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

Update docs/design/feature/offloader/README.md
Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
2026-08-15 07:00:22 +08:00 · 2026-08-13 23:37:08 +08:00 · 2026-08-13 23:37:01 +08:00 · 2026-08-12 11:35:07 +08:00 · 2026-08-12 09:30:34 +08:00 · 2026-08-12 08:55:41 +08:00
294 changed files with 2951 additions and 27017 deletions
@@ -76,9 +76,6 @@ steps:
          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
          - tests/e2e/offline_inference/test_qwen3_tts_customvoice.py
          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
        commands:
@@ -95,9 +92,6 @@ steps:
          - tests/e2e/online_serving/test_qwen3_tts_base.py
          - tests/e2e/offline_inference/test_qwen3_tts_base.py
          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
          - .buildkite/common/scripts/run_cov_split.sh
@@ -115,15 +109,27 @@ steps:
              -- -m 'advanced_model and cuda' --run-level 'advanced_model'
        mirror_hardwares: l4_1

+      - label: "TTS · Higgs-Audio-V3 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_higgs_audio_v3.py
+          - vllm_omni/model_executor/models/higgs_audio_v3/
+          - vllm_omni/model_executor/stage_input_processors/higgs_audio_v3.py
+          - vllm_omni/deploy/higgs_multimodal_qwen3.yaml
+        commands:
+          - |
+            timeout 20m bash -c "
+              export VLLM_WORKER_MULTIPROC_METHOD=spawn
+              export VLLM_USE_DEEP_GEMM=0
+              export VLLM_MOE_USE_DEEP_GEMM=0
+              pytest -s -v tests/e2e/online_serving/test_higgs_audio_v3.py -m 'advanced_model' --run-level 'advanced_model'
+            "
+        mirror_hardwares: l4_1
+
      - label: "Omni · Qwen3-Omni Test"
        source_file_dependencies:
          - tests/e2e/offline_inference/test_qwen3_omni.py
          - tests/e2e/online_serving/test_qwen3_omni.py
          - vllm_omni/model_executor/models/qwen3_omni/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
-          - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
          - vllm_omni/deploy/qwen3_omni_moe.yaml
        timeout_in_minutes: 50
@@ -135,11 +141,6 @@ steps:
        source_file_dependencies:
          - tests/e2e/offline_inference/test_minicpmo_4_5.py
          - tests/e2e/online_serving/test_minicpmo_4_5.py
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
          - vllm_omni/deploy/minicpmo_4_5.yaml
        timeout_in_minutes: 50
        commands:
@@ -161,35 +162,70 @@ steps:
          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
          - vllm_omni/experimental/fullduplex/
          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
          - vllm_omni/core/sched/
          - vllm_omni/outputs/multimodal_accumulation.py
          - vllm_omni/engine/stage_init_utils.py
          - vllm_omni/engine/async_omni_engine.py
-          - vllm_omni/engine/orchestrator.py
          - vllm_omni/entrypoints/cli/serve.py
-          - vllm_omni/entrypoints/openai/api_server.py
-          - vllm_omni/entrypoints/openai/duplex_capability.py
-          - vllm_omni/config/stage_config.py
        timeout_in_minutes: 50
        commands:
          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'advanced_model and cuda' --run-level 'advanced_model'
        mirror_hardwares: h100_1

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

+      - label: "Diffusion · Qwen Image Layered Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_layered.py
+          - vllm_omni/diffusion/models/qwen_image/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_qwen_image_layered.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+            "
+        mirror_hardwares: h100_1
+
      - label: "Diffusion · Wan22 Test"
        source_file_dependencies:
          - tests/e2e/offline_inference/test_wan22_t2v.py
          - tests/e2e/online_serving/test_wan22_t2v.py
          - vllm_omni/diffusion/models/wan2_2/
-          - vllm_omni/diffusion/models/dmd2/
        commands:
          - |
            timeout 40m bash -c "
@@ -211,3 +258,21 @@ steps:
              pytest -s -v tests/e2e/offline_inference/test_wan22_t2v.py tests/e2e/online_serving/test_wan22_t2v.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
            "
        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Z Image Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_z_image.py
+          - tests/e2e/online_serving/test_z_image.py
+          - vllm_omni/diffusion/models/z_image/
+        timeout_in_minutes: 30
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_z_image.py tests/e2e/online_serving/test_z_image.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+        mirror_hardwares: l4_1
+      - label: "Diffusion · FLUX.2 Klein Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_flux2_klein.py
+          - vllm_omni/diffusion/models/flux2_klein/
+        timeout_in_minutes: 30
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_flux2_klein.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+        mirror_hardwares: l4_1
@@ -159,7 +159,19 @@ steps:
          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
          - export VLLM_USE_DEEP_GEMM="0"
          - export VLLM_MOE_USE_DEEP_GEMM="0"
-          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
+          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_voxcpm2_tts_expansion.py
+        mirror_hardwares: l4_1
+
+      - label: ":full_moon: TTS · Accuracy Test · VoxCPM2"
+        timeout_in_minutes: 30
+        commands:
+          - |
+            timeout 30m bash -c "
+              export VLLM_LOGGING_LEVEL=DEBUG
+              export VLLM_WORKER_MULTIPROC_METHOD=spawn
+              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+              pytest -s -v tests/e2e/online_serving/test_voxcpm2_tts_expansion.py -m 'full_model and cuda and tts' --run-level 'full_model'
+            "
        mirror_hardwares: l4_1

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

+      - label: ":full_moon: TTS · VoxCPM2 · Perf Test"
+        key: nightly-tts-performance-voxcpm2
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_voxcpm2.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: TTS · Higgs-Audio-V3 · Perf Test"
+        key: nightly-tts-performance-higgs-audio-v3
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - export VLLM_USE_DEEP_GEMM="0"
+          - export VLLM_MOE_USE_DEEP_GEMM="0"
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_higgs_audio_v3.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
  - group: ":card_index_dividers: Tiny Model Tests [Multi-GPU]"
    key: nightly-tiny-model-test-group
    depends_on: upload-nightly-pipeline
@@ -205,7 +246,8 @@ steps:
      build.pull_request.labels includes "nightly-test" ||
      build.pull_request.labels includes "diffusion-x2iat-test"
    steps:
-      # Use explicit file shards so single-card cases do not reserve 2+ GPUs.
+      # Use explicit file shards so single-card cases do not reserve 2+ GPUs,
+      # and Bagel feature coverage is not duplicated via a broad tests/e2e sweep.

      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Single-GPU"
        timeout_in_minutes: 120
@@ -213,6 +255,15 @@ steps:
          - >-
            pytest -sv
            tests/e2e/online_serving/test_qwen_image_expansion.py
+            tests/e2e/online_serving/test_qwen_image_edit_expansion.py
+            tests/e2e/online_serving/test_qwen_image_layered_expansion.py
+            tests/e2e/online_serving/test_boogu_image.py
+            tests/e2e/online_serving/test_boogu_image_edit.py
+            tests/e2e/online_serving/test_boogu_image_expansion.py
+            tests/e2e/online_serving/test_boogu_image_edit_expansion.py
+            tests/e2e/online_serving/test_bagel_expansion.py
+            tests/e2e/online_serving/test_krea2_expansion.py
+            tests/e2e/offline_inference/test_krea2_expansion.py
            -m "full_model and diffusion and H100 and not distributed_cuda"
            --run-level "full_model"
        mirror_hardwares: h100_1
@@ -223,14 +274,47 @@ steps:
          - >-
            pytest -sv
            tests/e2e/online_serving/test_qwen_image_expansion.py
+            tests/e2e/online_serving/test_qwen_image_edit_expansion.py
+            tests/e2e/offline_inference/test_krea2_expansion.py
            -m "full_model and diffusion and H100 and distributed_cuda"
            --run-level "full_model"
        mirror_hardwares: h100_2

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

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

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

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

+      - label: ":full_moon: Diffusion X2V · LingBot Video MoE Function Test"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_lingbot_video_moe.py -k "video_generation" -m "full_model and cuda" --run-level "full_model"
+        mirror_hardwares: h100_1
+
      - label: ":full_moon: Diffusion X2V · Doc Test"
        timeout_in_minutes: 60
        commands:
@@ -302,6 +456,7 @@ steps:
        timeout_in_minutes: 90
        commands:
          - pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_ltx.py -m "full_model and cuda" --run-level "full_model"
        mirror_hardwares: h100_2

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

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

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

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

+      - label: "TTS · Higgs-Audio-V3 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_higgs_audio_v3.py
+          - vllm_omni/model_executor/models/higgs_audio_v3/
+          - vllm_omni/model_executor/stage_input_processors/higgs_audio_v3.py
+          - vllm_omni/deploy/higgs_multimodal_qwen3.yaml
+        commands:
+          - |
+            timeout 20m bash -c "
+              export VLLM_WORKER_MULTIPROC_METHOD=spawn
+              export VLLM_USE_DEEP_GEMM=0
+              export VLLM_MOE_USE_DEEP_GEMM=0
+              pytest -s -v tests/e2e/online_serving/test_higgs_audio_v3.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · Qwen Image Edit Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_edit.py
+          - vllm_omni/diffusion/models/qwen_image/
+        commands:
+          - |
+            timeout 20m bash -c "
+              pytest -s -v tests/e2e/online_serving/test_qwen_image_edit.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Bagel Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_bagel.py
+          - vllm_omni/model_executor/models/bagel/
+          - vllm_omni/diffusion/models/bagel/
+          - vllm_omni/model_executor/stage_input_processors/bagel.py
+          - vllm_omni/deploy/bagel.yaml
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_bagel.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
+
      - label: "Diffusion · Wan22 Test"
        source_file_dependencies:
          - tests/e2e/online_serving/test_wan22_t2v.py
          - vllm_omni/diffusion/models/wan2_2/
-          - vllm_omni/diffusion/models/dmd2/
        commands:
          - |
            timeout 40m bash -c "
@@ -314,9 +342,6 @@ steps:
        source_file_dependencies:
          - tests/e2e/online_serving/test_cosmos3.py
          - vllm_omni/diffusion/models/cosmos3/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py
-          - vllm_omni/experimental/world_models/
-          - vllm_omni/model_extras/cosmos3.py
        commands:
          - |
            timeout 40m bash -c "
@@ -329,8 +354,6 @@ steps:
        source_file_dependencies:
          - tests/e2e/online_serving/test_qwen_image.py
          - vllm_omni/diffusion/models/qwen_image/
-          - vllm_omni/diffusion/models/dmd2/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
        commands:
          - |
            timeout 40m bash -c "
@@ -338,3 +361,15 @@ steps:
              pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'core_model' --run-level 'core_model'
            "
        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Qwen Image Layered Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_layered.py
+          - vllm_omni/diffusion/models/qwen_image/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_qwen_image_layered.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
@@ -108,22 +108,6 @@ steps:
            --run-level "full_model"
        mirror_hardwares: h100_1

-      # Own step: the image has no NanoCodec, so this file import-skips out of
-      # the mark-selected TTS steps above. The overrides keep nemo_toolkit[tts]
-      # from downgrading transformers; see
-      # examples/offline_inference/text_to_speech/README.md.
-      - label: "TTS · Gepard-1.0 · L4"
-        timeout_in_minutes: 60
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - printf '%s\n' "transformers>=5.5.3" "huggingface-hub>=1.0" > /tmp/gepard-overrides.txt
-          - uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" "nemo_toolkit[tts]==2.7.3" --overrides /tmp/gepard-overrides.txt
-          - >-
-            pytest -s -v tests/e2e/offline_inference/test_gepard_tts.py
-            -m "tts and cuda"
-            --run-level "full_model"
-        mirror_hardwares: l4_1
-
      - label: "Diffusion · L4"
        timeout_in_minutes: 60
        commands:
@@ -149,4 +133,4 @@ steps:
            pytest -sv
            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
            --run-level "full_model"
-        mirror_hardwares: h100_4
+        mirror_hardwares: h100_2
@@ -96,6 +96,33 @@ steps:
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$EXIT
+  - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
+    key: nightly-diffusion-x2iat-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
+    steps:
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit-2511"
+        key: nightly-diffusion-x2iat-performance-qwen-image-edit-2511
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_8
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export CACHE_DIT_VERSION=1.5.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
  - group: ":card_index_dividers: Diffusion I2I Model Test"
    key: nightly-HunyuanImage3-x2i-test-group
    depends_on: upload-nightly-pipeline
@@ -254,7 +254,6 @@ outputs/
 results/
 generated/
 output_*/
-/tests/e2e/accuracy/artifacts/

 # Configuration overrides
 configs/local.yaml
@@ -66,7 +66,7 @@ repos:
      entry: python tools/pre_commit/check_tts_adapter.py
      language: python
      pass_filenames: false
-      files: ^(vllm_omni/entrypoints/openai/serving_speech\.py|tools/pre_commit/check_tts_adapter\.py)$
+      files: ^(vllm_omni/entrypoints/openai/(serving_speech\.py|tts_adapters/__init__\.py)|tools/pre_commit/check_tts_adapter\.py)$
      # The budget-is-now-slack reminder is printed on a PASSING run, and
      # pre-commit hides output from passing hooks unless the hook is verbose.
      verbose: true
@@ -116,7 +116,6 @@ nav:
        - Async Diffusion Output: design/feature/async_diffusion_output.md
        - Async Omni Output Materialization: design/feature/omni_async_output_materialization.md
        - Automatic Prefix Caching: design/feature/prefix_caching.md
-        - Realtime AR-Diffusion: design/feature/realtime_ar_diffusion.md
      - Communication:
        - OmniConnector Implementations:
          - Mooncake Store Connector: design/feature/omni_connectors/mooncake_store_connector.md
@@ -329,13 +329,11 @@ On PR builds, `upload_pipeline.py` logs `skip '…' (no changes under …)` for

 ### Per-model coverage

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

 **Naming convention**: `coverage-<model_id>-<mode>-<step_id>.xml.gz`, where
 `<model_id>` is the model's directory name under
@@ -412,8 +410,7 @@ runs a coverage job needs the same.
 List both `run_cov_split.sh` and `pyproject.toml` in every opted-in job's
 `source_file_dependencies` — both change what the job measures, so without them a
 change there is filtered out of normal PR builds and only surfaces in a later
-nightly. `tests/buildkite/test_upload_pipeline.py` covers the filter behavior with
-a synthetic job (it does not pin real merge labels).
+nightly. `tests/buildkite/test_upload_pipeline.py` asserts this for the pilots.
 Editing only the surrounding CI YAML still does not schedule them, so a PR that
 touches just the wiring needs a full E2E run (or the commands run on a GPU host)
 to produce artifacts. When checking a new model's
@@ -4,9 +4,6 @@
 > `experimental/ar_diffusion` contract. It follows the ownership and lifecycle
 > direction in [#5137](https://github.com/vllm-project/vllm-omni/issues/5137),
 > but it is not yet part of the stable module-design hierarchy.
->
-> The end-to-end request, identity, failure, and memory contracts are documented
-> in [Realtime AR-Diffusion sessions](feature/realtime_ar_diffusion.md).

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

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

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

 ### Batch Split

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

 ## Model Coverage

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

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

@@ -4,7 +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
-complete rank-local block sources and avoid an additional collective.
+the standard loader's rank-local weights and avoid an additional collective.

 For user-facing commands, see the
 [distributed layerwise offloading guide](../../../user_guide/diffusion/offloader/distributed_layerwise_offload.md)
@@ -14,20 +14,10 @@ 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 streams complete blocks independently and adds no
-DLO weight collective.
-
-Host storage is selected separately from the transfer protocol. The loader can
-produce a direct-checkpoint mmap plan for a proven-compatible runtime layout;
-otherwise it uses the ordinary loader. Consequently, no-AllGather replicas on
-the same node can share immutable checkpoint pages when direct mmap is
-selected, while the ordinary-loader fallback still keeps a private runtime
-copy per process.
-
-The Phase A shared-mmap support boundary is TP1. TP greater than one is an
-ordinary-loader compatibility path: DLO can consume the resulting TP-local
-tensors, but those configurations do not use checkpoint mmap and must not be
-used to claim shared-mmap host-memory savings.
+`--dlo-no-use-allgather` path is a rank-local compatibility mode: it is useful
+for standard-loader sharding, workstation bring-up, and systems where an
+additional DLO collective is undesirable, but it does not reduce host weight
+storage across ranks.

 The compatibility matrix below describes the current implementation. The
 unit-level guards are covered, but not every parallelism combination has a
@@ -51,36 +41,6 @@ TP is deliberately not used as DLO's AllGather group. HSDP has its own
 parameter-sharding lifecycle and is not allowed to be sharded a second time by
 DLO's AllGather path.

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

 With the default `dlo_use_allgather=True`, each rank stores approximately
@@ -101,15 +61,6 @@ Buffers:    [current slot]       [prefetch slot]       [current slot]
 The backend uses two shared device buffers, so accelerator weight residency is
 bounded by the largest streamed blocks rather than the complete model.

-When direct checkpoint mmap is selected, the checkpoint mappings are only the
-source used to prepare each rank's persistent shard. They can be closed after
-shard preparation. Across the AllGather group, those private shards total
-approximately one runtime model copy.
-
-An effective DLO group size of one performs no collective, even when
-`dlo_use_allgather=True`; it follows the rank-local transfer path described
-below.
-
 When DP is greater than one, the engine can process one request per DP rank in
 the same denoising wave. Because AllGather is a collective, all participating
 requests must take the same execution path at every denoising step.
@@ -117,20 +68,9 @@ requests must take the same execution path at every denoising step.
 ### Rank-local path without DLO AllGather

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

 This mode means:

@@ -140,18 +80,21 @@ This mode means:
  not shard weights across SP ranks.
 - TP/HSDP/SP collectives, if configured, are not disabled by this flag; only
  DLO's additional weight AllGather is disabled.
- Pure DP deployments share one checkpoint-backed copy per node when direct
-  mmap is selected; the ordinary-loader fallback keeps one private runtime
-  copy per rank.
+- Pure DP deployments keep a full host-side model copy per rank, subject to
+  shared OS page-cache behavior.
 - The scheduler does not require a synchronized DP request wave for DLO.

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

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

 ### Combined dimensions
@@ -179,15 +122,11 @@ AllGather DP multi-concurrency requires:
 The no-AllGather path does not impose these DLO-specific synchronized-wave
 requirements.

-Direct checkpoint mmap can back either transfer path. It is currently limited
-to proven TP1, non-HSDP, non-online-quantized layouts. Other layouts use the
-ordinary loader. 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).
+The mmap loader is used only by the supported DLO+AllGather path when the
+model has a compatible checkpoint layout. Online quantization is incompatible
+with that sharded mmap path; use `--dlo-no-use-allgather` or disable online
+quantization. Models that do not meet the mmap requirements use the regular
+loader path.

 ## Validation coverage

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

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

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

 ## Recommendations

@@ -271,9 +150,7 @@ change the Phase A direct-mmap TP1 support boundary.
  scaling path.
 - Use **SP + DLO AllGather** for long-sequence workloads when DP concurrency is
  not the goal.
- Use **no-AllGather** when independent replica execution is required. TP1
-  direct-mmap deployments can share checkpoint pages per node; other layouts
-  retain the ordinary loader's private host memory behavior and are outside
-  the Phase A shared-mmap support scope.
+- Use **no-AllGather** to bring up TP or workstation PCIe configurations, with
+  the expectation of higher host-memory use and lower validation confidence.
 - Prefer **HSDP alone** for production HSDP deployments until the combined
  HSDP + DLO no-AllGather path has broader end-to-end coverage.
@@ -76,12 +76,6 @@ equivalent. `auto-round` and `auto_round` use the Omni INC/AutoRound adapter.
 ModelOpt checkpoint metadata is detected from its method, producer metadata, or
 `quant_algo`, then mapped to the corresponding vLLM ModelOpt configuration.

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

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

 ### Communication

@@ -101,7 +101,7 @@ outputs = omni.generate(
 )

 # Access custom trajectory data
-output = outputs[0]
+output = outputs[0].request_output
 print(f"Trajectory timesteps shape: {output.metrics['trajectory_timesteps'].shape}")
 print(f"Trajectory latents shape: {output.latents.shape}")
 ```
@@ -47,7 +47,7 @@ if __name__ == "__main__":
    omni = Omni(model="Tongyi-MAI/Z-Image-Turbo")
    prompt = "a cup of coffee on the table"
    outputs = omni.generate(prompt)
-    images = outputs[0].images
+    images = outputs[0].request_output.images
    images[0].save("coffee.png")
 ```

@@ -75,7 +75,8 @@ if __name__ == "__main__":
    ]
    omni_outputs = omni.generate(prompts)
    for i_prompt, prompt_output in enumerate(omni_outputs):
-        this_images = prompt_output.images
+        this_request_output = prompt_output.request_output
+        this_images = this_request_output.images
        for i_image, image in enumerate(this_images):
            image.save(f"p{i_prompt}-img{i_image}.jpg")
            print("saved to", f"p{i_prompt}-img{i_image}.jpg")
@@ -37,19 +37,13 @@ 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` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   |   | — |
 | `LTX2Pipeline` | LTX-2 / LTX-2.3 one-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | | — |
 | `LTX2TwoStagePipeline` | LTX-2 / LTX-2.3 ordinary two-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` + matching Lightricks LoRA and upsampler | ✅︎ | ✅︎ | | | — |
-| `LTX2DistilledOneStagePipeline` | LTX-2 / LTX-2.3 merged-distilled one-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled`, `diffusers/LTX-2.3-Distilled-Diffusers` | ✅︎ | ✅︎ | | | — |
-| `LTX2DistilledTwoStagePipeline` | LTX-2 / LTX-2.3 merged-distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled`, `diffusers/LTX-2.3-Distilled-Diffusers` + matching Lightricks upsampler | ✅︎ | ✅︎ | | | — |
-| `LTX2Pipeline` | LTX-2.5 Full/SFT one-stage T2V and I2V | `Lightricks/LTX-2.5-Diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/LTX/LTX-2.5.md) |
-| `LTX2TwoStagePipeline` | LTX-2.5 Full/SFT two-stage T2V and I2V | `Lightricks/LTX-2.5-Diffusers` + `Lightricks/LTX-2.5` LoRA | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/LTX/LTX-2.5.md) |
-| `LTX2DistilledOneStagePipeline` | LTX-2.5 merged-distilled one-stage T2V and I2V | `Lightricks/LTX-2.5-Diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/LTX/LTX-2.5.md) |
-| `LTX2DistilledTwoStagePipeline` | LTX-2.5 merged-distilled two-stage T2V and I2V | `Lightricks/LTX-2.5-Diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/LTX/LTX-2.5.md) |
+| `LTX2DistilledPipeline` | LTX-2 / LTX-2.3 full-distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled`, `diffusers/LTX-2.3-Distilled-Diffusers` + `Lightricks/LTX-2.3` upsampler | ✅︎ | ✅︎ | | | — |
 | `LingBotVideoPipeline` | LingBot-Video dense and MoE T2I, T2V, TI2V | `robbyant/lingbot-video-dense-1.3b`, `robbyant/lingbot-video-moe-30b-a3b` | ✅︎ | | | | — |
 | `MiniMaxH3Pipeline` | MiniMax H3 T2VA, first/last-keyframe FL2VA, and mixed-reference Ref2VA | `MiniMaxAI/MiniMax-H3` | ✅︎ | ✅︎ | | | [Published](https://recipes.vllm.ai/MiniMaxAI/MiniMax-H3) |
 | `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
@@ -104,7 +98,6 @@ th {
 |`DyninOmniForConditionalGeneration` | Dynin-Omni | `snu-aidas/Dynin-Omni` | ✅︎ | | | | — |
 | `MiniCPMO45OmniForConditionalGeneration` | MiniCPM-o 4.5 | `openbmb/MiniCPM-o-4_5` | ✅︎ | | ✅︎ | | — |
 | `ErnieImagePipeline` | ERNIE-Image | `baidu/ERNIE-Image`, `baidu/ERNIE-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `GepardTalkerForConditionalGeneration` | Gepard-1.0 | `nineninesix/gepard-1.0` | ✅︎ | | | | — |
 |`HiDreamImagePipeline` | HiDream-I1-Full | `HiDream-ai/HiDream-I1-Full` | ✅︎ | ✅︎ | | | — |

 The `Recipe` column links the corresponding deployment recipe. `Published` is
@@ -118,10 +111,6 @@ support metadata until their recipe is audited.

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

-See the [LingBot-World 2.0 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Robbyant/LingBot-World-2.0.md)
-for offline and experimental realtime usage, supported controls, validation,
-and current limitations.
-
 <sup>H3</sup> MiniMax H3 AMD GPU support is validated on gfx942 (MI300X) and
 gfx950 (MI350) in BF16, using the AITER `FLASH_ATTN` diffusion attention backend.
 Other AMD SKUs (e.g. MI325X) are not listed until their
@@ -3,9 +3,7 @@
 Distributed layerwise offloading (DLO) extends block streaming to multi-device
 deployments. With AllGather enabled, each rank stores roughly `1 / dp_size` of
 the host weights and reconstructs each layer at runtime. Without AllGather,
-each rank streams a complete block independently. Compatible TP1 deployments
-can share checkpoint-backed host pages among processes on the same node;
-otherwise DLO streams the ordinary loader's rank-local tensors.
+each rank streams its standard-loader rank-local weights independently.

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

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

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

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

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

 ## Declarative topology

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

 ## Limitations

- Direct checkpoint mmap currently requires TP1. TP greater than one is
-  outside the Phase A shared-mmap support scope and falls back before model
-  mutation to the ordinary TP-aware loader. DLO can stream that runtime layout,
-  but it provides no shared-mmap host-memory benefit or guarantee.
+- Online FP8 quantization is rejected with the DLO plus AllGather mmap path.
+  Use `--dlo-no-use-allgather` or disable online quantization.
+- Tensor parallel size greater than one is rejected in the mmap path because
+  it bypasses TP-aware loader callbacks. The no-AllGather path retains the
+  standard loader and remains experimental with TP.
 - HSDP plus AllGather is rejected to avoid double sharding. HSDP without
  AllGather has limited end-to-end validation.
- 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.
@@ -161,7 +161,6 @@ The following tables show which models support each feature:
 | **Wan2.1-VACE**              |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2**                    |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2.3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
-| **LTX-2.5**                  |     ❌     | ❓ (one-stage) | ❓ (Ulysses only) | ✅ (Full only) |         ❓         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |    ❓ (FP8)     |        ❌         |
 | **Helios**                   |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ✅*        |
 | **HunyuanVideo-1.5 T2V I2V** |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **DreamID-Omni**             |     ❌     |     ❌      |           ❌           |       ✅        |         ❌         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
@@ -175,7 +174,7 @@ The following tables show which models support each feature:
 **Frame Interpolation Support**

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

 ### AudioGen

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

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

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

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

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

 You can also pass a negative prompt via the CLI argument `--negative-prompt`:
@@ -3,26 +3,7 @@
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/x_to_video_audio>.


-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.
+The `DreamID-Omni` pipeline generates short videos from text, image and video.

 ## Local CLI Usage
 ### Download the Model locally
@@ -110,8 +91,7 @@ python x_to_video_audio.py \
 Key arguments:
 - `--prompt`: text description (string).
 - `--model`: path to the model local directory.
- `--model-class-name`: optional diffusion pipeline class override.
- `--height/--width`: output resolution (defaults 704 * 1280).
+- `--height/--width`: output resolution (defaults 704 * 1024).
 - `--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).
@@ -121,9 +101,6 @@ 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
@@ -95,7 +95,7 @@ warmup_quack_fp8([(14040, 2048, 6144), (14040, 2048, 2048)])
 | HunyuanImage-3.0 | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | Yes | Yes | All layers; use the Hunyuan stage config for multi-stage runs | None | |
 | HunyuanVideo-1.5 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `720p_t2v`, `480p_i2v` | Yes | Yes | All layers | None | |
 | Cosmos3 | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | Yes | Not validated | All layers | None | |
-| MiniMax-H3 | `MiniMaxAI/MiniMax-H3` (`FL2VA` / `Ref2VA`) | Yes | Not validated | `quantization="fp8"` quantizes eligible DiT and text-encoder linears; mixed-precision input/output heads stay FP32 | None | ✅︎ |
+| MiniMax H3 | `MiniMaxAI/MiniMax-H3` | Yes | Not validated | DiT linears except patch, timestep, and final projections; not compatible with layerwise offload | None | |

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

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

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

 ```bash
@@ -24,6 +24,7 @@ from vllm.assets.video import VideoAsset, video_to_ndarrays
 from vllm.multimodal.media.audio import load_audio
 from vllm.utils.argparse_utils import FlexibleArgumentParser

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


@@ -1,288 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Generate a LingBot-World v2 video from an image and camera trajectory.
-
-The official checkpoint is licensed separately under CC BY-NC-SA and is
-restricted to non-commercial use. This vLLM-Omni integration remains
-Apache-2.0 licensed.
-
-Example:
-    python examples/offline_inference/diffusion/lingbot_world_v2.py \
-        --prompt "The camera moves slowly forward through the scene." \
-        --image /path/to/first_frame.png \
-        --action-dir /path/to/actions/forward \
-        --output lingbot_world_v2.mp4
-
-``--action-dir`` must contain ``poses.npy`` and ``intrinsics.npy``. The
-example configures its parent as the trusted action root and sends the
-contained directory through ``extra_args["action_path"]``.
-"""
-
-from __future__ import annotations
-
-import argparse
-import math
-from collections.abc import Sequence
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-
-_MODEL = "robbyant/lingbot-world-v2-14b-causal-fast-diffusers"
-_NUM_INFERENCE_STEPS = 4
-_MAX_SEQUENCE_LENGTH = 512
-_MAX_PIXEL_AREA = 480 * 832
-_MAX_RAW_FRAMES = 117
-_MAX_SOURCE_CAMERA_FRAMES = 4096
-_TEMPORAL_COMPRESSION = 4
-_LATENT_FRAMES_PER_BLOCK = 3
-_SPATIAL_ALIGNMENT = 16
-
-
-@dataclass(frozen=True)
-class LingBotPaths:
-    image: Path
-    action_dir: Path
-    action_root: Path
-    action_relative: Path
-    camera_frames: int
-    output: Path
-
-
-def _validate_camera_arrays(action_dir: Path) -> int:
-    """Validate the bounded camera contract before starting the engine."""
-
-    try:
-        poses = np.load(action_dir / "poses.npy", mmap_mode="r", allow_pickle=False)
-        intrinsics = np.load(action_dir / "intrinsics.npy", mmap_mode="r", allow_pickle=False)
-    except (OSError, TypeError, ValueError):
-        raise ValueError("Camera files must be valid numeric poses.npy and intrinsics.npy arrays.") from None
-    if poses.ndim != 3 or poses.shape[1:] != (4, 4):
-        raise ValueError("poses.npy must have shape [frames, 4, 4].")
-    if intrinsics.ndim != 2 or intrinsics.shape[1:] != (4,):
-        raise ValueError("intrinsics.npy must have shape [frames, 4].")
-    if poses.shape[0] != intrinsics.shape[0]:
-        raise ValueError("poses.npy and intrinsics.npy must contain the same frame count.")
-    if not 1 <= poses.shape[0] <= _MAX_SOURCE_CAMERA_FRAMES:
-        raise ValueError("Camera arrays must contain between 1 and 4096 source frames.")
-    if poses.dtype.kind not in "fiu" or intrinsics.dtype.kind not in "fiu":
-        raise ValueError("Camera arrays must contain real numeric values.")
-    if not np.isfinite(poses).all() or not np.isfinite(intrinsics).all():
-        raise ValueError("Camera arrays must contain only finite values.")
-    return int(poses.shape[0])
-
-
-def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Generate a LingBot-World v2 video from a first-frame image and camera trajectory."
-    )
-    parser.add_argument("--model", default=_MODEL, help="Official Hugging Face model ID or local checkpoint path.")
-    parser.add_argument("--prompt", required=True, help="Text description of the scene and desired motion.")
-    parser.add_argument("--image", required=True, help="Path to the first-frame image.")
-    parser.add_argument(
-        "--action-dir",
-        required=True,
-        help="Camera action directory containing poses.npy and intrinsics.npy.",
-    )
-    parser.add_argument("--height", type=int, default=480, help="Output height; must be divisible by 16.")
-    parser.add_argument("--width", type=int, default=832, help="Output width; must be divisible by 16.")
-    parser.add_argument(
-        "--num-frames",
-        type=int,
-        default=9,
-        help="Raw output frame count. Nine frames are one three-latent-frame causal block.",
-    )
-    parser.add_argument("--seed", type=int, default=42, help="Random seed.")
-    parser.add_argument(
-        "--tensor-parallel-size",
-        type=int,
-        default=1,
-        help="Number of GPUs used for tensor parallelism inside the DiT.",
-    )
-    parser.add_argument("--flow-shift", type=float, default=5.0, help="Positive FlowUniPC scheduler shift.")
-    parser.add_argument("--fps", type=int, default=16, help="Frames per second in the exported MP4.")
-    parser.add_argument("--output", default="lingbot_world_v2.mp4", help="Output MP4 path.")
-    parser.add_argument(
-        "--enforce-eager",
-        action="store_true",
-        help="Disable compilation and force eager execution.",
-    )
-    return parser.parse_args(argv)
-
-
-def resolve_cli_paths(args: argparse.Namespace) -> LingBotPaths:
-    """Canonicalize local inputs and construct a safe trusted-root mapping."""
-
-    image = Path(args.image).expanduser().resolve()
-    if not image.is_file():
-        raise ValueError("--image must point to an existing file.")
-
-    action_dir = Path(args.action_dir).expanduser().resolve()
-    if not action_dir.is_dir():
-        raise ValueError("--action-dir must point to an existing directory.")
-    if not all((action_dir / name).is_file() for name in ("poses.npy", "intrinsics.npy")):
-        raise ValueError("--action-dir must contain poses.npy and intrinsics.npy.")
-    camera_frames = _validate_camera_arrays(action_dir)
-
-    action_root = action_dir.parent.resolve()
-    try:
-        action_relative = action_dir.relative_to(action_root)
-    except ValueError:
-        raise ValueError("--action-dir must be contained by its canonical trusted action root.") from None
-    if not action_relative.parts:
-        raise ValueError("--action-dir must name a directory contained by a trusted action root.")
-
-    output = Path(args.output).expanduser().resolve()
-    return LingBotPaths(
-        image=image,
-        action_dir=action_dir,
-        action_root=action_root,
-        action_relative=action_relative,
-        camera_frames=camera_frames,
-        output=output,
-    )
-
-
-def _positive_finite(value: float, flag: str) -> float:
-    result = float(value)
-    if not math.isfinite(result) or result <= 0:
-        raise ValueError(f"{flag} must be a positive finite number.")
-    return result
-
-
-def build_omni_kwargs(
-    args: argparse.Namespace,
-    paths: LingBotPaths,
-    *,
-    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()
@@ -1,239 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Run LingBot-World 2.0 as an in-process realtime AR-Diffusion session.
-
-This example exercises the internal session API. It is not a public HTTP or
-WebSocket protocol. Each JSONL event advances the world by one three-latent
-frame AR block and writes the latent plus its identity metadata. The current
-scoped implementation supports at most ten ticks per generation epoch because
-its image condition is bounded to 117 pixel frames; reset or create a session
-to start a new world.
-"""
-
-from __future__ import annotations
-
-import argparse
-import asyncio
-import json
-import math
-import time
-from collections.abc import Sequence
-from pathlib import Path
-from typing import Any
-
-_MODEL = "robbyant/lingbot-world-v2-14b-causal-fast-diffusers"
-_CAMERA_ACTION_SCHEMA = "lingbot.camera_actions.v1"
-_FRAMES_PER_BLOCK = 3
-# ((117 pixel frames - 1) / VAE temporal factor 4 + 1) / 3 latent frames.
-_MAX_REALTIME_TICKS = 10
-
-
-def _camera_event_data(frames: list[list[str]]) -> dict[str, Any]:
-    """Build the event-side script consumed by LingBotCameraControlReducer."""
-    return {"mode": "script", "frames": frames}
-
-
-def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Run an in-process realtime LingBot-World 2.0 session.")
-    parser.add_argument("--model", default=_MODEL, help="Hugging Face model ID or local checkpoint path.")
-    parser.add_argument("--image", required=True, help="Initial RGB image.")
-    parser.add_argument("--prompt", required=True, help="Initial scene prompt.")
-    parser.add_argument(
-        "--events",
-        required=True,
-        help="JSONL file with one prompt/action event per AR block; at most 10 events.",
-    )
-    parser.add_argument("--output-dir", required=True, help="Directory for chunk latents and metadata.")
-    parser.add_argument("--session-id", default="lingbot-world", help="Persistent world session identifier.")
-    parser.add_argument("--height", type=int, default=480)
-    parser.add_argument("--width", type=int, default=832)
-    parser.add_argument("--seed", type=int, default=42)
-    parser.add_argument("--gpu-memory-fraction", type=float, default=0.1)
-    parser.add_argument("--tensor-parallel-size", type=int, default=1)
-    parser.add_argument("--enforce-eager", action="store_true")
-    return parser.parse_args(argv)
-
-
-def _load_events(path: Path) -> list[dict[str, Any]]:
-    events: list[dict[str, Any]] = []
-    for line_number, raw_line in enumerate(path.read_text().splitlines(), start=1):
-        line = raw_line.strip()
-        if not line:
-            continue
-        try:
-            value = json.loads(line)
-        except json.JSONDecodeError as exc:
-            raise ValueError(f"events line {line_number} is not valid JSON: {exc.msg}.") from None
-        if not isinstance(value, dict):
-            raise ValueError(f"events line {line_number} must be a JSON object.")
-        event_id = value.get("event_id")
-        if isinstance(event_id, bool) or not isinstance(event_id, int) or event_id < 0:
-            raise ValueError(f"events line {line_number} requires a non-negative integer event_id.")
-        prompt = value.get("prompt")
-        if prompt is not None and (not isinstance(prompt, str) or not prompt.strip()):
-            raise ValueError(f"events line {line_number} prompt must be a non-empty string.")
-        frames = value.get("frames")
-        if frames is not None:
-            if not isinstance(frames, list) or len(frames) != _FRAMES_PER_BLOCK:
-                raise ValueError(f"events line {line_number} frames must contain exactly three lists.")
-            for frame in frames:
-                if not isinstance(frame, list) or any(not isinstance(action, str) for action in frame):
-                    raise ValueError(f"events line {line_number} frames must contain only action strings.")
-        if prompt is None and frames is None:
-            raise ValueError(f"events line {line_number} must update prompt and/or frames.")
-        events.append({"event_id": event_id, "prompt": prompt, "frames": frames})
-    if not events:
-        raise ValueError("events file must contain at least one event.")
-    if len(events) > _MAX_REALTIME_TICKS:
-        raise ValueError(
-            "events file must contain at most "
-            f"{_MAX_REALTIME_TICKS} events because the current LingBot realtime "
-            "image-condition horizon is 117 pixel frames."
-        )
-    event_ids = [event["event_id"] for event in events]
-    if event_ids != sorted(set(event_ids)):
-        raise ValueError("event_id values must be unique and strictly increasing.")
-    return events
-
-
-def _validate_args(args: argparse.Namespace) -> tuple[Path, Path, Path]:
-    image = Path(args.image).expanduser().resolve()
-    events = Path(args.events).expanduser().resolve()
-    output_dir = Path(args.output_dir).expanduser().resolve()
-    if not image.is_file():
-        raise ValueError("--image must point to an existing file.")
-    if not events.is_file():
-        raise ValueError("--events must point to an existing JSONL file.")
-    if not args.prompt.strip():
-        raise ValueError("--prompt must contain non-whitespace text.")
-    if args.height <= 0 or args.width <= 0 or args.height % 16 or args.width % 16:
-        raise ValueError("--height and --width must be positive multiples of 16.")
-    if args.tensor_parallel_size <= 0:
-        raise ValueError("--tensor-parallel-size must be positive.")
-    if not math.isfinite(args.gpu_memory_fraction) or not 0 < args.gpu_memory_fraction <= 1:
-        raise ValueError("--gpu-memory-fraction must be in (0, 1].")
-    return image, events, output_dir
-
-
-async def run(argv: Sequence[str] | None = None) -> Path:
-    args = parse_args(argv)
-    image, events_path, output_dir = _validate_args(args)
-    events = _load_events(events_path)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Imports stay below pure input validation so --help and helper tests do not
-    # require a CUDA-enabled vLLM installation.
-    import torch
-
-    from vllm_omni.diffusion.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()
@@ -96,7 +96,7 @@ if __name__ == "__main__":
        ),
    )
    from diffusers.utils import export_to_video
-    frames = outputs[0].images
+    frames = outputs[0].request_output.images
    export_to_video(frames, "quick_test_i2v.mp4", fps=16)
 ```

@@ -68,7 +68,6 @@ from vllm_omni.model_extras import (
 from vllm_omni.model_extras import (
    get_extra_body_params,
    get_model_class_name,
-    should_preserve_reference_image_size,
 )
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.platforms import current_omni_platform
@@ -455,13 +454,7 @@ def main():

    media_inputs: dict[str, Any] = {}
    if image is not None:
-        preserve_image_size = should_preserve_reference_image_size(
-            model_class_name,
-            model=args.model,
-        )
-        media_inputs["image"] = (
-            image if preserve_image_size else image.resize((width, height), PIL.Image.Resampling.LANCZOS)
-        )
+        media_inputs["image"] = image.resize((width, height), PIL.Image.Resampling.LANCZOS)
    if last_image is not None:
        media_inputs["last_image"] = last_image.resize((width, height), PIL.Image.Resampling.LANCZOS)
    if mask_image is not None:
@@ -82,6 +82,9 @@ def parse_args():
    # ``max_num_seqs=1`` …) are passed as flat kwargs to ``Omni`` below
    # and ``create_default_diffusion`` materializes the stage config.

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


@@ -335,7 +335,7 @@ def main(args):
    print(f"use_tts (tts_bos): {use_tts}")

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

 Examples:

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

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

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


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


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

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

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

@@ -74,7 +74,7 @@ if __name__ == "__main__":
    omni = Omni(model="Qwen/Qwen-Image")
    prompt = "a cup of coffee on the table"
    outputs = omni.generate(prompt)
-    images = outputs[0].images
+    images = outputs[0].request_output.images
    images[0].save("coffee.png")
 ```

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

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

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

 ---

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

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

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


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


@@ -1,139 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Offline inference example for Gepard-1.0 TTS via vLLM-Omni.
-
-Single-stage native-AR pipeline: a Qwen3.5 backbone samples one 32-code FSQ
-frame per step; the NeMo NanoCodec decodes the frames to a 22.05 kHz mono
-waveform. Zero-shot: the default learned voice, no reference audio.
-
-Generation length and seed are stage settings rather than flags here. One
-output token is one audio frame, so ``max_tokens`` in the deploy YAML is the
-frame budget; pass a copy of the YAML via ``--deploy-config`` to change it.
-
-Usage:
-  python end2end.py --text "Hello, this is Gepard speaking."
-"""
-
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import soundfile as sf
-import torch
-
-import vllm_omni
-from vllm_omni.utils.tracking_parser import TrackingArgumentParser
-
-os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
-
-from vllm_omni import Omni  # noqa: E402
-
-MODEL = "nineninesix/gepard-1.0"
-SAMPLE_RATE = 22050
-# Absolute: the loader hands the path straight to OmegaConf, which resolves a
-# bare name against the current working directory, not the package.
-DEFAULT_DEPLOY_CONFIG = str(Path(vllm_omni.__file__).resolve().parent / "deploy" / "gepard.yaml")
-
-
-def build_request(text: str) -> dict:
-    """Build an Omni request payload for Gepard (zero-shot, no ref audio).
-
-    The model only consumes the [speaker slots, SOT, text, EOT, SOS] layout, so
-    a bare text prompt would have no speaker slots and no SOS.
-    """
-    from transformers import AutoTokenizer
-
-    from vllm_omni.model_executor.models.gepard.configuration_gepard import GepardConfig
-    from vllm_omni.model_executor.models.gepard.prompt import build_gepard_prompt_ids
-
-    # From the checkpoint's own sidecar, which is what the worker builds its
-    # config from too. A layout that disagrees with the trained one does not
-    # fail, it just degrades the speech.
-    cfg = GepardConfig.from_checkpoint(MODEL)
-    tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
-    prompt_token_ids = build_gepard_prompt_ids(
-        tokenizer(text, add_special_tokens=False)["input_ids"],
-        config=cfg,
-    )
-    return {
-        "prompt_token_ids": prompt_token_ids,
-        "additional_information": {"text": [text]},
-    }
-
-
-def save_audio(waveform: torch.Tensor, path: str, sample_rate: int = SAMPLE_RATE) -> None:
-    sf.write(path, waveform.float().numpy(), sample_rate)
-    seconds = waveform.numel() / sample_rate
-    print(
-        f"  Saved {path} ({tuple(waveform.shape)}, {sample_rate} Hz, "
-        f"{seconds:.2f}s, peak {float(waveform.abs().max()):.3f})"
-    )
-
-
-def main(args) -> None:
-    omni = Omni(
-        model=MODEL,
-        deploy_config=args.deploy_config,
-        stage_init_timeout=args.stage_init_timeout,
-        init_timeout=args.init_timeout,
-    )
-
-    # No explicit SamplingParams: one supplied here would replace the stage
-    # defaults rather than merge over them, dropping the pipeline's stop token.
-    output_dir = Path(args.output_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    print(f"Synthesizing: {args.text!r}")
-    inputs = build_request(text=args.text)
-
-    for i, stage_outputs in enumerate(omni.generate(inputs)):
-        for j, out in enumerate(stage_outputs.outputs):
-            mm = out.multimodal_output
-            if mm is None:
-                print(f"  [req {i}] No audio output.")
-                continue
-            # The consolidation path renames "model_outputs" to "audio".
-            audio = mm.get("audio")
-            if audio is None:
-                audio = mm.get("model_outputs")
-            if isinstance(audio, list):
-                audio = audio[0] if len(audio) else None
-            if audio is None:
-                print(f"  [req {i}] No waveform in multimodal_output (keys: {list(mm)}).")
-                continue
-            sr_tensor = mm.get("sr")
-            sr = int(sr_tensor.item()) if hasattr(sr_tensor, "item") else SAMPLE_RATE
-            save_audio(audio.cpu(), str(output_dir / f"output_{i}_{j}.wav"), sr)
-
-    print("Done.")
-
-
-def parse_args():
-    parser = TrackingArgumentParser(description="Gepard-1.0 offline TTS inference (zero-shot)")
-    parser.add_argument("--text", default="Hello, this is Gepard speaking.", help="Text to synthesize.")
-    parser.add_argument(
-        "--output-dir",
-        default=os.path.join(
-            os.environ.get("XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache")),
-            "gepard_output",
-        ),
-        help="Directory for WAV outputs (default: ~/.cache/gepard_output).",
-    )
-    parser.add_argument(
-        "--deploy-config",
-        default=DEFAULT_DEPLOY_CONFIG,
-        help=(
-            "Path to the deploy YAML (default: the packaged vllm_omni/deploy/gepard.yaml). This must "
-            "stay set: without the YAML's `pipeline: gepard` pin the architectures fallback routes "
-            "Qwen3_5ForCausalLM to the diffusion registry. Copy it to change max_tokens or seed."
-        ),
-    )
-    # The upstream defaults are too tight for a cold start.
-    parser.add_argument("--stage-init-timeout", type=int, default=900)
-    parser.add_argument("--init-timeout", type=int, default=1800)
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    main(parse_args())
@@ -32,10 +32,11 @@ from end2end import _estimate_prompt_len
 from vllm.utils.argparse_utils import FlexibleArgumentParser

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


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

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

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


@@ -570,8 +570,6 @@ def main():
        num_frames=args.num_frames,
        extra_args=extra_args,
    )
-    if args.frame_rate is not None:
-        sampling_kwargs["frame_rate"] = args.frame_rate
    if args.guidance_scale_high is not None:
        sampling_kwargs["guidance_scale_2"] = args.guidance_scale_high

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

-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.
+The `DreamID-Omni` pipeline generates short videos from text, image and video.

 ## Local CLI Usage
 ### Download the Model locally
@@ -166,8 +147,7 @@ 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.
- `--model-class-name`: optional diffusion pipeline class override.
- `--height/--width`: output resolution (defaults 704 * 1280).
+- `--height/--width`: output resolution (defaults 704 * 1024).
 - `--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).
@@ -177,9 +157,6 @@ 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`.
@@ -3,97 +3,32 @@

 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.param_utils import apply_declared_extra_args
+from vllm_omni.diffusion.utils.media_utils import mux_video_audio_bytes
 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=None,
-        help="Optional legacy model type override (for example, magi-human).",
+        default="dreamid-omni",
+        choices=["dreamid-omni", "magi-human"],
+        help="Model type.",
    )
-    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")
@@ -101,9 +36,9 @@ 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=parse_json_object,
+        type=str,
        default=None,
-        help="JSON dict of model-specific extra params (declared in vllm_omni/model_extras/), "
+        help="[magi-human] 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}\'.',
    )

@@ -136,20 +71,13 @@ 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 model transformer.",
+        help=("Online (dynamic) quantization method for the DreamID-Omni transformer. "),
    )
    parser.add_argument(
        "--enable-cpu-offload",
@@ -166,7 +94,7 @@ def parse_args() -> argparse.Namespace:
    parser.add_argument(
        "--use-hsdp",
        action="store_true",
-        help="Enable HSDP for supported transformer blocks.",
+        help="Enable HSDP for DreamID-Omni fused transformer blocks.",
    )
    parser.add_argument(
        "--hsdp-shard-size",
@@ -183,105 +111,20 @@ def parse_args() -> argparse.Namespace:
    return parser.parse_args()


-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 = []
+def load_image_and_audio(image_paths, audio_paths):
+    image = []
+    audio = []

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

-    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,
-    )
+    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


 def _extract_peak_memory_mb(result: Any) -> float:
@@ -314,10 +157,45 @@ def main() -> None:
    if args.prompt_file:
        with open(args.prompt_file) as f:
            text_prompt = json.load(f)
-            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.")
+            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,
+            },
+        )

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

-    omni_kwargs: dict[str, Any] = dict(
+    omni_kwargs = 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)
-    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
+    start = time.perf_counter()
+    outputs = omni.generate(prompt, sampling_params)
+    elapsed = time.perf_counter() - start

-        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 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")

-        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})
+    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)

-        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),
-        )
+    audio_np = None
+    if generated_audio is not None:
+        audio_np = np.squeeze(np.asarray(generated_audio)).astype(np.float32)

-        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()
+    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)")


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

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

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

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

 ## API Migration

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

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

 ```bash
 vllm serve rootonchair/LTX-2-19b-distilled --omni \
-  --model-class-name LTX2DistilledTwoStagePipeline --stage-init-timeout 600
+  --model-class-name LTX2DistilledPipeline --stage-init-timeout 600
 # LTX-2.3 full-distilled; the v1.1 x2 upsampler is resolved when absent
 vllm serve diffusers/LTX-2.3-Distilled-Diffusers --omni \
-  --model-class-name LTX2DistilledTwoStagePipeline --stage-init-timeout 600
+  --model-class-name LTX2DistilledPipeline --stage-init-timeout 600
 # Ordinary LTX-2.3 two-stage
 vllm serve diffusers/LTX-2.3-Diffusers --omni \
  --model-class-name LTX2TwoStagePipeline \
@@ -1,195 +0,0 @@
-# MiniMax-H3 on a single NPU 950PR
-
-This recipe runs MiniMax-H3 with online INT8 quantization on one NPU 950PR
-NPU (128 GB HiBL 1.0 HBM). It covers the single-card T2VA configuration at
-1024x576. For the eight-card Atlas 800I A3 BF16 route at 768P, see
-[MiniMax-H3-NPU.md](MiniMax-H3-NPU.md).
-
-## Capacity requirements
-
-| Resource | Requirement |
-| --- | ---: |
-| NPU | 1x NPU 950PR |
-| NPU HBM | 128 GiB (131,072 MiB reported by `npu-smi`) |
-| Observed HBM high-water mark | 118,442 MiB (90.4% of capacity) |
-| Checkpoint storage | 135 GiB per partition, local disk strongly preferred |
-| Container shared memory | 8 GiB, or a writable local filesystem for spill |
-
-The observed high-water mark leaves about 12.3 GiB of headroom, so this card is
-sized for exactly one resident task partition at 1024x576. Do not expect a
-larger output shape or concurrency greater than one to fit.
-
-Because a single 950PR holds the whole partition in HBM, this route does not
-need layerwise offload, and the 200 GiB system-RAM floor carried by the 72 GiB
-GPU recipes does not apply. The validated run completed inside a container
-limited to 32 GiB of RAM. Passing `--enable-layerwise-offload` on this card is
-actively harmful: it stages weights in non-reclaimable host memory and the
-container's OOM killer terminates the server with exit code -9.
-
-
-## Environment
-
- Host architecture: x86_64
-  driver: 25.7.rc1.6 (hal 7.35.23)
-  firmware: 9.0.0.105.229
- CANN toolkit: 9.1.0 (`/usr/local//cann-9.1.0`)
- npu-smi: 25.7.rc1.6
- Python: 3.12.13
- PyTorch: 2.10.0+cpu
- torch_npu: 2.10.0.post2
- vLLM: 0.26.0
- vLLM-Omni: 0.26.1.dev103+g584d78c67.npu (commit `584d78c6`)
-
-Install vLLM-Omni from a checkout with MiniMax-H3 support:
-
-```bash
-uv venv
-source .venv/bin/activate
-uv pip install -e .
-```
-
-## Start a server
-
-```bash
-export MODEL=/path/to/MiniMax-H3/FL2VA
-export PORT=8000
-export VLLM_WORKER_MULTIPROC_METHOD=spawn
-export VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400
-
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 1 \
-  --tensor-parallel-size 1 \
-  --usp 1 \
-  --ring 1 \
-  --text-encoder-tp-size 1 \
-  --vae-patch-parallel-size 1 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --init-timeout 14400 \
-  --stage-init-timeout 14400 \
-  --quantization int8
-```
-
-Do not pass `--diffusion-attention-backend CUDNN_ATTN`. cuDNN attention has no
-NPU implementation and fails at the first denoise step. Leave the backend
-unset: it resolves to `TORCH_SDPA`, or to `FLASH_ATTN` when MindIE-SD is
-installed (see [MiniMax-H3-NPU.md § Environment](MiniMax-H3-NPU.md#environment)).
-
-H3 is CFG-distilled, so `--cfg-parallel-size` must remain 1.
-
-## Validated evidence
-
-Measured on one NPU 950PR with the server command above plus
-`--enable-diffusion-pipeline-profiler`, generating a 5 s 1024x576 T2VA clip from
-the request in [§ T2VA request example](#t2va-request-example). MiniMax-H3
-requested 60 denoise steps and executed 59 denoise updates, so per-step latency
-is `denoise / 59`. `ffprobe` confirms every returned MP4 is 1024x576, 24 fps,
-124 video frames, 5.175 s, with a 32 kHz audio track of 165,600 samples.
-
-Five requests were issued back to back with no idle time between them. The card
-is measurably faster on the first request than in steady state, so both regimes
-are reported.
-
-| Stage | First request, cold card | Thermal steady state |
-| --- | ---: | ---: |
-| End-to-end request | 462.13 s | 506.38 s |
-| Text encode | 0.55 s | 0.05 s |
-| Denoise (59 updates) | 429.29 s | 471.74 s |
-| Per denoise update | 7,276 ms | 7,996 ms |
-| VAE decode | 8.43 s | 8.14 s |
-| Worker-to-server handoff | 24 s | 27 s |
-| MP4 muxing | 0.57 s | 0.50 s |
-| Server-reported `denoise_step_latency_ms` | 7,702 ms | 8,440 ms |
-| Peak HBM (`npu-smi`) | 118,442 MiB | 118,442 MiB |
-| Average NPU power | 566.9 W | 556.9 W |
-| Peak NPU temperature | 104 C | 104 C |
-
-Sample counts differ per row. The cold column is a single request. In the steady
-column, text encode, denoise and VAE decode are means over the four subsequent
-requests — denoise spanned 470.37 to 473.21 s, a 0.60% spread — while
-end-to-end, handoff and muxing are means over the two of those four that
-survived the handoff timeout described above.
-
-**Report the steady-state column.** The cold card completes denoise 9.9% faster
-while drawing only 1.8% more average power, and both regimes reach the same
-104 C ceiling, so the first request is buying a short window of higher clocks
-before the die saturates. Any benchmark that issues a single request against an
-idle card will overstate throughput by about 10%.
-
-**Do not read `denoise_step_latency_ms` as a per-step time.** The server divides
-the entire stage wall time by the 60 *requested* steps, so it absorbs text
-encoding, VAE decode and the 837 MiB handoff. All three successful requests
-satisfy `denoise_step_latency_ms == e2e_stage_wall_time_ms / 60` exactly. The
-per-update figure in the table is `diffuse / 59` from the pipeline profiler.
-
-Denoise accounts for 93% of the steady-state request and the handoff accounts
-for 5.3%, so keeping the transfer in shared memory should bring end-to-end down
-to roughly 480 s.
-
-Peak HBM is sampled externally with `npu-smi` at a 3 s interval. It is a
-reserved high-water mark rather than live allocation: it reads 111,272 MiB after
-weight loading and before the first request, and does not fall back after
-requests complete. Re-measure for longer outputs, a different output shape, or
-concurrency greater than one.
-
-### Startup
-
-Weight loading took **31 min 50 s** on the validated host (first log line
-08:04:59, `Application startup complete` 08:36:49), with the checkpoint on a
-shared GlusterFS network mount. This is why the server command sets
-`--init-timeout 14400` and `--stage-init-timeout 14400`; the stock 600 s and
-1800 s timeouts both abort mid-load with `TimeoutError` and exit code 143.
-Reduce both timeouts when the partition is staged on local disk.
-
-## T2VA request example
-
-```bash
-export API_URL="http://127.0.0.1:${PORT}/v1/videos/sync"
-
-curl -sS --max-time 1800 -X POST "${API_URL}" \
-  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
-  -F 'width=1024' \
-  -F 'height=576' \
-  -F 'aspect_ratio=16:9' \
-  -F 'fps=24' \
-  -F 'num_inference_steps=60' \
-  -F 'flow_shift=12' \
-  -F 'seed=1101' \
-  -F 'extra_params={"task":"t2va","duration":5,"audio_flow_shift":3.0}' \
-  -o t2va.mp4
-```
-
-## Known limitations
-
- INT8 online quantization is validated for T2VA on this card. Use BF16 for
-  FL2VA and Ref2VA, or re-measure before relying on it.
- Single-card serving loads one task partition at a time. For Ref2VA, stop the
-  server and restart it against the `Ref2VA` directory.
- Sustained load drives the die to 104 C and costs about 10% of denoise
-  throughput relative to a cold card. Warm the server with at least one full
-  request before measuring, and treat steady state as the reportable number.
- Requests can fail during result handoff on hosts where the IPC spill path is
-  slow. See [§ Result handoff and shared
-  memory](#result-handoff-and-shared-memory).
- Loading the checkpoint from a network mount takes over 30 minutes and forces
-  very large init timeouts. Stage the partition on local disk when possible.
- The configuration measured here is 1024x576 at 60 steps, which differs from
-  the 1344x768 at 50 steps used by the GPU recipes in this directory. Denoise
-  cost scales superlinearly with token count, so the numbers are not directly
-  comparable across recipes.
- The image ships a `triton` package whose  backend is not built
-  (`No module named 'triton._C.libtriton.'`). vLLM logs this as an error
-  at startup and disables Triton; the diffusion path does not need it and the
-  run completes normally.
-
-## Additional resources
-
- [MiniMax-H3.md](MiniMax-H3.md) — full GPU guide
- [MiniMax-H3-NPU.md](MiniMax-H3-NPU.md) — eight-card Atlas 800I A3 BF16 guide
- [Int8 quantization](../../docs/user_guide/quantization/int8.md)
- [Supported models](../../docs/models/supported_models.md)
- [Video API](../../docs/serving/videos_api.md)
@@ -357,19 +357,12 @@ No restart is needed: `task=fl2va` routes to `FL2VA/transformer`, while

 ### Online FP8 quantization

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

 Add this option to an existing H3 server command:

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

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

-The structured option replaces `--quantization fp8`. Online FP8 can be used
-with H3 layerwise offload and with distributed layerwise offload's full-weight
-per-rank path (`--dlo-no-use-allgather`). The sharded DLO AllGather path is not
-supported for runtime-created FP8 weights.
+The structured option replaces `--quantization fp8`. Online FP8 is currently
+incompatible with H3 layerwise offload because the offload path produces a
+weight stride rejected by the Cutlass FP8 kernel. Use resident FP8 with tensor
+parallelism and VAE tiling instead.

 ## AMD ROCm (gfx942 / gfx950)

@@ -779,13 +750,6 @@ audio spectral cosine 0.9589 (minimum 0.80), and audio RMS ratio 0.9342. The
 resident per-GPU peak was 68.52 GiB for BF16 and 53.51 GiB for FP8, a 22%
 reduction.

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

 TeaCache reuses DiT block residuals across denoising steps when consecutive
@@ -870,8 +834,7 @@ vllm serve "${MODEL_ROOT}/FL2VA" \
  H3 native `tile` mode only.
 - A U2 x Ring2 hybrid currently fails with an attention-mask length mismatch; use
  pure Ulysses.
- Runtime-created FP8 weights do not support the sharded DLO AllGather path;
-  use `--dlo-no-use-allgather` when combining online FP8 with DLO.
+- Online FP8 is not compatible with layerwise offload.
 - TeaCache and Cache-DiT cannot be enabled on the same server.
 - Image+audio Ref2VA accepts exactly one image and one audio reference.
 - Video Ref2VA accepts one or more video files, but not an additional standalone
@@ -51,7 +51,7 @@ NemotronH (~3B active parameters). Pick the pipeline by task:
 `model_type` auto-resolves `audex_tts.yaml`):

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

 ```bash
-vllm serve nvidia/Nemotron-Labs-Audex-30B-A3B \
+vllm-omni serve nvidia/Nemotron-Labs-Audex-30B-A3B \
    --host 0.0.0.0 --port 8097 \
    --trust-remote-code --omni \
    --stage-configs-path vllm_omni/deploy/audex_tts_30b.yaml
@@ -55,12 +55,10 @@ recipes/
 | [`MiniMaxAI/MiniMax-H3-MUSA.md`](./MiniMaxAI/MiniMax-H3-MUSA.md) | MiniMax H3 T2VA, FL2VA, and Ref2VA on MUSA | 4x MTT S5000 (TP4 Ref2VA profile) |
 | [`krea/Krea-2.md`](./krea/Krea-2.md) | Text-to-image (Turbo + Raw), offline + online, with LoRA | 1x H100 80GB |
 | [`LTX/LTX-2.md`](./LTX/LTX-2.md) | LTX-2/LTX-2.3 text-to-video and image-to-video with synchronized audio | H200 141GB / 96GB-class GPU |
-| [`LTX/LTX-2.5.md`](./LTX/LTX-2.5.md) | LTX-2.5-Diffusers: Full/SFT one-stage and distilled two-stage T2V/I2V with synchronized audio | NVIDIA B300; cuDNN-qualified |
 | [`MammothModa2/MammothModa2.md`](./MammothModa2/MammothModa2.md) | Preview and Dev text-to-image (AR → DiT); Dev text/image understanding | Preview: 1x L40S 48GB / 1x ≥40GB GPU; Dev: 1x NVIDIA GPU with sufficient cache headroom |
 | [`meituan-longcat/LongCat-Video-Avatar-1.5.md`](./meituan-longcat/LongCat-Video-Avatar-1.5.md) | Audio-driven avatar video generation (AT2V / AI2V, single- and multi-speaker, AVC continuation) | 1x H100 80GB |
 | [`mistralai/Voxtral-TTS.md`](./mistralai/Voxtral-TTS.md) | Online serving for TTS | 1x RTX 4090 24GB |
 | [`Robbyant/LingBot-Video.md`](./Robbyant/LingBot-Video.md) | Native dense and MoE text-to-video serving | Dense: 1x L20X; MoE: 1x L20X (~67.7 GiB peak) |
-| [`Robbyant/LingBot-World-2.0.md`](./Robbyant/LingBot-World-2.0.md) | Offline and experimental realtime interactive world generation | 1x H200/B200 or 2x B200 for TP=2 |
 | [`cosmos3/Cosmos3-Nano.md`](./cosmos3/Cosmos3-Nano.md) | Text-to-image, text-to-video, image-to-video, video-to-video generation, text to video with sound, action policy | 1x H200 141GB / B300 |
 | [`cosmos3/Cosmos3-Edge.md`](./cosmos3/Cosmos3-Edge.md) | T2I / T2V / I2V + action (Physical-AI) generation on the Nemotron-based Edge transformer | 1x GPU (~8 GiB) |
 | [`cosmos3/Cosmos3-Super.md`](./cosmos3/Cosmos3-Super.md) | 64B T2I / T2V / I2V / V2V generation (+ optional audio) / Action policy | 8x H200/H100/A100 / 2x H200 / B300 |
@@ -1,122 +0,0 @@
-# LingBot-World 2.0
-
-> Offline and experimental realtime interactive world generation
-
-## Summary
-
- Vendor: Robbyant
- Model: `robbyant/lingbot-world-v2-14b-causal-fast-diffusers`
- Task: image-conditioned interactive world generation
- Modes: offline trajectory replay and in-process realtime AR-Diffusion ticks
- Hardware validated: NVIDIA H200 and B200
- Maintainer: Community
-
-The checkpoint is separately licensed under CC BY-NC-SA and restricted to
-non-commercial use. The vLLM-Omni integration code remains Apache-2.0.
-
-## Offline generation
-
-The offline path consumes one source image and an action directory containing
-`poses.npy` with shape `[frames, 4, 4]` and `intrinsics.npy` with shape
-`[frames, 4]`.
-
-```bash
-python examples/offline_inference/diffusion/lingbot_world_v2.py \
-  --prompt "The camera moves slowly forward through the scene." \
-  --image /path/to/first_frame.png \
-  --action-dir /path/to/actions/forward \
-  --num-frames 81 \
-  --output lingbot_world_v2.mp4
-```
-
-The current causal-fast checkpoint uses four DMD sampling steps. Raw frame
-counts must be `9 + 12k`, up to 117 frames. Camera files may contain a
-longer bounded source trajectory (the official example contains 269 frames);
-the runtime consumes only the prefix needed by the request.
-
-## Realtime in-process generation
-
-The realtime example keeps AR-Diffusion KV and model-owned state across
-requests. Each JSONL line describes the prompt and/or three latent-frame camera
-actions applied at the next chunk boundary:
-
-```json
-{"event_id":1,"prompt":"A road through a forest","frames":[["j"],[],[]]}
-{"event_id":2,"frames":[["w"],["w"],["w"]]}
-{"event_id":3,"prompt":"The road enters a snowy valley","frames":[[],[],[]]}
-```
-
-Run:
-
-```bash
-python examples/offline_inference/diffusion/lingbot_world_v2_realtime.py \
-  --image /path/to/first_frame.png \
-  --events /path/to/events.jsonl \
-  --output-dir /tmp/lingbot-realtime \
-  --gpu-memory-fraction 0.6
-```
-
-The example writes one latent tensor and one metadata JSON file per chunk. It
-exercises the same `ARDiffusionSessionManager -> ARDiffusionOmniTickConsumer
-> AsyncOmni -> ARDiffusionEngine` path used by a future HTTP or WebSocket
-transport.
-
-This PR intentionally does not define a public realtime HTTP/WebSocket schema.
-An online serving client should be added together with the public transport
-API rather than exposing LingBot-specific event fields from the generic model
-runtime. The structured camera-interaction frontend and transport work is
-tracked separately in
-[vllm-project/vllm-omni#5527](https://github.com/vllm-project/vllm-omni/pull/5527).
-
-## Realtime identity and controls
-
- `session_id` identifies the persistent world and its worker-owned state.
- `event_id` identifies a prompt/control update and remains monotonic across reset.
- `chunk_index` is contiguous from zero and restarts from zero after reset.
- `request_id` correlates one chunk snapshot with its output metadata.
- AsyncOmni uses a separate UUID-suffixed internal engine routing ID.
-
-The generic runtime transports controls as opaque snapshots. LingBot's adapter
-accepts:
-
- `lingbot.camera_actions.v1` for per-latent-frame key states such as `w`, `a`,
-  `s`, `d`, `i`, `j`, `k`, and `l`;
- `lingbot.camera_trajectory.v1` for explicit pose/intrinsics trajectories.
-
-## Validation
-
-Real-checkpoint validation uses 480x832 output, four DMD steps, and seed 42.
-The exercised matrix includes:
-
- TP=1 and TP=2 execution;
- two interleaved resident sessions;
- action input and prompt switching;
- seven contiguous chunks crossing the sink plus recent rolling window;
- direct versus paged replay;
- CUDA Graph execution;
- reset, close, failure cleanup, and exact metadata matching; and
- nine-frame VAE decode.
-
-Official `generate.py` accuracy and performance numbers, together with
-generation artifacts, are recorded in the PR validation section for the exact
-tested commit.
-
-## Current limitations
-
- Only the 14B causal-fast checkpoint is supported.
- The realtime control plane is internal; there is no public server transport yet.
- AR-Diffusion stages currently require one replica because session-affine
-  routing across replicas is not implemented.
- One AR block is generated per request and `max_num_seqs` must be one.
- Stateful streaming VAE decode is not implemented; the realtime example emits
-  latent chunks.
- SP/USP, pipeline/CFG parallelism, HSDP, VAE parallelism, quantization,
-  Cache-DiT, TeaCache, causal-pretrain, and the 1.3B checkpoint are not claimed.
- No AMD GPU, Ascend NPU, or Intel GPU support is claimed.
-
-## References
-
- Checkpoint: <https://huggingface.co/robbyant/lingbot-world-v2-14b-causal-fast-diffusers>
- Official implementation: <https://github.com/robbyant/lingbot-world-v2>
- Offline example: [`examples/offline_inference/diffusion/lingbot_world_v2.py`](../../examples/offline_inference/diffusion/lingbot_world_v2.py)
- Realtime design: [`docs/design/feature/realtime_ar_diffusion.md`](../../docs/design/feature/realtime_ar_diffusion.md)
@@ -78,7 +78,7 @@ python examples/offline_inference/text_to_audio/text_to_audio.py \
 Start the online serving endpoint:

 ```bash
-vllm serve /path/to/stable-audio-open-1.0 \
+vllm-omni serve /path/to/stable-audio-open-1.0 \
  --host 0.0.0.0 \
  --port 8091 \
  --gpu-memory-utilization 0.9 \
@@ -39,9 +39,6 @@ platform section, document one or more tested hardware configurations.

 #### Command

-Serve commands use `vllm serve <model> --omni`. The `--omni` flag is what
-selects the Omni pipeline, so it is required and cannot be dropped.
-
 ```bash
 # Add the exact command(s) here
 ```
@@ -39,7 +39,7 @@ Other hardware is welcome as community validation lands.
 Launch the two-stage talker:

 ```bash
-vllm serve inclusionAI/Ming-omni-tts-0.5B \
+vllm-omni 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 serve zai-org/GLM-TTS --omni --trust-remote-code --port 8091
+vllm-omni serve zai-org/GLM-TTS --omni --trust-remote-code --port 8091
 ```

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

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

 Use a custom deploy config for advanced cases:

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

@@ -1,15 +1,15 @@
 # Common dependencies for all platforms
-# transformers: required unified multimodal model classes are unavailable
-# before 5.10.1; 5.10.0 is yanked, and 5.15 has a known construction
-# regression. transformers 5.9 also changed
-# _adjust_missing_and_unexpected_keys from
+# transformers: kept exactly aligned with the upstream vLLM constraint.
+# vLLM pins ">= 4.56.0, != 5.0.*, != 5.1.*, != 5.2.*, != 5.3.*, != 5.4.*, != 5.5.0".
+# The previous extra "< 5.9.0" exclusion was removed when re-aligning with
+# upstream: transformers 5.9 changed _adjust_missing_and_unexpected_keys from
 # list+list to set|set, breaking models that declare
 # _keys_to_ignore_on_load_unexpected as a list (e.g. OpenMOSS-Team/MOSS-TTS-Nano)
 # with "TypeError: unsupported operand type(s) for |: 'list' and 'set'".
 # That breakage is now handled in code instead of by pinning:
 # MossTTSNanoForGeneration wraps weight loading in
 # transformers_keys_to_ignore_compat (vllm_omni/model_executor/models/utils.py).
-transformers >= 5.10.1, < 5.15
+transformers >= 5.5.3
 av>=14.0.0
 omegaconf>=2.3.0
 diffusers==0.38.0
@@ -39,6 +39,8 @@ def _render(changed_files: list[str]) -> str:


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


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

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


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


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

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


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


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

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

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

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

        assert resolver(NotTheRightHfConfig()) is None
-
-
-class TestObjectStorageConfigResolution:
-    """Object-storage URIs must be materialized locally before any HF-style reads.
-
-    Regression coverage for review on the Run:AI PR: ``get_config`` and the
-    config.json/model_index.json fallbacks crashed with ``HFValidationError``
-    for ``s3://`` URIs, and the name-match fallback scanned the whole URI, so
-    a bucket named after another pipeline could hijack pipeline selection.
-    """
-
-    @pytest.fixture
-    def fake_object_storage(self, monkeypatch, tmp_path):
-        """Replace ObjectStorageModel with a fake that "pulls" into a tmp dir.
-
-        Returns a recorder: calls to the returned callable write files into
-        the materialized directory, and ``recorder.pulls`` records every
-        ``pull_files`` invocation.
-        """
-        pulls: list[tuple[str, list[str] | None, list[str] | None]] = []
-        materialized_files: list[tuple[str, str]] = []
-
-        class FakeObjectStorageModel:
-            def __init__(self, url: str):
-                self.dir = str(tmp_path)
-
-            def pull_files(self, model_path, allow_pattern=None, ignore_pattern=None):
-                pulls.append((model_path, allow_pattern, ignore_pattern))
-                for name, content in materialized_files:
-                    (tmp_path / name).write_text(content)
-
-        monkeypatch.setattr(config_factory_module, "ObjectStorageModel", FakeObjectStorageModel)
-
-        class Recorder:
-            def set_files(self, files: list[tuple[str, str]]) -> None:
-                materialized_files.clear()
-                materialized_files.extend(files)
-
-        recorder = Recorder()
-        recorder.pulls = pulls
-        return recorder
-
-    def test_passthrough_for_non_uri(self, fake_object_storage):
-        assert _materialize_object_storage_configs("org/model") == "org/model"
-        assert _materialize_object_storage_configs("/local/model") == "/local/model"
-        assert fake_object_storage.pulls == []
-
-    def test_materialize_pulls_configs_once_per_uri(self, fake_object_storage):
-        uri = "s3://bucket/model"
-
-        first = _materialize_object_storage_configs(uri)
-        second = _materialize_object_storage_configs(uri)
-
-        assert first == second
-        assert len(fake_object_storage.pulls) == 1
-        pulled_uri, allow_pattern, ignore_pattern = fake_object_storage.pulls[0]
-        assert pulled_uri == uri
-        assert "*.json" in allow_pattern
-        assert ignore_pattern is None
-
-    def test_try_infer_model_type_reads_materialized_config(self, fake_object_storage):
-        """A misleading bucket name must not beat the real config.json content."""
-        fake_object_storage.set_files(
-            [
-                (
-                    "config.json",
-                    '{"model_type": "qwen3_omni_moe", "architectures": ["Qwen3OmniMoeForConditionalGeneration"]}',
-                )
-            ]
-        )
-        uri = "s3://qwen3-tts-models/Qwen3-Omni-30B-A3B-Instruct"
-
-        model = StageConfigFactory.try_infer_model_type(model=uri, trust_remote_code=False)
-
-        assert model == "qwen3_omni_moe"
-        assert fake_object_storage.pulls  # materialization happened
-
-    def test_name_match_fallback_scans_basename_only(self, fake_object_storage):
-        # Empty config.json (CosyVoice3 style) forces the name-match fallback.
-        fake_object_storage.set_files([("config.json", "{}")])
-
-        deceptive = "s3://qwen3-tts-models/plain-checkpoint"
-        assert StageConfigFactory.try_infer_model_type(model=deceptive, trust_remote_code=False) is None
-
-        matching = "s3://any-bucket/my-cosyvoice3-model"
-        assert StageConfigFactory.try_infer_model_type(model=matching, trust_remote_code=False) == "cosyvoice3"
@@ -4,10 +4,7 @@ from types import SimpleNamespace
 import pytest
 from vllm.v1.engine import FinishReason

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

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

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

 from __future__ import annotations

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

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


-class _FakeChunkAdapter:
-    """Stands in for OmniChunkTransferAdapter's timeout-collection surface."""
-
-    def __init__(self, timed_out_ids, *, receives_chunks=True):
-        self._timed_out_ids = set(timed_out_ids)
-        self.receives_chunks = receives_chunks
-        self.calls = []
-
-    def collect_timed_out_request_ids(self, timeout_s):
-        self.calls.append(timeout_s)
-        return set(self._timed_out_ids)
-
-
-class _FakeChunkScheduler(OmniSchedulerMixin):
-    def __init__(self, requests, adapter):
-        self.requests = requests
-        self.chunk_transfer_adapter = adapter
-        self.finish_calls = []
-
-    def finish_requests(self, req_ids, status):
-        self.finish_calls.append((set(req_ids), status))
-
-
-def test_process_pending_chunk_timeouts_delegates_to_finish_requests():
-    """A request stalled in WAITING_FOR_CHUNK is failed, not left parked.
-
-    This is the async-chunk half of the safety net (RFC #4855 R1.1); before it
-    existed, the chunk path had no deadline at all and issue #3833 could park a
-    request forever.
-    """
-    req_id = "stalled-stream"
-    adapter = _FakeChunkAdapter(timed_out_ids={req_id})
-    scheduler = _FakeChunkScheduler({req_id: SimpleNamespace(request_id=req_id)}, adapter)
-
-    scheduler._process_pending_chunk_timeouts()
-
-    assert len(adapter.calls) == 1
-    assert adapter.calls[0] > 0
-    assert len(scheduler.finish_calls) == 1
-    finished_ids, status = scheduler.finish_calls[0]
-    assert finished_ids == {req_id}
-    assert getattr(status, "name", str(status)).endswith("FINISHED_ERROR")
-
-
-def test_process_pending_chunk_timeouts_skips_already_freed_request():
-    adapter = _FakeChunkAdapter(timed_out_ids={"already-freed"})
-    scheduler = _FakeChunkScheduler(requests={}, adapter=adapter)
-
-    scheduler._process_pending_chunk_timeouts()
-
-    assert scheduler.finish_calls == []
-
-
-def test_process_pending_chunk_timeouts_noop_on_a_producer_stage():
-    """Stage 0 does not receive chunks, so it has nothing to expire."""
-    adapter = _FakeChunkAdapter(timed_out_ids={"r1"}, receives_chunks=False)
-    scheduler = _FakeChunkScheduler({"r1": SimpleNamespace(request_id="r1")}, adapter)
-
-    scheduler._process_pending_chunk_timeouts()
-
-    assert adapter.calls == []
-    assert scheduler.finish_calls == []
-
-
-def test_process_pending_chunk_timeouts_noop_without_adapter():
-    class _NoAdapter(OmniSchedulerMixin):
-        def __init__(self):
-            self.requests = {}
-            self.chunk_transfer_adapter = None
-            self.finish_calls = []
-
-        def finish_requests(self, req_ids, status):
-            self.finish_calls.append((set(req_ids), status))
-
-    scheduler = _NoAdapter()
-    scheduler._process_pending_chunk_timeouts()
-    assert scheduler.finish_calls == []
-
-
-def test_process_pending_chunk_timeouts_disabled_when_timeout_zero(monkeypatch):
-    """The shared knob disables both transports' nets together."""
-    from vllm_omni.core.sched import omni_scheduler_mixin
-
-    monkeypatch.setattr(omni_scheduler_mixin, "DEFAULT_INPUT_WAIT_TIMEOUT_S", 0.0)
-
-    adapter = _FakeChunkAdapter(timed_out_ids={"r1"})
-    scheduler = _FakeChunkScheduler({"r1": SimpleNamespace(request_id="r1")}, adapter)
-    scheduler._process_pending_chunk_timeouts()
-
-    assert adapter.calls == []
-    assert scheduler.finish_calls == []
-
-
-class _FakeSendFailureAdapter:
-    """Stands in for the adapter's drained send-failure map (R1.2)."""
-
-    def __init__(self, failures):
-        self._failures = dict(failures)
-        self.collect_calls = 0
-
-    def collect_failed_send_request_ids(self):
-        self.collect_calls += 1
-        failures, self._failures = self._failures, {}
-        return failures
-
-
-class _FakeSendFailureScheduler(OmniSchedulerMixin):
-    def __init__(self, requests, adapter):
-        self.requests = requests
-        self.chunk_transfer_adapter = adapter
-
-
-def test_log_failed_chunk_sends_reports_a_live_request(caplog):
-    adapter = _FakeSendFailureAdapter({"req-live": "OSError: No space left on device"})
-    scheduler = _FakeSendFailureScheduler({"req-live": SimpleNamespace(request_id="req-live")}, adapter)
-
-    with caplog.at_level(logging.ERROR):
-        scheduler._log_failed_chunk_sends()
-
-    assert "req-live" in caplog.text
-    assert "No space left on device" in caplog.text
-
-
-def test_log_failed_chunk_sends_still_reports_a_departed_request(caplog):
-    """``collect_*`` drains the map, so a failure for a request that already left
-    this scheduler is the one place a drop could still go unrecorded."""
-    adapter = _FakeSendFailureAdapter({"req-gone": "OSError: No space left on device"})
-    scheduler = _FakeSendFailureScheduler({}, adapter)
-
-    with caplog.at_level(logging.WARNING):
-        scheduler._log_failed_chunk_sends()
-
-    assert adapter.collect_calls == 1
-    assert "req-gone" in caplog.text
-    assert "No space left on device" in caplog.text
-    assert "finished or aborted" in caplog.text
-
-
-def test_log_failed_chunk_sends_noop_without_adapter():
-    scheduler = _FakeSendFailureScheduler({}, None)
-    scheduler._log_failed_chunk_sends()
-
-
-# --- R1.4: VLLM_OMNI_INPUT_WAIT_TIMEOUT_S is parsed at import time, so each of
-# these reloads the module under a different environment. One knob now gates the
-# deadline on both transports, which is why a silent 0 matters more than it did
-# when only the full-payload path used it.
-
-
-@pytest.fixture
-def reload_with(monkeypatch):
-    """Reload the mixin module under a given env value, then put it back.
-
-    ``importlib.reload`` re-executes into the live module dict, so whatever the
-    last test parsed is what every later test in the session sees -- ``-k
-    zero_warns`` alone would leave ``DEFAULT_INPUT_WAIT_TIMEOUT_S = 0.0`` and
-    silently disable both deadlines. The rejection cases are worse: the
-    ValueError fires *after* the rebind, so ``nan`` lingers in the module.
-    ``monkeypatch`` restores the environment but not the module, so reload once
-    more on the way out.
-    """
-    import vllm_omni.core.sched.omni_scheduler_mixin as mod
-
-    def _reload(raw):
-        if raw is None:
-            monkeypatch.delenv("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", raising=False)
-        else:
-            monkeypatch.setenv("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", raw)
-        return importlib.reload(mod)
-
-    yield _reload
-
-    monkeypatch.undo()
-    importlib.reload(mod)
-
-
-@pytest.mark.parametrize(
-    "raw,expected",
-    [
-        (None, 600.0),
-        ("30", 30.0),
-        ("0.5", 0.5),
-        ("0", 0.0),  # explicit opt-out is still honoured
-    ],
-)
-def test_timeout_accepts_valid_values(reload_with, raw, expected):
-    mod = reload_with(raw)
-    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == expected
-
-
-@pytest.mark.parametrize("raw", ["-1", "-600", "-0.5"])
-def test_negative_timeout_is_rejected(reload_with, raw):
-    """`-1` is a common "no limit" idiom; substituting 600 would give an operator
-    who meant "never time out" mysterious failures ten minutes in. Fail loudly."""
-    with pytest.raises(ValueError, match="negative"):
-        reload_with(raw)
-
-
-@pytest.mark.parametrize("raw", ["nan", "inf", "-inf", "1e999"])
-def test_non_finite_timeout_is_rejected(reload_with, raw):
-    """nan compares false against every deadline check and inf is unreachable;
-    both disable the net while looking like a number."""
-    with pytest.raises(ValueError, match="not finite"):
-        reload_with(raw)
-
-
-def test_zero_warns_and_names_both_transports(reload_with, caplog):
-    with caplog.at_level(logging.WARNING):
-        mod = reload_with("0")
-    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == 0.0
-    text = caplog.text
-    assert "DISABLED" in text
-    assert "WAITING_FOR_INPUT" in text and "WAITING_FOR_CHUNK" in text
-
-
-def test_unparsable_timeout_falls_back(reload_with):
-    mod = reload_with("not-a-number")
-    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == 600.0
-
-
-def test_a_rejected_value_does_not_outlive_its_test(monkeypatch):
-    """The reason ``reload_with`` reloads on the way out, done here by hand.
-
-    ``nan`` is bound before the ValueError fires, so the reload that rejects it
-    still leaves it in the module -- and nan compares false against every
-    deadline check, which is exactly the silent-disable this item exists to
-    stop. Restoring the environment is not enough; the module has to be reloaded
-    too.
-    """
-    import vllm_omni.core.sched.omni_scheduler_mixin as mod
-
-    ambient = float(os.environ.get("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", "600"))
-
-    monkeypatch.setenv("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", "nan")
-    with pytest.raises(ValueError, match="not finite"):
-        importlib.reload(mod)
-    assert math.isnan(mod.DEFAULT_INPUT_WAIT_TIMEOUT_S)
-
-    monkeypatch.undo()
-    importlib.reload(mod)
-    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == ambient
-
-
 if __name__ == "__main__":
    pytest.main([__file__, "-v"])
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-stage BAGEL (TP=1, CFG=1), t2i Task",
@@ -61,7 +61,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-stage BAGEL (TP=1, CFG=1), i2i Task",
@@ -113,7 +113,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Two-stage BAGEL (bagel.yaml), TP=1, CFG=1, t2i Task",
@@ -164,7 +164,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Two-stage BAGEL (bagel.yaml), TP=1, CFG=1, i2i Task",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "tts"
    ],
    "server_params": {
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-device baseline with enforce-eager (no torch.compile)",
@@ -64,7 +64,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-device with torch.compile (default, no enforce-eager)",
@@ -117,7 +117,7 @@
          "num_cards": 2
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "CFG-parallel=2 with enforce-eager",
@@ -172,7 +172,7 @@
          "num_cards": 2
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "CFG-parallel=2 with torch.compile",
@@ -226,7 +226,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "CacheDiT with enforce-eager",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-device baseline (two input images)",
@@ -76,7 +76,7 @@
          "num_cards": 4
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + VAE patch parallel=4",
@@ -125,7 +125,7 @@
          "num_cards": 4
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + CacheDiT",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-device baseline",
@@ -73,7 +73,7 @@
          "num_cards": 4
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + VAE patch parallel=4",
@@ -121,7 +121,7 @@
          "num_cards": 4
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Ulysses SP=2 + CFG=2 + CacheDiT",
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "diffusion"
    ],
    "description": "Single-device baseline",
@@ -424,10 +424,10 @@
        "baseline": {
          "H100": {
            "median_audio_ttfp_ms": [
-              82.6344
+              82.1886
            ],
            "median_audio_rtf": [
-              0.1277
+              0.1241
            ]
          }
        }
@@ -453,13 +453,13 @@
        "baseline": {
          "H100": {
            "median_audio_ttfp_ms": [
-              170.3737
+              160.9168
            ],
            "median_audio_rtf": [
-              0.2076
+              0.1907
            ],
            "audio_throughput": [
-              37.0834
+              39.2332
            ]
          }
        }
@@ -485,13 +485,13 @@
        "baseline": {
          "H100": {
            "median_audio_ttfp_ms": [
-              101.0293
+              100.1265
            ],
            "median_audio_rtf": [
-              0.1498
+              0.1437
            ],
            "audio_throughput": [
-              10.1186
+              11.7605
            ]
          }
        }
@@ -10,7 +10,7 @@
          "num_cards": 1
        }
      },
-      "local_model",
+      "full_model",
      "tts"
    ],
    "server_params": {
@@ -18,7 +18,6 @@ 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"
@@ -57,21 +56,6 @@ 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
@@ -119,7 +103,6 @@ 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(
@@ -130,10 +113,7 @@ def make_runner(
    )
    runner.device = torch.device("cpu")
    runner.pipeline = pipeline
-    runner.ar_diffusion_kv_config = ARDiffusionKVConfig(
-        enable=True,
-        gpu_memory_fraction=gpu_memory_fraction,
-    )
+    runner.ar_diffusion_kv_config = ARDiffusionKVConfig(enable=True)
    runner.kv_cache = None
    runner._ar_diffusion_capability = None
    runner._ar_diffusion_kv_cache_spec = None
@@ -152,16 +132,6 @@ 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)
@@ -170,43 +140,6 @@ 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)
@@ -237,31 +170,6 @@ 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
@@ -290,67 +198,24 @@ def test_dreamzero_like_two_branches_are_independent():


 def test_lru_eviction_releases_blocks_and_notifies_pipeline():
-    pipeline = CapablePipeline(lingbot_like_spec(capacity=2))
+    pipeline = CapablePipeline(lingbot_like_spec(capacity=8))
    runner = make_runner(pipeline)
    kv = runner.kv_cache
    assert kv is not None
-    assert runner._session_capacity == 2
-    assert kv.session_capacity == 2
+    assert runner._session_capacity == 1
+    assert kv.session_capacity == 1
    free_total = kv.manager.block_pool.get_num_free_blocks()
    old = commit_one_frame(runner, "old", "main")
    k = torch.randn(1, 8, 4, 64)
    old.populate_cross_attention("main", "text", [(k, k)] * old.num_layers)
    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", "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
+    assert "old" not in kv._cross_sessions
+    assert kv.manager.block_pool.get_num_free_blocks() == free_total


 def test_forward_exception_releases_pending_allocation_and_model_state(monkeypatch):
@@ -367,10 +232,7 @@ def test_forward_exception_releases_pending_allocation_and_model_state(monkeypat
        raise RuntimeError("layer exploded")

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

    with pytest.raises(RuntimeError, match="layer exploded"):
        runner.execute_model(request)
@@ -401,10 +263,7 @@ 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(
-        request_id="broken-request",
-        sampling_params=SimpleNamespace(extra_args={"session_id": "broken"}),
-    )
+    request = SimpleNamespace(sampling_params=SimpleNamespace(extra_args={"session_id": "broken"}))

    with pytest.raises(RuntimeError, match="asynchronous kernel failed"):
        runner.execute_model(request)
@@ -1,231 +0,0 @@
-# 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"
@@ -30,8 +30,6 @@ 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(
@@ -97,6 +95,7 @@ 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


@@ -202,149 +201,12 @@ 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},
-    )
-    cross_bytes = 2 * 2 * L * 4 * 64 * torch.float16.itemsize * 2  # K+V, pos+neg, layers
-    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 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
+    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)
+    assert kv.num_blocks == expected
+    assert expected > 1 * (2 + 1) + 2  # above the local-slot floor, so the cross deduction is what's tested


 def _make_kv(
@@ -359,24 +221,15 @@ 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,
-            gpu_memory_fraction=1.0,
-        ),
+        ARDiffusionKVConfig(enable=True, chunk_size=BLOCK, window_chunks=window_chunks),
        num_layers=1,
        num_kv_heads=4,
        head_size=64,
        dtype=torch.float32,
        block_size=BLOCK,
        max_model_len=4096,
-        available_bytes=(managed_blocks + scratch_blocks) * page_bytes,
+        available_bytes=1 << 16,  # tiny -> the floor binds
        device=torch.device("cpu"),
        kv_branches=kv_branches,
        session_capacity=1,
@@ -54,15 +54,13 @@ 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)
-        # Allocation may transiently add one in-flight chunk.
+        # Resident window stays bounded by window + the in-flight chunk.
        assert len(kv.window_block_ids(adapter)) <= kv.spec.window_chunks + 1
-        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())
+        kv.commit_chunk(adapter)

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

    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")
-    kv, st = make_state(num_layers=2)
+    _, 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)
@@ -349,10 +349,6 @@ 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():
@@ -174,19 +174,6 @@ 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
@@ -1,505 +0,0 @@
-# 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")
@@ -1,108 +0,0 @@
-# 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
@@ -1,53 +0,0 @@
-# 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("")
@@ -5,7 +5,6 @@ from contextlib import nullcontext

 import pytest
 import torch
-from torch.nn.attention import SDPBackend

 from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata
 from vllm_omni.diffusion.attention.backends.cudnn_attn import CuDNNAttentionImpl
@@ -13,48 +12,6 @@ from vllm_omni.diffusion.attention.backends.cudnn_attn import CuDNNAttentionImpl
 pytestmark = [pytest.mark.diffusion, pytest.mark.cpu, pytest.mark.core_model]


-@pytest.mark.parametrize(
-    ("query_sequence_length", "key_sequence_length", "expected_backend"),
-    [
-        (1, 1, SDPBackend.MATH),
-        (8, 1, SDPBackend.MATH),
-        (1, 8, SDPBackend.MATH),
-        (8, 8, SDPBackend.CUDNN_ATTENTION),
-    ],
-)
-def test_cudnn_routes_single_token_attention_to_math(
-    monkeypatch, query_sequence_length, key_sequence_length, expected_backend
-):
-    selected_backends = []
-
-    def fake_sdpa_kernel(backends):
-        selected_backends.append(backends)
-        return nullcontext()
-
-    monkeypatch.setattr(
-        "vllm_omni.diffusion.attention.backends.cudnn_attn.sdpa_kernel",
-        fake_sdpa_kernel,
-    )
-    monkeypatch.setattr(
-        torch.nn.functional,
-        "scaled_dot_product_attention",
-        lambda query, _key, _value, **_kwargs: query,
-    )
-    impl = CuDNNAttentionImpl(
-        num_heads=2,
-        head_size=4,
-        softmax_scale=0.5,
-    )
-    query = torch.randn(1, query_sequence_length, 2, 4)
-    key = torch.randn(1, key_sequence_length, 2, 4)
-    value = torch.randn_like(key)
-
-    output = impl.forward_cuda(query, key, value)
-
-    assert output.shape == query.shape
-    assert selected_backends == [[expected_backend]]
-
-
 def test_cudnn_slices_valid_kv_prefix_without_padding_mask(monkeypatch):
    observed = {}

@@ -36,6 +36,7 @@ pytestmark = [pytest.mark.core_model, pytest.mark.cpu]

 SEPARATE_CFG_TRANSFORMERS = [
    DreamIdOmniModel,
+    LTX2VideoTransformer3DModel,
    HeliosTransformer3DModel,
    LongCatImageTransformer2DModel,
    Cosmos3VFMTransformer,
@@ -123,13 +124,6 @@ def test_cache_dit_configs_have_separate_cfg(transformer_model):
    assert transformer_model._cache_dit_adapter_config.has_separate_cfg is True


-def test_ltx2_cache_dit_uses_one_forward_per_denoise_step():
-    """LTX batches all guidance passes into one Transformer invocation."""
-    adapter_config = LTX2VideoTransformer3DModel._cache_dit_adapter_config
-
-    assert adapter_config.has_separate_cfg is False
-
-
@patch("vllm_omni.diffusion.cache.cachedit.model_specific.BlockAdapter")
@patch("vllm_omni.diffusion.cache.cachedit.model_specific.cache_dit")
 def test_separate_wan22_custom_enabler_has_separate_cfg(mock_cache_dit, mock_block_adapter):
@@ -53,10 +53,14 @@ def _diff_metrics(a: Image.Image, b: Image.Image) -> tuple[float, float]:
 def _extract_single_image(outputs) -> Image.Image:
    first_output = outputs[0]
    assert first_output.final_output_type == "image"
-    if not isinstance(first_output, OmniRequestOutput):
-        raise ValueError(f"Expected an OmniRequestOutput, got {type(first_output).__name__}")
+    if not hasattr(first_output, "request_output") or not first_output.request_output:
+        raise ValueError("No request_output found in OmniRequestOutput")

-    images = first_output.images
+    req_out = first_output.request_output
+    if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
+        raise ValueError("Invalid request_output structure or missing 'images' key")
+
+    images = req_out.images
    if images is None or len(images) != 1:
        raise ValueError(f"Expected 1 image, got {0 if images is None else len(images)}")
    return images[0]
@@ -72,7 +72,8 @@ def _to_float_array(data: Any) -> np.ndarray:


 def _extract_output_array(output: Any) -> np.ndarray:
-    payload = output.images[0]
+    req_out = output.request_output
+    payload = req_out.images[0]
    return _to_float_array(payload)


@@ -16,11 +16,6 @@ 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

@@ -115,85 +110,6 @@ def test_empty_source_prefix_keeps_full_model_strict_check():
        loader.load_weights(model)


-def test_stream_online_quant_weights_offloads_layers_after_processing():
-    from vllm.model_executor.model_loader.reload.layerwise import (
-        get_layerwise_info,
-    )
-
-    events: list[str] = []
-
-    class _OnlineQuantMethod:
-        uses_meta_device = True
-
-    class _TrackedLayer(nn.Linear):
-        def __init__(self, name: str):
-            super().__init__(2, 2, bias=False)
-            self.name = name
-            self.quant_method = _OnlineQuantMethod()
-            get_layerwise_info(self).load_numel_total = self.weight.numel()
-
-        def to(self, *args, **kwargs):
-            events.append(self.name)
-            return super().to(*args, **kwargs)
-
-    class _StreamingModel(nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.first = _TrackedLayer("first")
-            self.second = _TrackedLayer("second")
-
-    model = _StreamingModel()
-    weights = iter(
-        [
-            ("first.weight", torch.zeros((2, 2))),
-            ("second.weight", torch.zeros((2, 2))),
-        ]
-    )
-    streamed = DiffusersPipelineLoader._stream_online_quant_weights_to_cpu(model, weights)
-
-    assert next(streamed)[0] == "first.weight"
-    get_layerwise_info(model.first).reset()
-    assert next(streamed)[0] == "second.weight"
-    assert events == ["first"]
-
-    get_layerwise_info(model.second).reset()
-    with pytest.raises(StopIteration):
-        next(streamed)
-    assert events == ["first", "second"]
-
-
-def test_process_weights_skips_completed_online_quant_layer(monkeypatch):
-    from unittest.mock import Mock
-
-    from vllm.model_executor.layers.quantization.base_config import QuantizeMethodBase
-    from vllm.model_executor.model_loader.reload import layerwise
-
-    class _TrackedLayer(nn.Linear):
-        def __init__(self):
-            super().__init__(2, 2, bias=False)
-            self.to_calls: list[object] = []
-
-        def to(self, *args, **kwargs):
-            self.to_calls.append(args[0] if args else kwargs.get("device"))
-            return super().to(*args, **kwargs)
-
-    model = nn.Module()
-    model.layer = _TrackedLayer()
-    quant_method = Mock(spec=QuantizeMethodBase)
-    quant_method.uses_meta_device = True
-    model.layer.quant_method = quant_method
-    model.layer._already_called_process_weights_after_loading = True
-    finalize = Mock()
-    monkeypatch.setattr(layerwise, "finalize_layerwise_processing", finalize)
-
-    loader = _make_loader_with_weights([])
-    loader._process_weights_after_loading(model, torch.device("cuda"))
-
-    finalize.assert_called_once_with(model, model_config=None)
-    quant_method.process_weights_after_loading.assert_not_called()
-    assert model.layer.to_calls == []
-
-
 class _ConfigAwareModel(nn.Module):
    def __init__(self, *, od_config):
        super().__init__()
@@ -263,137 +179,6 @@ 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
@@ -1,72 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import contextlib
-
-import huggingface_hub
-import pytest
-
-from vllm_omni.diffusion.model_loader import hub_prefetch
-
-pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
-
-
-def test_prefetch_subfolders_propagates_revision(monkeypatch):
-    calls = []
-
-    def fake_snapshot_download(**kwargs):
-        calls.append(kwargs)
-
-    monkeypatch.setattr(huggingface_hub, "snapshot_download", fake_snapshot_download)
-    monkeypatch.setattr(hub_prefetch, "_repo_prefetch_lock", lambda _model: contextlib.nullcontext())
-
-    hub_prefetch.prefetch_subfolders(
-        "org/model",
-        ["transformer"],
-        revision="pinned-revision",
-    )
-
-    assert calls == [
-        {
-            "repo_id": "org/model",
-            "revision": "pinned-revision",
-            "allow_patterns": ["transformer/*", "transformer/**", "*.json", "*.txt"],
-        }
-    ]
-
-
-def test_from_pretrained_retry_preserves_revision(monkeypatch):
-    factory_calls = []
-    prefetch_calls = []
-
-    def fake_factory(model, **kwargs):
-        factory_calls.append((model, kwargs))
-        if len(factory_calls) == 1:
-            raise OSError("partial cache")
-        return "loaded"
-
-    def fake_prefetch(model, subfolders, **kwargs):
-        prefetch_calls.append((model, tuple(subfolders), kwargs))
-
-    monkeypatch.setattr(hub_prefetch, "prefetch_subfolders", fake_prefetch)
-    monkeypatch.setattr(hub_prefetch.time, "sleep", lambda _seconds: None)
-
-    result = hub_prefetch.from_pretrained_with_prefetch(
-        fake_factory,
-        "org/model",
-        subfolder="transformer",
-        prefetch_list=["transformer", "vae"],
-        revision="pinned-revision",
-        max_attempts=2,
-    )
-
-    assert result == "loaded"
-    assert len(factory_calls) == 2
-    assert all(call[1]["revision"] == "pinned-revision" for call in factory_calls)
-    assert prefetch_calls == [
-        (
-            "org/model",
-            ("transformer", "vae"),
-            {"local_files_only": False, "revision": "pinned-revision"},
-        )
-    ]
@@ -6,10 +6,7 @@ from types import SimpleNamespace

 import pytest

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

 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
@@ -35,37 +32,6 @@ 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()

@@ -7,17 +7,17 @@ from vllm_omni.diffusion.models.flux2.pipeline_flux2 import (
    Flux2Pipeline,
    _resolve_component_quant_config,
 )
-from vllm_omni.quantization import ComponentQuantizationConfig

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


 def test_resolve_component_quant_config_routes_components():
    text_encoder_config = object()
-    component_config = ComponentQuantizationConfig(
-        component_configs={"text_encoder": text_encoder_config},
-        default_config=None,
-    )
+    component_config = Mock()
+    component_config.resolve.side_effect = {
+        "text_encoder": text_encoder_config,
+        "transformer": None,
+    }.get

    assert _resolve_component_quant_config(component_config, "text_encoder") is text_encoder_config
    assert _resolve_component_quant_config(component_config, "transformer") is None
@@ -1 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
@@ -1,13 +0,0 @@
-{
-  "_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"]
-}
@@ -1,42 +0,0 @@
-{
-  "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]
-  }
-}
@@ -1,30 +0,0 @@
-{
-  "_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
-}
@@ -1,112 +0,0 @@
-{
-  "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"
-  ]
-}
@@ -1,294 +0,0 @@
-# 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())
@@ -1,543 +0,0 @@
-# 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]))
@@ -1,525 +0,0 @@
-# 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)
@@ -1,685 +0,0 @@
-# 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
@@ -1,39 +0,0 @@
-# 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 == []
@@ -1,881 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-"""Focused LTX-2.5 pipeline correctness tests."""
-
-import json
-from dataclasses import replace
-from types import SimpleNamespace
-
-import pytest
-import torch
-
-from vllm_omni.diffusion.data import DiffusionOutput
-from vllm_omni.diffusion.models.ltx2 import ltx2_components, ltx2_latents, ltx2_runtime
-from vllm_omni.diffusion.models.ltx2.ltx2_components import (
-    LTX25_DISTILLED_COMPONENT_PROFILE,
-    LTX25_DISTILLED_ONE_STAGE_COMPONENT_PROFILE,
-    LTX25_FULL_COMPONENT_PROFILE,
-    LTX25_TWO_STAGE_COMPONENT_PROFILE,
-    detect_ltx_model_version,
-    resolve_ltx_checkpoint_kind,
-    resolve_ltx_component_profile,
-)
-from vllm_omni.diffusion.models.ltx2.ltx2_denoise import (
-    LTXPhaseResult,
-    _first_frame_keyframes_mask,
-    _official_ltx_sigmas,
-)
-from vllm_omni.diffusion.models.ltx2.ltx2_guidance import (
-    LTX_GUIDANCE_EXECUTOR,
-    LTXGuidanceSpec,
-    LTXModalityGuidance,
-    velocity_from_x0,
-    x0_from_velocity,
-)
-from vllm_omni.diffusion.models.ltx2.ltx2_latents import LTXAVState
-from vllm_omni.diffusion.models.ltx2.ltx2_recipes import (
-    LTX2_DISTILLED_TWO_STAGE_RECIPE,
-    LTX2_ONE_STAGE_RECIPE,
-    LTX23_ONE_STAGE_RECIPE,
-    LTX25_DISTILLED_ONE_STAGE_RECIPE,
-    LTX25_DISTILLED_TWO_STAGE_RECIPE,
-    LTX25_FULL_RECIPE,
-    LTX25_TWO_STAGE_RECIPE,
-    LTX_DISTILLED_SIGMAS,
-    LTX_POSITIVE_ONLY_RECIPE,
-    resolve_ltx_pipeline_recipe,
-)
-from vllm_omni.diffusion.models.ltx2.ltx2_request import LTXRequestInputs
-from vllm_omni.diffusion.models.ltx2.ltx2_runtime import LTXRuntime
-from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import (
-    LTX2DistilledOneStagePipeline,
-    LTX2Pipeline,
-)
-from vllm_omni.diffusion.models.ltx2.pipeline_ltx2_two_stage import (
-    LTX2DistilledTwoStagePipeline,
-    LTX2TwoStagePipeline,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-@pytest.mark.parametrize("model_version", ["2", "2.3", "2.5"])
-def test_ltx_timestep_adaln_expands_per_token_like_official_without_sp(model_version):
-    pipe = object.__new__(LTX2Pipeline)
-    pipe.model_version = model_version
-    pipe.od_config = SimpleNamespace(parallel_config=SimpleNamespace(sequence_parallel_size=1))
-    ts = torch.tensor([750.0, 500.0])
-
-    kwargs = LTXRuntime._denoise_timestep_kwargs(
-        pipe,
-        ts,
-        SimpleNamespace(),
-        SimpleNamespace(),
-        video_token_count=4,
-        audio_token_count=3,
-    )
-
-    torch.testing.assert_close(kwargs["timestep"], ts[:, None].expand(-1, 4))
-    torch.testing.assert_close(kwargs["audio_timestep"], ts[:, None].expand(-1, 3))
-    assert kwargs["sigma"] is ts
-    assert kwargs["audio_sigma"] is ts
-
-
-def test_ltx_ancestral_step_clears_sp_audio_padding_after_scheduler_update(monkeypatch):
-    state = LTXAVState(
-        video=torch.zeros(1, 2, 2),
-        audio=torch.zeros(1, 4, 2),
-    )
-    forward_ctx = SimpleNamespace(
-        original_audio_num_frames=3,
-        sampler="euler_ancestral",
-    )
-    denoise_ctx = SimpleNamespace(latents=None, audio_latents=None)
-    pipe = SimpleNamespace(
-        _predict_noise_for_step=lambda *_args: (torch.zeros_like(state.video), torch.zeros_like(state.audio))
-    )
-
-    def fake_step(_pipeline, actual_forward_ctx, _denoise_ctx, *_args):
-        assert actual_forward_ctx.sampler == "euler_ancestral"
-        updated_audio = torch.ones_like(state.audio)
-        updated_audio[:, 3:] = 99.0
-        return torch.ones_like(state.video), updated_audio
-
-    monkeypatch.setattr(ltx2_runtime, "step_denoised_latents", fake_step)
-
-    actual = LTXRuntime._denoise_step(
-        pipe,
-        0,
-        torch.tensor(1.0),
-        state,
-        forward_ctx,
-        denoise_ctx,
-    )
-
-    torch.testing.assert_close(actual.audio[:, :3], torch.ones_like(actual.audio[:, :3]))
-    torch.testing.assert_close(actual.audio[:, 3:], torch.zeros_like(actual.audio[:, 3:]))
-
-
-def test_ltx25_first_causal_latent_frame_is_marked_for_keyframe_embedding():
-    reference = torch.zeros(2, 12, 8)
-
-    mask = _first_frame_keyframes_mask(reference, latent_num_frames=3)
-
-    assert mask.shape == (2, 12, 1)
-    torch.testing.assert_close(mask[:, :4], torch.ones(2, 4, 1))
-    torch.testing.assert_close(mask[:, 4:], torch.zeros(2, 8, 1))
-
-
-def _make_ltx_request_pipe(cls):
-    pipe = object.__new__(cls)
-    torch.nn.Module.__init__(pipe)
-    pipe.device = torch.device("cpu")
-    pipe.tokenizer_max_length = 99
-    pipe.vae_spatial_compression_ratio = 32
-    pipe.vae_temporal_compression_ratio = 8
-    return pipe
-
-
-def _resolve_request_inputs_for_test(
-    pipe,
-    req,
-    *,
-    guidance_scale=4.0,
-    negative_prompt=None,
-):
-    return pipe._resolve_request_inputs(
-        req,
-        prompt=None,
-        negative_prompt=negative_prompt,
-        height=None,
-        width=None,
-        num_frames=None,
-        frame_rate=None,
-        num_inference_steps=None,
-        guidance_scale=guidance_scale,
-        num_videos_per_prompt=1,
-        generator=None,
-        latents=None,
-        audio_latents=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        prompt_attention_mask=None,
-        negative_prompt_attention_mask=None,
-        decode_timestep=0.0,
-        decode_noise_scale=None,
-        output_type="np",
-        max_sequence_length=None,
-    )
-
-
-def test_ltx25_missing_gemma4_recommends_supported_transformers_range(monkeypatch):
-    pipe = SimpleNamespace(component_profile=replace(LTX25_FULL_COMPONENT_PROFILE, text_encoder_cls=None))
-    od_config = SimpleNamespace(model="Lightricks/LTX-2.5-Diffusers", dtype=torch.bfloat16)
-
-    monkeypatch.setattr(ltx2_components, "get_local_device", lambda: torch.device("cpu"))
-    monkeypatch.setattr(ltx2_components, "prefetch_subfolders", lambda *_args, **_kwargs: None)
-    monkeypatch.setattr(ltx2_components.AutoTokenizer, "from_pretrained", lambda *_args, **_kwargs: object())
-
-    with pytest.raises(ImportError) as exc_info:
-        ltx2_components.initialize_pipeline_components(pipe, od_config)
-
-    assert str(exc_info.value) == (
-        "LTX-2.5 requires Gemma4UnifiedForConditionalGeneration; install transformers>=5.10.1,<5.15."
-    )
-
-
-def test_ltx_converted_component_loading_propagates_revision(monkeypatch):
-    revision = "pinned-revision"
-    calls = {"components": []}
-    profile = replace(
-        LTX25_DISTILLED_COMPONENT_PROFILE,
-        text_encoder_cls=object,
-        video_vae_cls=object,
-        vocoder_cls=object,
-        vocoder_fallback_cls=None,
-        scheduler_use_dynamic_shifting=False,
-    )
-    pipeline = SimpleNamespace(component_profile=profile, pipeline_kind="distilled_two_stage")
-    od_config = SimpleNamespace(
-        model="org/converted-ltx25",
-        revision=revision,
-        dtype=torch.bfloat16,
-        quantization_config=None,
-    )
-
-    monkeypatch.setattr(ltx2_components, "get_local_device", lambda: torch.device("cpu"))
-    monkeypatch.setattr(ltx2_components, "_install_connector_attention", lambda _connectors, **_kwargs: None)
-    monkeypatch.setattr(ltx2_components, "_place_aux_components", lambda _pipeline: None)
-
-    def fake_prefetch(model, subfolders, **kwargs):
-        calls["prefetch"] = (model, tuple(subfolders), kwargs)
-
-    def fake_tokenizer(*args, **kwargs):
-        calls["tokenizer"] = (args, kwargs)
-        return SimpleNamespace(model_max_length=1024)
-
-    def fake_component(component_cls, model, subfolder, **kwargs):
-        calls["components"].append((component_cls, model, subfolder, kwargs))
-        if subfolder == "vae":
-            return SimpleNamespace(spatial_compression_ratio=32, temporal_compression_ratio=8)
-        if subfolder == "audio_vae":
-            return SimpleNamespace(
-                mel_compression_ratio=4,
-                temporal_compression_ratio=4,
-                config=SimpleNamespace(sample_rate=16_000, mel_hop_length=160),
-            )
-        return object()
-
-    def fake_transformer_config(model, subfolder, local_files_only, *, revision):
-        calls["transformer_config"] = (model, subfolder, local_files_only, revision)
-        return {"component": "transformer"}
-
-    monkeypatch.setattr(ltx2_components, "prefetch_subfolders", fake_prefetch)
-    monkeypatch.setattr(ltx2_components.AutoTokenizer, "from_pretrained", fake_tokenizer)
-    monkeypatch.setattr(ltx2_components, "_load_component", fake_component)
-    monkeypatch.setattr(ltx2_components, "load_transformer_config", fake_transformer_config)
-    monkeypatch.setattr(
-        ltx2_components,
-        "create_transformer_from_config",
-        lambda *_args, **_kwargs: SimpleNamespace(config=SimpleNamespace(patch_size=1, patch_size_t=1)),
-    )
-
-    def fake_scheduler(*args, **kwargs):
-        calls["scheduler"] = (args, kwargs)
-        return SimpleNamespace(config={"use_dynamic_shifting": False, "shift_terminal": None})
-
-    monkeypatch.setattr(ltx2_components.FlowMatchEulerDiscreteScheduler, "from_pretrained", fake_scheduler)
-
-    ltx2_components.initialize_pipeline_components(pipeline, od_config)
-
-    assert pipeline.weights_sources[0].revision == revision
-    assert calls["prefetch"][2]["revision"] == revision
-    assert calls["tokenizer"][1]["revision"] == revision
-    assert {call[2] for call in calls["components"]} == {
-        "text_encoder",
-        "connectors",
-        "vae",
-        "audio_vae",
-        "vocoder",
-        "latent_upsampler",
-    }
-    assert all(call[3]["revision"] == revision for call in calls["components"])
-    assert calls["transformer_config"] == (
-        od_config.model,
-        profile.transformer_subfolder,
-        False,
-        revision,
-    )
-    assert calls["scheduler"][1]["revision"] == revision
-
-
-def test_ltx_checkpoint_explicit_version_precedes_structural_heuristics(tmp_path):
-    (tmp_path / "model_index.json").write_text(json.dumps({"model_version": "2.3.1"}))
-    (tmp_path / "transformer").mkdir()
-    (tmp_path / "transformer" / "config.json").write_text(json.dumps({"ff_bias": False}))
-
-    assert detect_ltx_model_version(str(tmp_path)) == "2.3"
-
-
-def test_ltx25_checkpoint_selects_full_one_stage_profile(tmp_path, monkeypatch):
-    from vllm_omni.diffusion.models.ltx2 import ltx2_runtime
-
-    (tmp_path / "model_index.json").write_text(
-        json.dumps({"text_encoder": ["transformers", "Gemma4UnifiedForConditionalGeneration"]})
-    )
-
-    def stub_components(pipe, od_config):
-        pipe.od_config = od_config
-        pipe.vae_spatial_compression_ratio = 32
-
-    monkeypatch.setattr(ltx2_runtime, "initialize_pipeline_components", stub_components)
-    monkeypatch.setattr(LTXRuntime, "setup_diffusion_pipeline_profiler", lambda *_args, **_kwargs: None)
-
-    pipe = LTX2Pipeline(od_config=SimpleNamespace(model=str(tmp_path), enable_diffusion_pipeline_profiler=False))
-
-    assert pipe.model_version == "2.5"
-    assert pipe.component_profile is LTX25_FULL_COMPONENT_PROFILE
-    assert pipe.pipeline_recipe is LTX25_FULL_RECIPE
-    assert pipe.preserve_sp_padded_audio_duration
-    assert pipe.reports_stage_durations
-
-
-def test_ltx25_full_recipe_matches_official_sft_defaults():
-    (phase,) = LTX25_FULL_RECIPE.phases
-
-    assert (LTX25_FULL_RECIPE.height, LTX25_FULL_RECIPE.width) == (544, 960)
-    assert LTX25_FULL_RECIPE.num_frames == 121
-    assert LTX25_FULL_RECIPE.frame_rate == 24.0
-    assert LTX25_FULL_RECIPE.num_inference_steps == 30
-    assert phase.guidance.video == LTXModalityGuidance(
-        cfg_scale=3.0,
-        stg_scale=1.0,
-        modality_scale=3.0,
-        rescale_scale=0.7,
-        stg_blocks=(28,),
-    )
-    assert phase.guidance.audio == LTXModalityGuidance(
-        cfg_scale=7.0,
-        stg_scale=1.0,
-        modality_scale=3.0,
-        rescale_scale=0.7,
-        stg_blocks=(28,),
-    )
-    assert phase.sigmas is None
-    assert phase.noise_scale == 1.0
-    assert phase.use_official_sigma_schedule
-    assert LTX25_FULL_COMPONENT_PROFILE.transformer_subfolder == "transformer_full"
-    assert LTX25_FULL_COMPONENT_PROFILE.scheduler_use_dynamic_shifting
-    assert LTX25_FULL_COMPONENT_PROFILE.scheduler_shift_terminal == 0.1
-
-
-def test_ltx25_full_sigma_schedule_uses_official_default_sequence_anchor():
-    scheduler = SimpleNamespace(
-        config={
-            "base_image_seq_len": 1024,
-            "max_image_seq_len": 4096,
-            "base_shift": 0.95,
-            "max_shift": 2.05,
-            "shift_terminal": 0.1,
-        }
-    )
-
-    sigmas = _official_ltx_sigmas(
-        scheduler,
-        steps=30,
-        device=torch.device("cpu"),
-    )
-
-    torch.testing.assert_close(
-        sigmas[[0, 1, -2, -1]],
-        torch.tensor([1.0, 0.9949570, 0.1, 0.0]),
-        rtol=1e-5,
-        atol=1e-6,
-    )
-
-
-def test_ltx25_checkpoint_selects_distilled_two_stage_profile(tmp_path, monkeypatch):
-    from vllm_omni.diffusion.models.ltx2 import ltx2_runtime
-
-    (tmp_path / "model_index.json").write_text(
-        json.dumps({"text_encoder": ["transformers", "Gemma4UnifiedForConditionalGeneration"]})
-    )
-
-    def stub_components(pipe, od_config):
-        pipe.od_config = od_config
-        pipe.vae_spatial_compression_ratio = 32
-
-    monkeypatch.setattr(ltx2_runtime, "initialize_pipeline_components", stub_components)
-    monkeypatch.setattr(LTXRuntime, "setup_diffusion_pipeline_profiler", lambda *_args, **_kwargs: None)
-
-    pipe = LTX2DistilledTwoStagePipeline(
-        od_config=SimpleNamespace(
-            model=str(tmp_path),
-            enable_diffusion_pipeline_profiler=False,
-        )
-    )
-
-    assert pipe.component_profile is LTX25_DISTILLED_COMPONENT_PROFILE
-    assert pipe.pipeline_recipe is LTX25_DISTILLED_TWO_STAGE_RECIPE
-
-
-def test_ltx25_full_config_enrichment_uses_selected_transformer_subfolder(tmp_path):
-    from vllm_omni.diffusion.data import OmniDiffusionConfig
-
-    (tmp_path / "model_index.json").write_text(json.dumps({"_class_name": "LTX2Pipeline", "model_version": "2.5"}))
-    for subfolder, marker in (("transformer", "distilled"), ("transformer_full", "full")):
-        path = tmp_path / subfolder
-        path.mkdir()
-        (path / "config.json").write_text(json.dumps({"marker": marker}))
-
-    config = OmniDiffusionConfig(model=str(tmp_path), model_class_name="LTX2Pipeline")
-    config.enrich_config()
-
-    assert config.tf_model_config.get("marker") == "full"
-
-
-def test_ltx25_four_public_pipeline_semantics_are_disjoint():
-    assert resolve_ltx_component_profile("one_stage", "2.5") is LTX25_FULL_COMPONENT_PROFILE
-    assert resolve_ltx_pipeline_recipe("one_stage", "2.5") is LTX25_FULL_RECIPE
-    assert resolve_ltx_component_profile("two_stage", "2.5") is LTX25_TWO_STAGE_COMPONENT_PROFILE
-    assert resolve_ltx_pipeline_recipe("two_stage", "2.5") is LTX25_TWO_STAGE_RECIPE
-    assert resolve_ltx_component_profile("distilled_one_stage", "2.5") is LTX25_DISTILLED_ONE_STAGE_COMPONENT_PROFILE
-    assert resolve_ltx_pipeline_recipe("distilled_one_stage", "2.5") is LTX25_DISTILLED_ONE_STAGE_RECIPE
-    assert resolve_ltx_component_profile("distilled_two_stage", "2.5") is LTX25_DISTILLED_COMPONENT_PROFILE
-    assert resolve_ltx_pipeline_recipe("distilled_two_stage", "2.5") is LTX25_DISTILLED_TWO_STAGE_RECIPE
-
-    assert LTX2Pipeline.pipeline_kind == "one_stage"
-    assert LTX2TwoStagePipeline.pipeline_kind == "two_stage"
-    assert LTX2DistilledOneStagePipeline.pipeline_kind == "distilled_one_stage"
-    assert LTX2DistilledTwoStagePipeline.pipeline_kind == "distilled_two_stage"
-
-
-@pytest.mark.parametrize(
-    ("pipeline_kind", "expected"),
-    [
-        ("one_stage", "regular"),
-        ("two_stage", "regular"),
-        ("distilled_one_stage", "distilled"),
-        ("distilled_two_stage", "distilled"),
-        ("dmd2", None),
-    ],
-)
-def test_ltx_checkpoint_kind_is_derived_from_pipeline_kind(pipeline_kind, expected):
-    assert resolve_ltx_checkpoint_kind(pipeline_kind) == expected
-
-
-def test_ltx_checkpoint_kind_rejects_unknown_pipeline():
-    with pytest.raises(ValueError, match="Unsupported LTX pipeline kind"):
-        resolve_ltx_checkpoint_kind("unknown")
-
-
-def test_ltx25_distilled_two_stage_recipe_matches_model_card_resolution():
-    stage1, stage2 = LTX25_DISTILLED_TWO_STAGE_RECIPE.phases
-
-    assert (LTX25_DISTILLED_TWO_STAGE_RECIPE.height, LTX25_DISTILLED_TWO_STAGE_RECIPE.width) == (1088, 1920)
-    assert stage1.spatial_downscale == 2
-    assert stage1.sigmas == LTX_DISTILLED_SIGMAS
-    assert stage1.sampler == "euler_ancestral"
-    assert stage2.sampler == "euler"
-    assert stage2.sigmas == (0.909375, 0.725, 0.421875, 0.0)
-    assert stage2.input_transform == "spatial_upsample"
-
-    assert LTX25_DISTILLED_TWO_STAGE_RECIPE.negative_prompt == ""
-    assert not LTX25_DISTILLED_TWO_STAGE_RECIPE.allow_negative_prompt
-
-
-def test_ltx25_distilled_one_stage_uses_native_low_resolution():
-    (phase,) = LTX25_DISTILLED_ONE_STAGE_RECIPE.phases
-
-    assert (LTX25_DISTILLED_ONE_STAGE_RECIPE.height, LTX25_DISTILLED_ONE_STAGE_RECIPE.width) == (544, 960)
-    assert phase.spatial_downscale == 1
-    assert phase.sigmas == LTX_DISTILLED_SIGMAS
-    assert phase.sampler == "euler_ancestral"
-    assert not LTX25_DISTILLED_ONE_STAGE_RECIPE.supports_cache_dit
-
-
-def test_ltx25_regular_two_stage_uses_full_transformer_then_distilled_lora():
-    stage1, stage2 = LTX25_TWO_STAGE_RECIPE.phases
-
-    assert (LTX25_TWO_STAGE_RECIPE.height, LTX25_TWO_STAGE_RECIPE.width) == (1088, 1920)
-    assert LTX25_TWO_STAGE_RECIPE.num_inference_steps == 30
-    assert stage1.spatial_downscale == 2
-    assert stage1.guidance == LTX25_FULL_RECIPE.request_guidance
-    assert stage2.adapter_slot == "ltx_distilled"
-    assert LTX25_TWO_STAGE_RECIPE.allow_request_phase_sigmas
-    assert LTX25_TWO_STAGE_COMPONENT_PROFILE.transformer_subfolder == "transformer_full"
-    assert LTX25_TWO_STAGE_COMPONENT_PROFILE.distilled_lora_filename is not None
-
-
-@pytest.mark.parametrize(
-    "recipe",
-    [
-        LTX2_ONE_STAGE_RECIPE,
-        LTX23_ONE_STAGE_RECIPE,
-        LTX_POSITIVE_ONLY_RECIPE,
-    ],
-)
-def test_ltx_one_stage_recipes_declare_cache_dit_supported(recipe):
-    assert recipe.supports_cache_dit
-
-
-@pytest.mark.parametrize(
-    "recipe",
-    [
-        LTX25_FULL_RECIPE,
-        LTX25_DISTILLED_ONE_STAGE_RECIPE,
-        LTX2_DISTILLED_TWO_STAGE_RECIPE,
-        LTX25_DISTILLED_TWO_STAGE_RECIPE,
-    ],
-)
-def test_ltx25_and_multistage_recipes_declare_cache_dit_unsupported(recipe):
-    assert not recipe.supports_cache_dit
-
-
-def test_ltx_positive_only_guidance_preserves_official_x0_roundtrip():
-    sample = torch.full((1, 1), 1e8, dtype=torch.float32)
-    velocity = torch.ones((1, 1), dtype=torch.bfloat16)
-    sigma = torch.tensor(0.5)
-
-    actual = LTX_GUIDANCE_EXECUTOR._guide_modality(
-        sample,
-        {"cond": velocity},
-        sigma,
-        LTXModalityGuidance(),
-    )
-    expected = velocity_from_x0(sample, x0_from_velocity(sample, velocity, sigma), sigma)
-
-    assert actual.dtype == torch.float32
-    torch.testing.assert_close(actual, expected, rtol=0.0, atol=0.0)
-
-
-def test_ltx_velocity_from_x0_materializes_official_scalar_sigma():
-    item_calls = []
-
-    class TrackingTensor(torch.Tensor):
-        @staticmethod
-        def __new__(cls):
-            return torch.Tensor._make_subclass(cls, torch.tensor(0.725), False)
-
-        def item(self):
-            item_calls.append(True)
-            return super().item()
-
-    sample = torch.tensor([[1.0]], dtype=torch.bfloat16)
-    x0 = torch.tensor([[0.5]], dtype=torch.bfloat16)
-
-    velocity_from_x0(sample, x0, TrackingTensor())
-
-    assert item_calls == [True]
-
-
-def test_ltx_ancestral_positive_only_guidance_preserves_raw_velocity():
-    sample = torch.full((1, 1), 1e8, dtype=torch.float32)
-    velocity = torch.ones((1, 1), dtype=torch.bfloat16)
-
-    actual = LTX_GUIDANCE_EXECUTOR._guide_modality(
-        sample,
-        {"cond": velocity},
-        torch.tensor(0.5),
-        LTXModalityGuidance(),
-        preserve_positive_velocity=True,
-    )
-
-    assert actual is velocity
-
-
-@pytest.mark.parametrize("model_version", ["2.5", "2.3"])
-def test_ltx_t2v_denoise_state_uses_official_bfloat16_dtype(model_version):
-    expected_dtype = torch.bfloat16
-    captured = {}
-
-    def prepare_latents(**kwargs):
-        captured["video_dtype"] = kwargs["dtype"]
-        return torch.empty(1, 1, 1, dtype=kwargs["dtype"])
-
-    def prepare_audio_latents(*_args, **kwargs):
-        captured["audio_dtype"] = kwargs["dtype"]
-        return torch.empty(1, 1, 1, dtype=kwargs["dtype"]), 1, 1
-
-    pipeline = SimpleNamespace(
-        model_version=model_version,
-        transformer=SimpleNamespace(config=SimpleNamespace(in_channels=128)),
-        prepare_latents=prepare_latents,
-        prepare_audio_latents=prepare_audio_latents,
-        audio_sampling_rate=48_000,
-        audio_hop_length=160,
-        audio_vae_temporal_compression_ratio=4,
-        audio_vae_mel_compression_ratio=4,
-        audio_vae=SimpleNamespace(config=SimpleNamespace(mel_bins=64, latent_channels=8)),
-        _resolve_audio_latent_length=lambda length, _latents: length,
-    )
-    request = SimpleNamespace(
-        num_videos_per_prompt=1,
-        height=64,
-        width=64,
-        num_frames=1,
-        frame_rate=24.0,
-        generator=None,
-        latents=None,
-        audio_latents=None,
-        audio_latents_normalized=False,
-    )
-    prompt = SimpleNamespace(
-        batch_size=1,
-        positive_connector_prompt_embeds=torch.empty(1, dtype=torch.bfloat16),
-        positive_connector_audio_prompt_embeds=torch.empty(1, dtype=torch.bfloat16),
-    )
-
-    LTXRuntime._prepare_video_latents_stage(
-        pipeline,
-        request,
-        prompt,
-        device=torch.device("cpu"),
-        noise_scale=1.0,
-    )
-    LTXRuntime._prepare_audio_latents_stage(
-        pipeline,
-        request,
-        prompt,
-        device=torch.device("cpu"),
-        noise_scale=1.0,
-    )
-
-    assert captured == {"video_dtype": expected_dtype, "audio_dtype": expected_dtype}
-
-
-def test_ltx_seeded_latents_match_official_packed_rng_layout():
-    pipeline = SimpleNamespace(
-        model_version="2.5",
-        vae_spatial_compression_ratio=2,
-        vae_temporal_compression_ratio=2,
-        transformer_spatial_patch_size=1,
-        transformer_temporal_patch_size=1,
-        audio_vae_mel_compression_ratio=2,
-        od_config=SimpleNamespace(parallel_config=SimpleNamespace(sequence_parallel_size=1)),
-    )
-    device = torch.device("cpu")
-    actual_generator = torch.Generator(device=device).manual_seed(123)
-    expected_generator = torch.Generator(device=device).manual_seed(123)
-
-    actual_video = ltx2_latents.prepare_video_latents(
-        pipeline,
-        batch_size=1,
-        num_channels_latents=2,
-        height=4,
-        width=6,
-        num_frames=3,
-        dtype=torch.float32,
-        device=device,
-        generator=actual_generator,
-    )
-    actual_audio, original_length, padded_length = ltx2_latents.prepare_audio_latents(
-        pipeline,
-        batch_size=1,
-        num_channels_latents=2,
-        audio_latent_length=3,
-        num_mel_bins=4,
-        dtype=torch.float32,
-        device=device,
-        generator=actual_generator,
-    )
-
-    expected_video = torch.randn((1, 12, 2), generator=expected_generator)
-    expected_audio = torch.randn((1, 3, 4), generator=expected_generator)
-
-    torch.testing.assert_close(actual_video, expected_video, rtol=0.0, atol=0.0)
-    torch.testing.assert_close(actual_audio, expected_audio, rtol=0.0, atol=0.0)
-    assert (original_length, padded_length) == (3, 3)
-
-
-def test_ltx25_distilled_two_stage_executes_custom_phase_schedules():
-    request_inputs = LTXRequestInputs(
-        prompt="prompt",
-        negative_prompt="",
-        height=64,
-        width=64,
-        num_frames=1,
-        frame_rate=24.0,
-        num_inference_steps=8,
-        guidance=LTXGuidanceSpec.positive_only(),
-        num_videos_per_prompt=1,
-        generator=None,
-        latents=None,
-        audio_latents=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        prompt_attention_mask=None,
-        negative_prompt_attention_mask=None,
-        decode_timestep=0.25,
-        decode_noise_scale=0.5,
-        output_type="np",
-        max_sequence_length=16,
-    )
-    prompt_context = object()
-    phase_calls = []
-
-    def resolve_request_inputs(req, **kwargs):
-        return request_inputs
-
-    def run_phase(req, inputs, *, prompt_context=None, **kwargs):
-        phase_recipe = kwargs["phase_recipe"]
-        assert kwargs["image"] is source_image
-        phase_calls.append((phase_recipe, inputs, prompt_context))
-        if len(phase_calls) == 1:
-            assert prompt_context is None
-            assert inputs.decode_timestep == 0.25
-            assert inputs.decode_noise_scale == 0.5
-            context = prompt_context_sentinel
-            assert phase_recipe.name == "generate_lowres"
-            assert (inputs.height, inputs.width) == (32, 32)
-            assert inputs.num_inference_steps == 2
-            assert kwargs["sigmas"] == stage_1_sigmas
-            assert kwargs["noise_scale"] == 1.0
-            video = torch.ones(1, 128, 1, 1, 1)
-            audio = torch.full((1, 8, 1, 2), 2.0)
-            audio_for_next_phase = torch.full((1, 8, 1, 2), 5.0)
-        else:
-            assert prompt_context is prompt_context_sentinel
-            assert phase_recipe.name == "refine"
-            assert (inputs.height, inputs.width) == (64, 64)
-            assert inputs.latents.shape == (1, 128, 1, 2, 2)
-            torch.testing.assert_close(inputs.audio_latents, torch.full((1, 8, 1, 2), 5.0))
-            assert inputs.audio_latents_normalized
-            assert inputs.guidance_scale == 1.0
-            assert inputs.num_inference_steps == 2
-            assert kwargs["sigmas"] == stage_2_sigmas
-            assert kwargs["noise_scale"] == stage_2_sigmas[0]
-            assert inputs.decode_timestep == 0.0
-            assert inputs.decode_noise_scale is None
-            context = prompt_context
-            video = torch.full((1, 128, 1, 2, 2), 3.0)
-            audio = torch.full((1, 8, 1, 2), 4.0)
-            audio_for_next_phase = None
-        return LTXPhaseResult(
-            forward_context=SimpleNamespace(prompt_context=context),
-            video=video,
-            audio=audio,
-            audio_for_next_phase=audio_for_next_phase,
-        )
-
-    def decode_phase(phase):
-        return DiffusionOutput(output=(phase.video, phase.audio))
-
-    class FakeUpsampler(torch.nn.Module):
-        dtype = torch.float32
-
-        def forward(self, latents):
-            return latents.repeat_interleave(2, dim=-2).repeat_interleave(2, dim=-1)
-
-    prompt_context_sentinel = prompt_context
-    stage_1_sigmas = [1.0, 0.5, 0.0]
-    stage_2_sigmas = [0.8, 0.2, 0.0]
-    pipeline = object.__new__(LTX2TwoStagePipeline)
-    torch.nn.Module.__init__(pipeline)
-    pipeline.pipeline_recipe = LTX25_DISTILLED_TWO_STAGE_RECIPE
-    pipeline.device = torch.device("cpu")
-    pipeline.vae_spatial_compression_ratio = 32
-    pipeline.vae_temporal_compression_ratio = 8
-    pipeline.latent_upsampler = FakeUpsampler()
-    object.__setattr__(pipeline, "_resolve_request_inputs", resolve_request_inputs)
-    object.__setattr__(pipeline, "run_phase", run_phase)
-    object.__setattr__(pipeline, "decode_phase", decode_phase)
-
-    source_image = object()
-    output = pipeline.forward(
-        SimpleNamespace(sampling_params_list=[], prompts=[]),
-        image=source_image,
-        stage_1_sigmas=stage_1_sigmas,
-        stage_2_sigmas=stage_2_sigmas,
-    )
-
-    assert len(phase_calls) == 2
-    assert phase_calls[1][2] is prompt_context_sentinel
-    torch.testing.assert_close(output.output[0], torch.full((1, 128, 1, 2, 2), 3.0))
-    torch.testing.assert_close(output.output[1], torch.full((1, 8, 1, 2), 4.0))
-
-
-class TestLTXRequestParsing:
-    def test_request_resolves_custom_sigmas_from_extra_args(self):
-        from vllm_omni.diffusion.request import OmniDiffusionRequest
-        from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
-        from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-        sigmas = [1.0, 0.5, 0.0]
-        req = DiffusionRequestBatch(
-            [
-                OmniDiffusionRequest(
-                    prompt="prompt",
-                    sampling_params=OmniDiffusionSamplingParams(extra_args={"sigmas": sigmas}),
-                    request_id="ltx-custom-sigmas",
-                )
-            ]
-        )
-
-        assert _make_ltx_request_pipe(LTX2Pipeline)._resolve_request_sigmas(req, None) == sigmas
-
-    def test_request_resolves_per_stage_sigmas_from_extra_args(self):
-        from vllm_omni.diffusion.request import OmniDiffusionRequest
-        from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
-        from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-        stage_1_sigmas = [1.0, 0.4, 0.0]
-        req = DiffusionRequestBatch(
-            [
-                OmniDiffusionRequest(
-                    prompt="prompt",
-                    sampling_params=OmniDiffusionSamplingParams(extra_args={"stage_1_sigmas": stage_1_sigmas}),
-                    request_id="ltx-custom-phase-sigmas",
-                )
-            ]
-        )
-
-        resolved = _make_ltx_request_pipe(LTX2TwoStagePipeline)._resolve_request_phase_sigmas(
-            req, [1.0, 0.0], [0.8, 0.0]
-        )
-
-        assert resolved == (stage_1_sigmas, [0.8, 0.0])
-
-    @pytest.mark.parametrize("value", [-1, 52, 1.5, True])
-    def test_request_rejects_invalid_image_crf(self, value):
-        from vllm_omni.diffusion.request import OmniDiffusionRequest
-        from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
-        from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-        req = DiffusionRequestBatch(
-            [
-                OmniDiffusionRequest(
-                    prompt="prompt",
-                    sampling_params=OmniDiffusionSamplingParams(extra_args={"image_crf": value}),
-                    request_id="ltx-invalid-image-crf",
-                )
-            ]
-        )
-
-        with pytest.raises(ValueError, match="image_crf"):
-            _resolve_request_inputs_for_test(_make_ltx_request_pipe(LTX2Pipeline), req)
-
-
-class TestLTXForwardStages:
-    def test_gemma_tokenization_matches_official_bos_and_left_padding(self):
-        pipe = _make_ltx_request_pipe(LTX2Pipeline)
-
-        class FakeTokenizer:
-            bos_token_id = 2
-            eos_token_id = 1
-            eos_token = "<eos>"
-            pad_token_id = 0
-            pad_token = "<pad>"
-            padding_side = "right"
-
-            def __init__(self):
-                self.calls = []
-
-            def __call__(self, text, **kwargs):
-                self.calls.append((text, kwargs))
-                ids = [7, 8, 9, self.eos_token_id] if text == "long" else [6, self.eos_token_id]
-                return SimpleNamespace(input_ids=ids)
-
-            def pad(self, encoded, *, padding, max_length, return_tensors, return_attention_mask):
-                assert padding == "max_length"
-                assert return_tensors == "pt"
-                assert return_attention_mask is True
-                rows = []
-                masks = []
-                for ids in encoded["input_ids"]:
-                    pad = max_length - len(ids)
-                    rows.append([self.pad_token_id] * pad + ids)
-                    masks.append([0] * pad + [1] * len(ids))
-                return SimpleNamespace(input_ids=torch.tensor(rows), attention_mask=torch.tensor(masks))
-
-        class FakeTextEncoder(torch.nn.Module):
-            dtype = torch.float32
-
-            def __init__(self):
-                super().__init__()
-                self.seen_input_ids = None
-                self.seen_attention_mask = None
-
-            def forward(self, *, input_ids, attention_mask, output_hidden_states):
-                assert output_hidden_states is True
-                self.seen_input_ids = input_ids.clone()
-                self.seen_attention_mask = attention_mask.clone()
-                return SimpleNamespace(hidden_states=(input_ids.unsqueeze(-1).float(),))
-
-        tokenizer = FakeTokenizer()
-        text_encoder = FakeTextEncoder()
-        object.__setattr__(pipe, "tokenizer", tokenizer)
-        object.__setattr__(pipe, "text_encoder", text_encoder)
-
-        pipe._get_gemma_prompt_embeds(
-            [" long ", "short"],
-            max_sequence_length=4,
-            device=torch.device("cpu"),
-            dtype=torch.float32,
-        )
-
-        torch.testing.assert_close(text_encoder.seen_input_ids, torch.tensor([[2, 7, 8, 9], [0, 2, 6, 1]]))
-        torch.testing.assert_close(text_encoder.seen_attention_mask, torch.tensor([[1, 1, 1, 1], [0, 1, 1, 1]]))
-        assert tokenizer.padding_side == "left"
-        assert [call[0] for call in tokenizer.calls] == ["long", "short"]
@@ -3,7 +3,6 @@

 """Unit tests for LTX image-to-video input and conditioning behavior."""

-import sys
 from types import SimpleNamespace

 import pytest
@@ -24,7 +23,7 @@ def _make_ltx23_request_pipe(cls):


 class TestLTXImageToVideoForwardStages:
-    def test_legacy_i2v_pil_preprocessing_preserves_aspect_ratio_and_center_crops(self):
+    def test_i2v_pil_preprocessing_matches_official_center_crop(self):
        from vllm_omni.diffusion.models.ltx2.ltx2_conditioning import _preprocess_i2v_pil_images

        pixels = torch.zeros(4, 8, 3, dtype=torch.uint8)
@@ -36,136 +35,6 @@ class TestLTXImageToVideoForwardStages:

        torch.testing.assert_close(actual, expected)

-    def test_ltx25_i2v_pil_preprocessing_preserves_aspect_ratio_after_crf(self, monkeypatch):
-        import vllm_omni.diffusion.models.ltx2.ltx2_conditioning as conditioning
-
-        pixels = torch.zeros(4, 8, 3, dtype=torch.uint8)
-        pixels[:, :, 0] = torch.arange(8, dtype=torch.uint8)
-        image = Image.fromarray(pixels.numpy())
-        monkeypatch.setattr(conditioning, "_apply_image_conditioning_crf", lambda image_array, _crf: image_array)
-
-        actual = conditioning._preprocess_i2v_pil_images(image, height=4, width=4, crf=18)
-        expected = pixels[:, 2:6].permute(2, 0, 1).unsqueeze(0).float() / 127.5 - 1.0
-
-        torch.testing.assert_close(actual, expected)
-
-    def test_ltx25_i2v_applies_crf18_before_resize(self, monkeypatch):
-        import vllm_omni.diffusion.models.ltx2.ltx2_conditioning as conditioning
-        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
-
-        captured_crfs = []
-        captured_prepare_kwargs = []
-
-        def fake_preprocess(image, *, height, width, crf, device, dtype):
-            del image, device
-            captured_crfs.append(crf)
-            return torch.zeros(1, 3, height, width, dtype=dtype)
-
-        def fake_prepare_latents(**kwargs):
-            captured_prepare_kwargs.append(kwargs)
-            return kwargs["image"], None
-
-        monkeypatch.setattr(conditioning, "_preprocess_i2v_pil_images", fake_preprocess)
-        pipe = object.__new__(LTX2Pipeline)
-        torch.nn.Module.__init__(pipe)
-        pipe.model_version = "2.5"
-        object.__setattr__(pipe, "transformer", SimpleNamespace(config=SimpleNamespace(in_channels=4)))
-        object.__setattr__(pipe, "prepare_latents", fake_prepare_latents)
-        request_inputs = SimpleNamespace(
-            height=4,
-            width=4,
-            num_frames=3,
-            num_videos_per_prompt=1,
-            generator=None,
-            latents=None,
-        )
-        prompt_context = SimpleNamespace(
-            batch_size=1,
-            positive_connector_prompt_embeds=torch.zeros(1, 1, 1, dtype=torch.bfloat16),
-        )
-
-        pipe._prepare_video_latents_stage(
-            request_inputs,
-            prompt_context,
-            device=torch.device("cpu"),
-            noise_scale=0.0,
-            image=Image.new("RGB", (8, 4)),
-        )
-
-        assert captured_crfs == [18]
-        assert captured_prepare_kwargs[0]["dtype"] is torch.bfloat16
-        assert captured_prepare_kwargs[0]["image"].dtype is torch.bfloat16
-
-    def test_ltx25_i2v_rejects_inputs_that_cannot_receive_crf18(self):
-        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
-
-        pipe = object.__new__(LTX2Pipeline)
-        torch.nn.Module.__init__(pipe)
-        pipe.model_version = "2.5"
-
-        with pytest.raises(ValueError, match="requires PIL images"):
-            pipe._prepare_video_latents_stage(
-                SimpleNamespace(),
-                SimpleNamespace(),
-                device=torch.device("cpu"),
-                noise_scale=0.0,
-                image=torch.zeros(3, 4, 4),
-            )
-
-    def test_ltx25_i2v_tensor_input_can_opt_out_of_crf18(self):
-        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
-
-        captured_prepare_kwargs = []
-
-        def fake_prepare_latents(**kwargs):
-            captured_prepare_kwargs.append(kwargs)
-            return kwargs["image"], None
-
-        pipe = object.__new__(LTX2Pipeline)
-        torch.nn.Module.__init__(pipe)
-        pipe.model_version = "2.5"
-        object.__setattr__(pipe, "transformer", SimpleNamespace(config=SimpleNamespace(in_channels=4)))
-        object.__setattr__(pipe, "prepare_latents", fake_prepare_latents)
-        request_inputs = SimpleNamespace(
-            height=4,
-            width=4,
-            num_frames=3,
-            num_videos_per_prompt=1,
-            generator=None,
-            latents=None,
-            image_crf=0,
-        )
-        prompt_context = SimpleNamespace(
-            batch_size=1,
-            positive_connector_prompt_embeds=torch.zeros(1, 1, 1, dtype=torch.bfloat16),
-        )
-
-        pipe._prepare_video_latents_stage(
-            request_inputs,
-            prompt_context,
-            device=torch.device("cpu"),
-            noise_scale=0.0,
-            image=torch.zeros(3, 4, 4),
-        )
-
-        assert captured_prepare_kwargs[0]["image"].shape == (1, 3, 4, 4)
-
-    def test_ltx25_i2v_crf_preserves_single_pixel_dimension(self, monkeypatch):
-        from vllm_omni.diffusion.models.ltx2.ltx2_conditioning import _apply_image_conditioning_crf
-
-        image = torch.zeros(1, 4, 3, dtype=torch.uint8).numpy()
-        monkeypatch.setitem(sys.modules, "av", None)
-
-        assert _apply_image_conditioning_crf(image, 18) is image
-
-    def test_ltx25_i2v_reports_missing_pyav(self, monkeypatch):
-        from vllm_omni.diffusion.models.ltx2.ltx2_conditioning import _apply_image_conditioning_crf
-
-        monkeypatch.setitem(sys.modules, "av", None)
-
-        with pytest.raises(ImportError, match="PyAV with a libx264 encoder"):
-            _apply_image_conditioning_crf(torch.zeros(4, 4, 3, dtype=torch.uint8).numpy(), 18)
-
    def test_forward_resolves_request_image_and_delegates_to_shared_recipe_runtime(self):
        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
        from vllm_omni.diffusion.request import OmniDiffusionRequest
@@ -229,7 +98,7 @@ class TestLTXImageToVideoForwardStages:
        with pytest.raises(ValueError, match="cannot mix text-to-video and image-to-video"):
            pipe._resolve_request_image(mixed_req, None, request_inputs)

-    def test_denoise_timestep_kwargs_masks_video_and_keeps_audio_per_token(self):
+    def test_denoise_timestep_kwargs_masks_video_only(self):
        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline

        pipe = object.__new__(LTX2Pipeline)
@@ -320,18 +189,13 @@ class TestLTXImageToVideoConditioning:
        torch.testing.assert_close(conditioning_mask, torch.tensor([[1.0, 0.0, 0.0]]))
        torch.testing.assert_close(out, torch.tensor([[[40.0, 41.0], [20.0, 21.0], [30.0, 31.0]]]))

-    @pytest.mark.parametrize(
-        ("model_version", "sampled_shape"),
-        [("2.3", (1, 3, 2)), ("2.5", (1, 3, 2))],
-    )
-    def test_i2v_5d_latents_noise_uses_packed_rng_layout(self, monkeypatch, model_version, sampled_shape):
+    def test_ltx23_i2v_5d_latents_noise_preserves_conditioning_frame(self, monkeypatch):
        import vllm_omni.diffusion.models.ltx2.ltx2_conditioning as ltx2_conditioning
        import vllm_omni.diffusion.models.ltx2.ltx2_latents as ltx2_latents
        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline

        pipe = object.__new__(LTX2Pipeline)
        torch.nn.Module.__init__(pipe)
-        pipe.model_version = model_version
        pipe.vae_spatial_compression_ratio = 1
        pipe.vae_temporal_compression_ratio = 1
        pipe.transformer_spatial_patch_size = 1
@@ -373,20 +237,14 @@ class TestLTXImageToVideoConditioning:

        torch.testing.assert_close(conditioning_mask, torch.tensor([[1.0, 0.0, 0.0]]))
        torch.testing.assert_close(out, torch.tensor([[[40.0, 41.0], [1.0, 1.0], [1.0, 1.0]]]))
-        assert sampled_shapes == [sampled_shape]
+        assert sampled_shapes == [(1, 3, 2)]

-    @pytest.mark.parametrize(
-        ("model_version", "sampled_shape"),
-        [("2.3", (1, 3, 2)), ("2.5", (1, 3, 2))],
-    )
-    def test_i2v_image_noise_uses_packed_rng_layout(self, monkeypatch, model_version, sampled_shape):
+    def test_ltx23_i2v_image_noise_is_sampled_after_packing(self, monkeypatch):
        import vllm_omni.diffusion.models.ltx2.ltx2_conditioning as ltx2_conditioning
-        import vllm_omni.diffusion.models.ltx2.ltx2_latents as ltx2_latents
        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline

        pipe = object.__new__(LTX2Pipeline)
        torch.nn.Module.__init__(pipe)
-        pipe.model_version = model_version
        pipe.vae_spatial_compression_ratio = 1
        pipe.vae_temporal_compression_ratio = 1
        pipe.transformer_spatial_patch_size = 1
@@ -408,7 +266,6 @@ class TestLTXImageToVideoConditioning:
            return torch.ones(shape, device=device, dtype=dtype)

        monkeypatch.setattr(ltx2_conditioning, "randn_tensor", fake_randn_tensor)
-        monkeypatch.setattr(ltx2_latents, "randn_tensor", fake_randn_tensor)

        out, conditioning_mask = pipe.prepare_latents(
            image=torch.zeros(1, 3, 1, 1),
@@ -422,54 +279,10 @@ class TestLTXImageToVideoConditioning:
            device=torch.device("cpu"),
        )

-        assert sampled_shapes == [sampled_shape]
+        assert sampled_shapes == [(1, 3, 2)]
        torch.testing.assert_close(conditioning_mask, torch.tensor([[1.0, 0.0, 0.0]]))
        torch.testing.assert_close(out, torch.tensor([[[10.0, 11.0], [1.0, 1.0], [1.0, 1.0]]]))

-    def test_ltx25_re_noise_uses_official_scalar_noise_scale(self, monkeypatch):
-        import vllm_omni.diffusion.models.ltx2.ltx2_latents as ltx2_latents
-        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
-
-        pipe = object.__new__(LTX2Pipeline)
-        torch.nn.Module.__init__(pipe)
-        pipe.model_version = "2.5"
-        pipe.vae_spatial_compression_ratio = 1
-        pipe.vae_temporal_compression_ratio = 1
-        pipe.transformer_spatial_patch_size = 1
-        pipe.transformer_temporal_patch_size = 1
-        pipe.vae = SimpleNamespace(
-            latents_mean=torch.zeros(2),
-            latents_std=torch.ones(2),
-            config=SimpleNamespace(scaling_factor=1.0),
-        )
-        object.__setattr__(
-            pipe,
-            "_encode_i2v_image_latents",
-            lambda *_args, **_kwargs: torch.zeros(1, 2, 1, 1, 1, dtype=torch.bfloat16),
-        )
-        captured = {}
-
-        def fake_create_noised_state(latents, noise_scale, generator=None):
-            del generator
-            captured["noise_scale"] = noise_scale
-            return latents
-
-        monkeypatch.setattr(ltx2_latents, "create_noised_state", fake_create_noised_state)
-        pipe.prepare_latents(
-            image=torch.zeros(1, 3, 1, 1),
-            batch_size=1,
-            num_channels_latents=2,
-            height=1,
-            width=1,
-            num_frames=3,
-            noise_scale=0.909375,
-            dtype=torch.bfloat16,
-            device=torch.device("cpu"),
-            latents=torch.zeros(1, 2, 3, 1, 1, dtype=torch.bfloat16),
-        )
-
-        assert captured["noise_scale"] == 0.909375
-
    def test_ltx23_i2v_video_step_preserves_conditioning_frame(self):
        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline

@@ -572,99 +385,6 @@ class TestLTXImageToVideoConditioning:
        torch.testing.assert_close(audio, torch.tensor([[[-2.0]]]))
        assert pipeline.step_indices == [0, 1]

-    def test_ltx25_ancestral_adapter_matches_official_seeded_step_and_preserves_i2v_mask(self):
-        from vllm_omni.diffusion.models.ltx2.ltx2_denoise import LTXVideoAudioStepAdapter
-
-        device = torch.device("cpu")
-        sigmas = torch.tensor([1.0, 0.5, 0.0])
-        pipeline = SimpleNamespace(device=device, scheduler=SimpleNamespace(sigmas=sigmas))
-        mask = torch.tensor([[1.0, 0.0]])
-        adapter = LTXVideoAudioStepAdapter(
-            pipeline,
-            SimpleNamespace(sigmas=sigmas.clone()),
-            latent_num_frames=1,
-            latent_height=1,
-            latent_width=1,
-            image_conditioned=True,
-            sampler="euler_ancestral",
-            generator=torch.Generator(device=device).manual_seed(42),
-            conditioning_mask=mask,
-        )
-        video = torch.tensor([[[3.0], [2.0]]])
-        audio = torch.tensor([[[4.0]]])
-        video_velocity = torch.tensor([[[7.0], [0.25]]])
-        audio_velocity = torch.tensor([[[0.5]]])
-
-        reference_generator = torch.Generator(device=device).manual_seed(10042)
-        video_noise = torch.randn(video.shape, generator=reference_generator)
-        audio_noise = torch.randn(audio.shape, generator=reference_generator)
-
-        def official_step(sample, velocity, noise):
-            sigma = sigmas[0]
-            sigma_next = sigmas[1]
-            denoised = sample.float() - velocity.float() * sigma
-            sigma_down = sigma_next * (sigma_next / sigma)
-            ratio = sigma_down / sigma
-            deterministic = ratio * sample.float() + (1.0 - ratio) * denoised
-            alpha_next = 1.0 - sigma_next
-            alpha_down = 1.0 - sigma_down
-            coeff = (sigma_next**2 - sigma_down**2 * alpha_next**2 / alpha_down**2).sqrt()
-            return (alpha_next / alpha_down) * deterministic + noise * coeff
-
-        expected_video = official_step(video, video_velocity, video_noise)
-        expected_video[:, :1] = video[:, :1]
-        expected_audio = official_step(audio, audio_velocity, audio_noise)
-
-        ((actual_video, actual_audio),) = adapter.step(
-            (video_velocity, audio_velocity),
-            (sigmas[0], sigmas[0]),
-            (video, audio),
-        )
-        torch.testing.assert_close(actual_video, expected_video)
-        torch.testing.assert_close(actual_audio, expected_audio)
-
-        terminal_video_velocity = torch.full_like(actual_video, 0.125)
-        terminal_audio_velocity = torch.full_like(actual_audio, 0.25)
-        ((terminal_video, terminal_audio),) = adapter.step(
-            (terminal_video_velocity, terminal_audio_velocity),
-            (sigmas[1], sigmas[1]),
-            (actual_video, actual_audio),
-        )
-        expected_terminal_video = actual_video - terminal_video_velocity * sigmas[1]
-        expected_terminal_video[:, :1] = actual_video[:, :1]
-        expected_terminal_audio = actual_audio - terminal_audio_velocity * sigmas[1]
-        torch.testing.assert_close(terminal_video, expected_terminal_video)
-        torch.testing.assert_close(terminal_audio, expected_terminal_audio)
-
-    def test_ltx25_ancestral_quantizes_x0_to_model_dtype_before_fp32_step(self):
-        from vllm_omni.diffusion.models.ltx2.ltx2_denoise import _ancestral_euler_step_from_velocity
-
-        sample = torch.tensor([[[3.140625, -1.234375]]], dtype=torch.bfloat16)
-        velocity = torch.tensor([[[0.333984375, 2.71875]]], dtype=torch.bfloat16)
-        sigmas = torch.tensor([0.73, 0.41, 0.0], dtype=torch.float32)
-        generator = torch.Generator(device="cpu").manual_seed(10042)
-        reference_generator = torch.Generator(device="cpu").manual_seed(10042)
-
-        sigma, sigma_next = sigmas[:2]
-        denoised = (sample.float() - velocity.float() * sigma).to(sample.dtype).float()
-        sigma_down = sigma_next * (sigma_next / sigma)
-        sigma_down_ratio = sigma_down / sigma
-        expected = sigma_down_ratio * sample.float() + (1.0 - sigma_down_ratio) * denoised
-        alpha_next = 1.0 - sigma_next
-        alpha_down = 1.0 - sigma_down
-        coefficient = (sigma_next**2 - sigma_down**2 * alpha_next**2 / alpha_down**2).sqrt()
-        noise = torch.randn(
-            sample.shape,
-            generator=reference_generator,
-            dtype=sample.dtype,
-            device=sample.device,
-        )
-        expected = ((alpha_next / alpha_down) * expected + noise.float() * coefficient).to(sample.dtype)
-
-        actual = _ancestral_euler_step_from_velocity(sample, velocity, sigmas, 0, generator)
-
-        torch.testing.assert_close(actual, expected, rtol=0, atol=0)
-
    def test_i2v_guidance_uses_zero_sigma_for_conditioned_tokens(self):
        from vllm_omni.diffusion.models.ltx2.ltx2_denoise import LTXDenoiseContext
        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
@@ -202,68 +202,3 @@ def test_prepare_audio_latents_rejects_incompatible_provided_length():
            device=torch.device("cpu"),
            latents=latents,
        )
-
-
-def test_create_noised_state_matches_official_fp32_lerp():
-    latents = torch.linspace(-2, 2, 4096, dtype=torch.bfloat16).reshape(1, 32, 128)
-    expected_generator = torch.Generator().manual_seed(42)
-    noise = torch.randn(latents.shape, generator=expected_generator, dtype=latents.dtype)
-    expected = torch.lerp(latents.float(), noise.float(), torch.tensor(0.15, dtype=torch.bfloat16).float()).to(
-        latents.dtype
-    )
-
-    actual = latent_ops.create_noised_state(
-        latents,
-        torch.tensor(0.15, dtype=torch.bfloat16),
-        torch.Generator().manual_seed(42),
-    )
-
-    torch.testing.assert_close(actual, expected, rtol=0, atol=0)
-
-
-def test_create_conditioned_noised_state_matches_official_two_lerps():
-    latents = torch.linspace(-2, 2, 24, dtype=torch.bfloat16).reshape(1, 3, 8)
-    clean_latents = latents.clone()
-    clean_latents[:, 0] = 3.0
-    denoise_mask = torch.tensor([[[0.0], [1.0], [1.0]]])
-    expected_generator = torch.Generator().manual_seed(42)
-    noise = torch.randn(latents.shape, generator=expected_generator, dtype=latents.dtype)
-    noised = torch.lerp(latents.float(), noise.float(), 0.909375)
-    expected = torch.lerp(clean_latents.float(), noised, denoise_mask).to(latents.dtype)
-
-    actual = latent_ops.create_conditioned_noised_state(
-        latents,
-        clean_latents,
-        denoise_mask,
-        0.909375,
-        torch.Generator().manual_seed(42),
-    )
-
-    torch.testing.assert_close(actual, expected, rtol=0, atol=0)
-
-
-def test_prepare_supplied_video_latents_uses_official_token_major_layout():
-    pipeline = _make_pipeline(LTX2Pipeline)
-    supplied = torch.arange(512, dtype=torch.float32).reshape(1, 32, 16)
-    actual_video = pipeline.prepare_latents(
-        batch_size=1,
-        num_channels_latents=4,
-        height=64,
-        width=64,
-        num_frames=9,
-        dtype=torch.float32,
-        device=torch.device("cpu"),
-        latents=supplied,
-    )
-
-    torch.testing.assert_close(actual_video, supplied, rtol=0, atol=0)
-    assert actual_video.stride()[1:] == (1, actual_video.shape[1])
-
-
-def test_clear_audio_padding_keeps_padding_outside_sampler_state():
-    updated = torch.tensor([[[1.0, 2.0], [3.0, 4.0], [99.0, -99.0]]])
-
-    actual = latent_ops.clear_audio_padding(updated, 2)
-
-    torch.testing.assert_close(actual[:, :2], updated[:, :2])
-    torch.testing.assert_close(actual[:, 2:], torch.zeros_like(actual[:, 2:]))
@@ -28,15 +28,6 @@ def test_velocity_from_x0_uses_official_host_scalar_sigma(mocker):
 class TestCFGParallelHelpers:
    """Test LTX-2.3 CFG helper math without loading model weights."""

-    def test_single_guidance_pass_preserves_official_token_layout(self):
-        from vllm_omni.diffusion.models.ltx2.ltx2_guidance import _repeat_batch
-
-        tokens = torch.randn(1, 16, 32).transpose(1, 2).contiguous().transpose(1, 2)
-        repeated = _repeat_batch(tokens, 1)
-
-        assert repeated is tokens
-        assert repeated.stride() == tokens.stride()
-
    def test_combine_cfg_noise_matches_x0_space_formula(self):
        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline

@@ -20,7 +20,6 @@ from vllm_omni.diffusion.models.ltx2 import (
 from vllm_omni.diffusion.models.ltx2.ltx2_components import (
    LTX2_COMPONENT_PROFILE,
    LTX2_DISTILLED_COMPONENT_PROFILE,
-    LTX2_DISTILLED_ONE_STAGE_COMPONENT_PROFILE,
    LTX2_TWO_STAGE_COMPONENT_PROFILE,
    LTX23_COMPONENT_PROFILE,
    LTX23_DISTILLED_COMPONENT_PROFILE,
@@ -48,7 +47,6 @@ from vllm_omni.diffusion.models.ltx2.ltx2_guidance import (
 )
 from vllm_omni.diffusion.models.ltx2.ltx2_latents import LTXAVState
 from vllm_omni.diffusion.models.ltx2.ltx2_recipes import (
-    LTX2_DISTILLED_ONE_STAGE_RECIPE,
    LTX2_DISTILLED_TWO_STAGE_RECIPE,
    LTX2_ONE_STAGE_RECIPE,
    LTX2_TWO_STAGE_RECIPE,
@@ -66,14 +64,12 @@ from vllm_omni.diffusion.models.ltx2.ltx2_request import (
 )
 from vllm_omni.diffusion.models.ltx2.ltx2_runtime import LTXRuntime
 from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import (
-    LTX2DistilledOneStagePipeline,
    LTX2I2VDMD2Pipeline,
    LTX2Pipeline,
    LTX2T2VDMD2Pipeline,
 )
 from vllm_omni.diffusion.models.ltx2.pipeline_ltx2_two_stage import (
    LTX2DistilledPipeline,
-    LTX2DistilledTwoStagePipeline,
    LTX2TwoStagePipeline,
 )

@@ -129,21 +125,15 @@ def test_ltx_public_entries_share_runtime_and_keep_recipe_boundaries():

    assert issubclass(LTX2Pipeline, LTXRuntime)
    assert issubclass(LTX2TwoStagePipeline, LTXRuntime)
-    assert issubclass(LTX2DistilledOneStagePipeline, LTXRuntime)
-    assert issubclass(LTX2DistilledTwoStagePipeline, LTXRuntime)
    assert issubclass(LTX2DistilledPipeline, LTXRuntime)
-    assert LTX2DistilledPipeline is LTX2DistilledTwoStagePipeline
    assert LTX2Pipeline.component_profile is LTX2_COMPONENT_PROFILE
    assert LTX2Pipeline.pipeline_recipe is LTX2_ONE_STAGE_RECIPE
    assert LTX2TwoStagePipeline.component_profile is LTX2_TWO_STAGE_COMPONENT_PROFILE
    assert LTX2TwoStagePipeline.pipeline_recipe is LTX2_TWO_STAGE_RECIPE
-    assert LTX2DistilledOneStagePipeline.component_profile is LTX2_DISTILLED_ONE_STAGE_COMPONENT_PROFILE
-    assert LTX2DistilledOneStagePipeline.pipeline_recipe is LTX2_DISTILLED_ONE_STAGE_RECIPE
    assert LTX2DistilledPipeline.component_profile is LTX2_DISTILLED_COMPONENT_PROFILE
    for pipeline_cls, profile in (
        (LTX2Pipeline, LTX2_COMPONENT_PROFILE),
        (LTX2TwoStagePipeline, LTX2_TWO_STAGE_COMPONENT_PROFILE),
-        (LTX2DistilledOneStagePipeline, LTX2_DISTILLED_ONE_STAGE_COMPONENT_PROFILE),
        (LTX2DistilledPipeline, LTX2_DISTILLED_COMPONENT_PROFILE),
    ):
        assert pipeline_cls._dit_modules == list(profile.dit_modules)
@@ -264,7 +254,7 @@ def test_ltx_multistage_rejects_cache_dit_before_component_initialization(tmp_pa

    monkeypatch.setattr(ltx2_runtime, "initialize_pipeline_components", stub_components)

-    with pytest.raises(ValueError, match="not qualified for this LTX recipe"):
+    with pytest.raises(ValueError, match="only one-stage LTX recipes"):
        LTX2TwoStagePipeline(
            od_config=SimpleNamespace(
                model=str(tmp_path),
@@ -719,48 +709,15 @@ def test_ltx_component_loader_installs_omni_connector_attention(monkeypatch):

    assert len(installed) == 2
    assert all(processor.__class__.__name__ == "_LTXConnectorAttnProcessor" for processor in installed)
+    assert all(processor.has_learned_registers for processor in installed)
    assert all(kernel["role"] == "ltx2.connector" for kernel in kernels)
    assert all(kernel["role_category"] == "self" for kernel in kernels)
    assert all(kernel["skip_sequence_parallel"] for kernel in kernels)
    assert all(kernel["disable_kv_quant"] for kernel in kernels)
-    assert all(processor.has_learned_registers for processor in installed)
-    assert not any(processor.preserve_learned_register_mask for processor in installed)


-def test_ltx25_connector_attention_preserves_learned_register_mask(monkeypatch):
-    installed = []
-
-    class OmniAttention:
-        def __init__(self, **_kwargs):
-            pass
-
-    class Attention:
-        heads = 32
-        head_dim = 128
-        inner_kv_dim = 4096
-
-        def set_processor(self, processor):
-            installed.append(processor)
-
-    monkeypatch.setattr(ltx2_components, "OmniAttention", OmniAttention)
-    connectors = SimpleNamespace(
-        video_connector=SimpleNamespace(
-            learnable_registers=object(),
-            transformer_blocks=[SimpleNamespace(attn1=Attention())],
-        ),
-        audio_connector=SimpleNamespace(
-            learnable_registers=object(),
-            transformer_blocks=[SimpleNamespace(attn1=Attention())],
-        ),
-    )
-
-    _install_connector_attention(connectors, preserve_learned_register_mask=True)
-
-    assert all(processor.has_learned_registers for processor in installed)
-    assert all(processor.preserve_learned_register_mask for processor in installed)
-
-
-def test_ltx_connector_attention_dispatches_through_omni_kernel():
+@pytest.mark.parametrize("has_learned_registers", [False, True])
+def test_ltx_connector_attention_dispatches_through_omni_kernel(has_learned_registers):
    calls = []

    class Backend:
@@ -775,16 +732,12 @@ def test_ltx_connector_attention_dispatches_through_omni_kernel():
            calls.append((query, key, value, metadata))
            return query

-    class UpcastingNorm(torch.nn.Module):
-        def forward(self, tensor):
-            return tensor.float()
-
    attention = SimpleNamespace(
        heads=2,
        head_dim=4,
        inner_kv_dim=8,
-        norm_q=UpcastingNorm(),
-        norm_k=UpcastingNorm(),
+        norm_q=torch.nn.Identity(),
+        norm_k=torch.nn.Identity(),
        to_q=torch.nn.Identity(),
        to_k=torch.nn.Identity(),
        to_v=torch.nn.Identity(),
@@ -793,29 +746,24 @@ def test_ltx_connector_attention_dispatches_through_omni_kernel():
        rope_type="split",
        omni_attention=OmniAttention(),
    )
-    hidden_states = torch.randn(2, 3, 8, dtype=torch.bfloat16)
+    hidden_states = torch.randn(2, 3, 8)
    additive_mask = torch.zeros(2, 1, 3, 3)
-    rotary = (
-        torch.randn(2, 2, 3, 2, dtype=torch.float32),
-        torch.randn(2, 2, 3, 2, dtype=torch.float32),
-    )

-    output = ltx2_components._LTXConnectorAttnProcessor()(
+    output = ltx2_components._LTXConnectorAttnProcessor(has_learned_registers=has_learned_registers)(
        attention,
        hidden_states,
        attention_mask=additive_mask,
-        query_rotary_emb=rotary,
    )

    query, key, value, metadata = calls[0]
    assert query.shape == key.shape == value.shape == (2, 3, 2, 4)
-    assert query.dtype == key.dtype == value.dtype == torch.bfloat16
-    assert metadata.attn_mask.shape == (2, 3)
-    assert metadata.attn_mask.dtype == torch.bool
-    assert metadata.attn_mask.all()
-    expected_rotary = tuple(component.to(torch.bfloat16) for component in rotary)
-    expected = ltx2_components.apply_split_rotary_emb(hidden_states, expected_rotary, head_dim=4)
-    torch.testing.assert_close(output, expected, rtol=0, atol=0)
+    if has_learned_registers:
+        assert metadata is None
+    else:
+        assert metadata.attn_mask.shape == (2, 3)
+        assert metadata.attn_mask.dtype == torch.bool
+        assert metadata.attn_mask.all()
+    torch.testing.assert_close(output, hidden_states)


 def test_ltx2_distilled_pipeline_shares_recipe_runtime():
@@ -1759,7 +1707,6 @@ class TestLTXForwardStages:
        assert seen["request_inputs"].output_type == "latent"
        assert seen["kwargs"] == {
            "request_sigmas": request_sigmas,
-            "request_phase_sigmas": None,
            "image": None,
        }

@@ -1877,16 +1824,6 @@ class TestRegistryIntegration:
            "pipeline_ltx2_two_stage",
            "LTX2TwoStagePipeline",
        )
-        assert _DIFFUSION_MODELS["LTX2DistilledOneStagePipeline"] == (
-            "ltx2",
-            "pipeline_ltx2",
-            "LTX2DistilledOneStagePipeline",
-        )
-        assert _DIFFUSION_MODELS["LTX2DistilledTwoStagePipeline"] == (
-            "ltx2",
-            "pipeline_ltx2_two_stage",
-            "LTX2DistilledTwoStagePipeline",
-        )
        assert _DIFFUSION_MODELS["LTX2DistilledPipeline"] == (
            "ltx2",
            "pipeline_ltx2_two_stage",
@@ -1911,8 +1848,6 @@ class TestRegistryIntegration:
        expected = [
            "LTX2Pipeline",
            "LTX2TwoStagePipeline",
-            "LTX2DistilledOneStagePipeline",
-            "LTX2DistilledTwoStagePipeline",
            "LTX2DistilledPipeline",
            "LTX2T2VDMD2Pipeline",
            "LTX2I2VDMD2Pipeline",
@@ -1926,8 +1861,6 @@ class TestRegistryIntegration:
        [
            "LTX2Pipeline",
            "LTX2TwoStagePipeline",
-            "LTX2DistilledOneStagePipeline",
-            "LTX2DistilledTwoStagePipeline",
            "LTX2DistilledPipeline",
            "LTX2T2VDMD2Pipeline",
            "LTX2I2VDMD2Pipeline",
@@ -7,54 +7,18 @@ import pytest
 import torch
 from torch import nn

+from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata
 from vllm_omni.diffusion.cache.cachedit import CacheDiTAdapterConfig
-from vllm_omni.diffusion.models.ltx2 import ltx2_transformer
 from vllm_omni.diffusion.models.ltx2.ltx2_transformer import (
-    LTX2FeedForward,
+    LTX2AudioVideoAttnProcessor,
    LTX2VideoTransformer3DModel,
-    LTX2VideoTransformerBlock,
+    _LTX2ParallelAttention,
    _make_rms_norm,
-    apply_interleaved_rotary_emb,
-    apply_keyframes_absolute_embedding,
-    apply_split_rotary_emb,
 )

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


-def test_ltx_interleaved_rope_matches_official_model_dtype_arithmetic():
-    torch.manual_seed(0)
-    x = torch.randn(2, 3, 8, dtype=torch.bfloat16)
-    cos = torch.randn(2, 3, 8, dtype=x.dtype)
-    sin = torch.randn(2, 3, 8, dtype=x.dtype)
-    x_real, x_imag = x.unflatten(2, (4, 2)).unbind(-1)
-    rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(2)
-    expected = x * cos + rotated * sin
-
-    actual = apply_interleaved_rotary_emb(x, (cos, sin))
-
-    torch.testing.assert_close(actual, expected, rtol=0, atol=0)
-
-
-def test_ltx_split_rope_matches_official_model_dtype_arithmetic():
-    torch.manual_seed(1)
-    x = torch.randn(2, 3, 8, dtype=torch.bfloat16)
-    cos = torch.randn(2, 3, 4, dtype=x.dtype)
-    sin = torch.randn(2, 3, 4, dtype=x.dtype)
-    split_x = x.reshape(2, 3, 2, 4)
-    first_x, second_x = split_x.chunk(2, dim=-2)
-    cos_u = cos.unsqueeze(-2)
-    sin_u = sin.unsqueeze(-2)
-    expected = split_x * cos_u
-    expected[..., :1, :].addcmul_(-sin_u, second_x)
-    expected[..., 1:, :].addcmul_(sin_u, first_x)
-    expected = expected.reshape_as(x)
-
-    actual = apply_split_rotary_emb(x, (cos, sin), head_dim=8)
-
-    torch.testing.assert_close(actual, expected, rtol=0, atol=0)
-
-
 def test_ltx_rms_norm_no_affine_matches_official_torch_module():
    norm = _make_rms_norm(8, eps=1e-6, elementwise_affine=False)

@@ -72,53 +36,11 @@ def test_ltx_rms_norm_affine_weight_remains_parameter():
    assert "weight" in norm.state_dict()


-def test_ltx_attention_restores_projection_dtype_after_upcasting_qk_norms():
-    calls = []
-
-    class UpcastingNorm(nn.Module):
-        def forward(self, tensor):
-            return tensor.float()
-
-    class RecordingAttention:
-        def __call__(self, query, key, value, metadata):
-            calls.append((query, key, value, metadata))
-            return query
-
-    attention = SimpleNamespace(
-        heads=2,
-        head_dim=4,
-        to_qkv=None,
-        to_q=nn.Identity(),
-        to_k=nn.Identity(),
-        to_v=nn.Identity(),
-        norm_q=UpcastingNorm(),
-        norm_k=UpcastingNorm(),
-        to_gate_logits=None,
-        attn=RecordingAttention(),
-        to_out=(nn.Identity(), nn.Identity()),
-        rope_type="split",
-    )
-    hidden_states = torch.randn(2, 3, 8, dtype=torch.bfloat16)
-    rotary = (
-        torch.ones(2, 2, 3, 2, dtype=torch.float32),
-        torch.zeros(2, 2, 3, 2, dtype=torch.float32),
-    )
-
-    processor = ltx2_transformer.LTX2AudioVideoAttnProcessor()
-    processor._slice_rope_for_tp = lambda rope, _: rope
-    output = processor(attention, hidden_states, query_rotary_emb=rotary)
-
-    query, key, value, metadata = calls[0]
-    assert query.dtype == key.dtype == value.dtype == torch.bfloat16
-    assert metadata is None
-    torch.testing.assert_close(output, hidden_states)
-
-
-def test_ltx_transformer_has_fused_cfg_cache_dit_config():
+def test_ltx_transformer_has_separate_cfg_cache_dit_config():
    adapter_config = getattr(LTX2VideoTransformer3DModel, "_cache_dit_adapter_config")

    assert isinstance(adapter_config, CacheDiTAdapterConfig)
-    assert not adapter_config.has_separate_cfg
+    assert adapter_config.has_separate_cfg


 def test_ltx_transformer_exposes_hsdp_shard_conditions_for_blocks():
@@ -154,9 +76,8 @@ def test_ltx_sp_plan_shards_video_and_audio_timesteps_together(rope_type):


 def test_ltx_sp_keeps_cross_attention_key_padding_mask(monkeypatch):
-    processor_cls = ltx2_transformer.LTX2AudioVideoAttnProcessor
-    monkeypatch.setattr(processor_cls, "_is_sp_enabled", staticmethod(lambda: True))
-    processor = processor_cls()
+    monkeypatch.setattr(LTX2AudioVideoAttnProcessor, "_is_sp_enabled", staticmethod(lambda: True))
+    processor = LTX2AudioVideoAttnProcessor()
    hidden_states = torch.zeros(1, 3, 4)
    encoder_hidden_states = torch.zeros(1, 4, 4)
    key_padding_mask = torch.tensor([[True, True, True, False]])
@@ -192,7 +113,7 @@ def _fake_ltx_parallel_attention():

        return forward

-    layer = object.__new__(ltx2_transformer._LTX2ParallelAttention)
+    layer = object.__new__(_LTX2ParallelAttention)
    nn.Module.__init__(layer)
    layer.attn_backend = Backend
    layer.attention = SimpleNamespace(forward=run("native"))
@@ -202,281 +123,33 @@ def _fake_ltx_parallel_attention():


@pytest.mark.parametrize(("query_length", "key_length"), [(4, 4), (3, 4)])
-def test_ltx_masked_attention_uses_sdpa(query_length, key_length):
+def test_ltx_masked_cross_attention_uses_sdpa(query_length, key_length):
    layer, calls = _fake_ltx_parallel_attention()
    query = torch.zeros(1, query_length, 2, 4, dtype=torch.bfloat16)
    key = value = torch.zeros(1, key_length, 2, 4, dtype=torch.bfloat16)
    key_padding_mask = torch.tensor([[True] * (key_length - 1) + [False]])
-    metadata = ltx2_transformer.AttentionMetadata(attn_mask=key_padding_mask[:, None, None, :])
+    metadata = AttentionMetadata(attn_mask=key_padding_mask[:, None, None, :])

    layer._run_local_attention(query, key, value, metadata)

    assert calls == ["sdpa"]


-def test_ltx_unmasked_attention_keeps_native_backend():
+def test_ltx_masked_self_attention_uses_sdpa():
+    layer, calls = _fake_ltx_parallel_attention()
+    query = key = value = torch.zeros(1, 4, 2, 4, dtype=torch.bfloat16)
+    key_padding_mask = torch.tensor([[True, True, True, False]])
+    metadata = AttentionMetadata(attn_mask=key_padding_mask[:, None, None, :])
+
+    layer._run_local_attention(query, key, value, metadata)
+
+    assert calls == ["sdpa"]
+
+
+def test_ltx_unmasked_cross_attention_keeps_native_backend():
    layer, calls = _fake_ltx_parallel_attention()
    query = key = value = torch.zeros(1, 4, 2, 4, dtype=torch.bfloat16)

    layer._run_local_attention(query, key, value, None)

    assert calls == ["native"]
-
-
-def test_ltx25_feed_forward_bias_flag_controls_both_linears(monkeypatch):
-    class FakeParallelLinear(nn.Linear):
-        def __init__(self, dim_in, dim_out, *, bias=True, **kwargs):
-            super().__init__(dim_in, dim_out, bias=bias)
-
-    monkeypatch.setattr(ltx2_transformer, "ColumnParallelLinear", FakeParallelLinear)
-    monkeypatch.setattr(ltx2_transformer, "RowParallelLinear", FakeParallelLinear)
-
-    without_bias = LTX2FeedForward(8, inner_dim=16, bias=False)
-    with_bias = LTX2FeedForward(8, inner_dim=16, bias=True)
-
-    assert without_bias.net[0].proj.bias is None
-    assert without_bias.net[2].bias is None
-    assert with_bias.net[0].proj.bias is not None
-    assert with_bias.net[2].bias is not None
-
-
-def _build_tiny_ltx_transformer(monkeypatch, **overrides):
-    class FakeBlock(nn.Module):
-        def __init__(self, **kwargs):
-            super().__init__()
-            self.init_kwargs = kwargs
-
-        def forward(self, hidden_states, audio_hidden_states, *_args, **_kwargs):
-            return hidden_states, audio_hidden_states
-
-    monkeypatch.setattr(ltx2_transformer, "LTX2VideoTransformerBlock", FakeBlock)
-    kwargs = {
-        "in_channels": 4,
-        "out_channels": 4,
-        "num_attention_heads": 2,
-        "attention_head_dim": 4,
-        "cross_attention_dim": 8,
-        "vae_scale_factors": (1, 1, 1),
-        "pos_embed_max_pos": 4,
-        "base_height": 8,
-        "base_width": 8,
-        "audio_in_channels": 4,
-        "audio_out_channels": 4,
-        "audio_num_attention_heads": 2,
-        "audio_attention_head_dim": 4,
-        "audio_cross_attention_dim": 8,
-        "audio_scale_factor": 1,
-        "audio_pos_embed_max_pos": 4,
-        "audio_sampling_rate": 16,
-        "audio_hop_length": 4,
-        "num_layers": 1,
-        "caption_channels": 8,
-        "use_prompt_embeddings": False,
-        "cross_attn_mod": True,
-        "audio_cross_attn_mod": True,
-    }
-    kwargs.update(overrides)
-    return LTX2VideoTransformer3DModel(**kwargs)
-
-
-def test_ltx25_keyframe_absolute_embedding_matches_official():
-    hidden_states = torch.arange(32, dtype=torch.bfloat16).view(1, 4, 8)
-    keyframes_mask = torch.tensor([[[1.0], [1.0], [0.0], [0.0]]])
-    embedding = torch.linspace(-0.5, 0.5, 8, dtype=torch.bfloat16).view(1, 8)
-
-    actual = apply_keyframes_absolute_embedding(hidden_states, keyframes_mask, embedding)
-    expected = hidden_states + (keyframes_mask > 0).to(torch.bfloat16) * embedding
-
-    torch.testing.assert_close(actual, expected, rtol=0, atol=0)
-    assert apply_keyframes_absolute_embedding(hidden_states, None, embedding) is hidden_states
-
-
-def test_ltx25_constructor_flags_and_native_weight_loader(monkeypatch):
-    legacy_model = _build_tiny_ltx_transformer(monkeypatch)
-
-    assert legacy_model.config.ff_bias is True
-    assert legacy_model.config.audio_ff_bias is True
-    assert legacy_model.config.use_prompt_adaln_single is True
-    assert legacy_model.config.use_keyframes_abs_pos_embedding is False
-    assert hasattr(legacy_model, "prompt_adaln")
-    assert hasattr(legacy_model, "audio_prompt_adaln")
-    assert not hasattr(legacy_model, "keyframes_abs_pos_embedding")
-    assert legacy_model.transformer_blocks[0].init_kwargs["ff_bias"] is True
-    assert legacy_model.transformer_blocks[0].init_kwargs["audio_ff_bias"] is True
-
-    ltx25_model = _build_tiny_ltx_transformer(
-        monkeypatch,
-        ff_bias=False,
-        audio_ff_bias=True,
-        use_prompt_adaln_single=False,
-        use_keyframes_abs_pos_embedding=True,
-    )
-
-    assert ltx25_model.keyframes_abs_pos_embedding.shape == (1, 8)
-    torch.testing.assert_close(ltx25_model.keyframes_abs_pos_embedding, torch.zeros(1, 8))
-    assert not hasattr(ltx25_model, "prompt_adaln")
-    assert not hasattr(ltx25_model, "audio_prompt_adaln")
-    assert ltx25_model.transformer_blocks[0].init_kwargs["ff_bias"] is False
-    assert ltx25_model.transformer_blocks[0].init_kwargs["audio_ff_bias"] is True
-
-    monkeypatch.setattr(ltx2_transformer, "get_tensor_model_parallel_world_size", lambda: 1)
-    expected = torch.arange(8, dtype=torch.float32).view(1, 8)
-    loaded = ltx25_model.load_weights([("keyframes_abs_pos_embedding", expected)])
-
-    assert loaded == {"keyframes_abs_pos_embedding"}
-    torch.testing.assert_close(ltx25_model.keyframes_abs_pos_embedding, expected)
-
-
-def test_ltx_cross_attention_modulations_use_official_timesteps(monkeypatch):
-    class CaptureAda(nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.calls = []
-
-        def forward(self, timestep, *, batch_size, hidden_dtype):
-            self.calls.append(timestep.detach().clone())
-            embedding = torch.zeros(timestep.numel(), 8, dtype=hidden_dtype)
-            return embedding, embedding
-
-    class CaptureRope(nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.output_dtypes = []
-
-        def forward(self, _coords, *, device, out_dtype=None):
-            self.output_dtypes.append(out_dtype)
-            empty = torch.empty(0, device=device, dtype=out_dtype)
-            return empty, empty
-
-    model = _build_tiny_ltx_transformer(monkeypatch)
-    rope = CaptureRope()
-    model.rope = model.audio_rope = model.cross_attn_rope = model.cross_attn_audio_rope = rope
-    model.prompt_modulation = False
-    video_scale_shift = CaptureAda()
-    video_gate = CaptureAda()
-    audio_scale_shift = CaptureAda()
-    audio_gate = CaptureAda()
-    model.av_cross_attn_video_scale_shift = video_scale_shift
-    model.av_cross_attn_video_a2v_gate = video_gate
-    model.av_cross_attn_audio_scale_shift = audio_scale_shift
-    model.av_cross_attn_audio_v2a_gate = audio_gate
-
-    model(
-        hidden_states=torch.zeros(1, 2, 4),
-        audio_hidden_states=torch.zeros(1, 1, 4),
-        encoder_hidden_states=torch.zeros(1, 1, 8),
-        audio_encoder_hidden_states=torch.zeros(1, 1, 8),
-        timestep=torch.tensor([[0.0, 2.0]]),
-        audio_timestep=torch.tensor([[5.0]]),
-        sigma=torch.tensor([2.0]),
-        audio_sigma=torch.tensor([5.0]),
-        video_coords=torch.zeros(1, 3, 2, 2),
-        audio_coords=torch.zeros(1, 1, 1, 2),
-    )
-
-    torch.testing.assert_close(video_scale_shift.calls[0], torch.tensor([0.0, 2.0]))
-    torch.testing.assert_close(video_gate.calls[0], torch.tensor([5.0]))
-    torch.testing.assert_close(audio_scale_shift.calls[0], torch.tensor([5.0]))
-    torch.testing.assert_close(audio_gate.calls[0], torch.tensor([2.0]))
-    assert len(rope.output_dtypes) == 4
-    assert all(dtype is torch.float32 for dtype in rope.output_dtypes)
-
-
-def test_ltx25_static_prompt_table_is_used_without_prompt_adaln():
-    class CaptureAttention(nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.encoder_hidden_states = None
-
-        def forward(self, hidden_states, encoder_hidden_states=None, **kwargs):
-            if encoder_hidden_states is not None:
-                self.encoder_hidden_states = encoder_hidden_states.detach().clone()
-            return torch.zeros_like(hidden_states)
-
-    class ZeroOutput(nn.Module):
-        def forward(self, hidden_states):
-            return torch.zeros_like(hidden_states)
-
-    block = object.__new__(LTX2VideoTransformerBlock)
-    nn.Module.__init__(block)
-    block.video_cross_attn_adaln = True
-    block.audio_cross_attn_adaln = True
-    block.cross_attn_adaln = True
-    block.scale_shift_table = nn.Parameter(torch.zeros(9, 2))
-    block.audio_scale_shift_table = nn.Parameter(torch.zeros(9, 2))
-    block.prompt_scale_shift_table = nn.Parameter(torch.tensor([[1.0, 2.0], [3.0, 4.0]]))
-    block.audio_prompt_scale_shift_table = nn.Parameter(torch.tensor([[0.5, 1.0], [1.5, 2.0]]))
-    block.norm1 = nn.Identity()
-    block.audio_norm1 = nn.Identity()
-    block.norm2 = nn.Identity()
-    block.audio_norm2 = nn.Identity()
-    block.norm3 = nn.Identity()
-    block.audio_norm3 = nn.Identity()
-    block.attn1 = CaptureAttention()
-    block.audio_attn1 = CaptureAttention()
-    block.attn2 = CaptureAttention()
-    block.audio_attn2 = CaptureAttention()
-    block.ff = ZeroOutput()
-    block.audio_ff = ZeroOutput()
-
-    hidden_states = torch.ones(1, 1, 2)
-    audio_hidden_states = torch.ones(1, 1, 2)
-    encoder_hidden_states = torch.ones(1, 1, 2)
-    audio_encoder_hidden_states = torch.ones(1, 1, 2)
-    block(
-        hidden_states,
-        audio_hidden_states,
-        encoder_hidden_states,
-        audio_encoder_hidden_states,
-        temb=torch.zeros(1, 1, 18),
-        temb_audio=torch.zeros(1, 1, 18),
-        temb_ca_scale_shift=torch.empty(0),
-        temb_ca_audio_scale_shift=torch.empty(0),
-        temb_ca_gate=torch.empty(0),
-        temb_ca_audio_gate=torch.empty(0),
-        temb_prompt=None,
-        temb_prompt_audio=None,
-        use_a2v_cross_attention=False,
-        use_v2a_cross_attention=False,
-    )
-
-    torch.testing.assert_close(block.attn2.encoder_hidden_states, torch.tensor([[[5.0, 7.0]]]))
-    torch.testing.assert_close(block.audio_attn2.encoder_hidden_states, torch.tensor([[[3.0, 4.0]]]))
-
-
-def test_ltx_gated_attention_uses_tp_sharded_projection(monkeypatch):
-    column_calls = []
-
-    class FakeColumnParallelLinear(nn.Linear):
-        def __init__(self, input_size, output_size, *, bias=True, **kwargs):
-            column_calls.append((output_size, kwargs))
-            super().__init__(input_size, output_size, bias=bias)
-
-    class FakeRowParallelLinear(nn.Linear):
-        def __init__(self, input_size, output_size, *, bias=True, **_kwargs):
-            super().__init__(input_size, output_size, bias=bias)
-
-    class FakeParallelAttention(nn.Module):
-        def __init__(self, **_kwargs):
-            super().__init__()
-
-    monkeypatch.setattr(ltx2_transformer, "ColumnParallelLinear", FakeColumnParallelLinear)
-    monkeypatch.setattr(ltx2_transformer, "RowParallelLinear", FakeRowParallelLinear)
-    monkeypatch.setattr(ltx2_transformer, "_LTX2ParallelAttention", FakeParallelAttention)
-    monkeypatch.setattr(ltx2_transformer, "get_tensor_model_parallel_world_size", lambda: 2)
-
-    attention = ltx2_transformer.LTX2Attention(
-        query_dim=8,
-        heads=4,
-        kv_heads=4,
-        dim_head=2,
-        qk_norm="rms_norm",
-        apply_gated_attention=True,
-        pack_qkv=False,
-    )
-
-    gate_output_size, gate_kwargs = column_calls[-1]
-    assert gate_output_size == 4
-    assert gate_kwargs["gather_output"] is False
-    assert gate_kwargs["return_bias"] is False
-    assert attention.query_num_heads == 2
Author	SHA1	Message	Date
Hongsheng Liu	7ec78360bb	Merge branch 'main' into codex-split-attention-offload-guides	2026-08-15 07:00:22 +08:00
Hongsheng Liu	5569abf0a2	Update docs/design/feature/offloader/README.md Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 23:37:08 +08:00
Hongsheng Liu	10853fb4f3	Update docs/design/feature/offloader/README.md Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 23:37:01 +08:00
Hongsheng Liu	a52c19ea8b	docs: organize offloader feature designs Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 11:35:07 +08:00
Hongsheng Liu	8b61499662	docs: rename offloader guides Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 09:30:34 +08:00
Hongsheng Liu	7872663bf0	docs: split diffusion feature guides Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 08:55:41 +08:00