINGLE_EDIT_PROMPT_2511 = "Restyle this image into a Vincent van Gogh style impressionist painting."
+MODEL = "Qwen/Qwen-Image-Edit-2511"
+SINGLE_EDIT_PROMPT = "Restyle this image into a Vincent van Gogh style impressionist painting."
 MULTI_EDIT_PROMPT = (
    "Transform the first image into a Dadaism collage art. "
    "Transform the second image into a Vincent van Gogh style painting. "
@@ -25,9 +23,7 @@ SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
 PARALLEL_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"}, num_cards=2)


-# This test file targets two models, so I write a helper function.
-# If a similar test only involves one model, one can just define a global list variable.
-def _get_diffusion_feature_cases(model: str):
+def _get_diffusion_feature_cases(model: str = MODEL):
    return [
        pytest.param(
            OmniServerParams(
@@ -123,35 +119,7 @@ def _get_diffusion_feature_cases(model: str):

@pytest.mark.parametrize(
    "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit"),
-    indirect=True,
-)
-def test_qwen_image_edit(omni_server: OmniServer, openai_client: OpenAIClientHandler):
-    """Test all diffusion features with Qwen-Image-Edit in regular end-user scenarios."""
-    image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"
-
-    messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=EDIT_PROMPT)
-
-    # CFG parallel is only activated when a negative prompt and true_cfg_scale > 1.0 are both present
-    request_config = {
-        "model": omni_server.model,
-        "messages": messages,
-        "extra_body": {
-            "height": 512,
-            "width": 512,
-            "num_inference_steps": 2,
-            "negative_prompt": NEGATIVE_PROMPT,
-            "true_cfg_scale": 4.0,
-            "seed": 42,
-        },
-    }
-
-    openai_client.send_diffusion_request(request_config)
-
-
-@pytest.mark.parametrize(
-    "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit-2511"),
+    _get_diffusion_feature_cases(),
    indirect=True,
 )
 def test_qwen_image_edit_2511_single_image(omni_server: OmniServer, openai_client: OpenAIClientHandler):
@@ -166,7 +134,7 @@ def test_qwen_image_edit_2511_single_image(omni_server: OmniServer, openai_clien
    """
    image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"

-    messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=SINGLE_EDIT_PROMPT_2511)
+    messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=SINGLE_EDIT_PROMPT)

    request_config = {
        "model": omni_server.model,
@@ -186,7 +154,7 @@ def test_qwen_image_edit_2511_single_image(omni_server: OmniServer, openai_clien

@pytest.mark.parametrize(
    "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit-2511"),
+    _get_diffusion_feature_cases(),
    indirect=True,
 )
 def test_qwen_image_edit_2511_two_images(omni_server: OmniServer, openai_client: OpenAIClientHandler):
@@ -18,7 +18,7 @@ from tests.helpers.mark import hardware_marks
 from tests.helpers.media import decode_b64_image, generate_synthetic_image
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler, dummy_messages_from_mix_data

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

 MODEL = "Qwen/Qwen-Image-Layered"
 EDIT_PROMPT = "Decompose this image into layers."
@@ -16,7 +16,7 @@ from tests.helpers.media import load_test_audio_data_url
 from tests.helpers.runtime import OmniServerParams
 from tests.helpers.stage_config import get_deploy_config_path

-pytestmark = [pytest.mark.full_model, pytest.mark.tts]
+pytestmark = [pytest.mark.slow, pytest.mark.tts]

 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

@@ -26,7 +26,7 @@ import pytest
 from tests.helpers.mark import hardware_marks
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler

-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]

 MODEL = "Wan-AI/Wan2.1-VACE-1.3B-diffusers"
 PROMPT = "A cat walking slowly across a sunlit garden path"
@@ -411,6 +411,51 @@ async def test_async_chunk_prewarm_failure_reports_failing_downstream_stage(orch
            orchestrator_fixture.thread.join(timeout=5)


+@pytest.mark.asyncio
+async def test_async_chunk_prewarm_without_prompt_token_ids_fails_only_that_request(
+    orchestrator_factory,
+) -> None:
+    """R1.3 of #4855: an async-chunk request whose stage-0 prompt carries no
+    token ids cannot prewarm anything, so it must fail with a client error
+    instead of stalling.
+
+    Non-fatal is the point: ``fatal=True`` reads as "the engine died" and the
+    entrypoints act on it (``OmniLLM.generate`` raises out of its batch loop,
+    the serving layer calls ``terminate_if_errored``). One client's embeds-only
+    prompt must not reach other requests.
+    """
+    stage0 = FakeStageClient(stage_type="llm", final_output=False)
+    stage1 = FakeStageClient(stage_type="llm", final_output=True)
+    orchestrator_fixture = orchestrator_factory([stage0, stage1], async_chunk=True)
+
+    try:
+        await _enqueue_add_request(
+            orchestrator_fixture,
+            request_id="req-embeds",
+            # No prompt_token_ids: what an embeds-only prompt looks like here.
+            prompt=SimpleNamespace(request_id="req-embeds"),
+            original_prompt={"prompt": "embeds only"},
+            sampling_params_list=[_sampling_params(), _sampling_params()],
+            final_stage_id=1,
+        )
+
+        error_msg = await _wait_for_error_message(orchestrator_fixture, request_id="req-embeds")
+        assert error_msg.fatal is False
+        assert error_msg.status_code == 400
+        assert error_msg.error_type == "BadRequestError"
+        assert error_msg.stage_id == 0
+        assert "prompt_token_ids" in error_msg.error
+
+        # Nothing was prewarmed downstream, and the request is gone.
+        assert stage1.add_request_calls == []
+        await _wait_for(lambda: "req-embeds" not in orchestrator_fixture.orchestrator.request_states)
+        assert orchestrator_fixture.thread.is_alive()
+    finally:
+        if orchestrator_fixture.thread.is_alive():
+            orchestrator_fixture.request_sync_q.put_nowait(ShutdownRequestMessage())
+            orchestrator_fixture.thread.join(timeout=5)
+
+
 # ───────── Direct unit tests for the fault-isolation helpers ─────────


@@ -256,12 +256,13 @@ async def test_async_prewarm_skips_outgoing_only_stage() -> None:
        duplex_identity=SimpleNamespace(),
    )

-    await orchestrator._prewarm_async_chunk_stages(
+    prewarmed = await orchestrator._prewarm_async_chunk_stages(
        "req-prewarm",
        SimpleNamespace(prompt_token_ids=[1, 2], resumable=True),
        req_state,
    )

+    assert prewarmed is True
    assert stage1.submitted == []
    assert len(stage2.submitted) == 1
    assert 1 not in req_state.stage_submit_ts
@@ -277,6 +278,7 @@ async def test_async_prewarm_skips_outgoing_only_stage() -> None:
@pytest.mark.asyncio
 async def test_duplex_prewarm_runs_after_first_stage0_submission() -> None:
    port, stage_pools, request_states, prewarm, submission = _duplex_stage_port_submission()
+    prewarm.return_value = True

    result = await port.submit(submission)

@@ -285,6 +287,30 @@ async def test_duplex_prewarm_runs_after_first_stage0_submission() -> None:
    prewarm.assert_awaited_once_with("req-duplex", ANY, request_states["req-duplex"])


+@pytest.mark.asyncio
+async def test_duplex_submit_bails_out_when_prewarm_failed_the_request() -> None:
+    """A failed prewarm has already aborted the request and popped its state.
+
+    Returning a success result here would hand the control plane a replica for a
+    request that no longer exists, and the trailing bookkeeping would re-register
+    a running counter the cleanup just released -- a leak that never decrements.
+    """
+    port, stage_pools, request_states, prewarm, submission = _duplex_stage_port_submission()
+    counter = MagicMock()
+    port._running_counter = counter
+    prewarm.return_value = False
+    request_state = request_states["req-duplex"]
+
+    with pytest.raises(RuntimeError, match="prewarm failed"):
+        await port.submit(submission)
+
+    prewarm.assert_awaited_once()
+    assert request_state.duplex_stage_fences == {}
+    assert request_state.stage_submit_ts == {}
+    assert request_state.running_counter_registered is False
+    counter.increment.assert_not_called()
+
+
@pytest.mark.asyncio
 async def test_async_route_forwards_to_outgoing_only_stage() -> None:
    orchestrator = object.__new__(Orchestrator)
@@ -26,8 +26,13 @@ pytestmark = [pytest.mark.core_model, pytest.mark.cpu]


 def _serving(model_type: str = "audex") -> OmniOpenAIServingSpeech:
+    from vllm_omni.entrypoints.openai.tts_adapters import resolve_adapter
+    from vllm_omni.entrypoints.openai.tts_adapters.base import SpeechServingContext
+
    serving = OmniOpenAIServingSpeech.__new__(OmniOpenAIServingSpeech)
    serving._tts_model_type = model_type
+    adapter_cls = resolve_adapter(model_type)
+    serving._adapter = adapter_cls(SpeechServingContext(server=serving)) if adapter_cls is not None else None
    serving._mark_ref_audio_artifact_ready_for_request = lambda request_id: None
    serving._discard_ref_audio_artifact_warmup = lambda request_id: None
    return serving
@@ -154,7 +159,7 @@ class TestAudexCfgValidation:
        assert _adapter().validate(_speech_request(cfg_scale=None)) is None
        serving = TestAudexCfgInjection()._serving_with_fake_tokenizer()
        params = SimpleNamespace(extra_args={"cfg_scale": None})
-        out = serving._inject_audex_cfg_pair_args("req-1", {"prompt": "p"}, params)
+        out = serving._adapter._inject_audex_cfg_pair_args("req-1", {"prompt": "p"}, params)
        assert out.extra_args == {}

    @pytest.mark.parametrize("key", ["cfg_role", "cfg_pair_id", "cfg_null_prompt"])
@@ -177,21 +182,21 @@ class TestAudexCfgInjection:
    def test_no_extra_args_is_untouched(self):
        serving = self._serving_with_fake_tokenizer()
        params = SimpleNamespace(extra_args=None)
-        out = serving._inject_audex_cfg_pair_args("req-1", {"prompt": self._cond_prompt()}, params)
+        out = serving._adapter._inject_audex_cfg_pair_args("req-1", {"prompt": self._cond_prompt()}, params)
        assert out.extra_args is None
        assert params.extra_args is None

    def test_scale_one_leaves_params_byte_identical(self):
        serving = self._serving_with_fake_tokenizer()
        params = SimpleNamespace(extra_args={"cfg_scale": 1.0})
-        out = serving._inject_audex_cfg_pair_args("req-1", {"prompt": self._cond_prompt()}, params)
+        out = serving._adapter._inject_audex_cfg_pair_args("req-1", {"prompt": self._cond_prompt()}, params)
        assert out.extra_args == {}

    def test_guided_request_gains_pair_contract(self):
        serving = self._serving_with_fake_tokenizer()
        params = SimpleNamespace(extra_args={"cfg_scale": 1.5})
        cond = self._cond_prompt("Some transcription text here.")
-        out = serving._inject_audex_cfg_pair_args("req-42", {"prompt": cond}, params)
+        out = serving._adapter._inject_audex_cfg_pair_args("req-42", {"prompt": cond}, params)

        extra = out.extra_args
        assert extra["cfg_scale"] == 1.5
@@ -207,14 +212,14 @@ class TestAudexCfgInjection:
        serving = self._serving_with_fake_tokenizer()
        params = SimpleNamespace(extra_args={"cfg_scale": 1.5})
        with pytest.raises(ValueError, match="adapter-built text prompt"):
-            serving._inject_audex_cfg_pair_args("req-1", {"prompt_token_ids": [1]}, params)
+            serving._adapter._inject_audex_cfg_pair_args("req-1", {"prompt_token_ids": [1]}, params)

    def test_injection_never_mutates_input_params(self):
        """The input may be the engine's SHARED default params (review P2):
        pair state must land only on the returned clone."""
        serving = self._serving_with_fake_tokenizer()
        shared = SimpleNamespace(extra_args={"cfg_scale": 1.5}, temperature=0.1)
-        out = serving._inject_audex_cfg_pair_args("req-7", {"prompt": self._cond_prompt()}, shared)
+        out = serving._adapter._inject_audex_cfg_pair_args("req-7", {"prompt": self._cond_prompt()}, shared)
        assert out is not shared
        assert shared.extra_args == {"cfg_scale": 1.5}
        assert shared.temperature == 0.1
@@ -252,11 +257,11 @@ def test_guided_injection_sets_guided_temperature():
    serving = TestAudexCfgInjection()._serving_with_fake_tokenizer()
    cond = TestAudexCfgInjection()._cond_prompt()
    params = SimpleNamespace(extra_args={"cfg_scale": 1.5}, temperature=0.1)
-    out = serving._inject_audex_cfg_pair_args("req-1", {"prompt": cond}, params)
+    out = serving._adapter._inject_audex_cfg_pair_args("req-1", {"prompt": cond}, params)
    assert out.temperature == 0.05

    unguided = SimpleNamespace(extra_args={"cfg_scale": 1.0}, temperature=0.1)
-    out = serving._inject_audex_cfg_pair_args("req-2", {"prompt": cond}, unguided)
+    out = serving._adapter._inject_audex_cfg_pair_args("req-2", {"prompt": cond}, unguided)
    assert out.temperature == 0.1


@@ -340,7 +345,7 @@ class TestAudexTTAInjection:
    def test_default_injection_applies_rvq_and_cfg3(self):
        serving = self._serving()
        params = SimpleNamespace(extra_args=None)
-        out = serving._inject_audex_tta_args("req-9", self._prompt(), params)
+        out = serving._adapter._inject_audex_tta_args("req-9", self._prompt(), params)

        extra = out.extra_args
        rvq = extra["tta_rvq"]
@@ -355,15 +360,15 @@ class TestAudexTTAInjection:
    def test_explicit_scale_one_disables_cfg_but_keeps_rvq(self):
        serving = self._serving()
        params = SimpleNamespace(extra_args={"cfg_scale": 1.0})
-        out = serving._inject_audex_tta_args("req-1", self._prompt(), params)
+        out = serving._adapter._inject_audex_tta_args("req-1", self._prompt(), params)
        assert "tta_rvq" in out.extra_args
        assert "cfg_scale" not in out.extra_args
        assert "cfg_role" not in out.extra_args

    def test_rvq_contract_cached_per_process(self):
        serving = self._serving()
-        out_a = serving._inject_audex_tta_args("a", self._prompt(), SimpleNamespace(extra_args=None))
-        out_b = serving._inject_audex_tta_args("b", self._prompt(), SimpleNamespace(extra_args=None))
+        out_a = serving._adapter._inject_audex_tta_args("a", self._prompt(), SimpleNamespace(extra_args=None))
+        out_b = serving._adapter._inject_audex_tta_args("b", self._prompt(), SimpleNamespace(extra_args=None))
        assert out_a.extra_args["tta_rvq"] is out_b.extra_args["tta_rvq"]

    def test_injection_never_mutates_input_params(self):
@@ -372,8 +377,8 @@ class TestAudexTTAInjection:
        per-request pair state must land only on the returned clone."""
        serving = self._serving()
        shared = SimpleNamespace(extra_args=None)
-        out_1 = serving._inject_audex_tta_args("req-1", self._prompt(), shared)
-        out_2 = serving._inject_audex_tta_args("req-2", self._prompt(), shared)
+        out_1 = serving._adapter._inject_audex_tta_args("req-1", self._prompt(), shared)
+        out_2 = serving._adapter._inject_audex_tta_args("req-2", self._prompt(), shared)
        assert shared.extra_args is None
        assert out_1 is not shared and out_2 is not shared
        assert out_1.extra_args["cfg_pair_id"] == "req-1"
@@ -52,6 +52,7 @@ from vllm_omni.model_executor.models.ming_tts.constants import (
    KEY_SPEAKER_WAVEFORM,
    KEY_SPEAKER_WAVEFORM_LENGTHS,
    SPEAKER_EMBEDDING_DIM,
+    TEXT_EOS_TOKEN_ID,
 )
 from vllm_omni.outputs import OmniRequestOutput

@@ -3618,7 +3619,21 @@ class TestCosyVoice3Serving:
        assert error is not None
        assert "max_new_tokens" in error

-    def test_prepare_speech_generation_cosyvoice3(self, cosyvoice3_server, mocker: MockerFixture):
+    @pytest.mark.parametrize(
+        ("max_new_tokens", "expected_min_tokens", "expected_max_tokens"),
+        [
+            (None, 10, 2000),
+            (5, 5, 5),
+        ],
+    )
+    def test_prepare_speech_generation_cosyvoice3(
+        self,
+        cosyvoice3_server,
+        mocker: MockerFixture,
+        max_new_tokens: int | None,
+        expected_min_tokens: int,
+        expected_max_tokens: int,
+    ):
        cosyvoice3_server._build_cosyvoice3_prompt = mocker.AsyncMock(
            return_value={
                "prompt": "Hello",
@@ -3626,15 +3641,23 @@ class TestCosyVoice3Serving:
                "mm_processor_kwargs": {"prompt_text": "ref text", "sample_rate": 24000},
            }
        )
-        cosyvoice3_server._apply_cosyvoice3_dynamic_tokens = mocker.MagicMock(side_effect=lambda spl, req: spl)
+        cosyvoice3_server.model_config.hf_config = SimpleNamespace(
+            min_token_text_ratio=1, max_token_text_ratio=200, allowed_special=True
+        )
+        cosyvoice3_server._cosyvoice3_tokenizer = mocker.MagicMock()
+        mocker.patch("vllm_omni.model_executor.models.cosyvoice3.utils.extract_text_token", return_value=[None, 10])

        request = OpenAICreateSpeechRequest(
            input="Hello",
            ref_audio="data:audio/wav;base64,abc",
            ref_text="Reference text",
+            max_new_tokens=max_new_tokens,
        )
        request_id, generator, tts_params = asyncio.run(cosyvoice3_server._prepare_speech_generation(request))
+        sampling_params = cosyvoice3_server.engine_client.generate.call_args.kwargs["sampling_params_list"][0]

+        assert sampling_params.min_tokens == expected_min_tokens
+        assert sampling_params.max_tokens == expected_max_tokens
        assert request_id.startswith("speech-")
        assert generator == "generator"
        assert tts_params == {}
@@ -3718,6 +3741,90 @@ class TestGLMTTSServing:
        assert text_token_len == 3
        load_tokenizer.assert_called_once()

+    def test_prepare_speech_generation_glm_tts(self, glm_tts_server, mocker: MockerFixture):
+        glm_tts_server._build_glm_tts_prompt = mocker.AsyncMock(
+            return_value={
+                "prompt": "Hello",
+                "multi_modal_data": {"audio": (np.zeros(24000), 24000)},
+                "mm_processor_kwargs": {"prompt_text": "ref text", "sample_rate": 24000},
+            }
+        )
+        request = OpenAICreateSpeechRequest(
+            input="Hello",
+            ref_audio="data:audio/wav;base64,abc",
+            ref_text="Reference text",
+        )
+        glm_tts_server.model_config.hf_config = SimpleNamespace(min_token_text_ratio=1, max_token_text_ratio=200)
+        glm_tts_server._estimate_glm_tts_text_token_len = mocker.MagicMock(return_value=10)
+        request_id, generator, tts_params = asyncio.run(glm_tts_server._prepare_speech_generation(request))
+        sampling_params = glm_tts_server.engine_client.generate.call_args.kwargs["sampling_params_list"][0]
+
+        assert sampling_params.min_tokens == 10
+        assert sampling_params.max_tokens == 2000
+        assert request_id.startswith("speech-")
+        assert generator == "generator"
+        assert tts_params == {}
+        glm_tts_server._build_glm_tts_prompt.assert_awaited_once()
+
+
+@pytest.fixture
+def ming_tts_server(mocker: MockerFixture):
+    mocker.patch.object(OmniOpenAIServingSpeech, "_load_supported_speakers", return_value=set())
+    mocker.patch.object(OmniOpenAIServingSpeech, "_load_codec_frame_rate", return_value=None)
+
+    mock_engine_client = mocker.MagicMock()
+    mock_engine_client.errored = False
+    mock_engine_client.model_config = mocker.MagicMock(
+        model="inclusionAI/Ming-omni-tts-0.5B",
+        hf_config=SimpleNamespace(min_token_text_ratio=2, max_token_text_ratio=20),
+    )
+    mock_engine_client.default_sampling_params_list = [
+        SimpleNamespace(max_tokens=2048, min_tokens=None, extra_args=None)
+    ]
+    mock_engine_client.tts_batch_max_items = 32
+    mock_engine_client.generate = mocker.MagicMock(return_value="generator")
+    mock_engine_client.stage_configs = [
+        SimpleNamespace(
+            engine_args=SimpleNamespace(
+                model_stage="llm", model_arch="MingTTSForConditionalGeneration", worker_type="ar"
+            ),
+            tts_args={},
+        )
+    ]
+
+    mock_models = mocker.MagicMock()
+    mock_models.is_base_model.return_value = True
+    return OmniOpenAIServingSpeech(
+        engine_client=mock_engine_client,
+        models=mock_models,
+        request_logger=mocker.MagicMock(),
+    )
+
+
+class TestMingTTSServing:
+    def test_prepare_speech_generation_ming(self, ming_tts_server, mocker: MockerFixture):
+        ming_tts_server._adapter.build = mocker.AsyncMock(
+            return_value=PreparedRequest(
+                prompt={"prompt": "hello"},
+                tts_params={},
+                model_type="ming_tts",
+            )
+        )
+
+        request = OpenAICreateSpeechRequest(
+            input="Hello",
+            ref_audio="data:audio/wav;base64,abc",
+            ref_text="Reference text",
+            max_new_tokens=7,
+        )
+        asyncio.run(ming_tts_server._prepare_speech_generation(request))
+
+        assert ming_tts_server._tts_model_type == "ming_tts"
+        ming_tts_server._adapter.build.assert_awaited_once()
+        sampling_params = ming_tts_server.engine_client.generate.call_args.kwargs["sampling_params_list"][0]
+        assert sampling_params.stop_token_ids == [TEXT_EOS_TOKEN_ID]
+        assert sampling_params.max_tokens == 8
+

@pytest.fixture
 def ming_flash_omni_tts_server(mocker: MockerFixture):
@@ -3901,6 +4008,9 @@ class TestTTSAsyncOffloading:
                    model_type=adapter_model_type,
                )

+            def apply_sampling_overrides(self, sampling_params_list, request, prompt, request_id=None):
+                return sampling_params_list
+
        voxtral_server._tts_model_type = legacy_tts_model_type
        mocker.patch.object(voxtral_server, "_get_tts_adapter", return_value=FakeAdapter())
        log_info = mocker.patch("vllm_omni.entrypoints.openai.serving_speech.logger.info")
@@ -14,7 +14,6 @@ import itertools
 import pytest

 from vllm_omni.entrypoints.openai.tts_adapters import (
-    LEGACY_TTS_DETECTORS,
    TTS_ADAPTER_REGISTRY,
    all_tts_stage_keys,
    detect_tts_model_type,
@@ -282,27 +281,17 @@ def test_entry_stage_archs_is_ming_only():
    assert tts_entry_stage_archs() == frozenset({"MingTTSForConditionalGeneration"})


-def test_every_detected_type_is_adapter_backed_or_declared_legacy():
-    """Detection may only name a model that has an adapter or an explicit
-    :data:`LEGACY_TTS_DETECTORS` entry — never an undeclared string."""
-    legacy_names = {d.name for d in LEGACY_TTS_DETECTORS}
+def test_every_detected_type_is_adapter_backed():
+    """Detection may only name a model that has a registered adapter."""
    for stage, arch in itertools.product(_STAGES, _ARCHS):  # full cross product on purpose
        detected = detect_tts_model_type(stage, arch)
        if detected is None:
            continue
-        assert detected in TTS_ADAPTER_REGISTRY or detected in legacy_names, (
+        assert detected in TTS_ADAPTER_REGISTRY, (
            f"detection produced undeclared model type {detected!r} for stage={stage!r} arch={arch!r}"
        )


-def test_legacy_detectors_have_no_adapter():
-    """A legacy entry that gained an adapter must be deleted from the list."""
-    for detector in LEGACY_TTS_DETECTORS:
-        assert resolve_adapter(detector.name) is None, (
-            f"{detector.name!r} now has an adapter; remove it from LEGACY_TTS_DETECTORS"
-        )
-
-
 def test_no_ambiguous_detectors_at_equal_priority():
    """Two detectors that can match the same deployment must be ordered.

@@ -492,6 +492,24 @@ def test_resolve_stage_configs_injects_additional_config_into_diffusion_stage(mo
    assert stage_configs[1].engine_args.additional_config == {"torchair_graph_config": {"enabled": True}}


+@pytest.mark.parametrize(
+    ("legacy_arg", "value"),
+    [
+        ("stage_configs_path", "legacy.yaml"),
+        ("stage_configs", [{"stage_id": 0}]),
+    ],
+)
+def test_resolve_stage_configs_rejects_legacy_config_arguments(legacy_arg, value):
+    engine = AsyncOmniEngine.__new__(AsyncOmniEngine)
+
+    with pytest.raises(ValueError, match=rf"`{legacy_arg}`.*`deploy_config`"):
+        engine._resolve_stage_configs(
+            "dummy-model",
+            {legacy_arg: value},
+            trust_remote_code=False,
+        )
+
+
 def test_default_stage_config_includes_quantization_config():
    """Ensure structured quantization_config survives default diffusion-stage creation."""
    quantization_config = {
@@ -27,7 +27,7 @@ from vllm_omni.entrypoints.utils import (
    filter_dataclass_kwargs,
    filter_stages,
    load_and_resolve_stage_configs,
-    load_stage_configs_from_yaml,
+    load_stage_configs_from_model,
    resolve_model_config_path,
 )

@@ -417,8 +417,9 @@ class TestTryResolveOmniModelType:

 class TestLoadAndResolveStageConfigs:
    def test_load_and_resolve_with_kwargs(self, mocker: MockerFixture):
-        """Ensure that dtype survives default stage creation."""
-        kwargs = {"dtype": torch.float32}
+        """Ensure that kwargs survive default stage creation."""
+        engine_backend = "vllm_omni.experimental.ar_diffusion.engine.ARDiffusionEngine"
+        kwargs = {"dtype": torch.float32, "engine_backend": engine_backend}
        mocker.patch("vllm_omni.entrypoints.utils.resolve_model_config_path", return_value=None)
        mocker.patch("vllm_omni.entrypoints.utils.load_stage_configs_from_model", return_value=([], None))

@@ -431,6 +432,7 @@ class TestLoadAndResolveStageConfigs:
        assert config_path is None
        assert len(stage_configs) == 1
        assert "dtype" in stage_configs[0]["engine_args"]
+        assert stage_configs[0]["engine_args"]["engine_backend"] == engine_backend

    def test_deploy_config_preserves_cli_overrides_and_replicas(self, tmp_path, mocker: MockerFixture):
        deploy_path = tmp_path / "qwen3_multi.yaml"
@@ -518,71 +520,20 @@ class TestLoadAndResolveStageConfigs:
        assert filtered[0].final_output_type is None


-class TestLoadStageConfigsFromYaml:
-    """Regression tests for stage-config loading and merging."""
-
-    def test_deep_merges_stage_engine_args(self, mocker: MockerFixture):
-        yaml_config = create_config(
-            {
-                "async_chunk": True,
-                "stage_args": [
-                    {
-                        "stage_id": 0,
-                        "runtime": {"device": 0},
-                        "engine_args": {
-                            "parallel_config": {"tensor_parallel_size": 4},
-                        },
-                    }
-                ],
-            }
-        )
+class TestLoadStageConfigsFromModel:
+    def test_unresolved_model_does_not_fall_back_to_yaml(self, mocker: MockerFixture, caplog):
        mocker.patch(
-            "vllm_omni.entrypoints.utils.load_yaml_config",
-            return_value=yaml_config,
+            "vllm_omni.entrypoints.utils.StageConfigFactory.create_legacy_stage_configs_from_model",
+            return_value=(None, None),
        )
+        resolve_path = mocker.patch("vllm_omni.entrypoints.utils.resolve_model_config_path")

-        stages = load_stage_configs_from_yaml(
-            "fake.yaml",
-            base_engine_args={
-                "parallel_config": {
-                    "tensor_parallel_size": 1,
-                    "pipeline_parallel_size": 2,
-                },
-                "model": "base-model",
-            },
-        )
+        result = load_stage_configs_from_model("unregistered-model", trust_remote_code=False)

-        merged_engine_args = stages[0]["engine_args"]
-        assert merged_engine_args["parallel_config"]["tensor_parallel_size"] == 4
-        assert merged_engine_args["parallel_config"]["pipeline_parallel_size"] == 2
-        assert merged_engine_args["model"] == "base-model"
-        assert merged_engine_args["async_chunk"] is True
-
-    def test_merges_nested_stage_engine_args(self, mocker: MockerFixture):
-        yaml_config = create_config(
-            {
-                "stage_args": [
-                    {
-                        "stage_id": 0,
-                        "engine_args": {
-                            "nested": {"override": 2},
-                        },
-                    }
-                ],
-            }
-        )
-        mocker.patch(
-            "vllm_omni.entrypoints.utils.load_yaml_config",
-            return_value=yaml_config,
-        )
-
-        stages = load_stage_configs_from_yaml(
-            "fake.yaml",
-            base_engine_args={"nested": {"base": 1}},
-        )
-
-        assert stages[0]["engine_args"]["nested"]["base"] == 1
-        assert stages[0]["engine_args"]["nested"]["override"] == 2
+        assert result == ([], None)
+        resolve_path.assert_not_called()
+        assert "No registered PipelineConfig resolved" in caplog.text
+        assert "deploy_config" in caplog.text


 class TestCumulativeStreamingCoercion:
Author	SHA1	Message	Date
Hongsheng Liu	f1bc6c8569	Address video-audio example review feedback Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-17 16:45:55 +08:00
Hongsheng Liu	6bdbaeb038	Harden the shared video-audio example Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-17 13:48:27 +08:00
boatman	65b3d41b3a	[Migrate] Move sampling parameter overrides from legacy dispatches to TTS adapters (#5272 ) Signed-off-by: boatman <109857087+sphinxkkkbc@users.noreply.github.com>	2026-08-16 21:14:26 -07:00
wangyuandCursor	4616823125	[CI]demote slow nightly cases and trim Qwen-Image-Edit coverage (#5944 ) Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: [Your Name] <your.email@example.com> Co-authored-by: Cursor <cursoragent@cursor.com>	2026-08-17 11:21:28 +08:00
Yueqian LinandHongsheng Liu	611c083587	[BugFix][TTS] R1.1-R1.4: turn silent async-chunk failures into visible ones (#6033 ) Signed-off-by: Yueqian Lin <linyueqian@outlook.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-17 10:51:17 +08:00
Yukim1andHongsheng Liu	baba7d1e2e	[Refactor] Remove legacy stage_args YAML loader (#6200 ) Signed-off-by: zwhzzz0821 <2831474076@qq.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-16 11:22:28 +08:00
Hongsheng Liu	9d2bb23ff6	[Diffusion] Add loader-owned host-weight plans for DLO (TP=1) (#6213 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-16 11:02:38 +08:00
Jack Chen zhangbin.327 Hongsheng Liu	3875f8ded4	[Core] Add realtime AR-Diffusion tick sessions for LingBot World 2.0 (#5491 ) Signed-off-by: zhangbin.327 <zhangbin.327@bytedance.com> Signed-off-by: Jack47 <scsvip@gmail.com> Co-authored-by: zhangbin.327 <zhangbin.327@bytedance.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-16 09:11:20 +08:00
Yueqian Lin	9798bc3a8d	[Docs] Unify recipe serve commands on `vllm serve --omni` (#6221 ) Signed-off-by: Yueqian Lin <linyueqian@outlook.com>	2026-08-15 21:18:47 +00:00
Anurag	68bf8c2c42	[Bugfix] Prevent DiffusionResultPump crash on cancelled futures (#5793 ) (#5983 ) Signed-off-by: ANURAG KANADE <anuragkanade6@gmail.com>	2026-08-15 16:19:45 +00:00