[Fix] Restore MammothModa2 support

Signed-off-by: hsliuustc0106 <hsliuustc0106@users.noreply.github.com>
2026-08-27 23:40:02 +08:00
344 changed files with 2302 additions and 25567 deletions
@@ -1,7 +1,4 @@
 #!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
 # vllm-omni customized version
 # Based on: https://github.com/vllm-project/ci-infra/blob/main/buildkite/bootstrap-amd.sh
 # Last synced: 2025-12-15
@@ -9,9 +6,6 @@

 set -euo pipefail

-# The bootstrap runs from the repository root; ShellCheck does not follow
-# sourced files unless invoked with -x.
-# shellcheck disable=SC1091
 source .buildkite/common/scripts/resolve_skip_ci.sh

 if [[ -z "${RUN_ALL:-}" ]]; then
@@ -69,8 +63,6 @@ upload_pipeline() {
    # Install minijinja
    ls .buildkite || buildkite-agent annotate --style error 'Please merge upstream main branch for buildkite CI'
    curl -sSfL https://github.com/mitsuhiko/minijinja/releases/download/2.3.1/minijinja-cli-installer.sh | sh
-    # Installed by the minijinja bootstrap above and only present on the CI agent.
-    # shellcheck disable=SC1091
    source /var/lib/buildkite-agent/.cargo/env

    if [[ $BUILDKITE_PIPELINE_SLUG == "fastcheck" ]]; then
@@ -98,82 +90,22 @@ upload_pipeline() {
    cd .buildkite/amd

    # Select test definition file: merge suite for main, ready suite for PRs.
-    # For debugging, DEBUG_TEST_YAML accepts a comma-separated list containing
-    # "merge" and/or "ready" (case-insensitive). Multiple suites are combined
-    # before rendering so they share one amd-build step.
+    # For debugging, DEBUG_TEST_YAML can override the selection — accepts
+    # "merge" or "ready" (case-insensitive).
    if [[ -n "${DEBUG_TEST_YAML:-}" ]]; then
-        declare -a DEBUG_TEST_SPECS=()
-        declare -A SEEN_DEBUG_TESTS=()
-        IFS=',' read -ra REQUESTED_DEBUG_TESTS <<< "${DEBUG_TEST_YAML,,}"
-
-        for requested_test in "${REQUESTED_DEBUG_TESTS[@]}"; do
-            # Permit readable values such as "ready, merge," and ignore the
-            # empty item produced by a trailing comma.
-            requested_test="${requested_test//[[:space:]]/}"
-            [[ -z "$requested_test" ]] && continue
-
-            if [[ -n "${SEEN_DEBUG_TESTS[$requested_test]:-}" ]]; then
-                echo "ERROR: duplicate DEBUG_TEST_YAML suite '$requested_test'" >&2
+        case "${DEBUG_TEST_YAML,,}" in
+            merge)
+                TEST_YAML="test-amd-merge.yml"
+                ;;
+            ready)
+                TEST_YAML="test-amd-ready.yml"
+                ;;
+            *)
+                echo "ERROR: DEBUG_TEST_YAML must be 'merge' or 'ready', got '$DEBUG_TEST_YAML'" >&2
                exit 1
-            fi
-            SEEN_DEBUG_TESTS[$requested_test]=1
-
-            case "$requested_test" in
-                ready)
-                    DEBUG_TEST_SPECS+=("READY_TESTS:test-amd-ready.yml")
-                    ;;
-                merge)
-                    DEBUG_TEST_SPECS+=("MERGE_TESTS:test-amd-merge.yml")
-                    ;;
-                *)
-                    echo "ERROR: DEBUG_TEST_YAML entries must be 'merge' or 'ready', got '$requested_test'" >&2
-                    exit 1
-                    ;;
-            esac
-        done
-
-        if [[ ${#DEBUG_TEST_SPECS[@]} -eq 0 ]]; then
-            echo "ERROR: DEBUG_TEST_YAML did not contain a test suite" >&2
-            exit 1
-        elif [[ ${#DEBUG_TEST_SPECS[@]} -eq 1 ]]; then
-            TEST_YAML="${DEBUG_TEST_SPECS[0]#*:}"
-        else
-            TEST_YAML=$(mktemp "${TMPDIR:-/tmp}/amd-debug-tests.XXXXXX.yml")
-            python - "$TEST_YAML" "${DEBUG_TEST_SPECS[@]}" <<'PY'
-import sys
-
-import yaml
-
-
-output_path, *suite_specs = sys.argv[1:]
-combined = {"env": {}, "steps": []}
-
-for suite_spec in suite_specs:
-    group_name, input_path = suite_spec.split(":", 1)
-    with open(input_path, encoding="utf-8") as test_file:
-        suite = yaml.safe_load(test_file)
-
-    for name, value in (suite.get("env") or {}).items():
-        previous = combined["env"].get(name, value)
-        if previous != value:
-            raise ValueError(
-                f"Conflicting environment value for {name}: {previous!r} != {value!r}"
-            )
-        combined["env"][name] = value
-
-    suite_steps = []
-    for entry in suite.get("steps") or []:
-        if "group" in entry:
-            suite_steps.extend(entry.get("steps") or [])
-        else:
-            suite_steps.append(entry)
-    combined["steps"].append({"group": group_name, "steps": suite_steps})
-
-with open(output_path, "w", encoding="utf-8") as output_file:
-    yaml.safe_dump(combined, output_file, sort_keys=False)
-PY
-        fi
-        echo "DEBUG_TEST_YAML override: using ${DEBUG_TEST_SPECS[*]}"
+                ;;
+        esac
+        echo "DEBUG_TEST_YAML override: using $TEST_YAML"
    elif [[ $BUILDKITE_BRANCH == "main" ]]; then
        TEST_YAML="test-amd-merge.yml"
    else
@@ -198,20 +130,19 @@ PY
            > pipeline.yaml
    )
    cat pipeline.yaml
-    if [[ "$TEST_YAML" == "${TMPDIR:-/tmp}/amd-debug-tests."*.yml ]]; then
-        rm -f -- "$TEST_YAML"
-    fi
    buildkite-agent artifact upload pipeline.yaml
    buildkite-agent pipeline upload pipeline.yaml
    exit 0
 }

 get_diff() {
-    git diff --name-only --diff-filter=ACMDR "$(git merge-base origin/main HEAD)"
+    $(git add .)
+    echo $(git diff --name-only --diff-filter=ACMDR $(git merge-base origin/main HEAD))
 }

 get_diff_main() {
-    git diff --name-only --diff-filter=ACMDR HEAD~1
+    $(git add .)
+    echo $(git diff --name-only --diff-filter=ACMDR HEAD~1)
 }

 file_diff=$(get_diff)
@@ -250,7 +181,7 @@ for file in $file_diff; do
    # First check if file matches any pattern
    matches_pattern=0
    for pattern in "${patterns[@]}"; do
-        if [[ $file == "$pattern"* ]] || [[ $file == "$pattern" ]]; then
+        if [[ $file == $pattern* ]] || [[ $file == $pattern ]]; then
            matches_pattern=1
            break
        fi
@@ -260,7 +191,7 @@ for file in $file_diff; do
    if [[ $matches_pattern -eq 1 ]]; then
        matches_ignore=0
        for ignore in "${ignore_patterns[@]}"; do
-            if [[ $file == "$ignore"* ]] || [[ $file == "$ignore" ]]; then
+            if [[ $file == $ignore* ]] || [[ $file == $ignore ]]; then
                matches_ignore=1
                break
            fi
@@ -21,7 +21,7 @@ steps:

    - label: "Simple · Diffusion Test · Shard %N/%t"
      agent_pool: mi300_1
-      parallelism: 4
+      parallelism: 2
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -71,30 +71,26 @@ steps:
      commands:
        - export VLLM_LOGGING_LEVEL=DEBUG
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        - pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
-        - pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+        - |
+          bash -c '
+            set +e
+            pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
+            EXIT1=\$?
+            pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+            EXIT2=\$?
+            exit \$((EXIT1 | EXIT2))
+          '

-    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs) · Shard 1/2"
+    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs)"
      agent_pool: mi300_4
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
-      timeout_in_minutes: 40
      grade: Blocking
      commands:
        - export VLLM_LOGGING_LEVEL=DEBUG
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        - pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py
-
-    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs) · Shard 2/2"
-      agent_pool: mi300_4
-      depends_on: amd-build
-      mirror_hardwares: [amdproduction]
-      timeout_in_minutes: 40
-      grade: Blocking
-      commands:
-        - export VLLM_LOGGING_LEVEL=DEBUG
-        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        - pytest -s -v tests/diffusion/distributed/test_ulysses_uaa_perf.py
+        - timeout 20m pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py
+        - timeout 20m pytest -s -v tests/diffusion/distributed/test_ulysses_uaa_perf.py

    # merge-only tests
    - label: "Diffusion Tensor Parallelism Test"
@@ -175,45 +171,21 @@ steps:
      grade: Blocking
      commands:
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'

    - label: "Qwen3-TTS CustomVoice E2E Test · Shard %N/%t"
      agent_pool: mi300_1
-      parallelism: 3
+      parallelism: 2
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
-      timeout_in_minutes: 80
      commands:
        - |
-          timeout --signal=TERM --kill-after=2m 80m bash -c '
+          bash -c '
            export VLLM_LOGGING_LEVEL=DEBUG
            export VLLM_WORKER_MULTIPROC_METHOD=spawn
            export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-            TESTS=(
-              "tests/e2e/online_serving/test_qwen3_tts_customvoice.py::test_default_cuda_graph_startup[async_chunk]"
-              "tests/e2e/online_serving/test_qwen3_tts_customvoice.py::test_text_to_audio_002[async_chunk]"
-              "tests/e2e/offline_inference/test_qwen3_tts_customvoice.py::test_text_to_audio_001[no_cuda_graph]"
-            )
-            TEST="$${TESTS[$$BUILDKITE_PARALLEL_JOB]}"
-            pytest -s -v "$$TEST" -m "advanced_model" --run-level "advanced_model"
-          '
-
-    # TODO: Restore this case to the blocking sharded step once its long-running
-    # Whisper validation on AMD CI is resolved.
-    - label: "Qwen3-TTS CustomVoice async_chunk Test (quarantined)"
-      agent_pool: mi300_1
-      depends_on: amd-build
-      mirror_hardwares: [amdproduction]
-      grade: NonBlocking
-      timeout_in_minutes: 80
-      commands:
-        - |
-          timeout --signal=TERM --kill-after=2m 75m bash -c '
-            export VLLM_LOGGING_LEVEL=DEBUG
-            export VLLM_WORKER_MULTIPROC_METHOD=spawn
-            export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-            pytest -s -v "tests/e2e/online_serving/test_qwen3_tts_customvoice.py::test_text_to_audio_001[async_chunk]" -m "advanced_model" --run-level "advanced_model"
+            pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model" --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
          '

    - label: "Qwen3-TTS Base E2E Test"
@@ -191,7 +191,10 @@ steps:
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
-        - pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "slow" --run-level "core_model"
+        - |
+          timeout 45m bash -c '
+            pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "slow" --run-level "core_model"
+          '

 # TODO: Bagel test on ROCm is very unstable. @tjtanaa
 # Need to debug before reneable numerical changes across large PRs
@@ -122,7 +122,6 @@ steps:
      - label: "Omni · Qwen3-Omni Test"
        source_file_dependencies:
          - tests/e2e/offline_inference/test_qwen3_omni.py
-          - tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py
          - tests/e2e/online_serving/test_qwen3_omni.py
          - tests/helpers/runtime.py
          - vllm_omni/entrypoints/openai/serving_chat.py
@@ -133,10 +132,9 @@ steps:
          - 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
-          - vllm_omni/deploy/qwen3_omni_moe_thinking.yaml
        timeout_in_minutes: 50
        commands:
-          - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+          - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
        mirror_hardwares: h100_2

      - label: "Omni · MiniCPM-o 4.5 Test"
@@ -168,7 +166,6 @@ steps:
          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
          - 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_server_vad.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
@@ -219,15 +216,3 @@ 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 · HunyuanVideo-1.5 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_hunyuan_video_15_expansion.py
-          - vllm_omni/diffusion/models/hunyuan_video/
-        commands:
-          - |
-            timeout 40m bash -c "
-              export VLLM_IMAGE_FETCH_TIMEOUT=60
-              pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
-            "
-        mirror_hardwares: h100_1
@@ -12,22 +12,16 @@ steps:
    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
    steps:

-      - label: ":full_moon: Omni · Function Test with H100 · Single-GPU"
-        timeout_in_minutes: 90
+      - label: ":full_moon: Omni · Function Test with H100"
+        timeout_in_minutes: 120
        commands:
-          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni and cards_1" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
-        mirror_hardwares: h100_1
-
-      - label: ":full_moon: Omni · Function Test with H100 · 2-GPU"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni and cards_2" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
        mirror_hardwares: h100_2

      - label: ":full_moon: Omni · MiniCPM-o 4.5 Duplex Test"
        timeout_in_minutes: 50
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni and cards_1" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni" --run-level "full_model"
        mirror_hardwares: h100_1

      - label: ":full_moon: Omni · Doc Test with L4"
@@ -37,10 +31,10 @@ steps:
          - pytest -s -v tests/examples/ -m "full_model and omni and L4" --run-level "full_model"
        mirror_hardwares: l4_4

-      - label: ":full_moon: Omni · Doc Test with H100 · 2-GPU"
+      - label: ":full_moon: Omni · Doc Test with H100"
        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/examples/ -m "full_model and omni and H100 and cards_2" --run-level "full_model"
+          - pytest -s -v tests/examples/ -m "full_model and omni and H100" --run-level "full_model"
        mirror_hardwares: h100_2

      - label: ":full_moon: Omni · Accuracy Test"
@@ -142,29 +136,15 @@ steps:
          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
        mirror_hardwares: l4_1

-      - label: ":full_moon: TTS · Perf Test · Single-GPU"
-        key: nightly-tts-performance-single
+      - label: ":full_moon: TTS · Perf Test"
+        key: nightly-tts-performance
        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_tts.json -m "H100 and cards_1"
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
-        mirror_hardwares: h100_1
-
-      - label: ":full_moon: TTS · Perf Test · 2-GPU"
-        key: nightly-tts-performance-2gpu
-        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_tts.json -m "H100 and cards_2"
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$EXIT
@@ -199,6 +179,7 @@ steps:
      build.pull_request.labels includes "nightly-test" ||
      build.pull_request.labels includes "diffusion-x2iat-test"
    steps:
+      # Use explicit file shards so single-card cases do not reserve 2+ GPUs.

      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Single-GPU"
        timeout_in_minutes: 120
@@ -206,24 +187,24 @@ steps:
          - >-
            pytest -sv
            tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and cards_1"
+            -m "full_model and diffusion and H100 and not distributed_cuda"
            --run-level "full_model"
        mirror_hardwares: h100_1

-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · 2-GPU"
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Qwen-Image"
        timeout_in_minutes: 120
        commands:
          - >-
            pytest -sv
            tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and cards_2"
+            -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 L4 · Multi-GPU"
+      - 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 and not cards_1" --run-level "full_model" --ignore=tests/e2e/accuracy
+          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
        mirror_hardwares: l4_4

      - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
@@ -254,8 +235,8 @@ steps:
          - pytest -s -v tests/e2e/accuracy/test_hidream_o1_image.py -m full_model --run-level full_model
        mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · Single-GPU"
-        key: nightly-diffusion-x2iat-performance-qwen-image-single
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image"
+        key: nightly-diffusion-x2iat-performance-qwen-image
        timeout_in_minutes: 180
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
@@ -263,23 +244,7 @@ steps:
          - 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_vllm_omni.json -m "H100 and cards_1"
-            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 · Qwen-Image · 4-GPU"
-        key: nightly-diffusion-x2iat-performance-qwen-image-4gpu
-        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_vllm_omni.json -m "H100 and cards_4"
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_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"
@@ -295,34 +260,16 @@ steps:
      build.pull_request.labels includes "diffusion-x2v-test"
    steps:

-      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · Single-GPU"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test"
        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
-        mirror_hardwares: h100_1
-
-      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · 2-GPU"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda" --run-level "full_model"
        mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · Single-GPU"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
-        mirror_hardwares: h100_1
-
-      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · 2-GPU"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
-        mirror_hardwares: h100_2
-
-      - label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m "full_model and cuda" --run-level "full_model"
+          - 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 · Doc Test"
@@ -357,49 +304,17 @@ steps:
          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
        mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2V · Perf Test · Single-GPU"
-        key: nightly-diffusion-x2v-performance-single
+      - label: ":full_moon: Diffusion X2V · Perf Test"
+        key: nightly-diffusion-x2v-performance
        timeout_in_minutes: 180
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
          - |
            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and cards_1"
-            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 X2V · Perf Test · 2-GPU"
-        key: nightly-diffusion-x2v-performance-2gpu
-        timeout_in_minutes: 180
-        commands:
-          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and cards_2"
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
            EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json -m "H100 and cards_2"
-            EXIT2=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            if [ $$EXIT1 -ne 0 ]; then exit $$EXIT1; fi
-            exit $$EXIT2
-        mirror_hardwares: h100_2
-
-      - label: ":full_moon: Diffusion X2V · Perf Test · HunyuanVideo-1.5"
-        key: nightly-diffusion-x2v-performance-hunyuanvideo15
-        timeout_in_minutes: 180
-        commands:
-          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_t2v_vllm_omni.json
-            EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_i2v_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
            EXIT2=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
@@ -433,16 +348,10 @@ steps:
          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4' --run-level "full_model"
        mirror_hardwares: l4_4

-      - label: ":full_moon: Diffusion · Distributed Test with H100 · 2-GPU"
+      - label: ":full_moon: Diffusion · Distributed Test with H100"
        timeout_in_minutes: 60
        commands:
-          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_2' --run-level "full_model"
-        mirror_hardwares: h100_2
-
-      - label: ":full_moon: Diffusion · Distributed Test with H100 · 4-GPU"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_4' --run-level "full_model"
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100' --run-level "full_model"
        mirror_hardwares: h100_4

      - label: ":full_moon: Diffusion · Offloader Test"
@@ -477,25 +386,19 @@ steps:
    depends_on:
      - nightly-omni-performance-no-async-chunk
      - nightly-omni-performance-async-chunk
-      - nightly-tts-performance-single
-      - nightly-tts-performance-2gpu
-      - nightly-diffusion-x2iat-performance-qwen-image-single
-      - nightly-diffusion-x2iat-performance-qwen-image-4gpu
-      - nightly-diffusion-x2v-performance-single
-      - nightly-diffusion-x2v-performance-2gpu
+      - nightly-tts-performance
+      - nightly-diffusion-x2iat-performance-qwen-image
+      - nightly-diffusion-x2v-performance
    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-single
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance-2gpu
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance
      - 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-diffusion-x2iat-performance-qwen-image-single
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-4gpu
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance-single
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance-2gpu
+      - 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-x2v-performance
      - 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"
@@ -205,7 +205,6 @@ steps:
          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
          - 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_server_vad.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
@@ -338,15 +337,3 @@ 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 · HunyuanVideo-1.5 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_hunyuan_video_15_expansion.py
-          - vllm_omni/diffusion/models/hunyuan_video/
-        commands:
-          - |
-            timeout 40m bash -c "
-              export VLLM_IMAGE_FETCH_TIMEOUT=60
-              pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m 'core_model and diffusion' --run-level 'core_model'
-            "
-        mirror_hardwares: h100_1
@@ -44,16 +44,10 @@ steps:
          - pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
        mirror_hardwares: h100_4

-      - label: "Reliability Test · Invalid parameters · H100 · Single-GPU"
-        timeout_in_minutes: 90
+      - label: "Reliability Test · Invalid parameters · H100"
+        timeout_in_minutes: 120
        commands:
-          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100 and cards_1"
-        mirror_hardwares: h100_1
-
-      - label: "Reliability Test · Invalid parameters · H100 · 2-GPU"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100 and cards_2"
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100"
        mirror_hardwares: h100_2

      - label: "Reliability Test · Invalid parameters · L4"
@@ -111,37 +105,27 @@ steps:
    if: build.env("NON_CRITICAL") == "1"
    steps:

-      # Qwen2.5 expansion (CUDA 4-card / online 2-card) stays on this l4_4 job.
-      - label: "Omni · L4 · Multi-GPU"
+      - label: "Omni · L4"
        timeout_in_minutes: 90
        commands:
          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py -m "slow and L4 and omni and not cards_1" --run-level "core_model"
+          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
          - >-
            pytest -s -v tests/e2e/
            --ignore=tests/e2e/online_serving/test_qwen2_5_omni_expansion.py
            --ignore=tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py
-            -m "slow and L4 and omni and not cards_1"
+            -m "slow and L4 and omni"
            --run-level "full_model"
        mirror_hardwares: l4_4

-      - label: "Omni · H100 · Single-GPU"
+      - label: "Omni · H100"
        timeout_in_minutes: 90
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and H100 and omni and cards_1"
+            tests/e2e/ -m "slow and H100 and omni"
            --run-level "full_model"
-        mirror_hardwares: h100_1
-
-      - label: "Omni · H100 · 4-GPU"
-        timeout_in_minutes: 90
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/ -m "slow and H100 and omni and cards_4"
-            --run-level "full_model"
-        mirror_hardwares: h100_4
+        mirror_hardwares: h100_2

      - label: "TTS · L4"
        timeout_in_minutes: 120
@@ -185,21 +169,12 @@ steps:
            --run-level "full_model"
        mirror_hardwares: l4_1

-      - label: "Diffusion · L4 · Single-GPU"
+      - label: "Diffusion · L4"
        timeout_in_minutes: 60
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and diffusion and L4 and cards_1"
-            --run-level "full_model"
-        mirror_hardwares: l4_1
-
-      - label: "Diffusion · L4 · Multi-GPU"
-        timeout_in_minutes: 60
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/ -m "slow and diffusion and L4 and not cards_1"
+            tests/e2e/ -m "slow and diffusion and L4"
            --run-level "full_model"
        mirror_hardwares: l4_4

@@ -208,24 +183,15 @@ steps:
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and cards_1"
+            tests/e2e/ -m "slow and diffusion and H100 and not distributed_cuda"
            --run-level "full_model"
        mirror_hardwares: h100_1

-      - label: "Diffusion · H100 · 2-GPU"
+      - label: "Diffusion · H100 · Multi-GPU"
        timeout_in_minutes: 90
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and cards_2"
-            --run-level "full_model"
-        mirror_hardwares: h100_2
-
-      - label: "Diffusion · H100 · 4-GPU"
-        timeout_in_minutes: 90
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and cards_4"
+            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
            --run-level "full_model"
        mirror_hardwares: h100_4
@@ -10,7 +10,7 @@ steps:
          DOCKER_BUILDKIT: "1"
          # Buildkite will automatically replace this with the actual commit hash
          VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
-          VLLM_VERSION: "v0.28.0"
+          VLLM_VERSION: "v0.27.0"
        priority: 100
        timeout_in_minutes: 60
        soft_fail: false
@@ -5,12 +5,7 @@ set -ex

 omni_source_dir=$(git rev-parse --show-toplevel)

-: "${VLLM_VERSION:?VLLM_VERSION must be set}"
-# Official upstream XPU image. It is published from vllm's own
-# docker/Dockerfile.xpu, which is exactly what build_vllm_base() clones and
-# builds below, so pulling it is equivalent to building the fallback base.
-upstream_base_image="vllm/vllm-openai-xpu:${VLLM_VERSION}"
-local_base_image="xpu/vllm-omni-ci-base:${VLLM_VERSION}"
+base_image_name="xpu/vllm-omni-ci-base:${VLLM_VERSION:?VLLM_VERSION must be set}"
 image_name="xpu/vllm-omni-ci:${BUILDKITE_COMMIT:?BUILDKITE_COMMIT must be set}"
 container_name="xpu_${BUILDKITE_COMMIT}_$(
    tr -dc A-Za-z0-9 </dev/urandom | head -c 10
@@ -54,36 +49,9 @@ build_vllm_base() (
        "${vllm_source_dir}"
 )

-# Resolve the vLLM base image, in priority order:
-#   1. the published upstream image for VLLM_VERSION, pulled fresh (so moving
-#      tags like nightly are refreshed rather than served stale from disk);
-#   2. a local copy of that same image, when the registry is unreachable;
-#   3. VLLM_BASE, if that image is already on disk;
-#   4. otherwise build VLLM_BASE from the matching vLLM source tag (~30min).
-# VLLM_BASE names the fallback base and defaults to ${local_base_image}; set it
-# to reuse or produce a base under a different tag.
-resolve_vllm_base() {
-    if docker pull "${upstream_base_image}"; then
-        base_image_name="${upstream_base_image}"
-        return
-    fi
-
-    # Registry unreachable, but a previous run may have left the image behind.
-    if [ -n "$(docker images -q "${upstream_base_image}")" ]; then
-        echo "WARNING: could not pull ${upstream_base_image}; using local copy" >&2
-        base_image_name="${upstream_base_image}"
-        return
-    fi
-
-    echo "WARNING: ${upstream_base_image} unavailable remotely and locally" >&2
-    base_image_name="${VLLM_BASE:-${local_base_image}}"
-    if [ -z "$(docker images -q "${base_image_name}")" ]; then
-        echo "WARNING: building ${base_image_name} from vLLM ${VLLM_VERSION} source" >&2
-        build_vllm_base
-    fi
-}
-
-resolve_vllm_base
+if [ -z "$(docker images -q "${base_image_name}")" ]; then
+    build_vllm_base
+fi

 # Try building the docker image
 docker_build "${image_name}" --build-arg "VLLM_BASE=${base_image_name}" --build-arg "VLLM_VERSION=${VLLM_VERSION}"
@@ -10,18 +10,6 @@ steps:
        -m 'core_model and npu and A3 and omni'
        --run-level 'core_model'

-  - label: "Diffusion NPU layer tests"
-    key: ready-diffusion-npu-layer-tests
-    depends_on: upload-ready-pipeline
-    mirror_hardwares: a3_npu_2
-    commands:
-      - >-
-        pytest -s -v
-        tests/diffusion/layers/test_activation.py
-        tests/diffusion/layers/test_fused_qk_norm_rope_npu.py
-        -m 'core_model and npu and A3'
-        --run-level 'core_model'
-
  - group: ":card_index_dividers: Omni Model Test"
    key: ready-omni-group
    depends_on: upload-ready-pipeline
@@ -338,7 +338,7 @@ Classify the model's **CI priority** first:

 **L2 & L3 online — same file, dual marks on the baseline smoke:** The **first / simplest** case in `test_{slug}.py` (default deploy, minimal steps, single prompt) should carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** function so L2 (`test-ready.yml`) and L3 (`test-merge.yml`) share one smoke test. Heavier deploy variants or API paths in the same file use **`advanced_model` only**. When L3 moves to nightly, migrate those heavier cases into `test_{slug}_expansion.py` with `full_model` and remove the dedicated `test-merge.yml` job (see `test_longcat_image_expansion.py`, `test_qwen_image_expansion.py`).

-**L4 design (high priority):** Combine multiple supported features (Cache-DiT, TP, USP, CFG, HSDP, CPU offload, quantization) into **few parametrized** `OmniServerParams` rows so each feature appears in at least one case without exploding GPU jobs. Shard single-GPU vs multi-GPU cases across the nightly X2I/X2V function steps (`cards_1` vs `not cards_1`).
+**L4 design (high priority):** Combine multiple supported features (Cache-DiT, TP, USP, CFG, HSDP, CPU offload, quantization) into **few parametrized** `OmniServerParams` rows so each feature appears in at least one case without exploding GPU jobs. Shard single-GPU vs multi-GPU cases across the nightly X2I/X2V function steps (1-GPU vs `distributed_cuda`).

 **L4 design (medium / low):** One or two parametrized rows covering the best quality/perf trade-off; skip perf JSON unless the model is high priority.

@@ -34,7 +34,7 @@ but the changes are correct — stage the modified files and re-commit.
 | `check-forbidden-imports` | Stdlib `re`/`base64`, pickle, Hugging Face Hub API, or direct Triton/TileLang | `import regex as re` and `pybase64`; use `vllm.transformers_utils.repo_utils`. Do **not** add the file to `allowed_files` without review |
 | `check-torch-cuda-call` | New `torch.cuda.*` call site | Use `current_omni_platform` / `OmniPlatform`; do not grow `ALLOWED_FILES` without review |
 | `check-tts-adapter-migration` | New `self._tts_model_type` branch in `serving_speech.py` | Put per-model logic in `tts_adapters/`. Lower `MAX_MODEL_TYPE_BRANCHES` when removing branches; do not raise it without review |
-| `check-mark` | New `tests/**/test_*.py` missing level/hardware mark, or direct `pytest.mark.H100` | Add `core_model`/`advanced_model`/… plus `cpu`/`cuda`/`hardware_test(`; SKU marks only via helpers |
+| `check-test-ci-coverage` | New `tests/**/test_*.py` missing level or hardware mark | Add `core_model`/`advanced_model`/… plus `cpu`/`cuda`/`hardware_test(` |
 | `mypy-3.10` | Type error on changed `vllm_omni/` files | Fix the types; model trees are excluded. Extra versions: `pre-commit run --hook-stage manual mypy-3.12` |
 | `markdownlint-cli2` | Docs/README markdown lint | Accept auto-fix under `docs/` / `recipes/` / root README |
 | `check-buildkite` | Invalid `.buildkite/*.yml` | Fix the YAML; do not grow `SKIP_FILES` without review |
@@ -52,8 +52,8 @@ Always attach markers deliberately:
  - `tts` — speech synthesis / TTS-only models (`/v1/audio/speech`, voice clone, etc.)
  - `diffusion` — generative diffusion models (image / audio / text / video from noise)
 - **Cross-cutting area** (when relevant): `parallel`, `cache`, `example`, `benchmark`
- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, …; SKU (`H100`, `L4`, …) and `cards_{n}` via `@hardware_test(...)` / `hardware_marks(...)` only — never `pytest.mark.H100` (`check-mark` rejects it)
- Optional: `slow`. Multi-card: `num_cards=N` on the helper (filter with `cards_N` / `not cards_1`)
+- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
+- Optional: `slow`, distributed markers when multi-card is required

 **Baseline smoke (L2 + L3):** The simplest e2e case per model — default deploy, minimal request — should usually carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** test function so `test-ready.yml` and `test-merge.yml` share one test. `send_*_request` picks validation depth from `--run-level`. References: `test_voxcpm2_tts.py::test_text_to_audio_001`, `test_qwen3_tts_customvoice.py::test_text_to_audio_001`. Heavier scenarios use **`advanced_model` only**; L4 expansion uses **`full_model`**.

@@ -274,7 +274,7 @@ def test_text_to_video_001(omni_server, online_client) -> None:

 **3. Cross-cutting rules**

- New `.py` files need the Omni SPDX header (`Copyright contributors to the vLLM-Omni project`, not `vLLM project`). `tests/` may keep stdlib `re`/`base64`; pickle is still banned unless the file is already on the pickle allowlist. Every collected `tests/**/test_*.py` needs a CI **level** mark and a **hardware** platform mark/helper (`check-mark` runs locally; GHA skips it). Do not write `pytest.mark.H100` / `pytest.mark.L4`; use the helpers so `cards_{n}` is attached. Do not grow `allowed_files` to land a test. Policy: [docs/contributing/README.md](../../../docs/contributing/README.md#linting).
+- New `.py` files need the Omni SPDX header (`Copyright contributors to the vLLM-Omni project`, not `vLLM project`). `tests/` may keep stdlib `re`/`base64`; pickle is still banned unless the file is already on the pickle allowlist. Every collected `tests/**/test_*.py` needs a CI **level** mark and a **hardware** mark/helper (`check-test-ci-coverage` runs locally; GHA skips it). Do not grow `allowed_files` to land a test. Policy: [docs/contributing/README.md](../../../docs/contributing/README.md#linting).
 - Reuse existing fixtures for the chosen scenario; do not mix “online client” assumptions into offline `OmniRunner` tests without a clear reason.
 - Avoid external network dependency in assertions unless the scenario is explicitly “online serving” or doc examples that require a model hub (then align with CI secrets/cache).
 - Keep **one test function = one intent** (one modality combo, one endpoint contract, or one acceleration combo).
@@ -79,7 +79,7 @@ E2E (L2+) should not rely on mocks unless documenting a rare exception; prefer r
   - `full_model` — L4 nightly (`test-nightly.yml`); some expansion tests still carry both `advanced_model` and `full_model` during migration
 2. **Model type** (pick one): `omni`, `tts`, or `diffusion`
 3. **Cross-cutting** when relevant: `parallel`, `cache`, `example`, `benchmark`
-4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `cards_1`, …
+4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
 5. Multi-card: `@hardware_test(...)` in `tests/helpers/mark.py`

 ## Command Templates
@@ -244,7 +244,7 @@ pytest -s -v dfx/reliability/invalid_param_test/test_invalid_image_generation.py

 ```bash
 cd tests
-pytest -s -v -m "core_model and L4 and not cards_1" --run-level=core_model
+pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
 ```

 ### Concrete e2e paths (common in-tree)
@@ -27,4 +27,4 @@ jobs:
        # Don't run historical-debt hooks against the whole tree in CI yet.
        # They still run locally on changed files.
        env:
-          SKIP: check-mark,markdownlint-cli2,shellcheck,check-spdx-header,mypy-3.10
+          SKIP: check-test-ci-coverage,markdownlint-cli2,shellcheck,check-spdx-header,mypy-3.10
@@ -105,8 +105,8 @@ repos:
      <<: *mypy_common
      stages: [manual]

-    - id: check-mark
-      name: Ensure test files have CI marks (no direct SKU)
+    - id: check-test-ci-coverage
+      name: Ensure test files have pytestmarks
      entry: python tools/pre_commit/check_test_marks.py
      language: python
      types: [python]
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.28.0
+ARG VLLM_ASCEND_TAG=v0.27.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.28.0-a3
+ARG VLLM_ASCEND_TAG=v0.27.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.28.0
+ARG VLLM_ASCEND_TAG=v0.27.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.28.0-a3
+ARG VLLM_ASCEND_TAG=v0.27.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -1,33 +1,140 @@
-# Target vLLM version: v0.28.0. Keep VLLM_VERSION aligned with the target vLLM
-# release on every rebase.
-#
-# This file layers vLLM-Omni on top of the published upstream XPU image, which
-# already ships a self-consistent XPU runtime (torch-xpu, oneCCL/UCX+NIXL, the
-# pinned UMD/compute-runtime stack, and vLLM itself) built from
-# https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile.xpu. Docker
-# pulls that base automatically, so there is nothing to build here beyond the
-# Omni layers. Mirrors how docker/Dockerfile.ci consumes vllm/vllm-openai.
-#
-# If the tag does not exist yet (targeting an unreleased vLLM commit), build the
-# base from upstream's own Dockerfile and point VLLM_BASE at the result:
-#
-#   git clone --depth 1 --branch $VLLM_VERSION https://github.com/vllm-project/vllm /tmp/vllm
-#   docker build -t vllm-openai-xpu:local --target vllm-openai \
-#       -f /tmp/vllm/docker/Dockerfile.xpu /tmp/vllm
-#   docker build -f docker/Dockerfile.xpu --build-arg VLLM_BASE=vllm-openai-xpu:local .
-#
-# .buildkite/intel/scripts/run-xpu-test.sh automates that pull-then-build fallback.
-ARG VLLM_BASE_IMAGE=vllm/vllm-openai-xpu
-ARG VLLM_VERSION=v0.28.0
-ARG VLLM_BASE=${VLLM_BASE_IMAGE}:${VLLM_VERSION}
+# Argument to configure vllm base image if pre-built
+ARG VLLM_BASE=vllm-base
+
+FROM intel/deep-learning-essentials:2025.3.2-0-devel-ubuntu24.04 AS vllm-base
+
+WORKDIR /workspace/
+
+ARG PYTHON_VERSION=3.12
+ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/xpu"
+
+RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
+    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list
+
+RUN apt clean && apt-get update -y && \
+    apt-get install -y --no-install-recommends --fix-missing \
+    curl \
+    ffmpeg \
+    git \
+    libsndfile1 \
+    libsm6 \
+    libxext6 \
+    libgl1 \
+    lsb-release \
+    libaio-dev \
+    numactl \
+    wget \
+    vim \
+    python3.12 \
+    python3.12-dev \
+    python3-pip
+
+# Add oneAPI repo, pin oneAPI to 2025.3, then install pinned packages in one layer.
+RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
+    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list && \
+    printf '%s\n' \
+    'Package: intel-oneapi-* intel-deep-learning-essentials* intel-pti*' \
+    'Pin: version 2025.3*' \
+    'Pin-Priority: 1001' \
+    > /etc/apt/preferences.d/oneapi-2025.3.pref && \
+    apt-get update -y && \
+    apt-get install -y --no-install-recommends \
+    intel-oneapi-compiler-dpcpp-cpp-2025.3 \
+    intel-oneapi-mkl-devel-2025.3 \
+    intel-oneapi-dnnl-devel-2025.3 && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install UMD with fixed version
+RUN mkdir neo && \
+    cd neo && \
+    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.24.8/intel-igc-core-2_2.24.8+20344_amd64.deb && \
+    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.24.8/intel-igc-opencl-2_2.24.8+20344_amd64.deb && \
+    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/intel-ocloc_25.48.36300.8-0_amd64.deb && \
+    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/intel-opencl-icd_25.48.36300.8-0_amd64.deb && \
+    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/libigdgmm12_22.8.2_amd64.deb && \
+    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/libze-intel-gpu1_25.48.36300.8-0_amd64.deb && \
+    wget https://github.com/oneapi-src/level-zero/releases/download/v1.26.0/level-zero_1.26.0+u24.04_amd64.deb && \
+    dpkg -i *.deb && \
+    cd .. && \
+    rm -rf neo
+
+ENV PATH="/root/.local/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+
+# This oneccl contains the BMG support which is not the case for default version of oneapi 2025.3.
+ARG ONECCL_INSTALLER="intel-oneccl-2021.15.9.14_offline.sh"
+RUN wget "https://github.com/uxlfoundation/oneCCL/releases/download/2021.15.9/${ONECCL_INSTALLER}" && \
+    bash "${ONECCL_INSTALLER}" -a --silent --eula accept && \
+    rm "${ONECCL_INSTALLER}" && \
+    echo "source /opt/intel/oneapi/setvars.sh --force" >> /root/.bashrc && \
+    echo "source /opt/intel/oneapi/ccl/2021.15/env/vars.sh --force" >> /root/.bashrc
+RUN rm -f /opt/intel/oneapi/ccl/latest && \
+    ln -s /opt/intel/oneapi/ccl/2021.15 /opt/intel/oneapi/ccl/latest
+
+SHELL ["bash", "-c"]
+CMD ["bash", "-c", "source /root/.bashrc && exec bash"]
+
+WORKDIR /workspace/
+
+ENV UV_HTTP_TIMEOUT=500
+
+# Configure package index for XPU
+ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
+ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
+ENV UV_INDEX_STRATEGY="unsafe-best-match"
+ENV UV_LINK_MODE="copy"
+
+ARG VLLM_VERSION=v0.27.0
+RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
+WORKDIR /workspace/vllm
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip install --upgrade pip && \
+    uv pip install -r requirements/xpu.txt && \
+    uv pip install setuptools_scm &&\
+    uv pip install grpcio-tools protobuf nanobind && \
+    source /opt/intel/oneapi/setvars.sh --force && \
+    source /opt/intel/oneapi/ccl/2021.15/env/vars.sh --force && \
+    export CMAKE_PREFIX_PATH="$(python3 -c 'import site; print(site.getsitepackages()[0])'):${CMAKE_PREFIX_PATH}"
+
+ENV LD_LIBRARY_PATH="/opt/intel/oneapi/ccl/2021.15/lib:$LD_LIBRARY_PATH:/usr/local/lib/"
+
+ENV VLLM_TARGET_DEVICE=xpu
+ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip uninstall triton triton-xpu && \
+    uv pip install triton-xpu==3.7.2 && \
+    uv pip install --no-build-isolation --no-deps .
+
+CMD ["/bin/bash"]
+
+FROM vllm-base AS vllm-openai
+
+# install nixl from source code
+RUN uv pip install nixl
+
+# install additional dependencies for openai api server
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip install accelerate hf_transfer pytest pytest_asyncio lm_eval[api] modelscope
+
+# install development dependencies (for testing)
+RUN uv pip install -e tests/vllm_test_utils
+
+# ensure vllm is properly installed
+RUN python -c "import vllm, inspect; print(vllm.__file__)"
+RUN uv pip show vllm
+
+CMD ["/bin/bash"]
+
+ENTRYPOINT []

 FROM ${VLLM_BASE} AS vllm-omni

-# The base image ships ENTRYPOINT ["vllm", "serve"]; clear it so this stage stays
-# usable as a plain shell/test container regardless of which VLLM_BASE was used.
-ENTRYPOINT []
-CMD ["/bin/bash"]
-
 RUN apt-get update && \
    apt-get install -y espeak-ng ffmpeg git jq && \
    apt-get clean && \
@@ -49,7 +49,7 @@ vLLM-Omni's `pre-commit` hooks will now run automatically every time you commit.
    `--all-files` run stays green while historical debt is cleaned up. The
    current `SKIP` list in
    [`.github/workflows/pre-commit.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.github/workflows/pre-commit.yml)
-    is `check-mark`, `markdownlint-cli2`, `shellcheck`,
+    is `check-test-ci-coverage`, `markdownlint-cli2`, `shellcheck`,
    `check-spdx-header`, and `mypy-3.10`. Those hooks still run on **your
    commit** for changed files. A passing GitHub pre-commit check does not mean
    they passed locally. Hooks **not** on that list (forbidden imports,
@@ -66,7 +66,7 @@ already there). `check-pickle-imports` is gone: pickle is now one rule inside
 | `markdownlint-cli2` | Markdown in `docs/`, `recipes/`, `README.md`, `CONTRIBUTING.md` (not `.claude/` / `.cursor/` / `CLAUDE.md`) | skipped |
 | `mypy-3.10` | Type-check changed `vllm_omni/` files (model trees excluded). `tests/` uses `--follow-imports skip` | skipped |
 | `mypy-3.11` / `3.12` / `3.13` | Same checker, extra Python versions | not installed; `pre-commit run --hook-stage manual mypy-3.12` |
-| `check-mark` | Every `tests/**/test_*.py` has a CI level mark and a hardware platform mark or helper; no direct `pytest.mark.H100` / SKU | skipped |
+| `check-test-ci-coverage` | Every `tests/**/test_*.py` has a CI level mark and a hardware mark/helper | skipped |
 | `check-tts-adapter-migration` | `self._tts_model_type` branches in `serving_speech.py` must not increase | runs |
 | `shellcheck` | `*.sh` quoting / undefined vars | skipped |
 | `check-spdx-header` | Omni SPDX header on `.py` / `.pyi` / `.sh` / `.rs` / `.proto` | skipped |
@@ -144,13 +144,11 @@ pre-commit run --hook-stage manual mypy-3.12

 #### Test CI marks

-`check-mark` requires each collected test module under `tests/` to
+`check-test-ci-coverage` requires each collected test module under `tests/` to
 have at least one CI **level** mark (`core_model`, `advanced_model`,
-`full_model`, `local_model`, or `slow`) and a **hardware** platform mark
-(`cpu`, `cuda`, …) or helper (`hardware_test(` / `hardware_marks(`). SKU marks
-(`H100`, `L4`, …) must come from those helpers so `cards_{n}` is attached;
-direct `pytest.mark.H100` is rejected. GitHub Actions skips this hook; local
-commit does not. See the
+`full_model`, `local_model`, or `slow`) and a **hardware** mark (`cpu`, `cuda`,
+`H100`, …) or helper (`hardware_test(` / `hardware_marks(`). GitHub Actions
+skips this hook; local commit does not. See the
 [test writing guide](./ci/test_writing_guide.md).

 #### TTS adapter ratchet
@@ -402,7 +402,7 @@ loads the model once per mode.
 Before splitting, confirm each half actually collects at least one test under the job's forwarded filters — pytest exits with code 5 ("no tests collected") if a half is empty, which the script reports as a failure, red-ing a job that used to pass as one combined invocation.

 Also check the tests' `num_cards` against the job's `mirror_hardwares` preset.
-`_cuda_marks` turns `num_cards > 1` into `skipif(device_count() < num_cards)`, so a
+`cuda_marks` turns `num_cards > 1` into `skipif(device_count() < num_cards)`, so a
 test asking for more GPUs than the preset provides is silently skipped and its
 coverage artifact comes back empty. Splitting such a job produces a file that
 looks fine and measures nothing — "Diffusion · Z Image Test" is the current
@@ -81,8 +81,8 @@ Supported form:

 | Form | Example | Effect |
 | ---- | ------- | ------ |
-| Array (single platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "diffusion"]` | Hardware marks from `hardware_marks(...)`; `num_cards` adds `cards_{n}` (including `cards_1` when omitted). String entries become extra pytest marks. |
-| Array (multi-platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100", "rocm": "MI325"}, "num_cards": 2}}, "full_model", "omni"]` | Same as above, but declares **multiple platforms** on one item. All platforms must share the same `num_cards` (int or identical dict values). Different counts need `@hardware_test` or one `hardware_marks()` per `pytest.param`. Filter examples: `-m "full_model and H100 and cards_2 and cuda"`, `-m "full_model and MI325 and cards_2 and rocm"`. |
+| Array (single platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "diffusion"]` | Hardware marks from `hardware_marks(...)`; `num_cards` > 1 adds `distributed_*` (+ CUDA `skipif_cuda` when applicable). String entries become extra pytest marks. |
+| Array (multi-platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100", "rocm": "MI325", "npu": "A2"}, "num_cards": {"cuda": 2, "rocm": 2, "npu": 1}}}, "full_model", "omni"]` | Same as above, but declares **multiple platforms** in one case. Filter examples: `-m "full_model and H100 and cuda"`, `-m "full_model and MI325 and rocm"`. |

 Recommended for L4 perf cases:

@@ -157,7 +157,7 @@ pytest -s -v tests/dfx/perf/scripts/run_benchmark.py \
 ```

 <!-- markdownlint-disable-next-line MD051 -->
-See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `cards_1`, …).
+See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `distributed_cuda`, …).

 #### `server_params` Configuration

@@ -88,7 +88,7 @@ Failed jobs: 1/2
    ```bash
    cd tests
    pytest -s -v test_xxxx.py --run-level=core_model
-    pytest -s -v -m "core_model and L4 and not cards_1" --run-level=core_model
+    pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
    ```

 === "L3 level"
@@ -119,7 +119,7 @@ Failed jobs: 1/2

    ```bash
    pytest -s -v test_xxxx.py --run-level=advanced_model
-    pytest -s -v -m "advanced_model and L4 and not cards_1" --run-level=advanced_model
+    pytest -s -v -m "advanced_model and distributed_cuda and L4" --run-level=advanced_model
    ```

 === "L4 level"
@@ -153,12 +153,12 @@ Failed jobs: 1/2
    ```bash
    cd tests
    pytest -s -v test_xxxx.py --run-level=full_model
-    pytest -s -v -m "full_model and L4 and not cards_1" --run-level=full_model
+    pytest -s -v -m "full_model and distributed_cuda and L4" --run-level=full_model
    pytest -s -v -m "full_model and (omni or tts) and H100" --run-level=full_model
    ```
    If you only want to run specific test cases on a particular platform, you can use:
    ```bash
-    pytest -s -v -m "full_model and L4 and not cards_1"  --run-level=full_model
+    pytest -s -v -m "full_model and distributed_cuda and L4"  --run-level=full_model
    ```
    Note: ``run_benchmark.py`` and ``run_diffusion_benchmark.py`` accept an optional ``--test-config-file``. If omitted, each loads every ``*.json`` under ``tests/dfx/perf/tests/`` (omni/tts vs diffusion split by ``is_diffusion_perf_config``) and pytest ``-m`` filters by each case's JSON ``mark``:
    ```bash
@@ -48,12 +48,17 @@ Defined in `pyproject.toml`:
 | `MI325`            | Tests that require MI325 GPU (AMD/ROCm) *                                                               |
 | `A2`               | Tests that require A2 NPU *                                                                             |
 | `A3`               | Tests that require A3 NPU *                                                                             |
-| `cards_{n}`        | Tests that require *n* accelerator cards (`cards_1` … `cards_8`); auto-added from `num_cards` *         |
+| `distributed_cuda` | Tests that require multi cards on CUDA platform *                                                       |
+| `distributed_rocm` | Tests that require multi cards on ROCm platform  *                                                      |
+| `distributed_npu`  | Tests that require multi cards on NPU platform  *                                                       |
+| `skipif_cuda`      | Skip if the num of CUDA cards is less than the required *                                               |
+| `skipif_rocm`      | Skip if the num of ROCm cards is less than the required *                                               |
+| `skipif_npu`       | Skip if the num of NPU cards is less than the required *                                                |
 | `slow`             | Slow tests (may skip in quick CI)                                                                       |
 | `benchmark`        | Benchmark tests (decorator on runner test functions; perf JSON uses `full_model` + type marker instead) |
 | `local_model`      | Tests requiring local / non-HF-hub model weights                                                        |

-\* Means those markers are auto-added by `@hardware_test` (parametrization decorator) or `hardware_marks` (only returning the list of marks for flexibility). Do **not** write `pytest.mark.H100` / `pytest.mark.L4` (or other SKU names) by hand: `check-mark` rejects that so `cards_{n}` cannot be skipped.
+\* Means those markers are auto-added by `@hardware_test` (parametrization decorator) or `hardware_marks` (only returning the list of marks for flexibility).

 #### Example usage for markers

@@ -76,23 +81,25 @@ def test_video_to_audio()
 This decorator is intended to make hardware-aware, cross-platform test authoring easier and more robust for CI/CD environments. The `hardware_test` decorator in `vllm-omni/tests/helpers/mark.py` performs the following actions:

 1. **Applies platform and resource markers**  
-   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `H200`, `B200`, `MI325`, `B60`, `A2`, `A3`).
+   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `MI325`, `B60`, `A2`, `A3`).
   ```python
   @pytest.mark.cuda
   @pytest.mark.L4
   ```
-2. **Handles card-count filtering**  
-   Adds a filterable marker `cards_{n}` (including `cards_1` when `num_cards` is omitted / 1). Use this with the SKU and platform, e.g. `-m "H100 and cards_4 and cuda"` vs `-m "H100 and cards_2 and cuda"`. Any multi-card CUDA test is `-m "cuda and not cards_1"`.
-
-   When `res` lists more than one platform **and** `num_cards` differs by platform, `@hardware_test` collects **one variant per platform** so those `cards_{n}` marks do not stack. `-m "H100 and cards_4 and cuda"` then selects only the CUDA/H100 4-card variant, not a ROCm 2-card sibling. The same count on every platform stays a single item.
+2. **Handles multi-card (distributed) scenarios**  
+   For tests requiring multiple cards, it automatically adds distributed markers such as `distributed_cuda`, `distributed_rocm`, or `distributed_npu`.
+   ```python
+   @pytest.mark.distributed_cuda(num_cards=num_cards)
+   ```
 3. **Supports flexible card requirements**  
   Accepts `num_cards` as either a single integer for all platforms or as a dictionary with per-platform values. If not specified, defaults to 1 card per platform.

 4. **Integrates resource validation**  
-   On CUDA and XPU, when `num_cards > 1`, attaches pytest's built-in `skipif` if the machine has fewer devices than requested. This is not a named `skipif_*` marker and is not used for `-m` filtering.
+   On CUDA, adds a skip marker (`skipif_cuda`) if the system does not have the required number of devices.
+   Support for `skipif_rocm` and `skipif_npu` will be implemented later.

 5. **Works with pytest filtering**  
-   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., `-m "H100 and cards_2"`, `-m "L4 and not cards_1"`).
+   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., `-m "distributed_cuda and L4"`).

 ##### Example usage for decorator

@@ -110,14 +117,13 @@ This decorator is intended to make hardware-aware, cross-platform test authoring
        num_cards=2,
    )
    ```
- `res` must be a dict. SKU values come from `pyproject.toml` markers tagged `[hardware-resource]` plus the platform key (`[cuda]`, `[rocm]`, `[xpu]`, `[npu]`, `[musa]`).
+- `res` must be a dict; supported resources: CUDA (L4/H100), ROCm (MI325), XPU (B60), MUSA (S5000), NPU (A2/A3)
 - `num_cards` can be int (all platforms) or dict (per platform); defaults to 1 when missing
+- Distributed markers (`distributed_cuda`, `distributed_rocm`, `distributed_npu`) are auto-added for multi-card cases
 - Filtering examples:
-    - CUDA H100 4-card: `pytest -m "H100 and cards_4 and cuda"`
-    - CUDA H100 2-card: `pytest -m "H100 and cards_2 and cuda"`
-    - CUDA multi-card (any count): `pytest -m "L4 and not cards_1"`
-    - ROCm multi-card: `pytest -m "MI325 and not cards_1"`
-    - NPU multi-card: `pytest -m "npu and not cards_1"`
+    - CUDA only: `pytest -m "distributed_cuda and L4"`
+    - ROCm only: `pytest -m "distributed_rocm and MI325"`
+    - NPU only: `pytest -m "distributed_npu"`

 #### Function: `hardware_marks`

@@ -173,30 +179,37 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
   [tool.pytest.ini_options]
   markers = [
       # ... existing markers ...
-       "tpu: [hardware-platform] Tests that require TPU device",
-       # Add [gpu] next to [hardware-platform] if this platform should also get pytest.mark.gpu.
-       "TPU_V3: [hardware-resource] [tpu] Tests that require TPU v3 hardware",
-       "cards_1: [hardware-cards] Tests that require 1 accelerator card",
-       "cards_2: [hardware-cards] Tests that require 2 accelerator cards",
+       "tpu: Tests that require TPU device",
+       "TPU_V3: Tests that require TPU v3 hardware",
+       "distributed_tpu: Tests that require multiple TPU devices",
   ]
   ```
-   `check-mark` treats `[hardware-platform]` names (including `cpu`/`gpu`) as allowed `pytest.mark.<name>` values. `hardware_test(res=...)` still rejects `cpu`/`gpu` as `res` keys.
 2. **Implement a marker construction function for your platform** in `vllm-omni/tests/helpers/mark.py`:
   ```python
   # In vllm-omni/tests/helpers/mark.py

-   def _tpu_marks(*, res: str, num_cards: int):
-       _require_sku("tpu", res)
-       return [pytest.mark.tpu, getattr(pytest.mark, res), _cards_mark(num_cards)]
+   def tpu_marks(*, res: str, num_cards: int):
+       test_platform = pytest.mark.tpu
+       if res == "TPU_V3":
+           test_resource = pytest.mark.TPU_V3
+       else:
+           raise ValueError(
+               f"Invalid TPU resource type: {res}. Supported: TPU_V3")
+
+       if num_cards == 1:
+           return [test_platform, test_resource]
+       else:
+           test_distributed = pytest.mark.distributed_tpu(num_cards=num_cards)
+           # Optionally: add skipif_tpu when implemented
+           return [test_platform, test_resource, test_distributed]
   ```
-3. **Register the builder** in `_marks_for_platform`:
+3. **Update `hardware_marks` to recognize your new platform**:
+    In the relevant place (see the `hardware_marks` implementation), add:
    ```python
-    builders = {
-        # ... existing platforms ...
-        "tpu": _tpu_marks,
-    }
+    if platform == "tpu":
+        marks = tpu_marks(res=resource, num_cards=cards)
    ```
-    (`hardware_test` / `hardware_marks` both go through `_marks_for_platform`.)
+    (`hardware_test` calls `hardware_marks` internally, so both will pick up the change.)
 4. **(Recommended) Add a test using your new markers**:
   ```python
   @hardware_test(
@@ -210,11 +223,11 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
 **Summary**:  

 - Add pytest markers for your new platform/resources  
- Implement a private marker function (`_xxx_marks`)
- Register it in `_marks_for_platform`
- You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers and `cards_{n}` filtering.
+- Implement a marker function (`xxx_marks`)  
+- Plug into `hardware_marks`  
+- You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers, distribution, and isolation!

-See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`_cuda_marks`, `_rocm_marks`, `_npu_marks`).
+See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`cuda_marks`, `rocm_marks`, `npu_marks`).

 ## Test case style

@@ -438,7 +438,7 @@ selects the scheduler and worker family:
 Key topology fields belong to `StagePipelineConfig`:

 | Field | Description |
-| ------- | ------------- |
+|-------|-------------|
 | `model_stage` | Logical stage name (`ar_stage`, `decoder`, etc.). |
 | `execution_type` | Runtime family for this stage. |
 | `input_sources` | Upstream stage IDs that provide input. |
@@ -960,7 +960,7 @@ Common failures and fixes:
 | `check-forbidden-imports` | Stdlib `re`/`base64`, pickle, Hugging Face Hub API, or direct Triton/TileLang | `import regex as re` and `pybase64`; do not grow the allowlist without review |
 | `check-torch-cuda-call` | New `torch.cuda.*` call site | Use `current_omni_platform`; do not grow `ALLOWED_FILES` without review |
 | `check-tts-adapter-migration` | New `_tts_model_type` branch in `serving_speech.py` | Put logic in `tts_adapters/`; lower `MAX_MODEL_TYPE_BRANCHES` when removing branches |
-| `check-mark` | Test file missing level/hardware mark, or direct `pytest.mark.H100` | Add `core_model`/… plus `cpu`/`cuda`/`hardware_test(`; SKU marks only via helpers |
+| `check-test-ci-coverage` | Test file missing level or hardware mark | Add `core_model`/… plus `cpu`/`cuda`/`hardware_test(` |
 | `shellcheck` | Native `shellcheck` missing, or script warning | Install via apt/dnf/brew (or `shellcheck.exe` on Windows). See [Linting](../README.md#linting) |
 | `mypy-3.10` / `markdownlint-cli2` / `check-buildkite` | Types, docs markdown, or Buildkite YAML | See [Linting](../README.md#linting) |

@@ -26,8 +26,6 @@ Legend: ✅ supported, ⚠️ compatibility path or limited validation, ❌ unsu
 | **TP > 1** | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. |
 | **HSDP** | ❌ Rejected to avoid double-sharding parameters. | ⚠️ Limited end-to-end coverage. |
 | **Per-tensor online FP8 linears** | ✅ Ordinary loader finalizes weights and scales before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
-| **Online INT8 linears** | ✅ Ordinary loader finalizes the int8 weight (contiguous or transposed view) and fp32 scale before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
-| **Online MXFP8 linears** | ✅ Ordinary loader finalizes the fp8 weight (contiguous) and e8m0 block scale (contiguous or transposed view) before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
 | **Other online quantization methods** | ❌ Rejected until runtime packing and scale layouts are validated. | ⚠️ Allowed through the ordinary loader; validation is method-specific. |
 | **Model-level or standard layerwise CPU offload** | ❌ Disabled because DLO takes priority. | ❌ Disabled because DLO takes priority. |
 | **Resident leading layers** | ❌ Rejected. | ✅ Requires eligible resident paths in the model's `OffloadPlan`. |
@@ -440,15 +438,11 @@ requirements.

 Direct checkpoint mmap can back either transfer path. It is currently limited
 to proven TP1, non-HSDP, non-online-quantized layouts. Other layouts use the
-ordinary loader. Per-tensor online FP8, online INT8, and online MXFP8 linears
-can use DLO AllGather after the ordinary loader finalizes their runtime
-weights and scales; DLO then shards and reconstructs those tensors with their
-recorded layouts (they all keep plain 1-byte dtypes over ordinary strided
-views: online INT8 has an int8 weight plus fp32 scale, online MXFP8 has an
-fp8 weight plus e8m0 block scale, each with a contiguous or transposed-view
-finalized tensor, all covered by the same physical-order packing as online
-FP8). Other online methods must use `--dlo-no-use-allgather` or disable
-online quantization until their runtime layouts are validated.
+ordinary loader. Per-tensor online FP8 linears can use DLO AllGather after the
+ordinary loader finalizes their runtime weights and scales; DLO then shards and
+reconstructs those tensors with their recorded layouts. Other online methods
+must use `--dlo-no-use-allgather` or disable online quantization until their
+runtime layouts are validated.

 The Host Weight Runtime representation, publication, and no-AllGather consumer
 contracts are merged; see
@@ -465,8 +459,8 @@ Current source-level validation includes:
 - HSDP + DLO without AllGather acceptance at configuration level;
 - loader preflight fallback for TP, HSDP, online quantization, unknown custom
  loaders, missing keys, and shape/dtype mismatches;
- ordinary-loader fallback for per-tensor online FP8, online INT8, and online
-  MXFP8 linears followed by DLO sharding of finalized weights and scales;
+- ordinary-loader fallback for per-tensor online FP8 linears followed by DLO
+  sharding of finalized weights and scales;
 - exact loader-to-backend plan transfer and ordinary-loader fallback;
 - ordered publication hashing, overlapped durability, unordered parallel
  checksum fallback, and node-local source-digest reuse/invalidation;
@@ -22,60 +22,59 @@ We use **Qwen-Image** as the reference implementation for decode parallel, and *

 **VAE Patch Parallelism** is an acceleration technique for both **encoding** and **decoding**. Instead of processing the entire tensor at once, the tensor is:

- Split into multiple spatial tiles
+ Split into multiple spatial tiles

- Distributed across multiple ranks
+ Distributed across multiple ranks

- Encoded/Decoded in parallel
+ Encoded/Decoded in parallel

- Merged to reconstruct the final output
+ Merged to reconstruct the final output

 This approach:

- Distributes computation across multiple devices
+ Distributes computation across multiple devices

- Reduces peak memory usage per device
+ Reduces peak memory usage per device

- Accelerates encoding/decoding latency
+ Accelerates encoding/decoding latency

 ### When to Use Encode vs Decode Parallel

-| Operation           | Use Case                     | Example                           |
-| ------------------- | ---------------------------- | --------------------------------- |
-| **Decode Parallel** | Text-to-Image, Text-to-Video | Latent → Image/Video              |
-| **Encode Parallel** | Image-to-Video (I2V)         | Image → Latent (for conditioning) |
+| Operation | Use Case | Example |
+|-----------|----------|---------|
+| **Decode Parallel** | Text-to-Image, Text-to-Video | Latent → Image/Video |
+| **Encode Parallel** | Image-to-Video (I2V) | Image → Latent (for conditioning) |

 ### Architecture
-
 We introduce **DistributedVaeExecutor** as the core component responsible for distributed VAE encoding/decoding.

 The executor is model-agnostic and accepts three function parameters:

- split – Partition the latent into tiles
+ split – Partition the latent into tiles

- exec – Decode a single tile
+ exec – Decode a single tile

- merge – Combine decoded tiles into the final output
+ merge – Combine decoded tiles into the final output

 #### Execution Flow

- Call split(z) to generate a list of TileTask and a GridSpec
+ Call split(z) to generate a list of TileTask and a GridSpec

- Dispatch tasks across ranks using workload-based balancing
+ Dispatch tasks across ranks using workload-based balancing

- Each rank executes exec(task) on its assigned tiles
+ Each rank executes exec(task) on its assigned tiles

- Gather decoded tile results to rank 0
+ Gather decoded tile results to rank 0

- Rank 0 performs merge(...)
+ Rank 0 performs merge(...)

- (Optional) Broadcast final result to all ranks
+ (Optional) Broadcast final result to all ranks

 This design separates:

- Distributed execution logic
+ Distributed execution logic

- Model-specific tiling and merging logic
+ Model-specific tiling and merging logic

 #### Why split / exec / merge is necessary?

@@ -83,23 +82,23 @@ The latent tensor cannot be arbitrarily partitioned.

 During decoding:

- Each output pixel may depend on neighboring pixels
+ Each output pixel may depend on neighboring pixels

- The receptive field is model-dependent
+ The receptive field is model-dependent

 Therefore:

- Tiles must include overlap
+ Tiles must include overlap

- Merge must perform blending to avoid seams
+ Merge must perform blending to avoid seams

 ## Step-by-Step Implementation (Decode)

 ### Step 1: Implement DistributedAutoencoderKLQwenImage
-
 `QwenImagePipeline` use `AutoencoderKLQwenImage` for vae, so implement a distributed version:

-```python
+
+```
 class DistributedAutoencoderKLQwenImage(AutoencoderKLQwenImage, DistributedVaeMixin):
    @classmethod
    def from_pretrained(cls, *args: Any, **kwargs: Any):
@@ -107,35 +106,28 @@ class DistributedAutoencoderKLQwenImage(AutoencoderKLQwenImage, DistributedVaeMi
        model.init_distributed()
        return model
 ```
-
 **Key points**:
-
- Inherit both AutoencoderKLQwenImage and DistributedVaeMixin
- Call init_distributed() after loading weights
+ Inherit both AutoencoderKLQwenImage and DistributedVaeMixin
+ Call init_distributed() after loading weights

 ### Step 2: Implement split/exec/merge
-
 Reuse `AutoencoderKLQwenImage.tiled_decode` logic and divide it into three stages. And we need return tiles with `GridSpec` and `TileTask`:
-
-```python
+```
 class GridSpec:
    split_dims: tuple[int, ...]  # Tensor dimensions being split (e.g., (2, 3) for (B, C, H, W))
    grid_shape: tuple[int, ...]  # Tile grid layout (num_rows, num_cols)
    tile_spec: dict = field(default_factory=dict) # Metadata required for merging
    output_dtype: torch.dtype | None = None # Final output dtype
 ```
-
-```python
+```
 class TileTask:
    tile_id: int # task id
    grid_coord: tuple[int, ...]  # Tile position in grid
    tensor: torch.Tensor | list[torch.Tensor]  # The tile tensor
    workload: int | float = 1 # Used for load balancing (e.g., tile area)
 ```
-
 And tiled base split/exec/merge as follow:
-
-```python
+```
 def tile_split(self, z: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
    # mostly copy from AutoencoderKL
    _, _, num_frames, height, width = z.shape
@@ -218,14 +210,11 @@ def tile_merge(self, coord_tensor_map: dict[tuple[int, ...], torch.Tensor], grid
 ```

 ### Step 3: Override tiled_decode
-
 We need to override tiled_decode, the main logic is:
-
- check distributed is enabled
- select split/exec/merge
- Invoke self.distributed_executor.execute to decode
-
-```python
+ check distributed is enabled
+ select split/exec/merge
+ Invoke self.distributed_executor.execute to decode
+```
 def tiled_decode(self, z: torch.Tensor, return_dict: bool = True):
    if not self.is_distributed_enabled():
        return super().tiled_decode(z, return_dict=return_dict)
@@ -241,14 +230,11 @@ def tiled_decode(self, z: torch.Tensor, return_dict: bool = True):

    return DecoderOutput(sample=result)
 ```
-
 `broadcast_result` is set to True or False depending on the model; when enabled, the result will be used even on ranks other than 0.

 ### Step 4: Modify Pipeline
-
 Change vae model from AutoencoderKLQwenImage to DistributedAutoencoderKLQwenImage
-
-```diff
+```
 class YourModelPipeline(nn.Module):
    def __init__(
        self,
@@ -273,8 +259,8 @@ For models that require VAE encoding (e.g., Image-to-Video), you can also parall

 Similar to decode, split the input tensor into tiles. Key considerations:

- **Patchify handling**: If the model uses `patch_size`, scale tile parameters accordingly
- **Temporal chunking**: Video VAEs may have temporal compression (e.g., 4x)
+ **Patchify handling**: If the model uses `patch_size`, scale tile parameters accordingly
+ **Temporal chunking**: Video VAEs may have temporal compression (e.g., 4x)

 ```python
 def encode_tile_split(self, x: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
@@ -420,28 +406,23 @@ def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
 **Key differences from decode parallel:**

 | Aspect | Decode Parallel | Encode Parallel |
-| -------- | ----------------- | ----------------- |
+|--------|-----------------|-----------------|
 | `broadcast_result` | Often `False` (only rank 0 needs output) | `True` (all ranks need latents for diffusion) |
 | Patchify | Applied in merge (unpatchify) | Handled by parent `_encode()` before `tiled_encode()` |
 | Temporal chunking | Frame-by-frame | Chunk-based (e.g., 1 + 4n frames) |

 ## Testing
-
 Verify numerical consistency between:
+ vae_patch_parallel_size = 1

- vae_patch_parallel_size = 1
- vae_patch_parallel_size = N
+ vae_patch_parallel_size = N

 Example:
-
-```python
 torch.allclose(output_1, output_n, atol=1e-5)
-```

 Testing requirements:
-
- Fix random seed
- Use identical tiling strategy
+ Fix random seed
+ Use identical tiling strategy

 ```python
 m = Omni(
@@ -453,7 +434,6 @@ m = Omni(
        ),
    )
 ```
-
 When vae_patch_parallel_size is larger than the DiT world size, it will automatically fall back to using the DiT world size instead.

 ## Reference Implementations
@@ -461,7 +441,7 @@ When vae_patch_parallel_size is larger than the DiT world size, it will automati
 Complete examples in the codebase:

 | Model | Path | Decode Parallel | Encode Parallel |
-| ------- | ------ | ----------------- | ----------------- |
+|-------|------|-----------------|-----------------|
 | **Z-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl.py` | ✅ | ❌ |
 | **Wan2.2** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py` | ✅ | ✅ |
 | **Qwen-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py` | ✅ | ❌ |
@@ -476,7 +456,7 @@ The tile-parallel executor above assigns independent spatial **tiles** to ranks.
 ### How it differs from tile parallel

 | Aspect | Tile parallel (`"tile"`) | Spatially-sharded (`"spatial_shard_height"`/`"spatial_shard_width"`) |
-| -------- | -------------------------- | ----------------------------------------------- |
+|--------|--------------------------|-----------------------------------------------|
 | Unit of work | Independent overlapping tiles | A single global feature map sharded along H or W |
 | Cross-rank communication | Gather tiles to rank 0, stitch + blend | Per-conv **halo exchange** of boundary rows/cols (P2P) |
 | Output assembly | Blend overlapping tiles | All-gather shards on rank 0, trim padding (matches `broadcast_result=False`) |
@@ -501,7 +481,7 @@ serve.py (--vae-parallel-mode) / OmniEngineArgs
 ### Notes

 - The decoder is patched **in place** the first time spatial-shard decode runs and is bound to a single split dimension for the lifetime of the VAE instance.
- Numerical correctness vs. single-GPU decode is covered by `tests/diffusion/distributed/test_wan_spatial_shard.py::test_spatial_shard_decode_matches_reference` (multi-GPU, nightly `full_model` + `cards_2`).
+- Numerical correctness vs. single-GPU decode is covered by `tests/diffusion/distributed/test_wan_spatial_shard.py::test_spatial_shard_decode_matches_reference` (multi-GPU, nightly `full_model` + `distributed_cuda`).

 ---

@@ -37,7 +37,6 @@ validation_paths:
  - tests/engine/test_async_omni_engine_input.py
  - tests/engine/test_async_omni_engine_outputs.py
  - tests/engine/test_async_omni_engine_abort.py
-  - tests/e2e/offline_inference/test_qwen3_omni.py
  - tests/engine/test_async_omni_engine_stage_init.py
  - tests/engine/test_orchestrator.py
  - tests/engine/test_orchestrator_error_handling.py
@@ -114,15 +113,6 @@ Numbers become append-only after normative promotion.

 Test routing, output ordering, queue correlation, cancellation, failure
 propagation, shutdown ordering, and representative multi-stage execution.
-AR abort of a final-stage LLM request must deliver a terminal output with
-`finish_reason="abort"` and the generated prefix so collocated training can
-resume; diffusion abort remains whole-sample retry. Aborting a parallel-sampling
-child must drop `parent_requests` once no children remain. EngineCore control
-RPCs (`sleep` / `wake_up` / `pause_scheduler` / `resume_scheduler`) must
-propagate worker exceptions rather than returning `{"supported": False}`.
-When a request has multiple final output stages, only the last abort message
-is request-terminal. Output-processor abort state is committed only after the
-physical EngineCore abort succeeds.
 Changes that cross into stage runtime or public error behavior require review
 from that contract's owners.

@@ -43,7 +43,6 @@ depends_on:
 validation_paths:
  - tests/entrypoints/test_omni_entrypoints.py
  - tests/entrypoints/test_async_omni.py
-  - tests/entrypoints/test_async_omni_pause_sleep_routing.py
  - tests/entrypoints/test_async_omni_duplex.py
  - tests/entrypoints/test_serve.py
  - tests/entrypoints/test_stream_finish_reason.py
@@ -120,13 +119,7 @@ promotion.

 Test request validation, protocol conversion, model-adapter routing,
 streaming, session identity, disconnect/cancellation, and error mapping for
-each affected offline or serving entrypoint. Sleep must wait for in-flight
-`generate()` admission before EngineCore offload; `wake_up` does not resume
-admission — callers must `resume_generation()`. Sleeping tags are tracked per
-stage so `wake_up(stage_ids=[0])` does not skip a later `wake_up(stage_ids=[1])`.
-Streaming input pumps take an admission slot immediately before each EngineCore
-ADD or update, not while waiting for the next client chunk. Frontend abort
-keeps `request_states` until `generate()` consumes the terminal output.
+each affected offline or serving entrypoint.

 ## Promotion gate

@@ -257,53 +257,6 @@ Lookup checks the marker both before validation and after constructing the
 lease so a weaker overlapping lookup cannot return a lease after a stronger
 validation has denied the artifact.

-If publishing the external deny marker fails, lookup returns a retryable
-storage failure and marks the artifact directory with a persistent invalidation
-mode before releasing the shared lock. Cooperating readers check that mode both
-while opening and immediately before returning a lease. Lookup then makes a
-bounded attempt to take the exclusive artifact lock and quarantine the entry;
-the quarantine transition removes the internal mode marker. If a cooperating
-builder replaces the marked inode while the locks are exchanged, containment
-preserves that new publication instead of quarantining it. If neither the
-external marker nor the directory-mode marker can be persisted, cross-process
-exclusion cannot be guaranteed on an unavailable filesystem and the domain
-must remain failed rather than treating the artifact as an ordinary invalid
-cache entry.
-
-New publications carry the internal invalidation mode through the staging
-rename. Hardening clears it and is the logical authority transition for a
-process crash; the temporary- and artifact-parent syncs then make the rename
-durable, with an artifact-inode sync first making the hardened mode durable. A
-process that dies before hardening leaves a persistently denied
-entry, while one that dies after successful hardening leaves an authoritative
-ready entry. Any synchronous failure after rename is marked invalid and
-contained in quarantine before `PUBLICATION_FAILED` is returned, so that
-nonretryable result cannot later resolve as a ready hit. If the filesystem
-refuses both the inode marker and the quarantine move, the store instead returns
-a retryable storage failure with `outcome_uncertain` details; as with deny-marker
-double failure, cross-process exclusion cannot be guaranteed until storage
-recovers. Once the artifact-parent sync succeeds, a later artifact- or
-build-lock release error is logged and lookup resolves the committed entry
-instead of reporting a contradictory publication failure. The same authority
-rule applies when a competing publisher already installed byte-identical
-semantic content; staging-cleanup errors are retried without downgrading that
-validated ready entry to publication failure.
-
-Before widening a hardened artifact for a lifecycle move, the store durably
-sets the same invalidation mode. A process crash before rename therefore leaves
-the writable source denied. After rename, the store re-hardens the destination
-but retains the marker until both parent-directory syncs make the move durable;
-only then does it durably clear the marker. An interrupted move therefore leaves
-a marked transition that the next exact-key lifecycle operation re-hardens.
-Explicit cleanup uses the locked move and retries exact-key
-`.cleanup.*` tombstones left by an interrupted removal; a later build performs
-the same tombstone reconciliation before producing replacement content.
-
-An operational failure while inspecting a noncooperative competing publication
-is a retryable storage failure. The competing entry remains authoritative and
-is not mislabeled as corrupt or quarantined merely because its manifest could
-not be read transiently.
-
 `inspect_domain()` and `inspect_artifact()` return typed, read-only snapshots.
 Inspection validation does not create a deny marker. Reported lock activity is
 point-in-time evidence only; kernel locks are not a persistent owner registry.
@@ -23,19 +23,6 @@ vLLM-Omni currently recommends using the Docker image setup steps below.
 DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.xpu -t vllm-omni-xpu --shm-size=4g .
 ```

-This layers vLLM-Omni on top of the published `vllm/vllm-openai-xpu:<VLLM_VERSION>`
-base image, which Docker pulls automatically. To target a different vLLM release,
-pass `--build-arg VLLM_VERSION=<tag>`. If that tag has not been published yet, build
-the base from upstream's own Dockerfile first and point the build at it:
-
-```bash
-git clone --depth 1 --branch "$VLLM_VERSION" https://github.com/vllm-project/vllm /tmp/vllm
-DOCKER_BUILDKIT=1 docker build -t vllm-openai-xpu:local --target vllm-openai \
-  -f /tmp/vllm/docker/Dockerfile.xpu /tmp/vllm
-DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.xpu -t vllm-omni-xpu --shm-size=4g \
-  --build-arg VLLM_BASE=vllm-openai-xpu:local .
-```
-
 #### Launch the docker image

 ##### Launch with OpenAI API Server
@@ -26,15 +26,15 @@ th {
 | `InternVLAA1Pipeline` | InternVLA-A1 | `InternRobotics/InternVLA-A1-3B` | ✅︎ | ✅︎ | | | — |
 | `Gr00tN1d7Pipeline` | GR00T N1.7 | `nvidia/GR00T-N1.7-3B` | ✅︎ | | | | — |
 | `HunyuanImage3ForCausalMM` | HunyuanImage3.0 (DiT-only) | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` | ✅︎ | | | ✅︎ | [Published](https://recipes.vllm.ai/Qwen/Qwen-Image) |
-| `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen-Image-2512.md) |
+| `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` | ✅︎ | | | | [Published](https://recipes.vllm.ai/Qwen/Qwen-Image) |
+| `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen-Image-2512.md) |
 | `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` | ✅︎ | ✅︎ | | | — |
 | `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | ✅︎ | | | ✅︎ | — |
+| `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 | | | | — |
@@ -42,7 +42,7 @@ th {
 | `SanaWmPipeline` | SANA-WM | `BBBBruce/SANA-WM_bidirectional-stage1-diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/NVIDIA/SANA-WM.md) |
 | `Wan22S2VPipeline` | Wan2.2-S2V | `Wan-AI/Wan2.2-S2V-14B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   | ✅︎ | — |
+| `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` | ✅︎ | ✅︎ | | | — |
@@ -62,12 +62,12 @@ th {
 | `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `LongCatVideoAvatarPipeline` | LongCat-Video-Avatar-1.5 A2V/AI2V (native single-speaker and multi-speaker AI2V/AVC) | `meituan-longcat/LongCat-Video-Avatar-1.5` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/meituan-longcat/LongCat-Video-Avatar-1.5.md) |
-| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | ✅︎ | — |
+| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | | — |
 | `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | ✅︎ | — |
-| `VoxCPM2TalkerForConditionalGeneration` | VoxCPM2 | `openbmb/VoxCPM2` | ✅︎ | | | ✅︎ | — |
+| `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | | — |
+| `VoxCPM2TalkerForConditionalGeneration` | VoxCPM2 | `openbmb/VoxCPM2` | ✅︎ | | | | — |
 | `DotsTTSForConditionalGeneration` | dots.tts | `rednote-hilab/dots.tts-soar` | ✅︎ | | | | — |
 | `MammothModa2ForConditionalGeneration` | MammothModa2-Preview | `bytedance-research/MammothModa2-Preview` | ✅︎ | ✅︎ | | | — |
 | `MammothModa2ForConditionalGeneration` | MammothModa2-Dev (AR-only image understanding) | `bytedance-research/MammothModa2-Dev` | ✅︎ | | | | — |
@@ -78,10 +78,10 @@ th {
 | `OmniGen2Pipeline` | OmniGen2 | `OmniGen2/OmniGen2` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `StableAudioPipeline` | Stable-Audio-Open | `stabilityai/stable-audio-open-1.0` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `MiniMaxMusic3ForConditionalGeneration` | MiniMax Music 3 (text-to-music) | `MiniMaxAI/MiniMax-Music3` | ✅︎ | | | | — |
-| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | | | ✅︎ | [Published](https://recipes.vllm.ai/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice) |
+| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | | | | [Published](https://recipes.vllm.ai/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice) |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `MingTTSForConditionalGeneration` | Ming-omni-tts dense 0.5B | `inclusionAI/Ming-omni-tts-0.5B` | ✅︎ | | | ✅︎ | — |
+| `MingTTSForConditionalGeneration` | Ming-omni-tts dense 0.5B | `inclusionAI/Ming-omni-tts-0.5B` | ✅︎ | | | | — |
 | `GLMTTSForConditionalGeneration` | GLM-TTS | `zai-org/GLM-TTS` | ✅︎ | | | | — |
 | `MossTTSNanoForCausalLM` | MOSS-TTS-Nano | `OpenMOSS-Team/MOSS-TTS-Nano` | ✅︎ | | | | — |
 | `MossTTSDelayModel` | MOSS-TTS, MOSS-TTSD, MOSS-SoundEffect, MOSS-VoiceGenerator | `OpenMOSS-Team/MOSS-VoiceGenerator` | ✅︎ | | | | — |
@@ -101,10 +101,10 @@ th {
 | `HunyuanVideo15ImageToVideoPipeline` | HunyuanVideo-1.5-I2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v` | ✅︎ | ✅︎ | | | — |
 | `VoxtralTTSForConditionalGeneration` | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | ✅︎ | ✅︎ | | | — |
 | `CovoAudioForConditionalGeneration` | Covo-Audio-Chat | `tencent/Covo-Audio-Chat` | ✅︎ | | | | — |
-|`DyninOmniForConditionalGeneration` | Dynin-Omni | `snu-aidas/Dynin-Omni` | ✅︎ | | | ✅︎ | — |
+|`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` | ✅︎ | | | ✅︎ | — |
+| `GepardTalkerForConditionalGeneration` | Gepard-1.0 | `nineninesix/gepard-1.0` | ✅︎ | | | | — |
 |`HiDreamImagePipeline` | HiDream-I1-Full | `HiDream-ai/HiDream-I1-Full` | ✅︎ | ✅︎ | | | — |
 | `HiDreamO1ImagePipeline` | HiDream-O1-Image | `HiDream-ai/HiDream-O1-Image` | ✅︎ | | | | — |

@@ -17,8 +17,6 @@ globally.
 | --- | --- | --- |
 | `sparsity` | finite, `[0, 1]` | Nominal dropped-key-block fraction; default `0.8`; `0` disables sparsity |
 | `start_step` | integer, `>= 0` | Number of early denoise steps kept dense |
-| `end_step` | integer, `>= 0` | Number of final denoise steps kept dense (tail fallback) |
-| `precision` | `"bf16"`, `"fp8"`, `"mix"` | Kernel precision mode; default `"bf16"`. Requires MindIE-SD with `sparse_attention(precision=...)` |
 | `skip_layers` | selector such as `"0-3,38"` | DiT blocks kept dense |

 ```bash
@@ -54,12 +52,6 @@ platform raises. It is incompatible with ring sequence parallelism because
 the kernel needs the complete key sequence for ranking; use Ulysses sequence
 parallelism with `ring_degree=1`.

-The `precision` knob requires a MindIE-SD release whose `sparse_attention`
-accepts `precision=`; older releases accept it through `**kwargs` but
-silently ignore it and run the BF16 path. `RAINFUSION_ATTN` raises at runtime
-when a non-`bf16` precision is requested without that support, so pin a
-compatible MindIE-SD release when using `precision="mix"` or `"fp8"`.
-
 ## Geometry handling

 RainFusion handles arbitrary video grids by rearranging video spatially and,
@@ -12,17 +12,15 @@ LoRA adapters are lightweight, model-specific fine-tuning weights that can be ap
 ## LoRA Adapter Format

 ### PEFT (Parameter-Efficient Fine-Tuning) format (default)
-
 A typical PEFT-format LoRA adapter directory structure:

-```text
+```
 lora_adapter/
 ├── adapter_config.json
 └── adapter_model.safetensors
 ```

 The `adapter_config.json` file contains metadata about the LoRA adapter, including:
-
 - `r`: LoRA rank
 - `lora_alpha`: LoRA alpha scaling factor
 - `target_modules`: List of module names to apply LoRA to
@@ -69,7 +67,7 @@ For distilled few-step LoRAs, pass `lora_backend="distill"` together with one or
 For Qwen-Image and Wan pipelines, the distill backend calls `pipeline.load_lora_weights(...)` during worker initialization.

 | Pipeline | Supported distilled LoRA repo | Notes |
-| ---------- | -------------------------------- | ------- |
+|----------|--------------------------------|-------|
 | `QwenImagePipeline` | `lightx2v/Qwen-Image-2512-Lightning` | Used with Qwen-Image-2512 Lightning-style few-step inference. |
 | `Wan22Pipeline` | `lightx2v/Wan2.1-Distill-Loras`, `lightx2v/Wan2.2-Distill-Loras` | Wan2.1 uses one LoRA file. For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
 | `Wan22I2VPipeline` | `lightx2v/Wan2.2-Distill-Loras` | For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
@@ -242,37 +240,6 @@ Notes:
 - Output quality and speed depend on the replacement checkpoints and sampling params you choose.
 - If you only need to fuse distilled LoRAs into a Wan2.2 checkpoint at load time (without the full LightX2V convert + assemble pipeline), you can instead pass them directly via `--lora-backend distill --lora-path <high>.safetensors <low>.safetensors`. See the [Distill backend](#distill-backend-fuse-distilled-lora-at-init) section above.

-## MiniMax-H3 adapter-declared schedules
-
-MiniMax-H3 supports two few-step mechanisms that must not be conflated:
-
- **Checkpoint-pinned schedule**: a merged release writes `base_schedule` into
-  `model_index.json`. Requests must pass `num_inference_steps` as the interval
-  count (for example `4` for `[1.0, 0.7, 0.4, 0.15, 0.0]`).
- **Runtime Turbo LoRA**: LightX2V Turbo artifacts request five sigma points and
-  enforce `flow_shift=6`, `audio_flow_shift=3`.
- **Runtime native LoRA**: FlashGen-style artifacts declare
-  `key_format=minimax-h3-native` and embed `base_schedule` in safetensors
-  metadata. When active, the adapter schedule overrides the base checkpoint and
-  requests must use the interval-count contract (`num_inference_steps=4`).
-  Request-mode generation may omit the field and take the count from the adapter
-  schedule; step execution requires it explicitly, because the step scheduler
-  derives the total step count from the request before the adapter schedule is
-  known.
-
-Native artifacts also declare `qkv_layout=grouped`. The H3 loader reorders fused
-`qkv_proj` LoRA rows with the same `_reorder_grouped_qkv_to_qkv` helper used for
-base-weight loading, then binds the packed Q/K/V slices through the legacy PEFT
-manager without modifying `DiffusionLoRAManager`.
-
-Both runtime adapters run with distributed layerwise offload in request-mode
-generation, where the manager keeps the LoRA A/B buffers resident on the compute
-device while DLO streams the base blocks. MiniMax-H3 step execution still
-rejects DLO, so `--step-execution` cannot be combined with
-`--enable-distributed-layerwise-offload`. Model-level CPU offload and standard
-layerwise offload are rejected in every mode, because the dynamic LoRA tensors
-are neither parameters nor registered buffers and therefore do not participate
-in those weight lifecycles.

 ## See Also

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 """
 Image-to-Video generation example using Wan2.2 I2V/TI2V models, LTX2/LTX-2.3,
@@ -111,13 +111,6 @@ def build_image_to_video_prompt(
    return result


-def _validate_video_output_type(output_type: str) -> None:
-    if output_type not in {"image", "video"}:
-        raise ValueError(
-            f"Unexpected output type '{output_type}', expected 'video' or legacy 'image' for video generation."
-        )
-
-
 def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description=(
@@ -672,7 +665,10 @@ def main():
        frames = frames[0] if frames else None

    if isinstance(frames, OmniRequestOutput):
-        _validate_video_output_type(frames.final_output_type)
+        if frames.final_output_type != "image":
+            raise ValueError(
+                f"Unexpected output type '{frames.final_output_type}', expected 'image' for video generation."
+            )
        if frames.multimodal_output and "audio" in frames.multimodal_output:
            audio = frames.multimodal_output["audio"]
            audio_sample_rate = frames.multimodal_output.get("audio_sample_rate", audio_sample_rate)
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 import argparse
 import json
@@ -174,13 +174,6 @@ def build_text_to_video_prompt(prompt: str, negative_prompt: str | None) -> dict
    return result


-def _validate_video_output_type(output_type: str) -> None:
-    if output_type not in {"image", "video"}:
-        raise ValueError(
-            f"Unexpected output type '{output_type}', expected 'video' or legacy 'image' for video generation."
-        )
-
-
 def _normalize_float_tensor(tensor: torch.Tensor, source_range: str) -> torch.Tensor:
    """Normalize decoded frames according to the pipeline's output contract."""
    if not tensor.is_floating_point():
@@ -627,7 +620,10 @@ def main():
        frames = frames[0] if frames else None

    if isinstance(frames, OmniRequestOutput):
-        _validate_video_output_type(frames.final_output_type)
+        if frames.final_output_type != "image":
+            raise ValueError(
+                f"Unexpected output type '{frames.final_output_type}', expected 'image' for video generation."
+            )
        if frames.multimodal_output and "audio" in frames.multimodal_output:
            audio = frames.multimodal_output["audio"]
            audio_sample_rate = frames.multimodal_output.get("audio_sample_rate", audio_sample_rate)
@@ -159,33 +159,6 @@ python examples/online_serving/minicpmo/realtime_duplex_demo.py \
    --output-dir /tmp/minicpmo_realtime_duplex_demo
 ```

-Video input uses the same session. PyAV demuxes JPEG frames at `--video-fps`
-(default 1.0) and vLLM `load_audio` extracts a 16 kHz mono WAV unless
-`--input-wav` overrides the soundtrack. Frames keep their capture size
-(`--frame-max-side 0`); the server `process_image` normalizes at 448.
-
-`--stack-frames N` raises the visual refresh rate the way official duplex does:
-each 1 s unit also samples the `N-1` sub-frames captured inside it and tiles
-them into a single composite image sent next to that unit's base frame. The
-audio cadence never changes — a unit is always one second — and the wire always
-carries 2 images per unit however large `N` is, because the sub-frames share one
-composite. Official uses 5 for high refresh rate mode:
-
-```bash
-python examples/online_serving/minicpmo/realtime_duplex_demo.py \
-    --url ws://localhost:8099/v1/realtime?duplex=1 \
-    --model openbmb/MiniCPM-o-4_5 \
-    --input-video /path/to/clip.mp4 \
-    --stack-frames 5 \
-    --ref-audio /path/to/MiniCPM-o-Demo/assets/ref_audio/ref_minicpm_signature.wav \
-    --output-dir /tmp/minicpmo_realtime_duplex_video_demo
-```
-
-Detail inside a composite is capped by `scale_resolution=448` at
-`max_slice_nums=1`: official suggests HD slicing (`max_slice_nums=[2, 1]`) for
-stacked frames, which the duplex adapter does not implement yet. Reading small
-text or digits out of a wide scene is limited by that, not by frame timing.
-
 ## Open the experimental browser client

 The browser UI serves the page and proxies the same-origin Realtime WebSocket to
@@ -12,9 +12,7 @@ from __future__ import annotations
 import argparse
 import asyncio
 import base64
-import io
 import json
-import math
 import sys
 import time
 from pathlib import Path
@@ -24,8 +22,8 @@ if str(REPO_ROOT) not in sys.path:
    sys.path.insert(0, str(REPO_ROOT))

 from vllm_omni.experimental.fullduplex.client import (  # noqa: E402
-    PCM16_BYTES_PER_SAMPLE,
-    PCM16_SAMPLE_RATE,
+    PCM16_BYTES_PER_SAMPLE,  # noqa: F401 - compatibility export
+    PCM16_SAMPLE_RATE,  # noqa: F401 - compatibility export
    RealtimeDuplexClient,
    RealtimeEventCollector,
    build_realtime_url,
@@ -40,10 +38,6 @@ from vllm_omni.experimental.fullduplex.client import (  # noqa: E402
 from vllm_omni.experimental.fullduplex.client import (  # noqa: E402
    reference_audio_data_url as _ref_audio_data_url,
 )
-from vllm_omni.experimental.fullduplex.video_stacking import (  # noqa: E402
-    concat_frames_b64,
-    unit_subframe_offsets,
-)


 class _StreamingOutputWriter:
@@ -165,258 +159,14 @@ def _event_count_after(
    return sum(event.get("type") == event_type for event in events[index + 1 :])


-def _probe_video_frames_and_fps(video_path: Path) -> tuple[int, float]:
-    """Frame count and average FPS via PyAV (same approach as Daily-Omni)."""
-    import av
-
-    with av.open(str(video_path)) as container:
-        stream = container.streams.video[0]
-        avg_fps = float(stream.average_rate) if stream.average_rate else 0.0
-        num_frames = stream.frames or 0
-        if num_frames <= 0:
-            duration: float | None = None
-            if stream.duration is not None and stream.time_base is not None:
-                duration = float(stream.duration * stream.time_base)
-            elif container.duration is not None:
-                duration = container.duration / av.time_base
-            if duration and avg_fps:
-                num_frames = int(math.floor(duration * avg_fps))
-        if num_frames <= 0:
-            stream.thread_type = "AUTO"
-            num_frames = sum(1 for _ in container.decode(stream))
-    if num_frames <= 0:
-        raise ValueError(f"No decodable video frames in {video_path}")
-    return num_frames, avg_fps or 1.0
-
-
-def _upright(image, rotation: int):
-    """Apply a container display-matrix rotation, in degrees, to ``image``.
-
-    PyAV hands back the stored raster and leaves the display matrix for the
-    caller, unlike the ffmpeg CLI which autorotates. Phone clips routinely
-    carry +/-90, so skipping this feeds the model a sideways frame: arrows and
-    digits stop being readable and the model mirrors the motion it reports.
-    """
-    from PIL import Image
-
-    quarter_turns = {
-        90: Image.Transpose.ROTATE_90,
-        180: Image.Transpose.ROTATE_180,
-        270: Image.Transpose.ROTATE_270,
-    }
-    turn = quarter_turns.get(int(rotation) % 360)
-    return image.transpose(turn) if turn is not None else image
-
-
-def _decode_video_frames_rgb(video_path: Path, frame_idx: list[int]) -> list:
-    """Decode presentation-order ``frame_idx`` into upright RGB ``PIL.Image``s."""
-    import av
-
-    wanted = sorted({int(i) for i in frame_idx})
-    if not wanted:
-        return []
-
-    decoded: dict[int, object] = {}
-    with av.open(str(video_path)) as container:
-        stream = container.streams.video[0]
-        stream.thread_type = "AUTO"
-        cursor = 0
-        for position, frame in enumerate(container.decode(stream)):
-            if wanted[cursor] > position:
-                continue
-            image = _upright(frame.to_image(), getattr(frame, "rotation", 0) or 0)
-            while cursor < len(wanted) and wanted[cursor] <= position:
-                decoded[wanted[cursor]] = image
-                cursor += 1
-            if cursor >= len(wanted):
-                break
-
-    if not decoded:
-        raise ValueError(f"Decoded no frames from {video_path} for indices {wanted[:8]}")
-    last_image = decoded[max(decoded)]
-    return [decoded.get(int(i), last_image) for i in frame_idx]
-
-
-def _resize_max_side(image, max_side: int):
-    width, height = image.size
-    longest = max(width, height)
-    if longest <= max_side or max_side <= 0:
-        return image
-    scale = max_side / float(longest)
-    new_size = (max(1, int(round(width * scale))), max(1, int(round(height * scale))))
-    return image.resize(new_size)
-
-
-def _jpeg_bytes(image, *, quality: int = 95) -> bytes:
-    buf = io.BytesIO()
-    image.convert("RGB").save(buf, format="JPEG", quality=quality)
-    return buf.getvalue()
-
-
-def _to_pcm16_bytes(audio_np) -> bytes:
-    import numpy as np
-
-    arr = np.asarray(audio_np).reshape(-1)
-    if arr.dtype != np.int16:
-        arr = (np.clip(arr, -1.0, 1.0) * 32767.0).astype(np.int16)
-    return arr.tobytes()
-
-
-def _pcm16_wav_duration_s(path: Path) -> float:
-    pcm16 = read_pcm16_wav(path)
-    return len(pcm16) / float(PCM16_SAMPLE_RATE * PCM16_BYTES_PER_SAMPLE)
-
-
-def _extract_video_frames(
-    video_path: Path,
-    work_dir: Path,
-    *,
-    fps: float,
-    max_side: int,
-    duration_s: float,
-    stack_frames: int = 1,
-) -> tuple[list[str], list[str | None]]:
-    """Demux JPEG frames only. Frame count follows ``duration_s``, not soundtrack.
-
-    Returns the base frames (one per model unit) and the parallel stacked track.
-    With ``stack_frames=N`` each unit also samples its ``N-1`` interior
-    sub-frames and tiles them into one composite, which is how official duplex
-    raises the visual refresh rate while keeping one second of audio per unit.
-    A unit whose interior falls past the end of the clip stacks nothing.
-    """
-    num_frames, avg_fps = _probe_video_frames_and_fps(video_path)
-    sample_fps = max(float(fps), 1e-6)
-    unit_s = 1.0 / sample_fps
-    timeline_s = max(float(duration_s), unit_s)
-    num_samples = max(1, int(math.ceil(timeline_s * sample_fps - 1e-9)))
-    offsets = unit_subframe_offsets(stack_frames, unit_s=unit_s)
-
-    def frame_at(seconds: float) -> int:
-        # Official get_video_frame_audio_segments uses truncating int(), not
-        # round(), so 29.97 fps lands on the same presentation index.
-        return min(int(seconds * avg_fps), num_frames - 1)
-
-    base_idx = [frame_at(unit * unit_s) for unit in range(num_samples)]
-    sub_times = [
-        [unit * unit_s + offset for offset in offsets if unit * unit_s + offset < timeline_s]
-        for unit in range(num_samples)
-    ]
-    sub_idx = [frame_at(seconds) for times in sub_times for seconds in times]
-    images = _decode_video_frames_rgb(video_path, base_idx + sub_idx)
-    sub_images = iter(images[len(base_idx) :])
-
-    frame_dir = work_dir / "frames"
-    frame_dir.mkdir(parents=True, exist_ok=True)
-    for stale in frame_dir.glob("*.jpg"):
-        stale.unlink()
-    frames_b64: list[str] = []
-    stacked_b64: list[str | None] = []
-    for index, (image, times) in enumerate(zip(images[: len(base_idx)], sub_times), start=1):
-        jpeg = _jpeg_bytes(_resize_max_side(image, max_side))
-        (frame_dir / f"frame_{index:05d}.jpg").write_bytes(jpeg)
-        frames_b64.append(base64.b64encode(jpeg).decode("ascii"))
-        interior = [_resize_max_side(next(sub_images), max_side) for _ in times]
-        if not interior:
-            stacked_b64.append(None)
-            continue
-        composite = concat_frames_b64(interior)
-        (frame_dir / f"stack_{index:05d}.jpg").write_bytes(base64.b64decode(composite))
-        stacked_b64.append(composite)
-    return frames_b64, stacked_b64
-
-
-def _extract_video_soundtrack(video_path: Path, work_dir: Path) -> Path:
-    """Write a 16 kHz mono WAV from the video soundtrack."""
-    try:
-        from vllm.multimodal.media.audio import load_audio
-    except ImportError as exc:  # pragma: no cover
-        raise SystemExit("--input-video needs vLLM audio helpers (load_audio)") from exc
-
-    audio_np, sr = load_audio(str(video_path), sr=PCM16_SAMPLE_RATE, mono=True)
-    work_dir.mkdir(parents=True, exist_ok=True)
-    audio_path = work_dir / "input_16k.wav"
-    write_pcm16_wav(audio_path, _to_pcm16_bytes(audio_np), sample_rate_hz=int(sr))
-    return audio_path
-
-
-def _extract_video_input(
-    video_path: Path,
-    work_dir: Path,
-    *,
-    fps: float,
-    max_side: int,
-    stack_frames: int,
-) -> tuple[Path, list[str], list[str | None]]:
-    """Demux a video into duplex inputs: 16 kHz mono WAV plus JPEG frames.
-
-    Use this only when the video soundtrack is the audio source. When the
-    caller already has ``--input-wav``, extract frames against that WAV
-    duration instead so a silent clip cannot fail ``load_audio``.
-    """
-    audio_path = _extract_video_soundtrack(video_path, work_dir)
-    frames_b64, stacked_b64 = _extract_video_frames(
-        video_path,
-        work_dir,
-        fps=fps,
-        max_side=max_side,
-        duration_s=_pcm16_wav_duration_s(audio_path),
-        stack_frames=stack_frames,
-    )
-    return audio_path, frames_b64, stacked_b64
-
-
-def _resolve_duplex_av_inputs(
-    *,
-    input_wav: str | None,
-    input_video: str | None,
-    work_dir: Path,
-    fps: float,
-    max_side: int,
-    stack_frames: int = 1,
-) -> tuple[str, list[str], list[str | None]]:
-    """Return ``(wav_path, frames_b64, stacked_b64)``. External WAV wins over video audio."""
-    if input_video:
-        video_path = Path(input_video)
-        if input_wav:
-            frames, stacked = _extract_video_frames(
-                video_path,
-                work_dir,
-                fps=fps,
-                max_side=max_side,
-                duration_s=_pcm16_wav_duration_s(Path(input_wav)),
-                stack_frames=stack_frames,
-            )
-            return input_wav, frames, stacked
-        extracted_wav, frames, stacked = _extract_video_input(
-            video_path,
-            work_dir,
-            fps=fps,
-            max_side=max_side,
-            stack_frames=stack_frames,
-        )
-        return str(extracted_wav), frames, stacked
-    if not input_wav:
-        raise SystemExit("provide --input-wav, --input-video, or both")
-    return input_wav, [], []
-
-
 async def run_demo(args: argparse.Namespace) -> dict[str, object]:
-    output_dir = Path(args.output_dir)
-    stream_writer = _StreamingOutputWriter(output_dir)
-
-    input_wav, video_frames, stacked_video_frames = _resolve_duplex_av_inputs(
-        input_wav=args.input_wav,
-        input_video=args.input_video,
-        work_dir=output_dir / "video_input",
-        fps=args.video_fps,
-        max_side=args.frame_max_side,
-        stack_frames=args.stack_frames,
-    )
-
-    input_pcm16 = read_pcm16_wav(Path(input_wav))
+    input_pcm16 = read_pcm16_wav(Path(args.input_wav))
    if not input_pcm16:
        raise ValueError("input WAV has no audio")

+    output_dir = Path(args.output_dir)
+    stream_writer = _StreamingOutputWriter(output_dir)
+
    url = build_realtime_url(
        args.url,
        args.model,
@@ -434,12 +184,10 @@ async def run_demo(args: argparse.Namespace) -> dict[str, object]:
            timeout_s=args.timeout_s,
        )
        stream_event_cursor = len(client.events.events)
-        frames_sent = await client.stream_pcm16(
+        await client.stream_pcm16(
            input_pcm16,
            chunk_ms=args.chunk_ms,
            realtime=not args.no_realtime_pacing,
-            video_frames=video_frames,
-            stacked_video_frames=stacked_video_frames,
        )
        commit_event_cursor = len(client.events.events)
        stream_decision = _latest_model_decision(client.events.events, stream_event_cursor)
@@ -562,10 +310,6 @@ async def run_demo(args: argparse.Namespace) -> dict[str, object]:
                    committed_index,
                ),
            },
-            "input_video": args.input_video,
-            "video_frames_extracted": len(video_frames),
-            "video_frames_sent": frames_sent,
-            "video_stacked_frames_extracted": sum(frame is not None for frame in stacked_video_frames),
            "audio_bytes": len(audio),
            "audio_chunk_count": len(stream_writer.audio_chunk_paths),
            "audio_chunk_files": [str(path) for path in stream_writer.audio_chunk_paths],
@@ -612,48 +356,7 @@ def parse_args() -> argparse.Namespace:
    parser.add_argument("--url", default="ws://localhost:8099/v1/realtime?duplex=1")
    parser.add_argument("--model", default="openbmb/MiniCPM-o-4_5")
    parser.add_argument("--session-id")
-    parser.add_argument(
-        "--input-wav",
-        help="Mono 16 kHz PCM16 clip to stream. Optional when --input-video carries the audio track.",
-    )
-    parser.add_argument(
-        "--input-video",
-        help=(
-            "Video to stream as omni duplex input. PyAV demuxes frames and "
-            "vLLM load_audio extracts a 16 kHz mono WAV (unless --input-wav overrides the audio)."
-        ),
-    )
-    parser.add_argument(
-        "--video-fps",
-        type=float,
-        default=1.0,
-        help="Frame extraction rate. The wire contract carries ~1 frame per second of audio.",
-    )
-    parser.add_argument(
-        "--frame-max-side",
-        type=int,
-        default=0,
-        help=(
-            "Optional client-side downscale of the longest side, in pixels. "
-            "0 (default) keeps the capture resolution so the server "
-            "process_image can normalize at scale_resolution=448 from a "
-            "higher-quality source. The web demo never resizes on the client."
-        ),
-    )
-    parser.add_argument(
-        "--stack-frames",
-        type=int,
-        default=1,
-        help=(
-            "Frames sampled per 1 s audio unit. 1 (default) sends the unit's "
-            "frame alone. N > 1 also samples the N-1 sub-frames inside the unit "
-            "and tiles them into one composite sent next to the base frame, so "
-            "motion (arrows, doors, digits changing) is visible at N fps while "
-            "the audio stays 1 s per unit. Official uses 5 for high refresh "
-            "rate mode; the wire carries 2 images per unit at any N because the "
-            "sub-frames share one composite."
-        ),
-    )
+    parser.add_argument("--input-wav", required=True)
    parser.add_argument(
        "--ref-audio",
        required=True,
@@ -669,7 +372,7 @@ def parse_args() -> argparse.Namespace:
        "--temperature",
        type=float,
        default=None,
-        help="Stage 0 sampling temperature; omit to preserve the model default.",
+        help="Stage0 sampling temperature; response-required soft-interrupt uses 0.0.",
    )
    parser.add_argument("--no-realtime-pacing", action="store_true")
    parser.add_argument("--require-audio", action="store_true")
@@ -71,11 +71,11 @@ dev = [
    "voxcpm==2.0.3", # for VoxCPM2 path-locating in tests
    "torchdiffeq==0.2.5", # for covo_audio Test
    "FlagEmbedding==1.4.0", # for hunyuan_image3 tests
+    "kernels==0.14.1", # for accuracy tests comparison with diffusers output (0.15.0 incompatible with diffusers 0.38.0)
    "s3tokenizer==0.3.0", # for tts test
    # Provides top-level cosyvoice2; do not co-install stepaudio2-minicpmo,
    # whose top-level stepaudio2 package conflicts with this distribution.
    "step-audio2==1.0.0", # Step-Audio2 / MiniCPM-o 4.5 Token2Wav (flashcosyvoice / hyperpyyaml)
-    "silero-vad==6.1", # Realtime server-VAD duplex E2E
 ]

 demo = [
@@ -85,10 +85,6 @@ demo = [
    "cn2an>=0.5.22",  # Chinese number normalization for TTS (e.g. "1次" -> "一次")
 ]

-server-vad = [
-    "silero-vad==6.1",
-]
-
 forced-aligner = [
    "qwen-asr>=0.0.6",  # faithful Japanese/Korean word segmentation for the TTS forced aligner
 ]
@@ -248,11 +244,11 @@ addopts = [
    "--dist=loadgroup"
 ]
 markers = [
-    # ci/cd required — tag with [ci-level] for tests.helpers.mark.get_level_markers().
-    "core_model: [ci-level] L1&L2 tests (run in each PR)",
-    "advanced_model: [ci-level] L3 level tests (run on each merge)",
-    "full_model: [ci-level] L4 level tests (run nightly)",
-    "local_model: [ci-level] tests (run locally)",
+    # ci/cd required
+    "core_model: L1&L2 tests (run in each PR)",
+    "advanced_model: L3 level tests (run on each merge)",
+    "full_model: L4 level tests (run nightly)",
+    "local_model: tests (run locally)",
    # function module markers
    "diffusion: Diffusion model tests",
    "omni: Omni model tests",
@@ -262,41 +258,38 @@ markers = [
    "sp: Sequence parallelism tests",
    "example: Doc example code tests",
    # platform markers
-    # Tag with [hardware-platform] for tests.helpers.mark.get_supported_platforms().
-    # Add [gpu] on accelerator platforms that should also get pytest.mark.gpu.
-    # cpu/gpu are pytest marks; hardware_test(res=...) rejects them as keys.
-    "cpu: [hardware-platform] Tests that run on CPU",
-    "gpu: [hardware-platform] Tests that run on GPU (auto-added)",
-    "cuda: [hardware-platform] [gpu] Tests that run on CUDA (auto-added)",
-    "rocm: [hardware-platform] [gpu] Tests that run on AMD/ROCm (auto-added)",
-    "xpu: [hardware-platform] [gpu] Tests that run on XPU (auto-added)",
-    "npu: [hardware-platform] Tests that run on NPU/Ascend (auto-added)",
-    "musa: [hardware-platform] [gpu] Tests that run on MUSA/Moore Threads (auto-added)",
+    "cpu: Tests that run on CPU",
+    "gpu: Tests that run on GPU (auto-added)",
+    "cuda: Tests that run on CUDA (auto-added)",
+    "rocm: Tests that run on AMD/ROCm (auto-added)",
+    "xpu: Tests that run on XPU (auto-added)",
+    "npu: Tests that run on NPU/Ascend (auto-added)",
+    "musa: Tests that run on MUSA/Moore Threads (auto-added)",
    # specified computation resources marks (auto-added)
-    # [hardware-resource] + [cuda]/[rocm]/[xpu]/[npu]/[musa] for hardware_test(res=...).
-    # Do not put [gpu] on SKU lines: that would make gpu a res key.
-    "H100: [hardware-resource] [cuda] Tests that require H100 GPU",
-    "H800: [hardware-resource] [cuda] Tests that require H800 GPU",
-    "H200: [hardware-resource] [cuda] Tests that require H200 GPU",
-    "L4: [hardware-resource] [cuda] Tests that require L4 GPU",
-    "B200: [hardware-resource] [cuda] Tests that require B200 GPU",
-    "MI325: [hardware-resource] [rocm] Tests that require MI325 GPU (AMD/ROCm)",
-    "B60: [hardware-resource] [xpu] Tests that require Intel Arc Pro B60 XPU",
-    "S5000: [hardware-resource] [musa] Tests that require S5000 GPU (Moore Threads/MUSA)",
-    "A2: [hardware-resource] [npu] Tests that require A2 NPU",
-    "A3: [hardware-resource] [npu] Tests that require A3 NPU",
-    # Tag with [hardware-cards] for tests.helpers.mark.get_supported_card_counts().
-    "cards_1: [hardware-cards] Tests that require 1 accelerator card",
-    "cards_2: [hardware-cards] Tests that require 2 accelerator cards",
-    "cards_3: [hardware-cards] Tests that require 3 accelerator cards",
-    "cards_4: [hardware-cards] Tests that require 4 accelerator cards",
-    "cards_5: [hardware-cards] Tests that require 5 accelerator cards",
-    "cards_6: [hardware-cards] Tests that require 6 accelerator cards",
-    "cards_7: [hardware-cards] Tests that require 7 accelerator cards",
-    "cards_8: [hardware-cards] Tests that require 8 accelerator cards",
+    # Tag with [hardware-resource] for tests.helpers.mark.get_hardware_mark_list().
+    "H100: [hardware-resource] Tests that require H100 GPU",
+    "H800: [hardware-resource] Tests that require H800 GPU",
+    "H200: [hardware-resource] Tests that require H200 GPU",
+    "L4: [hardware-resource] Tests that require L4 GPU",
+    "B200: [hardware-resource] Tests that require B200 GPU",
+    "MI325: [hardware-resource] Tests that require MI325 GPU (AMD/ROCm)",
+    "B60: [hardware-resource] Tests that require Intel Arc Pro B60 XPU",
+    "S5000: [hardware-resource] Tests that require S5000 GPU (Moore Threads/MUSA)",
+    "A2: [hardware-resource] Tests that require A2 NPU",
+    "A3: [hardware-resource] Tests that require A3 NPU",
+    "distributed_cuda: Tests that require multi cards on CUDA platform",
+    "distributed_rocm: Tests that require multi cards on ROCm platform",
+    "distributed_xpu: Tests that require multi cards on XPU platform",
+    "distributed_npu: Tests that require multi cards on NPU platform",
+    "distributed_musa: Tests that require multi cards on MUSA platform",
+    "skipif_cuda: Skip if the num of CUDA cards is less than the required",
+    "skipif_rocm: Skip if the num of ROCm cards is less than the required",
+    "skipif_xpu: Skip if the num of XPU cards is less than the required",
+    "skipif_npu: Skip if the num of NPU cards is less than the required",
+    "skipif_musa: Skip if the num of MUSA cards is less than the required",
    # more detailed markers
    "sp: Sequence parallelism tests (multi-GPU)",
-    "slow: [ci-level] Slow tests (may skip in quick CI)",
+    "slow: Slow tests (may skip in quick CI)",
    "benchmark: Benchmark tests",
    "xdist: xdist-parallelizable tests",
 ]
@@ -10,22 +10,14 @@ 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 canonical
-Diffusion VAE checkpoint and the official upsampler and LoRA sidecars used by
-the Full/SFT two-stage pipeline, so accept both model licenses and authenticate
-before first use.
-
-The vLLM-Omni integration code is provided under the project's
-[Apache-2.0 license](../../LICENSE). The LTX-2.5 model weights are separate
-artifacts covered by the
-[LTX-2.x Community License](https://github.com/Lightricks/LTX-2/blob/main/LICENSE.md),
-not the vLLM-Omni license. Users are responsible for reviewing and complying
-with the model license before downloading or using the weights.
+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 |
@@ -36,40 +28,6 @@ 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.

-## Video decoder
-
-LTX-2.5 uses the canonical Native Diffusion VAE decoder by default. To opt in
-to the legacy convolutional VAE, set the startup-only stage-0 model extra
-`ltx2_use_conv_vae: true`. The choice applies to all requests and all four
-pipeline classes above. DiffVAE loads its NATTEN kernel from Hugging Face Hub
-during startup. `decode_timestep` and `decode_noise_scale` condition only the
-legacy ConvVAE and have no effect when DiffVAE is selected.
-
-For offline Python usage:
-
-```python
-omni = Omni(
-    model="Lightricks/LTX-2.5-Diffusers",
-    model_class_name="LTX2Pipeline",
-    stage_overrides='{"0":{"extras":{"ltx2_use_conv_vae":true}}}',
-)
-```
-
-For online serving:
-
-```bash
-vllm serve Lightricks/LTX-2.5-Diffusers \
-  --omni \
-  --model-class-name LTX2Pipeline \
-  --stage-overrides '{"0":{"extras":{"ltx2_use_conv_vae":true}}}'
-```
-
-Both decoders are untiled by default. Set `vae_use_tiling` for memory-saving
-serial tiling; DiffVAE tiles only above 80 frames or 768 pixels in either spatial
-dimension. DiffVAE also supports distributed VAE decode; see the
-[VAE Parallelism Guide](../../docs/user_guide/diffusion/parallelism/vae_parallelism.md).
-DiffVAE is decoder-only, so I2V still uses the convolutional VAE encoder.
-
 ## Prerequisites

 ```bash
@@ -84,7 +42,7 @@ Install matching vLLM and vLLM-Omni versions, and ensure `ffmpeg` and
 ## 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 |
@@ -1,82 +0,0 @@
-# MiniMax H3 disaggregated text encoder
-
-This opt-in topology runs the Qwen3-VL text encoder as a vLLM stage and sends
-its hidden states and token-role metadata to an encoder-free diffusion stage.
-The standard MiniMax H3 recipes remain single-stage and continue to load the
-text encoder inside the diffusion pipeline.
-
-## Start the server
-
-Choose the topology explicitly and load its deployment defaults:
-
-```bash
-vllm-omni serve MiniMaxAI/MiniMax-H3 \
-  --omni \
-  --deploy-config vllm_omni/deploy/minimax_h3_disaggregated.yaml
-```
-
-The default deployment assigns stage 0 to GPUs 0-1 with tensor parallel size
-2 and `max_num_seqs: 1`. Stage 1 uses GPUs 2-5 with diffusion tensor parallel
-size 1, Ulysses degree 4, and VAE patch parallel size 4. Adjust the
-`devices`, `tensor_parallel_size`, and stage 1 `parallel_config` values in a
-deployment override for the available hardware. Diffusion quantization,
-layerwise offload, distributed layerwise offload, VAE parallelism, and USP
-settings use the same stage 1 options documented in [MiniMax-H3.md](MiniMax-H3.md).
-
-For example, this five-GPU topology assigns one GPU to the encoder and four
-to the diffusion stage. `--stage-overrides` keeps placement and parallelism
-scoped to the owning stage rather than broadcasting an override to both:
-
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3,4 \
-vllm-omni serve MiniMaxAI/MiniMax-H3 \
-  --omni \
-  --deploy-config vllm_omni/deploy/minimax_h3_disaggregated.yaml \
-  --stage-overrides '{"0":{"devices":"0","tensor_parallel_size":1},"1":{"devices":"1,2,3,4","tensor_parallel_size":1,"ulysses_degree":4,"vae_patch_parallel_size":4}}'
-```
-
-For memory-constrained deployments, start from the CPU-offload or distributed
-layerwise-offload profiles in [MiniMax-H3.md](MiniMax-H3.md). Apply memory and
-quantization options only to Stage 1 with `--stage-overrides`; retain the
-encoder's BF16 configuration and the video/audio VAEs' FP32 precision. Select
-one offload strategy per deployment:
-
-```bash
-# Stage 1 model-level CPU offload.
--stage-overrides '{"1":{"enable_cpu_offload":true}}'
-
-# Stage 1 distributed layerwise offload. Tune resident layers for available RAM.
--stage-overrides '{"1":{"enable_distributed_layerwise_offload":true,"dlo_use_allgather":false,"dlo_resident_layers":20}}'
-
-# Stage 1 online FP8 quantization of the DiT only.
--stage-overrides '{"1":{"diffusion_quantization_config":"{\"transformer\":{\"method\":\"fp8\"}}"}}'
-```
-
-The Stage 1 VAE patch-parallel options remain independent of offload and
-quantization. See [MiniMax-H3.md](MiniMax-H3.md) for memory requirements and
-hardware-qualified profiles before combining these options.
-
-Stage 1 sets `model_loaded.text_encoder: false`; it must not load or download
-text-encoder weights. This H3 topology explicitly keeps its single-replica
-diffusion stage inline, avoiding serialization of decoded video through a
-subprocess. It expects both stages to run in one deployment; cross-node payload
-transport is outside this configuration.
-
-The `/v1/videos` request schema and `extra_params.task` values (`t2va`,
-`fl2va`, and `ref2va`) are unchanged from the single-stage recipe.
-
-## Turbo LoRA
-
-MiniMax-H3 Turbo uses five sigma points, `flow_shift=6`, and
-`audio_flow_shift=3`. Start Turbo deployments with the dedicated defaults so
-requests that omit sampling controls do not inherit the 50-step base schedule:
-
-```bash
-vllm-omni serve MiniMaxAI/MiniMax-H3 \
-  --omni \
-  --lora-path /path/to/MiniMax-H3-Turbo \
-  --deploy-config vllm_omni/deploy/minimax_h3_disaggregated_turbo.yaml
-```
-
-The base deployment intentionally retains 50 inference steps for non-LoRA
-quality. Turbo LoRA supports T2VA and FL2VA requests, not Ref2VA.
@@ -860,63 +860,6 @@ fixed HBM usage. Model-level and standard layerwise offload remain unsupported.
 The five requested sigma points produce the four denoiser evaluations expected
 by the Turbo artifact.

-### FlashGen native LoRA
-
-The FlashGen 4-step T2VA artifact uses the native MiniMax-H3 module layout and
-declares its distilled sigma schedule in safetensors metadata. It is published on
-[ModelScope](https://modelscope.cn/models/FlashGen/Minimax-H3-4step-lora-flashgen):
-
-```text
-FlashGen/Minimax-H3-4step-lora-flashgen/minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors
-```
-
-Download only that file:
-
-```bash
-python -m pip install modelscope
-export FLASHGEN_DIR=/path/to/minimax-h3-flashgen-lora
-export FLASHGEN_FILE=minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors
-modelscope download FlashGen/Minimax-H3-4step-lora-flashgen \
-  --local_dir "${FLASHGEN_DIR}" \
-  --include "${FLASHGEN_FILE}"
-export FLASHGEN_LORA="${FLASHGEN_DIR}/${FLASHGEN_FILE}"
-```
-
-Start from a non-offloaded or DLO FL2VA server command and add
-`--task-type fl2va --lora-backend peft --lora-path "${FLASHGEN_LORA}"`.
-Each request must use T2VA and the distilled interval-count contract:
-
-```bash
-F 'num_inference_steps=4' \
-F 'extra_params={"task":"t2va","duration":5.2}' \
-F "lora={\"name\":\"h3-flashgen-v1.0\",\"path\":\"${FLASHGEN_LORA}\",\"scale\":1.0}"
-```
-
-This path rejects Ref2VA and checkpoints that already pin `base_schedule` in
-`model_index.json`. The adapter metadata carries
-`base_schedule=1.0,0.7,0.4,0.15,0.0`, so `num_inference_steps=4` means four
-denoiser evaluations, not five sigma points. Request-mode generation may omit
-the field and take the count from the adapter schedule; `--step-execution`
-requires it explicitly, because the step scheduler reads the total step count
-off the request at admission, before the adapter schedule is known.
-
-DLO is supported in request-mode generation on the same terms as the Turbo
-adapter: the request-switchable LoRA A/B buffers stay resident on the
-accelerator while DLO streams only the base blocks, so budget for that
-additional fixed HBM usage. The native artifact is rank 64 over 259 target
-modules, and its packed `qkv_proj` and `fc1` layers reuse the full-input A
-tensor per slice while B carries slice-local output rows, so the resident
-footprint exceeds the on-disk payload; measure it for your parallel layout
-rather than assuming the checkpoint size. Pure Ulysses replicates the adapter
-on every rank, while DiT tensor parallelism shards the B buffers. Model-level
-and standard layerwise offload remain unsupported, and `--step-execution`
-cannot be combined with `--enable-distributed-layerwise-offload`.
-
-To validate a deployment, post the same fixed-seed T2VA request twice with the
-adapter and twice without it, then compare the four output digests. The adapter
-is bound and deterministic when each pair matches internally and the two pairs
-differ from each other.
-
 ## Key parameters

 | Parameter | Recommended value | Notes |
@@ -18,11 +18,11 @@ examples in this repository.

 Qwen3-TTS supports three task types, each backed by a dedicated model checkpoint:

-| Task Type     | Model                                    | Description                                                   |
-| ------------- | ---------------------------------------- | ------------------------------------------------------------- |
-| `CustomVoice` | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice`   | Predefined speaker voices with optional style/emotion control |
-| `VoiceDesign` | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign`   | Generate speech from a natural language voice description     |
-| `Base`        | `Qwen/Qwen3-TTS-12Hz-1.7B-Base`          | Voice cloning from reference audio + transcript               |
+| Task Type     | Model                                    | Description                                                  |
+| ------------- | ---------------------------------------- | ------------------------------------------------------------ |
+| `CustomVoice` | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice`  | Predefined speaker voices with optional style/emotion control |
+| `VoiceDesign` | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign`  | Generate speech from a natural language voice description     |
+| `Base`        | `Qwen/Qwen3-TTS-12Hz-1.7B-Base`         | Voice cloning from reference audio + transcript              |

 Smaller 0.6B variants are also available for `CustomVoice` and `Base`.

@@ -166,15 +166,6 @@ python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py --query-ty
 - Key flags: `--omni` is required. `--deploy-config` points to the bundled two-stage pipeline config.
 - Async chunking: Enabled by default in `qwen3_tts.yaml` for streaming-friendly first-audio latency. Raw audio streaming requires `stream=true`, `stream_format="audio"`, and `response_format="pcm"`.
 - Task/model matching: Each task type requires its matching model checkpoint. Using a CustomVoice model for a Base (voice clone) request will fail.
- Base codec termination: Base requests without an explicit `max_new_tokens`
-  use a text-scaled safety ceiling (at least 192 codec frames and no more than
-  the configured model limit). If the Talker reaches that ceiling without
-  codec EOS, non-streaming serving discards the incomplete audio and retries
-  once with a fresh seed; an explicit `seed` or `max_new_tokens` disables the
-  retry. SSE and WebSocket clients receive structured errors and must discard
-  previously emitted audio when the error contains `"action":"discard"`;
-  raw PCM streams terminate with a connection error after any partial bytes
-  already sent.
 - Known limitations: The server serves one model variant at a time. To switch task types (e.g., CustomVoice to Base), restart the server with the corresponding model.

 ## Hardware Support
@@ -13,8 +13,6 @@ transformers >= 5.10.1, < 5.15
 av>=14.0.0
 omegaconf>=2.3.0
 diffusers==0.40.0
-# Hub kernels, including LTX-2.5 NATTEN
-kernels==0.14.1
 safetensors>=0.8.0
 accelerate==1.12.0
 soundfile>=0.13.1
@@ -279,79 +279,17 @@ def test_unmeasured_tpot_stays_missing_after_tokenizer_fallback():
    assert metrics.request_goodput == 0.0


-def test_zero_itl_does_not_create_zero_tpot():
+def test_measured_zero_itl_is_not_treated_as_missing():
    output = _make_output(100, output_tokens=3)
    output.itl = [0.0, 0.0]
    output.duplex_session_metrics = {"mean_ttft_ms": 100.0}

    metrics = _calculate_test_metrics([output], {"tpot": 1.0})

-    assert (metrics.num_tpot_samples, metrics.num_itl_samples) == (0, 2)
-    assert math.isnan(metrics.mean_tpot_ms)
+    assert (metrics.num_tpot_samples, metrics.num_itl_samples) == (1, 2)
+    assert metrics.mean_tpot_ms == 0.0
    assert metrics.mean_itl_ms == 0.0
-    assert metrics.request_goodput == 0.0
-
-
-def test_stage_output_tokens_without_client_timing_omit_tpot(capsys):
-    output = _make_output(100, output_tokens=5)
-    output.itl = []
-    output.text_latency = 0.0
-    output.ttft = 0.1
-
-    metrics, _ = calculate_metrics(
-        input_requests=[],
-        outputs=[output],
-        dur_s=1.0,
-        tokenizer=None,
-        selected_percentiles=[50.0, 99.0],
-        goodput_config_dict={},
-        task_type=TaskType.GENERATION,
-        selected_percentile_metrics=["tpot"],
-        max_concurrency=None,
-        request_rate=float("inf"),
-        benchmark_duration=1.0,
-    )
-
-    printed = capsys.readouterr().out
-    assert metrics.num_tpot_samples == 0
-    assert math.isnan(metrics.mean_tpot_ms)
-    assert "Time per Output Token" not in printed
-    assert "Mean TPOT" not in printed
-
-
-def test_consistent_client_latency_can_supply_tpot_fallback():
-    output = _make_output(100, output_tokens=10)
-    output.itl = []
-    output.ttft = 0.1
-    output.text_latency = 1.0
-
-    metrics = _calculate_test_metrics([output])
-
-    assert metrics.num_tpot_samples == 1
-    assert metrics.mean_tpot_ms == pytest.approx(100.0)
-
-
-def test_single_token_responses_do_not_report_zero_tpot(capsys):
-    output = _make_output(100, output_tokens=1)
-
-    metrics, _ = calculate_metrics(
-        input_requests=[],
-        outputs=[output],
-        dur_s=1.0,
-        tokenizer=None,
-        selected_percentiles=[50.0],
-        goodput_config_dict={},
-        task_type=TaskType.GENERATION,
-        selected_percentile_metrics=["tpot"],
-        max_concurrency=None,
-        request_rate=float("inf"),
-        benchmark_duration=1.0,
-    )
-
-    printed = capsys.readouterr().out
-    assert metrics.num_tpot_samples == 0
-    assert math.isnan(metrics.mean_tpot_ms)
-    assert "Time per Output Token" not in printed
+    assert metrics.request_goodput == 1.0


 def test_duplex_goodput_does_not_pair_measurements_from_different_requests():
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 """
 Unit tests for patch.py
@@ -17,7 +17,6 @@ from vllm.benchmarks.lib.endpoint_request_func import RequestFuncInput

 from vllm_omni.benchmarks.patch.patch import (
    MixRequestFuncOutput,
-    _apply_stage0_token_timings,
    async_request_openai_chat_omni_completions,
    async_request_openai_realtime_duplex,
 )
@@ -103,24 +102,7 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
                received_at_s=now + 0.03,
            )
            self.events.add(
-                {
-                    "type": "response.done",
-                    "response": {
-                        "id": response_id,
-                        "metadata": {
-                            "vllm_omni": {
-                                "stage_metrics": {
-                                    "0": {
-                                        "num_tokens_out": 3,
-                                        "vllm_ttft_ms": 20.0,
-                                        "vllm_tpot_ms": 10.0,
-                                        "vllm_itls_ms": [10.0, 10.0],
-                                    }
-                                }
-                            }
-                        },
-                    },
-                },
+                {"type": "response.done", "response": {"id": response_id}},
                received_at_s=now + 0.04,
            )

@@ -161,7 +143,6 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
    assert client.configure_kwargs["native_duplex"] is False
    assert client.configure_kwargs["extra_body"] == {
        "ref_audio": "data:audio/wav;base64,AAAA",
-        "return_stage_metrics": True,
    }
    assert client.sent == [
        event
@@ -186,10 +167,6 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
    assert output.audio_ttfp > output.ttft
    assert output.audio_rtf > 0
    assert output.latency > 0
-    assert output.output_tokens == 12
-    assert output.itl == [0.01, 0.01] * 4
-    assert output.text_latency == pytest.approx(output.ttft + 0.08)
-    assert output.tpot_measured is True
    assert output.tts_turn_pcm_bytes == [b"\x00\x00" * 2400] * 4
    assert output.tts_output_pcm_bytes == b"\x00\x00" * 9600
    session_id = output.duplex_request_metrics[0]["session_id"]
@@ -221,54 +198,6 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
    }


-def test_stage0_token_timings_use_weighted_tpot_when_itls_are_incomplete():
-    output = MixRequestFuncOutput(ttft=0.1)
-
-    measured = _apply_stage0_token_timings(
-        output,
-        [
-            {"output_token_count": 3, "itls_ms": [], "tpot_ms": 10.0},
-            {"output_token_count": 2, "itls_ms": [], "tpot_ms": 40.0},
-        ],
-        expected_output_tokens=5,
-    )
-
-    assert measured is True
-    assert output.itl == []
-    assert output.tpot_measured is True
-    assert (output.text_latency - output.ttft) / 4 == pytest.approx(0.02)
-
-
-def test_stage0_zero_itls_fall_back_to_positive_tpot():
-    output = MixRequestFuncOutput(ttft=0.1)
-
-    measured = _apply_stage0_token_timings(
-        output,
-        [{"output_token_count": 3, "itls_ms": [0.0, 0.0], "tpot_ms": 25.0}],
-        expected_output_tokens=3,
-    )
-
-    assert measured is True
-    assert output.itl == []
-    assert output.tpot_measured is True
-    assert (output.text_latency - output.ttft) / 2 == pytest.approx(0.025)
-
-
-def test_stage0_token_count_without_timing_is_not_measured():
-    output = MixRequestFuncOutput(ttft=0.1)
-
-    measured = _apply_stage0_token_timings(
-        output,
-        [{"output_token_count": 3, "itls_ms": [], "tpot_ms": None}],
-        expected_output_tokens=3,
-    )
-
-    assert measured is False
-    assert output.itl == []
-    assert output.text_latency == output.ttft
-    assert output.tpot_measured is False
-
-
 def create_sse_chunk(data_dict):
    """Helper to create SSE formatted chunk"""
    return f"data: {json.dumps(data_dict)}\n\n".encode()
@@ -1,6 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
 """Tests for Daily-Omni ``--daily-omni-pack-mode`` packing recipes.

 The ``minicpm-interleave`` mode is an accuracy-critical protocol: MiniCPM-o only reaches
@@ -30,8 +27,6 @@ _MODULE_NAME = "vllm_omni.benchmarks.data_modules.daily_omni_dataset"

 if _MODULE_NAME not in sys.modules:
    _spec = importlib.util.spec_from_file_location(_MODULE_NAME, _MODULE_PATH)
-    if _spec is None or _spec.loader is None:
-        raise ImportError(f"Cannot load {_MODULE_NAME} from {_MODULE_PATH}")
    _mod = importlib.util.module_from_spec(_spec)
    sys.modules[_MODULE_NAME] = _mod
    _spec.loader.exec_module(_mod)
@@ -92,9 +87,7 @@ def _stub_extract(monkeypatch, dataset: DailyOmniDataset, counter: list[int]) ->
    def fake_extract(video_path, *, audio_path, include_audio, **_kw):
        counter.append(1)
        frames = [Image.new("RGB", (16, 16), color=(i, i, i)) for i in range(_NUM_FRAMES)]
-        segments: list[np.ndarray] = (
-            [np.zeros(1600, dtype=np.float32) for _ in range(_NUM_FRAMES)] if include_audio else []
-        )
+        segments = [np.zeros(1600, dtype=np.float32) for _ in range(_NUM_FRAMES)] if include_audio else []
        return frames, segments

    monkeypatch.setattr(dataset, "_extract_minicpm_frame_audio_segments", fake_extract)
@@ -585,7 +585,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa

@pytest.mark.asyncio
@pytest.mark.parametrize(
-    ("request_metrics", "expected_itl", "expected_text_latency", "expected_tpot_measured"),
+    ("request_metrics", "expected_itl", "expected_text_latency"),
    [
        (
            [
@@ -594,7 +594,6 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
            ],
            [0.01, 0.02, 0.03],
            0.16,
-            True,
        ),
        (
            [
@@ -603,7 +602,6 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
            ],
            [],
            0.18,
-            True,
        ),
        (
            [
@@ -612,7 +610,6 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
            ],
            [],
            0.1,
-            False,
        ),
    ],
 )
@@ -622,7 +619,6 @@ async def test_adapter_reports_exact_or_weighted_token_timing(
    request_metrics: list[dict[str, object]],
    expected_itl: list[float],
    expected_text_latency: float,
-    expected_tpot_measured: bool,
 ):
    case, options = _case(tmp_path), _session_options(tmp_path)
    request = _request(case, options)
@@ -647,7 +643,7 @@ async def test_adapter_reports_exact_or_weighted_token_timing(
    assert output.success
    assert output.itl == expected_itl
    assert output.text_latency == pytest.approx(expected_text_latency)
-    assert output.tpot_measured is expected_tpot_measured
+    assert output.tpot_measured is True


 def test_batch_finalization_publishes_only_measured_results(tmp_path: Path, monkeypatch: pytest.MonkeyPatch):
@@ -34,7 +34,6 @@ from vllm_omni.config.stage_config import (
    build_stage_runtime_overrides,
    load_deploy_config,
    merge_pipeline_deploy,
-    normalize_pipeline_cli_overrides,
    pipeline_cfg_resolver,
 )
 from vllm_omni.engine.arg_utils import SHARED_FIELDS, internal_blacklist_keys
@@ -430,20 +429,6 @@ class TestStageConfigFactory:
        assert cfg["final_output"] is True
        assert cfg["final_output_type"] == "image"

-    @pytest.mark.parametrize(
-        "model_class_name",
-        [
-            "MiniMaxH3Pipeline",
-            "WanPipeline",
-            "LTX2Pipeline",
-            "Cosmos3OmniDiffusersPipeline",
-        ],
-    )
-    def test_default_video_diffusion_output_type(self, model_class_name):
-        configs = StageConfigFactory.create_default_diffusion({"model_class_name": model_class_name})
-
-        assert configs[0]["final_output_type"] == "video"
-
    def test_default_diffusion_with_parallel_config(self):
        """Test diffusion config calculates devices from parallel_config."""

@@ -1159,7 +1144,7 @@ class TestDeployConfigLoading:
        stages = merge_pipeline_deploy(pipeline, deploy)

        assert deploy.session_mode == "duplex"
-        assert deploy.active_stream_window == max_sessions
+        assert deploy.active_stream_window == 1
        assert deploy.duplex_session.max_sessions == max_sessions
        assert [stage.session_mode for stage in stages] == ["duplex", "duplex", "duplex"]
        assert [stage.to_omegaconf().session_mode for stage in stages] == ["duplex", "duplex", "duplex"]
@@ -1339,13 +1324,6 @@ stages:
            ("step_audio2_ci.yaml", "step_audio_2_asr", 1, "text", "step_audio_2_asr"),
            ("hunyuan_video_15.yaml", "hunyuan_video_15", 1, "video", None),
            ("wan2_2_ti2v.yaml", "wan2_2_ti2v", 1, "video", None),
-            (
-                "minimax_h3_disaggregated.yaml",
-                "minimax_h3_disaggregated",
-                2,
-                "video",
-                "minimax_h3_disaggregated",
-            ),
        ],
    )
    def test_load_new_registry_backed_deploy_configs(
@@ -1366,91 +1344,9 @@ stages:
        assert len(stages) == stage_count
        assert stages[-1].final_output is True
        assert stages[-1].final_output_type == final_output_type
-        if pipeline_name == "minimax_h3_disaggregated":
-            assert stages[0].yaml_engine_args["model_path_resolver"].endswith(".resolve_minimax_h3_model_root")
-            assert (
-                stages[1].yaml_engine_args["model_path_resolver"].endswith(".resolve_minimax_h3_diffusion_model_path")
-            )
-            assert "model_path_resolver" not in stages[0].yaml_extras
-            assert "model_path_resolver" not in stages[1].yaml_extras
        if deploy.trust_remote_code is not None:
            assert {s.yaml_engine_args.get("trust_remote_code") for s in stages} == {deploy.trust_remote_code}

-    def test_minimax_h3_resolvers_reach_production_engine_args(self, tmp_path: Path, monkeypatch):
-        from vllm_omni.engine import stage_init_utils
-
-        model_root = tmp_path / "MiniMax-H3"
-        (model_root / "FL2VA" / "text_encoder").mkdir(parents=True)
-        (model_root / "Ref2VA" / "text_encoder").mkdir(parents=True)
-        monkeypatch.setattr(stage_init_utils, "resolve_worker_cls", lambda _engine_args: None)
-
-        deploy = load_deploy_config(get_deploy_config_path("minimax_h3_disaggregated.yaml"))
-        assert deploy.stages[1].engine_extras["model_loaded"] == {"text_encoder": False}
-        stages = merge_pipeline_deploy(OMNI_PIPELINES["minimax_h3_disaggregated"], deploy)
-        resolved = [stage_init_utils.build_engine_args_dict(stage.to_omegaconf(), str(model_root)) for stage in stages]
-
-        assert resolved[0]["model"] == str(model_root / "FL2VA" / "text_encoder")
-        assert resolved[1]["model"] == str(model_root / "FL2VA")
-        assert all("model_path_resolver" not in engine_args for engine_args in resolved)
-
-    def test_minimax_h3_text_encoder_tp_reaches_only_stage_zero(self):
-        pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
-        stages, _ = StageConfigFactory._create_legacy_from_registry(
-            pipeline,
-            {"text_encoder_tp_size": 4},
-        )
-
-        assert stages[0].runtime_overrides["tensor_parallel_size"] == 4
-        assert "text_encoder_tp_size" not in stages[0].runtime_overrides
-        assert "text_encoder_tp_size" not in stages[1].runtime_overrides
-        assert stages[1].yaml_engine_args["parallel_config"]["tensor_parallel_size"] == 1
-
-    def test_minimax_h3_disaggregated_defaults_match_validated_topology(self):
-        pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
-        deploy = load_deploy_config(Path(get_deploy_config_path("minimax_h3_disaggregated.yaml")))
-        stages = merge_pipeline_deploy(pipeline, deploy)
-
-        assert stages[0].yaml_engine_args["max_num_seqs"] == 1
-        assert stages[0].yaml_engine_args["model_path_resolver"].endswith(".resolve_minimax_h3_model_root")
-        assert stages[1].yaml_engine_args["model_path_resolver"].endswith(".resolve_minimax_h3_diffusion_model_path")
-        parallel = stages[1].yaml_engine_args["parallel_config"]
-        assert parallel["tensor_parallel_size"] == 1
-        assert parallel["ulysses_degree"] == 4
-        assert parallel["vae_patch_parallel_size"] == 4
-        assert stages[1].yaml_engine_args["inline_diffusion"] is True
-        assert stages[1].yaml_extras["default_sampling_params"]["num_inference_steps"] == 50
-
-        turbo = load_deploy_config(Path(get_deploy_config_path("minimax_h3_disaggregated_turbo.yaml")))
-        turbo_stages = merge_pipeline_deploy(pipeline, turbo)
-        turbo_sampling = turbo_stages[1].yaml_extras["default_sampling_params"]
-        assert turbo_sampling["num_inference_steps"] == 5
-        assert turbo_sampling["extra_args"] == {"flow_shift": 6.0, "audio_flow_shift": 3.0}
-
-    def test_minimax_h3_text_encoder_tp_alias_targets_stage_zero(self):
-        pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
-
-        assert normalize_pipeline_cli_overrides(pipeline, {}) == {}
-        assert normalize_pipeline_cli_overrides(pipeline, {"text_encoder_tp_size": 4}) == {
-            "stage_0_tensor_parallel_size": 4
-        }
-
-    def test_minimax_h3_stage_zero_tp_override_wins_alias_conflict(self):
-        pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
-
-        with pytest.warns(UserWarning, match="stage_0_tensor_parallel_size=2 takes precedence"):
-            normalized = normalize_pipeline_cli_overrides(
-                pipeline,
-                {"text_encoder_tp_size": 4, "stage_0_tensor_parallel_size": 2},
-            )
-
-        assert normalized == {"stage_0_tensor_parallel_size": 2}
-
-    def test_minimax_h3_rejects_text_encoder_tp_on_diffusion_stage(self):
-        pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
-
-        with pytest.raises(ValueError, match="stage_1_text_encoder_tp_size cannot be set"):
-            normalize_pipeline_cli_overrides(pipeline, {"stage_1_text_encoder_tp_size": 4})
-
    @pytest.mark.parametrize(
        ("config_json", "model_index", "expected_pipeline"),
        [
@@ -1486,29 +1382,6 @@ stages:
        assert pipeline is not None
        assert pipeline.model_type == expected_pipeline

-    def test_minimax_h3_disaggregation_is_not_auto_discovered(self):
-        def get_model_file(filename, _model, revision=None):
-            del revision
-            if filename == "config.json":
-                return None
-            if filename == "model_index.json":
-                return {"_class_name": "MiniMaxH3Pipeline"}
-            return None
-
-        with (
-            patch.object(StageConfigFactory, "get_hf_config", return_value=None),
-            patch(
-                "vllm_omni.config.config_factory.get_hf_file_to_dict",
-                side_effect=get_model_file,
-            ),
-        ):
-            pipeline = StageConfigFactory.get_pipeline_config(
-                model="/models/unrelated-checkpoint-name",
-                trust_remote_code=False,
-            )
-
-        assert pipeline is None
-
    @pytest.mark.parametrize("deploy_name", ["step_audio_2.yaml", "step_audio_2_async_chunk.yaml"])
    def test_step_audio2_deploy_configs_fit_two_gpus(self, deploy_name):
        deploy = load_deploy_config(Path(get_deploy_config_path(deploy_name)))
@@ -2399,20 +2272,6 @@ class TestPlatformOverrides:
        assert rocm.stages[0].enforce_eager is None
        assert rocm.stages[1].enforce_eager is True

-    def test_higgs_audio_v3_rocm_uses_triton_attention(self):
-        deploy_path = Path(get_deploy_config_path("higgs_multimodal_qwen3.yaml"))
-
-        base = load_deploy_config(deploy_path)
-        assert base.stages[0].engine_extras["attention_backend"] == "FLASHINFER"
-
-        rocm = _apply_platform_overrides(base, platform="rocm")
-        assert rocm.stages[0].engine_extras["attention_backend"] == "TRITON_ATTN"
-
-        pipeline = resolve_pipeline_config("higgs_multimodal_qwen3")
-        assert isinstance(pipeline, PipelineConfig)
-        stages = merge_pipeline_deploy(pipeline, rocm)
-        assert stages[0].yaml_engine_args["attention_backend"] == "TRITON_ATTN"
-
    def test_qwen3_omni_cuda_uses_thinker_rotary_custom_op(self):
        deploy_path = Path(get_deploy_config_path("qwen3_omni_moe.yaml"))
        pipeline = resolve_pipeline_config(
@@ -92,23 +92,10 @@ def _from_pipeline_key(
    )


-def test_minimax_h3_text_encoder_tp_targets_only_structured_stage_zero() -> None:
-    config = _from_pipeline_key(
-        "minimax_h3_disaggregated",
-        cli_overrides={"text_encoder_tp_size": 4},
-    )
-
-    assert config.stage_by_id(0).parallel_config.tensor_parallel_size == 4
-    diffusion = config.stage_by_id(1)
-    assert diffusion.parallel_config.tensor_parallel_size == 1
-    assert diffusion.parallel_config.text_encoder_tp_size == 1
-
-
 def test_duplex_session_capacity_propagates_to_every_structured_stage() -> None:
    omni_config = _from_pipeline_key("personaplex")

    assert [stage.model_config.duplex_max_sessions for stage in omni_config.stage_configs] == [2, 2]
-    assert [stage.model_config.session_mode for stage in omni_config.stage_configs] == ["duplex", "duplex"]


 def test_non_duplex_deploy_keeps_model_session_capacity_at_one(tmp_path: Path) -> None:
@@ -118,7 +105,6 @@ def test_non_duplex_deploy_keeps_model_session_capacity_at_one(tmp_path: Path) -
    omni_config = _from_pipeline_key("personaplex", deploy_config_path=str(deploy_path))

    assert [stage.model_config.duplex_max_sessions for stage in omni_config.stage_configs] == [1, 1]
-    assert [stage.model_config.session_mode for stage in omni_config.stage_configs] == ["turn", "turn"]


@pytest.mark.parametrize("model_type", sorted(OMNI_PIPELINES))
@@ -146,7 +132,6 @@ def test_vllm_omni_config_from_pipeline_config_matches_merge_pipeline_deploy(mod

        engine_args = legacy_stage.yaml_engine_args
        assert omni_stage.model_config.duplex_max_sessions == engine_args.get("duplex_max_sessions", 1)
-        assert omni_stage.model_config.session_mode == engine_args.get("session_mode", "turn")
        assert omni_stage.model_config.enforce_eager == engine_args.get("enforce_eager", False)
        assert omni_stage.load_config.load_format == engine_args.get("load_format", "auto")
        assert omni_stage.load_config.tokenizer_mode == engine_args.get("tokenizer_mode", "auto")
@@ -594,7 +579,6 @@ def test_sub_config_fields_match_structured_scopes():
        "interleave_mm_strings",
        "media_io_kwargs",
        "active_stream_window",
-        "session_mode",
        "duplex_max_sessions",
        "enable_sleep_mode",
        "default_sampling_params",
@@ -653,11 +637,10 @@ def test_sub_config_fields_match_structured_scopes():
        "ring_degree",
        "allgather_degree",
        "ulysses_mode",
-        "ulysses_a2a_permute",
        "cfg_parallel_size",
        "vae_patch_parallel_size",
-        "vae_parallel_mode",
        "text_encoder_tp_size",
+        "vae_parallel_mode",
        "use_hsdp",
        "mask_sp_padding",
        "hsdp_shard_size",
@@ -859,23 +842,6 @@ def test_from_pipeline_config_derives_sequence_parallel_size_from_allgather_degr
    assert stage.parallel_config.world_size == 2


-def test_from_pipeline_config_preserves_deployed_ulysses_a2a_permute() -> None:
-    pipeline = _resolve_pipeline_or_skip("dreamzero")
-    deploy = DeployConfig(
-        stages=[
-            StageDeployConfig(
-                stage_id=0,
-                ulysses_a2a_permute=True,
-            )
-        ]
-    )
-
-    stage = VllmOmniConfig.from_pipeline_config(pipeline, user_deploy_config=deploy).stage_by_id(0)
-
-    assert isinstance(stage, VllmOmniDiffusionStageConfig)
-    assert stage.parallel_config.ulysses_a2a_permute is True
-
-
 def test_diffusion_parallel_config_accepts_four_way_guidance_parallelism():
    cfg = OmniStageDiffusionParallelConfig(cfg_parallel_size=4)

@@ -970,35 +936,6 @@ def test_structured_diffusion_config_rejects_non_boolean_compile_dynamic():
        omni_config_module._DiffusionConfigProjection(diffusion_compile_dynamic="false")


-def test_from_pipeline_config_routes_ltx2_conv_vae_extra(tmp_path):
-    deploy_path = tmp_path / "dreamzero_ltx2_extras.yaml"
-    deploy_path.write_text(
-        "\n".join(
-            [
-                "pipeline: dreamzero",
-                "async_chunk: false",
-                "stages:",
-                "  - stage_id: 0",
-                "    extras:",
-                "      ltx2_use_conv_vae: true",
-            ]
-        )
-    )
-
-    stage = _from_pipeline_key("dreamzero", deploy_config_path=str(deploy_path)).stage_by_id(0)
-
-    assert stage.diffusion_config.extras["ltx2_use_conv_vae"] is True
-
-
-def test_stage_override_routes_ltx2_conv_vae_extra():
-    stage = _from_pipeline_key(
-        "dreamzero",
-        cli_overrides={"stage_0_extras": {"ltx2_use_conv_vae": True}},
-    ).stage_by_id(0)
-
-    assert stage.diffusion_config.extras["ltx2_use_conv_vae"] is True
-
-
 def test_structured_diffusion_config_rejects_invalid_compile_granularity():
    with pytest.raises(ValidationError, match="diffusion_compile_granularity"):
        omni_config_module._DiffusionConfigProjection(diffusion_compile_granularity="block")
@@ -66,10 +66,3 @@ def test_register_resolver_requires_model_type(custom_resolver, clean_pipeline_r
    """Ensure that registering a custom resolver to OMNI_PIPELINES requires an explicit model_type."""
    with pytest.raises(ValueError):
        register_pipeline(custom_resolver)
-
-
-def test_minimax_h3_disaggregation_is_explicit_opt_in():
-    assert "minimax_h3" not in OMNI_PIPELINES
-    pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
-    assert isinstance(pipeline, PipelineConfig)
-    assert pipeline.model_type == "minimax_h3_disaggregated"
@@ -25,9 +25,9 @@ pytest_plugins = (
    "tests.helpers.fixtures.clean",
    "tests.helpers.fixtures.log",
    "tests.helpers.fixtures.media",
-    "tests.helpers.fixtures.pytest_collection",
-    "tests.helpers.fixtures.pytest_run_args",
+    "tests.helpers.fixtures.run_args",
    "tests.helpers.fixtures.runtime",
+    "tests.helpers.fixtures.xdist",
 )


@@ -1,89 +0,0 @@
-"""Tests for ``OmniARScheduler._drop_aborted_queued_requests``.
-
-``schedule()`` sweeps ``FINISHED_ABORTED`` requests out of the queues before
-handing control to upstream, because upstream ``Scheduler.schedule()`` raises
-``RuntimeError: Invalid request status: FINISHED_ABORTED`` for any request it
-admits in a finished state -- which kills the stage's engine core, not just
-the request.
-
-Regression for the sweep missing ``skipped_waiting``, the third queue
-upstream admits from. An aborted duplex session parked there was re-selected
-by ``_select_waiting_queue_for_scheduling`` on a later tick and crashed the
-stage.
-"""
-
-from __future__ import annotations
-
-import pytest
-from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
-from vllm.v1.request import RequestStatus
-
-import vllm_omni.core.sched.omni_ar_scheduler as ar_sched_mod
-from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-_POLICIES = [
-    pytest.param(SchedulingPolicy.FCFS, id="fcfs"),
-    pytest.param(SchedulingPolicy.PRIORITY, id="priority"),
-]
-
-
-class _StubRequest:
-    """Minimal ``Request`` stub with the surface the sweep exercises.
-
-    Carries the ``(priority, arrival_time)`` ordering ``PriorityRequestQueue``
-    relies on so the same stub works under both scheduling policies.
-    """
-
-    def __init__(self, request_id: str, status: RequestStatus, arrival_time: float = 0.0) -> None:
-        self.request_id = request_id
-        self.status = status
-        self.priority = 0
-        self.arrival_time = arrival_time
-
-    def __lt__(self, other: _StubRequest) -> bool:
-        return (self.priority, self.arrival_time) < (other.priority, other.arrival_time)
-
-
-def _make_scheduler(policy: SchedulingPolicy, *, waiting=(), skipped_waiting=(), running=()):
-    """Bare scheduler with just the surface the sweep reads."""
-    scheduler = OmniARScheduler.__new__(OmniARScheduler)
-    scheduler.waiting = create_request_queue(policy)
-    for req in waiting:
-        scheduler.waiting.add_request(req)
-    scheduler.skipped_waiting = create_request_queue(policy)
-    for req in skipped_waiting:
-        scheduler.skipped_waiting.add_request(req)
-    scheduler.running = list(running)
-    return scheduler
-
-
-@pytest.mark.parametrize("policy", _POLICIES)
-def test_schedule_sweeps_skipped_waiting_before_upstream_selection(
-    monkeypatch: pytest.MonkeyPatch,
-    policy: SchedulingPolicy,
-) -> None:
-    """The public schedule path must remove aborted skipped requests first."""
-    aborted = _StubRequest("req-aborted", RequestStatus.FINISHED_ABORTED)
-    scheduler = _make_scheduler(policy, skipped_waiting=[aborted])
-    scheduler.chunk_transfer_adapter = None
-    scheduler.input_coordinator = None
-    scheduler.num_waiting_for_streaming_input = 0
-    scheduler._process_pending_omni_inputs = lambda model_mode: None
-    scheduler._resync_streaming_input_counter = lambda: None
-    scheduler._should_defer_waiting_admission = lambda: False
-    scheduler._restore_omni_wait_queues = lambda: None
-    scheduler._postprocess_omni_schedule_output = lambda *args, **kwargs: None
-    scheduler.get_finished_requests_needing_kv_transfer = lambda: {}
-    scheduler._wrap_omni_scheduler_output = lambda output, **kwargs: output
-
-    sentinel = object()
-
-    def upstream_schedule(_self, _throttle_prefills: bool = False):
-        assert list(scheduler.skipped_waiting) == []
-        return sentinel
-
-    monkeypatch.setattr(ar_sched_mod.VLLMScheduler, "schedule", upstream_schedule)
-
-    assert OmniARScheduler.schedule(scheduler) is sentinel
@@ -142,16 +142,13 @@ def test_exact_drain_delivers_new_segment_frame() -> None:
    session.num_computed_tokens = 6
    session.num_output_placeholders = 1
    session.num_in_flight_tokens = 1
-    session.async_tokens_to_discard = 1

    _replace_streaming_session(session)
    assert session.num_stale_output_tokens == 1
-    assert session.async_tokens_to_discard == 1

    sched = _make_drain_sched(session)
    assert _run_step(sched, session, num_scheduled=1, token=42) is False  # late frame dropped
    assert session.num_stale_output_tokens == 0
-    assert session.async_tokens_to_discard == 0
    assert _run_step(sched, session, num_scheduled=1, token=43) is True  # new segment survives


@@ -18,19 +18,17 @@ from vllm.sampling_params import SamplingParams
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.request import Request, RequestStatus, StreamingUpdate
-from vllm_omni.core.sched.omni_ar_scheduler import OmniARAsyncScheduler, OmniARScheduler
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler

 # isort: on

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


-def _make_scheduler(*, stage_id: int = 0, session_mode: str = "turn") -> OmniARScheduler:
+def _make_scheduler(*, stage_id: int = 0) -> OmniARScheduler:
    sched = OmniARScheduler.__new__(OmniARScheduler)
    sched._new_prompt_len_snapshot = {}
-    sched.vllm_config = SimpleNamespace(
-        model_config=SimpleNamespace(stage_id=stage_id, session_mode=session_mode),
-    )
+    sched.vllm_config = SimpleNamespace(model_config=SimpleNamespace(stage_id=stage_id))
    sched.num_waiting_for_streaming_input = 0
    sched.log_stats = False
    sched.chunk_transfer_adapter = None
@@ -187,7 +185,6 @@ def test_resumable_segment_boundary_keeps_pre_transition_send_watermark() -> Non

    def replace_with_next_segment(request: Request) -> bool:
        request.num_computed_tokens = 0
-        request._omni_segment_generation = 1
        request.status = RequestStatus.WAITING
        return False

@@ -204,7 +201,6 @@ def test_resumable_segment_boundary_keeps_pre_transition_send_watermark() -> Non
        True,
        new_token_ids=[42],
        confirmed_num_computed_tokens=26,
-        segment_generation=0,
    )


@@ -290,140 +286,6 @@ def test_queued_streaming_update_on_async_stop_fences_in_flight_once() -> None:
    assert session.num_stale_output_tokens == session.num_in_flight_tokens == 1


-def test_stale_async_frame_is_dropped_before_output_processing() -> None:
-    session = _make_request()
-    session.status = RequestStatus.RUNNING
-    session.num_in_flight_tokens = 2
-    session.num_computed_tokens = session.num_tokens + 1
-    session.num_output_placeholders = 1
-    session.async_tokens_to_discard = 1
-    session.sampling_params = SimpleNamespace(num_logprobs=1)
-    num_computed_tokens = session.num_computed_tokens
-    num_output_placeholders = session.num_output_placeholders
-
-    sched = MagicMock()
-    sched.requests = {session.request_id: session}
-    sched.perf_metrics = None
-    sched.structured_output_manager.should_advance.return_value = False
-
-    def discard_stale_output(request: Request, token_ids: list[int]) -> tuple[list[int], bool]:
-        request.async_tokens_to_discard = 0
-        return token_ids, False
-
-    sched._update_request_with_output.side_effect = discard_stale_output
-    sched._process_kv_transfer_trigger.return_value = False
-    sched.chunk_transfer_adapter = MagicMock()
-    sched.running = [session]
-    sched.waiting_for_transfer_free = set()
-    sched.transfer_triggered_requests = set()
-    sched.active_kv_transfers = set()
-    sched.pending_stop_after_extraction = set()
-    sched.connector = None
-    sched.kv_cache_manager.take_events.return_value = None
-    sched.kv_cache_manager.estimate_cached_tokens.return_value = 0
-    sched.finished_req_ids_dict = {}
-    sched.make_stats.return_value = None
-
-    scheduler_output = MagicMock(spec=SchedulerOutput)
-    scheduler_output.num_scheduled_tokens = {session.request_id: 1}
-    scheduler_output.scheduled_spec_decode_tokens = {}
-    scheduler_output.num_invalid_spec_tokens = 0
-
-    model_runner_output = MagicMock(spec=ModelRunnerOutput)
-    model_runner_output.sampled_token_ids = [[42]]
-    model_runner_output.logprobs = None
-    model_runner_output.prompt_logprobs_dict = {}
-    model_runner_output.pooler_output = None
-    model_runner_output.multimodal_outputs = None
-    model_runner_output.inter_stage_outputs = [{"hidden": object()}]
-    model_runner_output.num_nans_in_logits = None
-    model_runner_output.kv_connector_output = None
-    model_runner_output.cudagraph_stats = None
-    model_runner_output.req_id_to_index = {session.request_id: 0}
-    model_runner_output.routed_experts = None
-
-    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
-
-    assert session.async_tokens_to_discard == 0
-    assert session.status == RequestStatus.RUNNING
-    assert session.num_computed_tokens == num_computed_tokens
-    assert session.num_output_placeholders == num_output_placeholders
-    sched.chunk_transfer_adapter.save_async.assert_not_called()
-
-    session.sampling_params = SamplingParams(max_tokens=8)
-    next_payload = {"hidden": object()}
-    sched._update_request_with_output.side_effect = None
-    sched._update_request_with_output.return_value = ([43], False)
-    model_runner_output.sampled_token_ids = [[43]]
-    model_runner_output.inter_stage_outputs = [next_payload]
-
-    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
-
-    sched.chunk_transfer_adapter.save_async.assert_called_once_with(
-        next_payload,
-        session,
-        False,
-        new_token_ids=[43],
-        confirmed_num_computed_tokens=None,
-    )
-
-
-def test_legacy_stale_async_marker_bypasses_real_placeholder_accounting() -> None:
-    request = _make_request()
-    request.status = RequestStatus.RUNNING
-    request.num_in_flight_tokens = 1
-    request.num_output_placeholders = 0
-    request.async_tokens_to_discard = 1
-    request.num_stale_output_tokens = 0
-
-    sched = OmniARAsyncScheduler.__new__(OmniARAsyncScheduler)
-    sched.requests = {request.request_id: request}
-    sched.perf_metrics = None
-    sched.chunk_transfer_adapter = None
-    sched.connector = None
-    sched.ec_connector = None
-    sched.waiting_for_transfer_free = set()
-    sched.transfer_triggered_requests = set()
-    sched.active_kv_transfers = set()
-    sched.pending_stop_after_extraction = set()
-    sched.finished_req_ids_dict = {}
-    sched._new_prompt_len_snapshot = {}
-    sched.kv_cache_manager = MagicMock()
-    sched.kv_cache_manager.take_events.return_value = None
-    sched._remove_stopped_requests_from_queues = MagicMock()
-    sched._handle_failed_kv_load_outputs = MagicMock(return_value=[])
-    sched._cleanup_kv_tracking = MagicMock()
-    sched._aggregate_kv_connector_stats = MagicMock(return_value=None)
-    sched._publish_kv_cache_events = MagicMock()
-    sched._attach_finished_request_sets = MagicMock()
-    sched._attach_scheduler_stats = MagicMock()
-    sched._capture_omni_connector_output = MagicMock()
-
-    scheduler_output = MagicMock(spec=SchedulerOutput)
-    scheduler_output.num_scheduled_tokens = {request.request_id: 1}
-    scheduler_output.scheduled_spec_decode_tokens = {}
-    scheduler_output.num_invalid_spec_tokens = 0
-
-    model_runner_output = MagicMock(spec=ModelRunnerOutput)
-    model_runner_output.sampled_token_ids = [[42]]
-    model_runner_output.logprobs = None
-    model_runner_output.prompt_logprobs_dict = {}
-    model_runner_output.pooler_output = None
-    model_runner_output.multimodal_outputs = None
-    model_runner_output.inter_stage_outputs = None
-    model_runner_output.num_nans_in_logits = None
-    model_runner_output.kv_connector_output = None
-    model_runner_output.cudagraph_stats = None
-    model_runner_output.req_id_to_index = {request.request_id: 0}
-    model_runner_output.routed_experts = None
-
-    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
-
-    assert request.async_tokens_to_discard == 0
-    assert request.num_output_placeholders == 0
-    assert request._output_token_ids == []
-
-
 def test_stage0_streaming_update_discards_outstanding_async_placeholder_token() -> None:
    sched = _make_scheduler(stage_id=0)
    session = _make_request()
@@ -584,15 +446,11 @@ def test_chunk_segment_cleanup_keeps_requeued_resumable_receiver() -> None:
        return result

    sched = OmniARScheduler.__new__(OmniARScheduler)
-    sched.vllm_config = SimpleNamespace(model_config=SimpleNamespace(session_mode="duplex"))
    sched.running = []
    sched.waiting = queue()
    sched.skipped_waiting = queue(session)
    sched.num_waiting_for_streaming_input = 1
-    sched.chunk_transfer_adapter = SimpleNamespace(
-        receives_chunks=True,
-        segment_finished_requests={session.request_id},
-    )
+    sched.chunk_transfer_adapter = SimpleNamespace(segment_finished_requests={session.request_id})

    sched._resume_downstream_chunk_receiver(session)
    sched._remove_stopped_requests_from_queues(set(), {session})
@@ -602,37 +460,3 @@ def test_chunk_segment_cleanup_keeps_requeued_resumable_receiver() -> None:
    assert session not in sched.skipped_waiting.requests
    assert sched.num_waiting_for_streaming_input == 0
    assert session.request_id not in sched.chunk_transfer_adapter.segment_finished_requests
-
-
-@pytest.mark.parametrize(
-    ("receives_chunks", "session_mode"),
-    [
-        (False, "duplex"),
-        (True, "turn"),
-    ],
-)
-def test_chunk_segment_cleanup_keeps_explicit_update_stage_parked(
-    receives_chunks: bool,
-    session_mode: str,
-) -> None:
-    """Only duplex connector-driven receivers resume without an update."""
-    session = _make_request()
-    session.status = RequestStatus.WAITING_FOR_STREAMING_REQ
-
-    sched = OmniARScheduler.__new__(OmniARScheduler)
-    sched.vllm_config = SimpleNamespace(model_config=SimpleNamespace(session_mode=session_mode))
-    sched.num_waiting_for_streaming_input = 1
-    sched.chunk_transfer_adapter = SimpleNamespace(
-        receives_chunks=receives_chunks,
-        segment_finished_requests={session.request_id},
-    )
-    sched.skipped_waiting = MagicMock()
-    sched._enqueue_waiting_request = MagicMock()
-
-    sched._resume_downstream_chunk_receiver(session)
-
-    assert session.status == RequestStatus.WAITING_FOR_STREAMING_REQ
-    assert sched.num_waiting_for_streaming_input == 1
-    assert session.request_id not in sched.chunk_transfer_adapter.segment_finished_requests
-    sched.skipped_waiting.remove_requests.assert_not_called()
-    sched._enqueue_waiting_request.assert_not_called()
@@ -76,9 +76,6 @@ def _make_scheduler(
        else:
            hf_config.voxcpm2_runtime_config = runtime_config
    sched.vllm_config = SimpleNamespace(model_config=model_config)
-    # Upstream ``Scheduler.__init__`` always creates ``skipped_waiting``; the
-    # abort sweep at the top of ``schedule()`` reads it.
-    sched.skipped_waiting = _MockQueue()
    return sched


@@ -166,15 +166,3 @@ def test_fast_path_schedule_defer_disabled_leaves_fence() -> None:
    OmniGenerationScheduler.schedule(sched)

    assert sched.sched_step_seq == 0
-
-
-def test_generation_schedule_sweeps_and_resyncs_around_pending_inputs() -> None:
-    sched, request = _make_fast_path_sched(defer_block_free=False)
-    calls: list[str] = []
-    sched._drop_aborted_queued_requests.side_effect = lambda: calls.append("sweep")
-    sched._process_pending_omni_inputs.side_effect = lambda model_mode: calls.append(model_mode)
-    sched._resync_streaming_input_counter.side_effect = lambda: calls.append("resync")
-
-    OmniGenerationScheduler.schedule(sched)
-
-    assert calls == ["sweep", "generation", "sweep", "resync"]
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# 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;
@@ -253,38 +253,6 @@ def test_log_failed_chunk_sends_noop_without_adapter():
    scheduler._log_failed_chunk_sends()


-class _FakeReceiveFailureAdapter:
-    def __init__(self, failures):
-        self._failures = dict(failures)
-
-    def collect_failed_receive_request_ids(self):
-        failures, self._failures = self._failures, {}
-        return failures
-
-
-class _FakeReceiveFailureScheduler(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_chunk_receive_failures_delegates_to_finish_requests():
-    request_id = "invalid-chunk"
-    adapter = _FakeReceiveFailureAdapter({request_id: "prompt exceeds limit"})
-    scheduler = _FakeReceiveFailureScheduler({request_id: SimpleNamespace(request_id=request_id)}, adapter)
-
-    scheduler._process_chunk_receive_failures()
-
-    assert len(scheduler.finish_calls) == 1
-    finished_ids, status = scheduler.finish_calls[0]
-    assert finished_ids == {request_id}
-    assert getattr(status, "name", str(status)).endswith("FINISHED_ERROR")
-
-
 # --- 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
@@ -1,168 +0,0 @@
-"""Tests for the ``num_waiting_for_streaming_input`` resync placed in
-``OmniARScheduler.finish_requests`` / ``OmniGenerationScheduler.finish_requests``
-and in ``OmniARScheduler.schedule``.
-
-Upstream keeps that counter incrementally (``+1`` when a resumable request
-parks in a waiting queue as ``WAITING_FOR_STREAMING_REQ``, ``-1`` when the
-next update arrives or the request is finished) and
-``Scheduler.get_num_unfinished_requests`` *subtracts* it from the waiting
-queues. Omni rewrites ``request.status`` outside both hooks -- the
-chunk-transfer adapter's park/restore and
-``_realign_request_status_to_queues`` -- so a counted request can leave
-``WAITING_FOR_STREAMING_REQ`` without the matching decrement and inflate the
-counter for the lifetime of the process.
-
-An inflated counter does not degrade throughput, it hangs the stage:
-``EngineCore.has_work()`` reads false while a live request sits in
-``waiting``, so the engine blocks in ``input_queue.get()`` and never calls
-``schedule()`` again. Regression for the Qwen3-Omni duplex symptom where the
-server answered the first conversation after a boot and was silent from the
-second on while still reporting healthy.
-"""
-
-from __future__ import annotations
-
-import pytest
-from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
-from vllm.v1.request import RequestStatus
-
-import vllm_omni.core.sched.omni_ar_scheduler as ar_sched_mod
-import vllm_omni.core.sched.omni_generation_scheduler as gen_sched_mod
-from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
-from vllm_omni.core.sched.omni_generation_scheduler import OmniGenerationScheduler
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-class _StubRequest:
-    def __init__(self, request_id: str, status: RequestStatus) -> None:
-        self.request_id = request_id
-        self.status = status
-
-    def is_finished(self) -> bool:
-        return RequestStatus.is_finished(self.status)
-
-
-_SCHEDULER_PARAMS = [
-    pytest.param(OmniARScheduler, ar_sched_mod, id="ar"),
-    pytest.param(OmniGenerationScheduler, gen_sched_mod, id="generation"),
-]
-
-
-def _make_scheduler(scheduler_cls, *, requests, running, waiting, counter, skipped=None):
-    scheduler = scheduler_cls.__new__(scheduler_cls)
-    scheduler.chunk_transfer_adapter = None
-    scheduler.input_coordinator = None
-    scheduler.requests = requests
-    scheduler.running = running
-    scheduler.waiting = waiting
-    scheduler.skipped_waiting = skipped if skipped is not None else []
-    scheduler.num_waiting_for_streaming_input = counter
-    return scheduler
-
-
-@pytest.mark.parametrize(("scheduler_cls", "scheduler_mod"), _SCHEDULER_PARAMS)
-def test_finish_requests_clears_counter_left_by_a_stomped_status(
-    monkeypatch: pytest.MonkeyPatch,
-    scheduler_cls,
-    scheduler_mod,
-) -> None:
-    """The abort case that wedged the talker.
-
-    The request was counted while parked for streaming input, then had its
-    status rewritten to ``RUNNING`` by omni's queue surgery, so upstream's
-    ``finish_requests`` took the running branch and never decremented. With
-    no parked requests left, the counter must land at 0.
-    """
-    aborted = _StubRequest("req-aborted", RequestStatus.RUNNING)
-    scheduler = _make_scheduler(
-        scheduler_cls,
-        requests={},  # upstream already dropped it
-        running=[],
-        waiting=[],
-        counter=1,
-    )
-
-    monkeypatch.setattr(
-        scheduler_mod.VLLMScheduler,
-        "finish_requests",
-        lambda self, request_ids, finished_status: [aborted],
-    )
-
-    scheduler_cls.finish_requests(scheduler, [aborted.request_id], RequestStatus.FINISHED_ABORTED)
-
-    assert scheduler.num_waiting_for_streaming_input == 0
-
-
-def test_resync_counts_parked_requests_in_skipped_waiting() -> None:
-    """``get_num_unfinished_requests`` sums ``waiting`` and ``skipped_waiting``,
-    so the resync has to look at both or it under-counts and reintroduces the
-    spin the counter exists to prevent."""
-    parked = _StubRequest("req-skipped", RequestStatus.WAITING_FOR_STREAMING_REQ)
-    scheduler = _make_scheduler(
-        OmniARScheduler,
-        requests={parked.request_id: parked},
-        running=[],
-        waiting=[],
-        counter=0,
-        skipped=[parked],
-    )
-
-    scheduler._resync_streaming_input_counter()
-
-    assert scheduler.num_waiting_for_streaming_input == 1
-
-
-def test_ar_schedule_resyncs_before_delegating_upstream(monkeypatch: pytest.MonkeyPatch) -> None:
-    """Pins placement in ``schedule``: the chunk-transfer adapter rewrites
-    status during ``process_pending_chunks``, so the resync has to run after
-    that and before upstream schedules, while ``self.waiting`` is still the
-    real queue."""
-    parked = _StubRequest("req-resumed", RequestStatus.RUNNING)
-    scheduler = OmniARScheduler.__new__(OmniARScheduler)
-    scheduler.chunk_transfer_adapter = None
-    scheduler.input_coordinator = None
-    scheduler.requests = {parked.request_id: parked}
-    scheduler.running = [parked]
-    scheduler.waiting = []
-    scheduler.skipped_waiting = []
-    scheduler.num_waiting_for_streaming_input = 1  # leaked by the status rewrite
-    scheduler.policy = "fcfs"
-
-    seen: dict[str, int] = {}
-
-    monkeypatch.setattr(
-        ar_sched_mod.OmniSchedulerMixin, "_consume_pending_connector_output", lambda self, model_mode: None
-    )
-    monkeypatch.setattr(ar_sched_mod.OmniSchedulerMixin, "_process_pending_input_timeouts", lambda self: None)
-    monkeypatch.setattr(OmniARScheduler, "_should_defer_waiting_admission", lambda self: False)
-
-    def fake_schedule(self, throttle_prefills=False):
-        seen["counter"] = self.num_waiting_for_streaming_input
-        raise _StopScheduleError
-
-    class _StopScheduleError(Exception):
-        pass
-
-    monkeypatch.setattr(ar_sched_mod.VLLMScheduler, "schedule", fake_schedule)
-
-    with pytest.raises(_StopScheduleError):
-        OmniARScheduler.schedule(scheduler)
-
-    assert seen["counter"] == 0
-
-
-def test_generation_scheduler_sweep_removes_aborted_requests_from_all_queues() -> None:
-    aborted = _StubRequest("req-generation-aborted", RequestStatus.FINISHED_ABORTED)
-    scheduler = OmniGenerationScheduler.__new__(OmniGenerationScheduler)
-    scheduler.waiting = create_request_queue(SchedulingPolicy.FCFS)
-    scheduler.waiting.add_request(aborted)
-    scheduler.skipped_waiting = create_request_queue(SchedulingPolicy.FCFS)
-    scheduler.skipped_waiting.add_request(aborted)
-    scheduler.running = [aborted]
-
-    scheduler._drop_aborted_queued_requests()
-
-    assert list(scheduler.waiting) == []
-    assert list(scheduler.skipped_waiting) == []
-    assert scheduler.running == []
@@ -1,8 +1,4 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
 import json
-import math
 import os
 import threading
 from collections.abc import Callable
@@ -163,18 +159,6 @@ def benchmark_params(request):

 def assert_result(result, params, num_prompt) -> None:
    assert result["completed"] == num_prompt, "Request failures exist"
-    baseline = params.get("baseline")
-    hardware = result.get("Hardware")
-    hardware_baseline = baseline.get(hardware) if isinstance(baseline, dict) and isinstance(hardware, str) else None
-    if isinstance(hardware_baseline, dict) and "mean_tpot_ms" in hardware_baseline:
-        num_tpot_samples = result.get("num_tpot_samples")
-        mean_tpot_ms = result.get("mean_tpot_ms")
-        assert isinstance(num_tpot_samples, int) and not isinstance(num_tpot_samples, bool) and num_tpot_samples > 0, (
-            "TPOT baseline is configured, but no measurable TPOT samples were produced"
-        )
-        assert (
-            isinstance(mean_tpot_ms, int | float) and not isinstance(mean_tpot_ms, bool) and math.isfinite(mean_tpot_ms)
-        ), "TPOT baseline is configured, but mean_tpot_ms is not finite"
    expected_audio_turns = params.get("expected_duplex_audio_turns_per_session")
    if expected_audio_turns is not None:
        session_metrics = result.get("duplex_session_metrics")
@@ -1,106 +0,0 @@
-[
-  {
-    "test_name": "test_hunyuanvideo15_i2v_single_device",
-    "mark": [
-      {
-        "hardware_marks": {
-          "res": {
-            "cuda": "H100"
-          },
-          "num_cards": 1
-        }
-      },
-      "full_model",
-      "diffusion"
-    ],
-    "description": "Single-device baseline",
-    "server_type": "vllm-omni",
-    "server_params": {
-      "model": "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v",
-      "serve_args": {
-        "flow-shift": 5.0,
-        "enable-diffusion-pipeline-profiler": true
-      }
-    },
-    "benchmark_params": [
-      {
-        "name": "832x480_frames33_steps4",
-        "dataset": "random",
-        "task": "i2v",
-        "num-prompts": 10,
-        "max-concurrency": 1,
-        "num-input-images": 1,
-        "seed": 42,
-        "enable-negative-prompt": true,
-        "random-request-config": [
-          {
-            "width": 832,
-            "height": 480,
-            "num_inference_steps": 4,
-            "num_frames": 33,
-            "fps": 24,
-            "weight": 1
-          }
-        ],
-        "extra-body": {
-          "guidance_scale": 6.0,
-          "flow_shift": 5.0
-        }
-      }
-    ]
-  },
-  {
-    "test_name": "test_hunyuanvideo15_i2v_cachedit_tp2_vae_patch2",
-    "mark": [
-      {
-        "hardware_marks": {
-          "res": {
-            "cuda": "H100"
-          },
-          "num_cards": 2
-        }
-      },
-      "full_model",
-      "diffusion"
-    ],
-    "description": "CacheDiT + TP=2 + VAE patch parallel=2 + VAE tiling",
-    "server_type": "vllm-omni",
-    "server_params": {
-      "model": "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v",
-      "serve_args": {
-        "flow-shift": 5.0,
-        "cache-backend": "cache_dit",
-        "tensor-parallel-size": 2,
-        "vae-patch-parallel-size": 2,
-        "vae-use-tiling": true,
-        "enable-diffusion-pipeline-profiler": true
-      }
-    },
-    "benchmark_params": [
-      {
-        "name": "832x480_frames33_steps4",
-        "dataset": "random",
-        "task": "i2v",
-        "num-prompts": 10,
-        "max-concurrency": 1,
-        "num-input-images": 1,
-        "seed": 42,
-        "enable-negative-prompt": true,
-        "random-request-config": [
-          {
-            "width": 832,
-            "height": 480,
-            "num_inference_steps": 4,
-            "num_frames": 33,
-            "fps": 24,
-            "weight": 1
-          }
-        ],
-        "extra-body": {
-          "guidance_scale": 6.0,
-          "flow_shift": 5.0
-        }
-      }
-    ]
-  }
-]
@@ -1,104 +0,0 @@
-[
-  {
-    "test_name": "test_hunyuanvideo15_t2v_single_device",
-    "mark": [
-      {
-        "hardware_marks": {
-          "res": {
-            "cuda": "H100"
-          },
-          "num_cards": 1
-        }
-      },
-      "full_model",
-      "diffusion"
-    ],
-    "description": "Single-device baseline",
-    "server_type": "vllm-omni",
-    "server_params": {
-      "model": "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
-      "serve_args": {
-        "flow-shift": 5.0,
-        "enable-diffusion-pipeline-profiler": true
-      }
-    },
-    "benchmark_params": [
-      {
-        "name": "832x480_frames33_steps4",
-        "dataset": "random",
-        "task": "t2v",
-        "num-prompts": 10,
-        "max-concurrency": 1,
-        "seed": 42,
-        "enable-negative-prompt": true,
-        "random-request-config": [
-          {
-            "width": 832,
-            "height": 480,
-            "num_inference_steps": 4,
-            "num_frames": 33,
-            "fps": 24,
-            "weight": 1
-          }
-        ],
-        "extra-body": {
-          "guidance_scale": 6.0,
-          "flow_shift": 5.0
-        }
-      }
-    ]
-  },
-  {
-    "test_name": "test_hunyuanvideo15_t2v_cachedit_tp2_vae_patch2",
-    "mark": [
-      {
-        "hardware_marks": {
-          "res": {
-            "cuda": "H100"
-          },
-          "num_cards": 2
-        }
-      },
-      "full_model",
-      "diffusion"
-    ],
-    "description": "CacheDiT + TP=2 + VAE patch parallel=2 + VAE tiling",
-    "server_type": "vllm-omni",
-    "server_params": {
-      "model": "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
-      "serve_args": {
-        "flow-shift": 5.0,
-        "cache-backend": "cache_dit",
-        "tensor-parallel-size": 2,
-        "vae-patch-parallel-size": 2,
-        "vae-use-tiling": true,
-        "enable-diffusion-pipeline-profiler": true
-      }
-    },
-    "benchmark_params": [
-      {
-        "name": "832x480_frames33_steps4",
-        "dataset": "random",
-        "task": "t2v",
-        "num-prompts": 10,
-        "max-concurrency": 1,
-        "seed": 42,
-        "enable-negative-prompt": true,
-        "random-request-config": [
-          {
-            "width": 832,
-            "height": 480,
-            "num_inference_steps": 4,
-            "num_frames": 33,
-            "fps": 24,
-            "weight": 1
-          }
-        ],
-        "extra-body": {
-          "guidance_scale": 6.0,
-          "flow_shift": 5.0
-        }
-      }
-    ]
-  }
-]
@@ -43,7 +43,7 @@
        "extra_body": {
          "save_duplex_request_metrics": true
        },
-        "percentile-metrics": "ttft,tpot,itl,e2el,audio_ttfp,audio_rtf,audio_duration",
+        "percentile-metrics": "ttft,e2el,audio_ttfp,audio_rtf,audio_duration",
        "baseline": {
          "H100": {
            "request_throughput": [
@@ -46,6 +46,9 @@
        "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
        "baseline": {
          "H100": {
+            "mean_e2el_ms": [
+              3646.797
+            ],
            "mean_audio_rtf": [
              0.1098
            ]
@@ -79,6 +82,9 @@
        "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
        "baseline": {
          "H100": {
+            "mean_e2el_ms": [
+              4059.0878
+            ],
            "mean_audio_rtf": [
              0.1335
            ]
@@ -114,6 +120,9 @@
        "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
        "baseline": {
          "H100": {
+            "mean_e2el_ms": [
+              5639.5769
+            ],
            "mean_audio_rtf": [
              0.1798
            ]
@@ -45,6 +45,13 @@
        "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
        "baseline": {
          "H100": {
+            "mean_e2el_ms": [
+              15793.1209,
+              30658.6632,
+              37463.4943,
+              47990.5839,
+              64980.2199
+            ],
            "mean_audio_rtf": [
              0.169,
              0.2041,
@@ -80,6 +87,9 @@
        "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
        "baseline": {
          "H100": {
+            "mean_e2el_ms": [
+              4556.4347
+            ],
            "mean_audio_rtf": [
              0.138
            ]
@@ -113,6 +123,9 @@
        "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
        "baseline": {
          "H100": {
+            "mean_e2el_ms": [
+              4768.0972
+            ],
            "mean_audio_rtf": [
              0.1609
            ]
@@ -148,6 +161,9 @@
        "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
        "baseline": {
          "H100": {
+            "mean_e2el_ms": [
+              6395.3517
+            ],
            "mean_audio_rtf": [
              0.2048
            ]
@@ -1,6 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
 """Tests for DFX runner metadata field exclusion."""

 import json
@@ -108,7 +105,7 @@ def test_resolve_pytest_marks_hardware_dict_with_extra():
    assert "H100" in names
    assert "cuda" in names
    assert "gpu" in names
-    assert "cards_2" in names
+    assert "distributed_cuda" in names
    assert "full_model" in names
    assert "diffusion" in names
    assert "local_model" in names
@@ -515,41 +512,3 @@ def test_omni_duplex_expected_audio_turns_rejects_incomplete_session():
            {"expected_duplex_audio_turns_per_session": 4},
            1,
        )
-
-
-def test_omni_tpot_baseline_accepts_measured_finite_sample():
-    from tests.dfx.perf.scripts.run_benchmark import assert_result
-
-    assert_result(
-        {
-            "completed": 1,
-            "Hardware": "H100",
-            "num_tpot_samples": 1,
-            "mean_tpot_ms": 10.0,
-        },
-        {"baseline": {"H100": {"mean_tpot_ms": 20.0}}},
-        1,
-    )
-
-
-@pytest.mark.parametrize(
-    ("num_tpot_samples", "mean_tpot_ms", "match"),
-    [
-        (0, float("nan"), "no measurable TPOT samples"),
-        (1, float("nan"), "mean_tpot_ms is not finite"),
-    ],
-)
-def test_omni_tpot_baseline_rejects_missing_or_nonfinite_sample(num_tpot_samples, mean_tpot_ms, match):
-    from tests.dfx.perf.scripts.run_benchmark import assert_result
-
-    with pytest.raises(AssertionError, match=match):
-        assert_result(
-            {
-                "completed": 1,
-                "Hardware": "H100",
-                "num_tpot_samples": num_tpot_samples,
-                "mean_tpot_ms": mean_tpot_ms,
-            },
-            {"baseline": {"H100": {"mean_tpot_ms": 20.0}}},
-            1,
-        )
@@ -101,12 +101,7 @@ class TestAttentionSpec:
    def test_block_sparse_defaults_applied_when_backend_selected(self):
        spec = AttentionSpec(backend="RAINFUSION_ATTN")
        assert spec.block_sparse.sparsity == 0.8
-        assert spec.backend_kwargs() == {
-            "sparsity": 0.8,
-            "start_step": 0,
-            "end_step": 0,
-            "precision": "bf16",
-        }
+        assert spec.backend_kwargs() == {"sparsity": 0.8, "start_step": 0}

    def test_block_sparse_skip_layers_selector_expanded(self):
        spec = AttentionSpec(
@@ -117,8 +112,6 @@ class TestAttentionSpec:
        assert spec.backend_kwargs() == {
            "sparsity": 0.9,
            "start_step": 12,
-            "end_step": 0,
-            "precision": "bf16",
            "skip_layers": [0, 1, 2, 38],
        }

@@ -128,44 +121,12 @@ class TestAttentionSpec:

    @pytest.mark.parametrize(
        "block_sparse",
-        [{"sparsity": 1.5}, {"start_step": -1}, {"end_step": -1}, {"precision": "bogus"}],
+        [{"sparsity": 1.5}, {"start_step": -1}],
    )
    def test_block_sparse_invalid_values(self, block_sparse):
        with pytest.raises(ValueError):
            AttentionSpec(backend="RAINFUSION_ATTN", block_sparse=block_sparse)

-    def test_block_sparse_end_step_precision_forwarded(self):
-        spec = AttentionSpec(
-            backend="RAINFUSION_ATTN",
-            block_sparse={"sparsity": 0.9, "start_step": 5, "end_step": 3, "precision": "fp8"},
-        )
-        assert spec.backend_kwargs() == {
-            "sparsity": 0.9,
-            "start_step": 5,
-            "end_step": 3,
-            "precision": "fp8",
-        }
-
-    def test_block_sparse_precision_enum_input_normalized(self):
-        """RainFusionPrecision enum members must normalize to their string value.
-
-        Regression: the str,Enum members passed the value check but str() later
-        produced "RainFusionPrecision.MIX" instead of "mix" (StrEnum fixes it).
-        """
-        from vllm_omni.diffusion.attention.backends.rainfusion_attn import RainFusionConfig
-        from vllm_omni.diffusion.data import RainFusionPrecision
-
-        spec = AttentionSpec(
-            backend="RAINFUSION_ATTN",
-            block_sparse={"sparsity": 0.8, "precision": RainFusionPrecision.MIX},
-        )
-        kwargs = spec.backend_kwargs()
-        assert kwargs["precision"] == "mix"
-        # The config path is where str(value) used to mangle enum members.
-        cfg = RainFusionConfig.from_backend_kwargs(kwargs)
-        assert cfg.precision == "mix"
-        assert type(cfg.precision) is str
-

 class TestAttentionConfig:
    def test_empty_config(self):
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 """Geometry contract RAINFUSION_ATTN enforces before handing a forward to rf_v2.

@@ -11,25 +11,18 @@ to the always-kept prefix segment before generating the block mask.
 """

 import dataclasses
-import sys
-import types
-from unittest import mock

 import pytest
 import torch

-from vllm_omni.diffusion.attention.backends import rainfusion_attn
-from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata, VideoTokenLayout, VideoTokenSpan
+from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata, VideoTokenLayout
 from vllm_omni.diffusion.attention.backends.rainfusion_attn import (
    _BLOCK_SIZE,
-    RainFusionAttentionBackend,
    RainFusionAttentionImpl,
    RainFusionPlan,
 )
 from vllm_omni.platforms import current_omni_platform

-pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.npu]
-
 PREFIX_ROWS = 710  # 14 text rows + 696 audio rows
 ALIGNED_GRID = (62, 24, 40)  # 1280x768 -> 59520 video rows, 465 blocks
 MISALIGNED_GRID = (62, 24, 42)  # 1344x768 -> 62496 video rows, 488 blocks + 32 rows
@@ -104,62 +97,6 @@ def test_video_segment_must_be_the_tail_of_packed_document_zero():
    assert make_impl()._resolve_plan(metadata) is None


-def test_ref2va_multi_video_spans_resolve():
-    layout = VideoTokenLayout(
-        used_len=12000,
-        video_spans=(
-            VideoTokenSpan(start=128, latent_grid=(4, 16, 64), role="reference"),
-            VideoTokenSpan(start=5000, latent_grid=(4, 16, 64), role="target"),
-        ),
-    )
-    plan = make_impl()._resolve_plan(AttentionMetadata(extra={"max_seqlen_q": 12000}, video_layout=layout))
-
-    assert plan is not None
-    assert plan.used_len == 12000
-    assert plan.video_spans == [
-        {"start": 128, "latent_shape": [4, 16, 64]},
-        {"start": 5000, "latent_shape": [4, 16, 64]},
-    ]
-
-
-def test_invalid_ref2va_video_spans_fall_back_to_dense():
-    layout = VideoTokenLayout(
-        used_len=12000,
-        video_spans=(
-            VideoTokenSpan(start=128, latent_grid=(4, 16, 64), role="reference"),
-            VideoTokenSpan(start=4000, latent_grid=(4, 16, 64), role="target"),
-        ),
-    )
-    assert make_impl()._resolve_plan(AttentionMetadata(extra={"max_seqlen_q": 12000}, video_layout=layout)) is None
-
-
-def test_validate_available_rejects_legacy_mindiesd(monkeypatch):
-    mindiesd = types.ModuleType("mindiesd")
-
-    def sparse_attention(query, key, value, **kwargs):
-        return query
-
-    mindiesd.sparse_attention = sparse_attention
-    monkeypatch.setitem(sys.modules, "mindiesd", mindiesd)
-    monkeypatch.setattr("importlib.util.find_spec", lambda _: object())
-
-    with pytest.raises(ValueError, match="video_spans"):
-        RainFusionAttentionBackend.validate_available()
-
-
-def test_validate_available_accepts_new_mindiesd(monkeypatch):
-    mindiesd = types.ModuleType("mindiesd")
-
-    def sparse_attention(query, key, value, *, video_spans=None, **kwargs):
-        return query
-
-    mindiesd.sparse_attention = sparse_attention
-    monkeypatch.setitem(sys.modules, "mindiesd", mindiesd)
-    monkeypatch.setattr("importlib.util.find_spec", lambda _: object())
-
-    RainFusionAttentionBackend.validate_available()
-
-
@pytest.mark.parametrize("grid", [(4, 24, 40), (1, 24, 40)])
 def test_short_video_stays_dense(grid):
    assert make_impl()._resolve_plan(make_metadata(grid)) is None
@@ -232,9 +169,7 @@ def test_irregular_video_tail_reproduces_dense_attention_with_updated_mindiesd()
    heads, head_dim = 4, 128
    q, k, v = (torch.randn(1, used, heads, head_dim, dtype=torch.bfloat16, device="npu") for _ in range(3))

-    # Explicit bf16: the dense comparison exercises the plain sparse kernel,
-    # and older MindIE-SD releases do not accept precision= (gate would raise).
-    impl = make_impl(precision="bf16")
+    impl = make_impl()
    impl.rainfusion = dataclasses.replace(impl.rainfusion, sparsity=0.0)
    plan = RainFusionPlan(prefix_len=prefix_len, used_len=used, latent_shape=list(grid))
    out = impl._forward_sparse_npu(q, k, v, plan)
@@ -247,90 +182,3 @@ def test_irregular_video_tail_reproduces_dense_attention_with_updated_mindiesd()
    ).transpose(1, 2)
    error = (out.float() - reference.float()).abs().mean() / reference.float().abs().mean()
    assert error < 2e-3, f"mean relative error {error:.4%} against dense attention"
-
-
-# end_step tail fallback: the last ``end_step`` denoise steps must stay dense.
-
-
-def test_end_step_tail_window_stays_dense():
-    """step_idx inside the last end_step window must resolve no sparse plan."""
-    impl = make_impl(end_step=3)
-    fc = mock.Mock()
-    fc.denoise_step_idx = 47
-    fc.total_denoise_steps = 50
-    with (
-        mock.patch.object(rainfusion_attn, "is_forward_context_available", return_value=True),
-        mock.patch.object(rainfusion_attn, "get_forward_context", return_value=fc),
-    ):
-        assert impl._resolve_plan(make_metadata(ALIGNED_GRID)) is None
-
-
-def test_end_step_before_tail_window_still_sparse():
-    """step_idx before the tail window must still resolve a sparse plan."""
-    impl = make_impl(end_step=3)
-    fc = mock.Mock()
-    fc.denoise_step_idx = 46
-    fc.total_denoise_steps = 50
-    with (
-        mock.patch.object(rainfusion_attn, "is_forward_context_available", return_value=True),
-        mock.patch.object(rainfusion_attn, "get_forward_context", return_value=fc),
-    ):
-        assert impl._resolve_plan(make_metadata(ALIGNED_GRID)) is not None
-
-
-def test_end_step_zero_never_triggers_tail_fallback():
-    """end_step=0 must not fall back even on the final denoise step."""
-    impl = make_impl(end_step=0)
-    fc = mock.Mock()
-    fc.denoise_step_idx = 49
-    fc.total_denoise_steps = 50
-    with (
-        mock.patch.object(rainfusion_attn, "is_forward_context_available", return_value=True),
-        mock.patch.object(rainfusion_attn, "get_forward_context", return_value=fc),
-    ):
-        assert impl._resolve_plan(make_metadata(ALIGNED_GRID)) is not None
-
-
-# precision capability gate: a mindiesd without sparse_attention(precision=)
-# must fail fast instead of silently running the BF16 path.
-
-
-def _fake_mindiesd_module():
-    """Install a minimal stand-in so the import inside _forward_sparse_npu succeeds."""
-    import types
-
-    fake = types.ModuleType("mindiesd")
-    fake.sparse_attention = lambda *args, **kwargs: None
-    return fake
-
-
-def test_precision_non_bf16_requires_mindiesd_support():
-    """precision != bf16 against a mindiesd lacking the kwarg must raise RuntimeError."""
-    import sys
-
-    impl = make_impl(precision="mix")
-    sys.modules["mindiesd"] = _fake_mindiesd_module()
-    try:
-        with mock.patch.object(rainfusion_attn, "_mindiesd_supports_precision", return_value=False):
-            with pytest.raises(RuntimeError, match="requires MindIE-SD"):
-                impl._forward_sparse_npu(None, None, None, None)
-    finally:
-        sys.modules.pop("mindiesd", None)
-
-
-def test_precision_non_bf16_passes_gate_when_supported():
-    """precision != bf16 with a capable mindiesd must not raise from the gate."""
-    import sys
-
-    impl = make_impl(precision="mix")
-    sys.modules["mindiesd"] = _fake_mindiesd_module()
-    try:
-        with mock.patch.object(rainfusion_attn, "_mindiesd_supports_precision", return_value=True):
-            # q/k/v shapes: [B, S, N, D]; plan geometry must match S.
-            q = torch.randn(1, 8, 4, 128)
-            plan = RainFusionPlan(prefix_len=0, used_len=8, latent_shape=[2, 2, 2])
-            # The gate must pass; the fake mindiesd returns None so no crash.
-            out = impl._forward_sparse_npu(q, q, q, plan)
-            assert out is None
-    finally:
-        sys.modules.pop("mindiesd", None)
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 """
 Unit tests for TeaCache extractor functions.
@@ -18,7 +18,6 @@ Currently implemented:
 - TestMiniMaxH3Extractor: MiniMaxH3DiTModel extractor
 """

-import math
 from abc import ABC, abstractmethod
 from types import SimpleNamespace
 from unittest.mock import MagicMock, Mock, patch
@@ -651,29 +650,6 @@ class TestMiniMaxH3Extractor(BaseExtractorTest):
        torch.testing.assert_close(actual_video, expected_video)
        torch.testing.assert_close(actual_audio, expected_audio)

-    def test_extractor_uses_prepared_rope_table(
-        self,
-        minimax_h3_module,
-        sample_inputs,
-        monkeypatch,
-    ):
-        """TeaCache must consume the denoise-loop RoPE table without rebuilding it."""
-        rope_table = minimax_h3_module.prepare_rope_table(
-            sample_inputs["img_position_ids"],
-            seq_len=sample_inputs["x"].shape[1],
-        )
-
-        def unexpected_rope_build(*args, **kwargs):
-            raise AssertionError("TeaCache extractor rebuilt the prepared RoPE table")
-
-        monkeypatch.setattr(minimax_h3_module, "prepare_rope_table", unexpected_rope_build)
-        monkeypatch.setattr(minimax_h3_module.rope, "forward", unexpected_rope_build)
-        context = extract_minimax_h3_context(
-            minimax_h3_module,
-            **{**sample_inputs, "rope_table": rope_table},
-        )
-        context.run_transformer_blocks()
-
    def test_run_transformer_blocks_forwards_packed_layout(
        self,
        minimax_h3_module,
@@ -729,222 +705,6 @@ class TestMiniMaxH3Extractor(BaseExtractorTest):
        assert video_logits.shape[0] == sample_inputs["img_pos_info"]["position_ids"].shape[0]
        assert audio_logits.shape[0] == sample_inputs["audio_pos_info"]["position_ids"].shape[0]

-    @pytest.mark.sp
-    def test_teacache_strict_sp_keeps_cache_state_rank_local(
-        self,
-        minimax_h3_module,
-        sample_inputs,
-        monkeypatch,
-    ):
-        """Strict SP must not mix local TeaCache state with gathered block rows."""
-        from vllm_omni.diffusion.attention.ops import minimax_h3_modulation
-        from vllm_omni.diffusion.cache.teacache.config import TeaCacheConfig
-        from vllm_omni.diffusion.cache.teacache.hook import TeaCacheHook
-        from vllm_omni.diffusion.distributed import parallel_state
-
-        seq_len = sample_inputs["x"].shape[1]
-        local_len = seq_len // 2
-        hidden_size = minimax_h3_module.hidden_size
-        video_width = minimax_h3_module.arch.latents_dim * math.prod(minimax_h3_module.arch.patch_size)
-        audio_width = minimax_h3_module.arch.audio_latents_dim
-        captured_modulation_indices: list[torch.Tensor] = []
-
-        class _StrictSPPrepare(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.global_indices: list[torch.Tensor] = []
-
-            def forward(self, hidden, rope_table, combined_indices):
-                assert hidden.shape[0] == local_len
-                assert rope_table.shape[0] == local_len
-                self.global_indices.append(combined_indices.clone())
-                return hidden, rope_table, combined_indices.narrow(0, 0, local_len)
-
-        class _StrictSPGather(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.inputs: list[torch.Tensor] = []
-                self.outputs: list[torch.Tensor] = []
-
-            def forward(self, tensor):
-                self.inputs.append(tensor.clone())
-                if tensor.shape[-1] == hidden_size:
-                    raise AssertionError("TeaCache must not gather hidden states in strict SP")
-                assert tensor.shape == (local_len, video_width + audio_width)
-                # Model the all-gather of compact logits from every SP rank.
-                # postprocess() indexes the resulting global layout with
-                # infer_out_pos/audio_pos, including rows owned by another rank.
-                gathered = tensor.repeat(seq_len // local_len, 1)
-                assert gathered.shape == (seq_len, video_width + audio_width)
-                self.outputs.append(gathered)
-                return gathered
-
-        class _StrictSPGroup:
-            def __init__(self):
-                super().__init__()
-                self.local_decisions: list[int] = []
-                self.ops = []
-
-            def all_reduce(self, decision, op):
-                self.local_decisions.append(int(decision.item()))
-                self.ops.append(op)
-                # Simulate another rank requiring the second step to execute
-                # the collective block path even though this rank would cache.
-                if len(self.local_decisions) == 2:
-                    decision.fill_(1)
-                return decision
-
-        class _StrictSPBlock(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.block_indices: list[torch.Tensor] = []
-
-            def adaln_proj(self, t_emb):
-                del t_emb
-                zeros = torch.zeros(3, hidden_size, dtype=torch.float32)
-                return zeros, zeros
-
-            def norm1(self, hidden):
-                return hidden.clone()
-
-            def forward(self, hidden, *, combined_indices, rope_table, **kwargs):
-                del kwargs
-                assert hidden.shape[0] == local_len
-                assert rope_table.shape[0] == local_len
-                self.block_indices.append(combined_indices.clone())
-                return hidden + 1
-
-        class _StrictSPFinalLayer(nn.Module):
-            def forward(self, hidden, *, t_emb, inverse_indices):
-                del t_emb, inverse_indices
-                assert hidden.shape == (local_len, hidden_size)
-                return hidden[:, :video_width].float(), hidden[:, :audio_width].float()
-
-        local_prepare = _StrictSPPrepare()
-        gather = _StrictSPGather()
-        sp_group = _StrictSPGroup()
-        block = _StrictSPBlock()
-        minimax_h3_module.local_sp_prepare = local_prepare
-        minimax_h3_module.sp_gather = gather
-        minimax_h3_module.blocks = nn.ModuleList([block])
-        minimax_h3_module.final_layer = _StrictSPFinalLayer()
-        monkeypatch.setattr(minimax_h3_module, "_rope_local_span", lambda _seq_len: (0, local_len))
-        monkeypatch.setattr(
-            minimax_h3_module,
-            "_embed",
-            lambda **kwargs: (
-                torch.arange(local_len * hidden_size, dtype=torch.float32).reshape(local_len, hidden_size),
-                torch.zeros(3, hidden_size, dtype=torch.float32),
-            ),
-        )
-        monkeypatch.setattr(
-            minimax_h3_module,
-            "prepare_rope_table",
-            lambda *args, **kwargs: torch.zeros(local_len, 6, dtype=torch.float32),
-        )
-        monkeypatch.setattr(parallel_state, "get_sp_group", lambda: sp_group)
-
-        original_indexed_scale_shift = minimax_h3_modulation.indexed_scale_shift_
-
-        def capture_indexed_scale_shift(hidden, shift, scale, indices):
-            captured_modulation_indices.append(indices.clone())
-            return original_indexed_scale_shift(hidden, shift, scale, indices)
-
-        monkeypatch.setattr(minimax_h3_modulation, "indexed_scale_shift_", capture_indexed_scale_shift)
-
-        local_img_pos = torch.tensor([0, 1])
-        local_audio_pos = torch.tensor([2, 3])
-        token_tags = torch.full((seq_len,), -1, dtype=torch.long)
-        token_tags[local_img_pos] = 0
-        token_tags[local_audio_pos] = 2
-        inverse_indices = torch.zeros(seq_len, dtype=torch.long)
-        inputs = {
-            **sample_inputs,
-            "inverse_indices": inverse_indices,
-            "token_tags": token_tags,
-            "update_mask": torch.ones(local_img_pos.numel(), dtype=torch.bool),
-            "prompt_embeds": torch.empty(0, 6),
-            "img_pos_info": {"position_ids": local_img_pos},
-            "audio_pos_info": {"position_ids": local_audio_pos},
-            "text_pos_info": {"position_ids": torch.empty(0, dtype=torch.long)},
-            "img_pos_for_infer_output_info": {"position_ids": local_img_pos},
-            "refiner_packed_seq_params": {
-                "cu_seqlens_q": torch.tensor([0, 0], dtype=torch.int32),
-                "max_seqlen_q": 0,
-            },
-        }
-        expected_local_indices = (inverse_indices * 3 + token_tags.clamp(min=0))[:local_len]
-
-        hook = TeaCacheHook(
-            TeaCacheConfig(
-                transformer_type="MiniMaxH3DiTModel",
-                rel_l1_thresh=0.3,
-            )
-        )
-        hook.initialize_hook(minimax_h3_module)
-
-        hook.new_forward(minimax_h3_module, **inputs)
-        hook.new_forward(minimax_h3_module, **inputs)
-        video_logits, audio_logits = hook.new_forward(minimax_h3_module, **inputs)
-        state = hook.state_manager.get_state()
-
-        assert state.previous_residual is not None
-        assert state.previous_residual.shape == (local_len, hidden_size)
-        assert len(captured_modulation_indices) == 3
-        assert all(torch.equal(indices, expected_local_indices) for indices in captured_modulation_indices)
-        assert sp_group.local_decisions == [1, 0, 0]
-        assert len(sp_group.ops) == 3
-        assert all(op == torch.distributed.ReduceOp.MAX for op in sp_group.ops)
-        assert len(local_prepare.global_indices) == 2
-        assert all(indices.shape == (seq_len,) for indices in local_prepare.global_indices)
-        assert len(block.block_indices) == 2
-        assert all(torch.equal(indices, expected_local_indices) for indices in block.block_indices)
-        assert len(gather.inputs) == 3
-        assert video_logits.shape == (local_img_pos.numel(), video_width)
-        assert audio_logits.shape == (local_audio_pos.numel(), audio_width)
-
-    @pytest.mark.cpu
-    def test_teacache_non_strict_sp_gathers_hidden_once(
-        self,
-        minimax_h3_module,
-        sample_inputs,
-        monkeypatch,
-    ):
-        """The non-strict SP cache path must not gather global hidden rows twice."""
-        from vllm_omni.diffusion.cache.teacache.config import TeaCacheConfig
-        from vllm_omni.diffusion.cache.teacache.hook import TeaCacheHook
-
-        class _Prepare(nn.Module):
-            def forward(self, hidden, rope_table, combined_indices):
-                return hidden, rope_table, combined_indices
-
-        class _Gather(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.inputs: list[torch.Tensor] = []
-
-            def forward(self, tensor):
-                self.inputs.append(tensor.clone())
-                return tensor
-
-        seq_len = sample_inputs["x"].shape[1]
-        gather = _Gather()
-        minimax_h3_module.sp_prepare = _Prepare()
-        minimax_h3_module.sp_gather = gather
-        monkeypatch.setattr(minimax_h3_module, "_rope_local_span", lambda _seq_len: (0, seq_len))
-
-        hook = TeaCacheHook(
-            TeaCacheConfig(
-                transformer_type="MiniMaxH3DiTModel",
-                rel_l1_thresh=0.3,
-            )
-        )
-        hook.initialize_hook(minimax_h3_module)
-        hook.new_forward(minimax_h3_module, **sample_inputs)
-
-        assert len(gather.inputs) == 1
-        assert gather.inputs[0].shape == (seq_len, minimax_h3_module.hidden_size)
-
    def test_invalid_module_raises_error(self):
        """Test that invalid module without blocks raises ValueError."""
        invalid_module = Mock()
@@ -9,7 +9,6 @@ import torch
 from diffusers import DiffusionPipeline  # pyright: ignore[reportPrivateImportUsage]
 from PIL import Image

-import vllm_omni.diffusion.data as diffusion_data
 from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniServer, OmniServerParams, OnlineOmniClient, dummy_messages_from_mix_data
 from vllm_omni.diffusion.data import (
@@ -630,46 +629,6 @@ class TestPipelineArgumentsHandling:
        with pytest.raises(ValueError):
            pipeline.forward(DiffusionRequestBatch(requests=[problematic_request]))

-        interpolation_request = _make_request(
-            sampling_params=OmniDiffusionSamplingParams(
-                enable_frame_interpolation=True,
-            ),
-        )
-        with pytest.raises(ValueError, match="Frame interpolation is not supported with the Diffusers backend"):
-            pipeline.forward(DiffusionRequestBatch(requests=[interpolation_request]))
-
-    def test_diffusers_config_preserves_checkpoint_class_for_metadata(self, mocker):
-        class MockWanImageToVideoPipeline:
-            pass
-
-        od_config = _make_od_config(model_class_name=None)
-        mocker.patch(
-            "vllm_omni.diffusion.utils.hf_utils.get_diffusion_model_index",
-            return_value={"_class_name": "WanImageToVideoPipeline"},
-        )
-        mocker.patch.object(
-            diffusion_data,
-            "diffusers",
-            SimpleNamespace(WanImageToVideoPipeline=MockWanImageToVideoPipeline),
-        )
-        mocker.patch.object(OmniDiffusionConfig, "update_multimodal_support")
-
-        od_config.enrich_config()
-
-        assert od_config.model_class_name == "WanImageToVideoPipeline"
-        assert od_config.diffusers_pipeline_cls is MockWanImageToVideoPipeline
-
-    def test_diffusers_config_falls_back_to_adapter_without_pipeline_index(self, mocker):
-        od_config = _make_od_config(model_class_name=None)
-        mocker.patch(
-            "vllm_omni.diffusion.utils.hf_utils.get_diffusion_model_index",
-            return_value=None,
-        )
-
-        od_config.enrich_config()
-
-        assert od_config.model_class_name == "DiffusersAdapterPipeline"
-

@pytest.mark.advanced_model
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@@ -1,117 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from types import SimpleNamespace
-
-import pytest
-import torch
-
-from vllm_omni.diffusion.distributed import a2a_permute
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-def test_jit_build_includes_cuda_headers_from_nvidia_wheels(tmp_path, monkeypatch) -> None:
-    cu13_include = tmp_path / "nvidia" / "cu13" / "include"
-    nccl_include = tmp_path / "nvidia" / "nccl" / "include"
-    nccl_lib = tmp_path / "nvidia" / "nccl" / "lib" / "libnccl.so.2"
-    cu13_include.mkdir(parents=True)
-    nccl_include.mkdir(parents=True)
-    nccl_lib.parent.mkdir(parents=True)
-    (cu13_include / "cusparse.h").touch()
-    (nccl_include / "nccl.h").touch()
-    nccl_lib.touch()
-
-    load_kwargs = {}
-    monkeypatch.setattr(a2a_permute.sysconfig, "get_paths", lambda: {"purelib": str(tmp_path)})
-    monkeypatch.setattr(
-        torch.utils.cpp_extension,
-        "load",
-        lambda **kwargs: load_kwargs.update(kwargs),
-    )
-    monkeypatch.setattr(a2a_permute.symm_mem, "set_backend", lambda _backend: None)
-    monkeypatch.setattr(a2a_permute, "_BUILT", False)
-
-    a2a_permute.ensure_a2a_permute_available()
-
-    assert set(load_kwargs["extra_include_paths"]) == {str(cu13_include), str(nccl_include)}
-    assert load_kwargs["extra_ldflags"] == [str(nccl_lib)]
-
-
-@dataclass
-class _FakeAllocation:
-    size: int
-    device: torch.device
-
-
-class _FakeHandle:
-    rank = 0
-
-    def __init__(self, allocation: _FakeAllocation) -> None:
-        self.allocation = allocation
-        self.views: list[tuple[tuple[int, ...], torch.dtype]] = []
-
-    def get_buffer(self, rank, shape, dtype):
-        assert rank == self.rank
-        self.views.append((shape, dtype))
-        return self.allocation, shape, dtype
-
-
-def test_workspace_reuses_peak_capacity_across_shapes(monkeypatch) -> None:
-    allocations: list[_FakeAllocation] = []
-    all_reduce_calls: list[object] = []
-    synchronize_calls: list[torch.device] = []
-    stream_id = [1]
-    a2a_permute._SYMM_WORKSPACES.clear()
-
-    monkeypatch.setattr(a2a_permute.torch.cuda, "is_current_stream_capturing", lambda: False)
-    monkeypatch.setattr(
-        a2a_permute.torch.cuda,
-        "current_stream",
-        lambda _device: SimpleNamespace(cuda_stream=stream_id[0]),
-    )
-    monkeypatch.setattr(a2a_permute, "_resolve_process_group", lambda _name: object())
-    monkeypatch.setattr(a2a_permute.torch, "ones", lambda *_args, **_kwargs: object())
-    monkeypatch.setattr(
-        a2a_permute.dist,
-        "all_reduce",
-        lambda _tensor, group: all_reduce_calls.append(group),
-    )
-    monkeypatch.setattr(
-        a2a_permute.torch.accelerator,
-        "synchronize",
-        lambda device: synchronize_calls.append(device),
-    )
-
-    def empty(size, *, dtype, device):
-        assert dtype == torch.uint8
-        allocation = _FakeAllocation(size=size, device=torch.device(device))
-        allocations.append(allocation)
-        return allocation
-
-    monkeypatch.setattr(a2a_permute.symm_mem, "empty", empty)
-    monkeypatch.setattr(a2a_permute.symm_mem, "rendezvous", lambda allocation, _group: _FakeHandle(allocation))
-
-    device = torch.device("cuda:0")
-    first = a2a_permute._get_symm_buffer((2, 3), torch.float16, device, "group")
-    smaller = a2a_permute._get_symm_buffer((1, 4), torch.float16, device, "group")
-    larger = a2a_permute._get_symm_buffer((4, 3), torch.float16, device, "group")
-
-    assert first[0].size == 12
-    assert smaller[0] is first[0]
-    assert larger[0].size == 24
-    assert [allocation.size for allocation in allocations] == [12, 24]
-    assert len(a2a_permute._SYMM_WORKSPACES) == 1
-    assert len(all_reduce_calls) == 1
-    assert synchronize_calls == [device, device]
-
-    stream_id[0] = 2
-    with pytest.raises(RuntimeError, match="single CUDA stream"):
-        a2a_permute._get_symm_buffer((1, 4), torch.float16, device, "group")
-
-    a2a_permute.clear_a2a_permute_workspaces()
-    assert not a2a_permute._SYMM_WORKSPACES
-    assert synchronize_calls == [device, device, device]
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 from types import SimpleNamespace

@@ -194,47 +194,6 @@ def test_moe_ep_maps_diffusion_sp_cfg_dp_to_vllm_groups(monkeypatch):
    assert ep_groups == [parallel_state.vllm_parallel_state._EP.local_group]


-@pytest.mark.cpu
-@pytest.mark.core_model
-@pytest.mark.parametrize(
-    ("fully_shard_degree", "error_message"),
-    [(0, "fully_shard_degree must be positive"), (3, "must be divisible by fully_shard_degree")],
-)
-def test_invalid_hsdp_shard_size_fails_before_group_creation(monkeypatch, fully_shard_degree: int, error_message: str):
-    """Reject invalid HSDP shard sizes before allocating any process groups."""
-    world_size = 4
-    created_groups: list[object] = []
-
-    def fail_if_group_created(*args, **kwargs):
-        del args, kwargs
-        created_groups.append(object())
-        raise AssertionError("a process group was created before HSDP validation")
-
-    fake_world_group = SimpleNamespace(
-        rank_in_group=0,
-        local_rank=0,
-        device_group=object(),
-    )
-    monkeypatch.setattr(parallel_state.torch.distributed, "is_initialized", lambda: True)
-    monkeypatch.setattr(parallel_state.torch.distributed, "get_world_size", lambda: world_size)
-    monkeypatch.setattr(parallel_state, "get_world_group", lambda: fake_world_group)
-    monkeypatch.setattr(parallel_state, "init_model_parallel_group", fail_if_group_created)
-
-    for name in ("_DP", "_CFG", "_SP", "_PP", "_FS", "_HSDP_REPLICATE", "_EXPERT_PARALLEL_GROUP_RANKS"):
-        monkeypatch.setattr(parallel_state, name, None)
-    for name in ("_TP", "_PCP", "_DP", "_EP", "_PP"):
-        monkeypatch.setattr(parallel_state.vllm_parallel_state, name, None, raising=False)
-
-    with pytest.raises(ValueError, match=error_message):
-        parallel_state.initialize_model_parallel(
-            fully_shard_degree=fully_shard_degree,
-            use_hsdp=True,
-            backend="gloo",
-        )
-
-    assert created_groups == []
-
-
@pytest.mark.cpu
@pytest.mark.core_model
 def test_cfg_parallel_keeps_diffusion_dp_without_ep(monkeypatch):
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Tests for the Sequence Parallelism (SP) framework.

 These tests verify the SP plan mechanism and hooks work correctly without
@@ -17,7 +17,6 @@ import pytest
 import torch
 import torch.nn as nn

-from tests.helpers.mark import hardware_marks
 from vllm_omni.diffusion.distributed.sp_plan import (
    SequenceParallelInput,
    SequenceParallelOutput,
@@ -420,6 +419,7 @@ class TestHookRegistration:
 # =============================================================================


+@pytest.mark.L4
 class TestModelSpPlans:
    """Test that model _sp_plan definitions are valid.

@@ -427,8 +427,6 @@ class TestModelSpPlans:
    May require GPU for model imports.
    """

-    pytestmark = hardware_marks(res={"cuda": "L4"})
-
    def test_zimage_transformer_sp_plan(self):
        """Test ZImageTransformer2DModel _sp_plan structure.

@@ -9,7 +9,6 @@ Uses ``Tongyi-MAI/Z-Image-Turbo`` as the parity model.

 from __future__ import annotations

-import gc
 import os
 import time
 from pathlib import Path
@@ -76,13 +75,8 @@ def _run_generate(
    if num_requests < 2:
        raise ValueError("num_requests must be >= 2 (1 warmup + >=1 timed)")

-    gc.collect()
    current_omni_platform.empty_cache()
    device_index = current_omni_platform.current_device()
-    with current_omni_platform.device(device_index):
-        free_bytes, total_bytes = current_omni_platform.mem_get_info()
-    initial_used_mb = (total_bytes - free_bytes) / (1024**2)
-
    monitor = DeviceMemoryMonitor(device_index=device_index, interval=0.02)
    monitor.start()
    try:
@@ -119,10 +113,7 @@ def _run_generate(
                pass

            median_time_s = float(np.median(per_request_times_s))
-            # DeviceMemoryMonitor samples absolute device usage. Normalize by
-            # this run's starting usage so compiler and allocator caches retained
-            # by the preceding TP=1 run are not charged to the TP=2 run.
-            peak_memory_mb = max(0.0, monitor.peak_used_mb - initial_used_mb)
+            peak_memory_mb = monitor.peak_used_mb
            return _extract_single_image([last_output]), median_time_s, peak_memory_mb
    finally:
        monitor.stop()
@@ -1,32 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
-import pytest
-import torch
-import torch.nn.functional as F
-
-from tests.helpers.mark import hardware_test
-from vllm_omni.diffusion.layers.activation import SiluAndMul
-
-pytestmark = [pytest.mark.core_model, pytest.mark.diffusion]
-
-
-@pytest.mark.cpu
-def test_silu_and_mul_native_matches_packed_reference() -> None:
-    packed = torch.randn(257, 256, dtype=torch.float32)
-    gate, up = packed.chunk(2, dim=-1)
-
-    torch.testing.assert_close(SiluAndMul().forward_native(packed), F.silu(gate) * up)
-
-
-@hardware_test(res={"npu": "A3"}, num_cards=1)
-def test_silu_and_mul_npu_matches_packed_reference() -> None:
-    packed = torch.randn(257, 256, device="npu", dtype=torch.bfloat16)
-    gate, up = packed.chunk(2, dim=-1)
-
-    torch.testing.assert_close(
-        SiluAndMul()(packed),
-        F.silu(gate) * up,
-        atol=2e-2,
-        rtol=2e-2,
-    )
@@ -1,194 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import sys
-import types
-
-import pytest
-import torch
-import torch.nn.functional as F
-
-from tests.helpers.mark import hardware_test
-
-pytestmark = [pytest.mark.core_model, pytest.mark.diffusion]
-
-_HEAD_DIM = 128
-_ROTARY_DIM = 96
-_EPS = 1e-5
-
-
-def _reference(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    q_weight: torch.Tensor,
-    k_weight: torch.Tensor,
-    rope_table: torch.Tensor,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    half = _ROTARY_DIM // 2
-
-    def apply(x: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
-        x = F.rms_norm(x, (_HEAD_DIM,), weight, _EPS)
-        cos = rope_table[..., :half].unsqueeze(1)
-        sin = rope_table[..., half:].unsqueeze(1)
-        return torch.cat(
-            (
-                x[..., :half] * cos - x[..., half:_ROTARY_DIM] * sin,
-                x[..., half:_ROTARY_DIM] * cos + x[..., :half] * sin,
-                x[..., _ROTARY_DIM:],
-            ),
-            dim=-1,
-        )
-
-    return apply(q, q_weight), apply(k, k_weight)
-
-
-def _inputs() -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-    torch.manual_seed(7)
-    tokens, heads = 11, 4
-    q = torch.randn(tokens, heads, _HEAD_DIM, dtype=torch.bfloat16)
-    k = torch.randn(tokens, heads, _HEAD_DIM, dtype=torch.bfloat16)
-    q_weight = torch.randn(_HEAD_DIM, dtype=torch.bfloat16)
-    k_weight = torch.randn(_HEAD_DIM, dtype=torch.bfloat16)
-    freqs = torch.randn(tokens, _ROTARY_DIM // 2)
-    rope_table = torch.cat((torch.cos(freqs), torch.sin(freqs)), dim=-1).to(torch.bfloat16)
-    return q, k, q_weight, k_weight, rope_table
-
-
-@pytest.mark.cpu
-def test_npu_qk_norm_rope_uses_torch_npu_fused_primitives_without_mindiesd(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    from vllm_omni.diffusion.layers import fused_qk_norm_rope as fused
-
-    calls: dict[str, int] = {"rms_norm": 0, "rotary_mul": 0}
-
-    def npu_rms_norm(x: torch.Tensor, gamma: torch.Tensor, epsilon: float):
-        calls["rms_norm"] += 1
-        return (F.rms_norm(x, (_HEAD_DIM,), gamma, epsilon),)
-
-    def npu_rotary_mul(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):
-        calls["rotary_mul"] += 1
-        assert x.shape[-1] == _ROTARY_DIM
-        assert cos.shape == (1, x.shape[1], 1, _ROTARY_DIM)
-        assert sin.shape == cos.shape
-        half = _ROTARY_DIM // 2
-        return torch.cat(
-            (
-                x[..., :half] * cos[..., :half] - x[..., half:] * sin[..., :half],
-                x[..., half:] * cos[..., half:] + x[..., :half] * sin[..., half:],
-            ),
-            dim=-1,
-        )
-
-    monkeypatch.setitem(
-        sys.modules,
-        "torch_npu",
-        types.SimpleNamespace(npu_rms_norm=npu_rms_norm, npu_rotary_mul=npu_rotary_mul),
-    )
-    monkeypatch.setattr(fused, "find_spec", lambda _: None)
-    q, k, q_weight, k_weight, rope_table = _inputs()
-
-    actual_q, actual_k = fused._npu_qk_norm_rope(
-        q,
-        k,
-        q_weight,
-        k_weight,
-        rope_table,
-        _EPS,
-        _ROTARY_DIM,
-    )
-    expected_q, expected_k = _reference(q, k, q_weight, k_weight, rope_table)
-
-    assert calls == {"rms_norm": 2, "rotary_mul": 2}
-    torch.testing.assert_close(actual_q, expected_q, atol=0, rtol=0)
-    torch.testing.assert_close(actual_k, expected_k, atol=0, rtol=0)
-
-
-@pytest.mark.cpu
-def test_npu_qk_norm_rope_uses_mindiesd_and_normalizes_packed_layout(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    from vllm_omni.diffusion.layers import fused_qk_norm_rope as fused
-
-    calls: dict[str, int] = {"rms_norm": 0, "mindiesd_rope": 0}
-
-    def npu_rms_norm(x: torch.Tensor, gamma: torch.Tensor, epsilon: float):
-        calls["rms_norm"] += 1
-        return (F.rms_norm(x, (_HEAD_DIM,), gamma, epsilon),)
-
-    def rotary_position_embedding(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, **kwargs):
-        calls["mindiesd_rope"] += 1
-        assert x.dim() == 4
-        assert x.shape[0] == 1
-        assert x.shape[-1] == _ROTARY_DIM
-        assert cos.shape == (1, x.shape[1], 1, _ROTARY_DIM)
-        assert sin.shape == cos.shape
-        assert kwargs == {
-            "rotated_mode": "rotated_half",
-            "head_first": False,
-            "fused": True,
-        }
-        half = _ROTARY_DIM // 2
-        return torch.cat(
-            (
-                x[..., :half] * cos[..., :half] - x[..., half:] * sin[..., :half],
-                x[..., half:] * cos[..., half:] + x[..., :half] * sin[..., half:],
-            ),
-            dim=-1,
-        )
-
-    monkeypatch.setitem(sys.modules, "torch_npu", types.SimpleNamespace(npu_rms_norm=npu_rms_norm))
-    monkeypatch.setitem(
-        sys.modules,
-        "mindiesd",
-        types.SimpleNamespace(rotary_position_embedding=rotary_position_embedding),
-    )
-    monkeypatch.setattr(fused, "find_spec", lambda _: object())
-    q, k, q_weight, k_weight, rope_table = _inputs()
-
-    actual_q, actual_k = fused._npu_qk_norm_rope(
-        q,
-        k,
-        q_weight,
-        k_weight,
-        rope_table,
-        _EPS,
-        _ROTARY_DIM,
-    )
-    expected_q, expected_k = _reference(q, k, q_weight, k_weight, rope_table)
-
-    assert calls == {"rms_norm": 2, "mindiesd_rope": 2}
-    torch.testing.assert_close(actual_q, expected_q, atol=0, rtol=0)
-    torch.testing.assert_close(actual_k, expected_k, atol=0, rtol=0)
-
-
-@hardware_test(res={"npu": "A3"}, num_cards=1)
-def test_fused_qk_norm_rope_npu_matches_reference(monkeypatch: pytest.MonkeyPatch) -> None:
-    """Exercise the public Ascend dispatch with real torch_npu/MindIE operators."""
-    from vllm_omni.diffusion.layers import fused_qk_norm_rope as fused
-
-    q, k, q_weight, k_weight, rope_table = (tensor.to("npu") for tensor in _inputs())
-    calls = 0
-    original_npu_impl = fused._npu_qk_norm_rope
-
-    def capture_npu_impl(*args, **kwargs):
-        nonlocal calls
-        calls += 1
-        return original_npu_impl(*args, **kwargs)
-
-    monkeypatch.setattr(fused, "_npu_qk_norm_rope", capture_npu_impl)
-    actual_q, actual_k = fused.fused_qk_norm_rope(
-        q,
-        k,
-        q_weight,
-        k_weight,
-        rope_table,
-        _EPS,
-        head_dim=_HEAD_DIM,
-        rotary_dim=_ROTARY_DIM,
-    )
-    expected_q, expected_k = _reference(q, k, q_weight, k_weight, rope_table)
-
-    assert calls == 1
-    torch.testing.assert_close(actual_q, expected_q, atol=2e-2, rtol=2e-2)
-    torch.testing.assert_close(actual_k, expected_k, atol=2e-2, rtol=2e-2)
@@ -273,15 +273,12 @@ def test_required_hwr_miss_fails_before_ordinary_loading_or_publication(
    assert loader.take_host_weight_plan() is None


-def _make_dlo_online_quant_config(dp_size: int = 2) -> OmniDiffusionConfig:
+def _make_dlo_online_quant_config() -> OmniDiffusionConfig:
    return OmniDiffusionConfig(
        model="",
        dtype=torch.float32,
        quantization_config="fp8",
-        parallel_config=DiffusionParallelConfig(
-            data_parallel_size=dp_size,
-            sequence_parallel_size=1,
-        ),
+        parallel_config=DiffusionParallelConfig(data_parallel_size=2),
        enable_distributed_layerwise_offload=True,
        dlo_use_allgather=True,
    )
@@ -827,93 +824,6 @@ def test_dlo_allgather_online_fp8_uses_ordinary_loader(monkeypatch):
    assert loader.take_host_weight_plan() is None


-def test_dlo_allgather_online_int8_uses_ordinary_loader(monkeypatch):
-    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
-    from vllm_omni.quantization.int8_config import NPUInt8OnlineLinearMethod
-
-    od_config = _make_dlo_online_quant_config()
-    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
-    model = nn.Module()
-    model.transformer = nn.Linear(2, 2, bias=False)
-    model.transformer.quant_method = object.__new__(NPUInt8OnlineLinearMethod)
-    model.transformer.quant_method.uses_meta_device = True
-    calls: list[object] = []
-    allowlist_models: list[nn.Module] = []
-
-    original_allowlist_check = loader._unsupported_dlo_allgather_online_quant_methods
-
-    def check_allowlist(candidate: nn.Module) -> tuple[str, ...]:
-        allowlist_models.append(candidate)
-        return original_allowlist_check(candidate)
-
-    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
-    monkeypatch.setattr(loader, "_unsupported_dlo_allgather_online_quant_methods", check_allowlist)
-    loader._request_offload_after_quant = lambda _model: 1  # type: ignore[method-assign]
-    loader.load_weights = (  # type: ignore[method-assign]
-        lambda _model, *, stream_online_quant_to_cpu=False: calls.append(("load", stream_online_quant_to_cpu))
-    )
-    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,
-            "online quantization requires the ordinary loader",
-        ),
-    )
-
-    assert loader.load_model(load_device="cpu", device=torch.device("cpu")) is model
-    assert allowlist_models == [model]
-    assert calls == [("load", True), "process"]
-    assert loader.take_host_weight_plan() is None
-
-
-def test_dlo_online_quant_group_size_one_skips_allgather_gate(monkeypatch):
-    """A DLO group of one runs no weight collective, so an otherwise
-    unvalidated online method must not be rejected by the AllGather gate."""
-
-    class UnsupportedOnlineMethod:
-        uses_meta_device = True
-
-    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
-
-    od_config = _make_dlo_online_quant_config(dp_size=1)
-    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
-    model = nn.Module()
-    model.transformer = nn.Linear(2, 2, bias=False)
-    model.transformer.quant_method = UnsupportedOnlineMethod()
-    calls: list[object] = []
-    allowlist_models: list[nn.Module] = []
-
-    original_allowlist_check = loader._unsupported_dlo_allgather_online_quant_methods
-
-    def check_allowlist(candidate: nn.Module) -> tuple[str, ...]:
-        allowlist_models.append(candidate)
-        return original_allowlist_check(candidate)
-
-    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
-    monkeypatch.setattr(loader, "_unsupported_dlo_allgather_online_quant_methods", check_allowlist)
-    loader._request_offload_after_quant = lambda _model: 1  # type: ignore[method-assign]
-    loader.load_weights = (  # type: ignore[method-assign]
-        lambda _model, *, stream_online_quant_to_cpu=False: calls.append(("load", stream_online_quant_to_cpu))
-    )
-    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,
-            "online quantization requires the ordinary loader",
-        ),
-    )
-
-    assert loader.load_model(load_device="cpu", device=torch.device("cpu")) is model
-    assert allowlist_models == []
-    assert calls == [("load", True), "process"]
-
-
 def test_dlo_allgather_rejects_unvalidated_online_quant_method(monkeypatch):
    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod

@@ -937,54 +847,10 @@ def test_dlo_allgather_rejects_unvalidated_online_quant_method(monkeypatch):
        ),
    )

-    with pytest.raises(ValueError, match="per-tensor FP8, INT8, and MXFP8 linears"):
+    with pytest.raises(ValueError, match="per-tensor FP8 linears"):
        loader.load_model(load_device="cpu")


-def test_dlo_allgather_online_mxfp8_uses_ordinary_loader(monkeypatch):
-    mxfp8_config = pytest.importorskip("vllm_omni.quantization.mxfp8_config")
-    NPUMxfp8OnlineLinearMethod = mxfp8_config.NPUMxfp8OnlineLinearMethod
-
-    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
-
-    od_config = _make_dlo_online_quant_config()
-    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
-    model = nn.Module()
-    model.transformer = nn.Linear(2, 2, bias=False)
-    model.transformer.quant_method = object.__new__(NPUMxfp8OnlineLinearMethod)
-    model.transformer.quant_method.uses_meta_device = True
-    calls: list[object] = []
-    allowlist_models: list[nn.Module] = []
-
-    original_allowlist_check = loader._unsupported_dlo_allgather_online_quant_methods
-
-    def check_allowlist(candidate: nn.Module) -> tuple[str, ...]:
-        allowlist_models.append(candidate)
-        return original_allowlist_check(candidate)
-
-    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
-    monkeypatch.setattr(loader, "_unsupported_dlo_allgather_online_quant_methods", check_allowlist)
-    loader._request_offload_after_quant = lambda _model: 1  # type: ignore[method-assign]
-    loader.load_weights = (  # type: ignore[method-assign]
-        lambda _model, *, stream_online_quant_to_cpu=False: calls.append(("load", stream_online_quant_to_cpu))
-    )
-    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,
-            "online quantization requires the ordinary loader",
-        ),
-    )
-
-    assert loader.load_model(load_device="cpu", device=torch.device("cpu")) is model
-    assert allowlist_models == [model]
-    assert calls == [("load", True), "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
@@ -1,12 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# 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
-from typing import Any

 import pytest
 import torch
@@ -171,10 +170,7 @@ def _resolve_request_inputs_for_test(


 def test_ltx25_missing_gemma4_recommends_supported_transformers_range(monkeypatch):
-    pipe = SimpleNamespace(
-        component_profile=replace(LTX25_FULL_COMPONENT_PROFILE, text_encoder_cls=None),
-        use_diffusion_decoder=True,
-    )
+    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"))
@@ -191,19 +187,7 @@ def test_ltx25_missing_gemma4_recommends_supported_transformers_range(monkeypatc

 def test_ltx_converted_component_loading_propagates_revision(monkeypatch):
    revision = "pinned-revision"
-    calls: dict[str, Any] = {"components": []}
-    natten_processor = object()
-
-    class FakeDiffusionDecoder:
-        def set_attn_processor(self, processor):
-            calls["diffusion_decoder_processor"] = processor
-
-        def enable_tiling(self):
-            calls["diffusion_decoder_tiling"] = True
-
-        def set_parallel_size(self, parallel_size, mode):
-            calls["diffusion_decoder_parallel"] = (parallel_size, mode)
-
+    calls = {"components": []}
    profile = replace(
        LTX25_DISTILLED_COMPONENT_PROFILE,
        text_encoder_cls=object,
@@ -212,19 +196,12 @@ def test_ltx_converted_component_loading_propagates_revision(monkeypatch):
        vocoder_fallback_cls=None,
        scheduler_use_dynamic_shifting=False,
    )
-    pipeline = SimpleNamespace(
-        component_profile=profile,
-        pipeline_kind="distilled_two_stage",
-        use_diffusion_decoder=True,
-    )
+    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,
-        extras={},
-        vae_use_tiling=False,
-        parallel_config=SimpleNamespace(vae_patch_parallel_size=2, vae_parallel_mode="tile"),
    )

    monkeypatch.setattr(ltx2_components, "get_local_device", lambda: torch.device("cpu"))
@@ -250,10 +227,6 @@ def test_ltx_converted_component_loading_propagates_revision(monkeypatch):
            )
        return object()

-    def fake_native_diffusion_decoder(model, **kwargs):
-        calls["native_diffusion_decoder"] = (model, kwargs)
-        return FakeDiffusionDecoder()
-
    def fake_transformer_config(model, subfolder, local_files_only, *, revision):
        calls["transformer_config"] = (model, subfolder, local_files_only, revision)
        return {"component": "transformer"}
@@ -261,13 +234,7 @@ def test_ltx_converted_component_loading_propagates_revision(monkeypatch):
    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_ltx25_native_diffusion_decoder", fake_native_diffusion_decoder)
    monkeypatch.setattr(ltx2_components, "load_transformer_config", fake_transformer_config)
-    monkeypatch.setattr(
-        ltx2_components,
-        "LTX2VideoVaeNeighborhoodNattenProcessor",
-        lambda: natten_processor,
-    )
    monkeypatch.setattr(
        ltx2_components,
        "create_transformer_from_config",
@@ -294,14 +261,6 @@ def test_ltx_converted_component_loading_propagates_revision(monkeypatch):
        "latent_upsampler",
    }
    assert all(call[3]["revision"] == revision for call in calls["components"])
-    assert "diffusion_decoder" not in calls["prefetch"][1]
-    assert calls["native_diffusion_decoder"] == (
-        od_config.model,
-        {"local_files_only": False, "dtype": torch.bfloat16, "revision": revision},
-    )
-    assert calls["diffusion_decoder_processor"] is natten_processor
-    assert calls["diffusion_decoder_tiling"] is True
-    assert calls["diffusion_decoder_parallel"] == (2, "tile")
    assert calls["transformer_config"] == (
        od_config.model,
        profile.transformer_subfolder,
@@ -311,85 +270,6 @@ def test_ltx_converted_component_loading_propagates_revision(monkeypatch):
    assert calls["scheduler"][1]["revision"] == revision


-def test_ltx25_native_diffusion_decoder_uses_diffusers_config_and_canonical_artifact(monkeypatch):
-    revision = "diffusers-revision"
-    config = {"decoder_stage_channels": [8, 8, 8, 8, 8]}
-    calls: dict[str, Any] = {}
-
-    class FakeDecoder:
-        def init_distributed(self):
-            calls["init_distributed"] = True
-
-    expected = FakeDecoder()
-
-    def fake_load_config(model, **kwargs):
-        calls["config"] = (model, kwargs)
-        return config
-
-    def fake_resolve(model, repo_id, filename, **kwargs):
-        calls["artifact"] = (model, repo_id, filename, kwargs)
-        return "/models/native-diffvae.safetensors"
-
-    def fake_load(cls, path, passed_config, dtype):
-        calls["load"] = (path, passed_config, dtype)
-        return expected
-
-    monkeypatch.setattr(
-        ltx2_components.DistributedLTX2VideoDiffusionDecoderModel,
-        "load_config",
-        staticmethod(fake_load_config),
-    )
-    monkeypatch.setattr(ltx2_components, "resolve_ltx_artifact", fake_resolve)
-    monkeypatch.setattr(
-        ltx2_components.DistributedLTX2VideoDiffusionDecoderModel,
-        "from_ltx25_native_checkpoint",
-        classmethod(fake_load),
-    )
-
-    actual = ltx2_components._load_ltx25_native_diffusion_decoder(
-        "Lightricks/LTX-2.5-Diffusers",
-        local_files_only=False,
-        dtype=torch.bfloat16,
-        revision=revision,
-    )
-
-    assert actual is expected
-    assert calls["config"] == (
-        "Lightricks/LTX-2.5-Diffusers",
-        {
-            "subfolder": "diffusion_decoder",
-            "local_files_only": False,
-            "revision": revision,
-        },
-    )
-    assert calls["artifact"] == (
-        "Lightricks/LTX-2.5-Diffusers",
-        "Lightricks/LTX-2.5",
-        "vae/ltx-2.5-video-vae-bf16.safetensors",
-        {
-            "model_revision": revision,
-            "artifact_revision": ltx2_components.LTX25_NATIVE_ARTIFACT_REVISION,
-        },
-    )
-    assert calls["load"] == ("/models/native-diffvae.safetensors", config, torch.bfloat16)
-    assert calls["init_distributed"] is True
-
-
-def test_ltx25_natten_failure_has_actionable_omni_remedy(monkeypatch):
-    class MissingNattenProcessor:
-        def __init__(self):
-            raise ImportError("kernels is unavailable")
-
-    monkeypatch.setattr(
-        ltx2_components,
-        "LTX2VideoVaeNeighborhoodNattenProcessor",
-        MissingNattenProcessor,
-    )
-
-    with pytest.raises(RuntimeError, match=r"kernels==0\.14\.1.*supported GPU.*allow Hub access"):
-        ltx2_components._create_ltx25_natten_processor()
-
-
 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()
@@ -531,46 +411,6 @@ def test_ltx25_four_public_pipeline_semantics_are_disjoint():
    assert LTX2DistilledTwoStagePipeline.pipeline_kind == "distilled_two_stage"


-@pytest.mark.parametrize(
-    "pipeline_cls",
-    [
-        LTX2Pipeline,
-        LTX2TwoStagePipeline,
-        LTX2DistilledOneStagePipeline,
-        LTX2DistilledTwoStagePipeline,
-    ],
-)
-@pytest.mark.parametrize(
-    ("extras", "use_diffusion_decoder"),
-    [({}, True), ({"ltx2_use_conv_vae": True}, False)],
-)
-def test_ltx25_all_public_pipelines_default_to_diffvae_with_conv_opt_in(
-    tmp_path, monkeypatch, pipeline_cls, extras, use_diffusion_decoder
-):
-    (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(ltx2_runtime, "build_ltx_phase_adapter", lambda _pipe: None)
-    monkeypatch.setattr(LTXRuntime, "setup_diffusion_pipeline_profiler", lambda *_args, **_kwargs: None)
-
-    pipe = pipeline_cls(
-        od_config=SimpleNamespace(
-            model=str(tmp_path),
-            extras=extras,
-            enable_diffusion_pipeline_profiler=False,
-        )
-    )
-
-    assert pipe.use_diffusion_decoder is use_diffusion_decoder
-    assert pipe._vae_modules.count("diffusion_decoder") == int(use_diffusion_decoder)
-
-
@pytest.mark.parametrize(
    ("pipeline_kind", "expected"),
    [
@@ -833,7 +673,7 @@ def test_ltx25_distilled_two_stage_executes_custom_phase_schedules():
        max_sequence_length=16,
    )
    prompt_context = object()
-    phase_calls: list[Any] = []
+    phase_calls = []

    def resolve_request_inputs(req, **kwargs):
        return request_inputs
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

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

@@ -258,39 +258,6 @@ class TestLTXImageToVideoConditioning:

        assert LTX2Pipeline.support_image_input is True

-    def test_ltx25_i2v_encode_uses_diffusion_decoder_statistics(self, monkeypatch):
-        import vllm_omni.diffusion.models.ltx2.ltx2_conditioning as ltx2_conditioning
-        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
-
-        pipe = object.__new__(LTX2Pipeline)
-        torch.nn.Module.__init__(pipe)
-        pipe.use_diffusion_decoder = True
-        pipe.vae = SimpleNamespace(
-            encode=lambda image: image,
-            latents_mean=torch.full((2,), 100.0),
-            latents_std=torch.full((2,), 10.0),
-            config=SimpleNamespace(scaling_factor=1.0),
-        )
-        pipe.diffusion_decoder = SimpleNamespace(
-            latents_mean=torch.tensor([1.0, 3.0]),
-            latents_std=torch.tensor([2.0, 4.0]),
-            config=SimpleNamespace(scaling_factor=2.0),
-        )
-        monkeypatch.setattr(
-            ltx2_conditioning,
-            "retrieve_latents",
-            lambda *_args, **_kwargs: torch.tensor([[[[[3.0]]], [[[7.0]]]]]),
-        )
-
-        actual = pipe._encode_i2v_image_latents(
-            torch.zeros(1, 3, 1, 1),
-            batch_size=1,
-            generator=None,
-            dtype=torch.float32,
-        )
-
-        torch.testing.assert_close(actual, torch.full((1, 2, 1, 1, 1), 2.0))
-
    def test_ltx23_i2v_rejects_multi_image_prompt_list(self):
        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline

@@ -1,6 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
 from types import SimpleNamespace

 import pytest
@@ -50,39 +47,6 @@ def test_prepare_video_latents_matches_official_values_and_token_major_layout():
    assert actual.stride()[1:] == (1, actual.shape[1])


-def test_prepare_video_latents_uses_diffusion_decoder_statistics():
-    pipeline = _make_pipeline(LTX2Pipeline)
-    pipeline.use_diffusion_decoder = True
-    pipeline.vae_spatial_compression_ratio = 1
-    pipeline.vae_temporal_compression_ratio = 1
-    pipeline.transformer_spatial_patch_size = 1
-    pipeline.transformer_temporal_patch_size = 1
-    pipeline.vae = SimpleNamespace(
-        latents_mean=torch.full((2,), 100.0),
-        latents_std=torch.full((2,), 10.0),
-        config=SimpleNamespace(scaling_factor=1.0),
-    )
-    pipeline.diffusion_decoder = SimpleNamespace(
-        latents_mean=torch.tensor([1.0, 3.0]),
-        latents_std=torch.tensor([2.0, 4.0]),
-        config=SimpleNamespace(scaling_factor=2.0),
-    )
-
-    actual = pipeline.prepare_latents(
-        batch_size=1,
-        num_channels_latents=2,
-        height=1,
-        width=1,
-        num_frames=1,
-        noise_scale=0.0,
-        dtype=torch.float32,
-        device=torch.device("cpu"),
-        latents=torch.tensor([[[[[3.0]]], [[[7.0]]]]]),
-    )
-
-    torch.testing.assert_close(actual, torch.full((1, 1, 2), 2.0))
-
-
 def test_prepare_audio_latents_samples_directly_in_packed_token_space():
    pipeline = _make_pipeline(LTX2Pipeline)

@@ -1,10 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 """Unit tests for the fixed LTX refinement-phase adapter."""

 from types import SimpleNamespace
-from typing import Any

 import pytest
 import torch
@@ -149,13 +148,10 @@ def test_ltx_phase_adapter_selects_execution_from_quantization(
 ):
    pipeline = _pipeline(dtype=dtype, quantization_config=quantization_config)
    manifest = object()
-    captured: dict[str, Any] = {}
+    captured = {}

    def resolve_artifact(*_args, **kwargs):
-        assert kwargs == {
-            "model_revision": None,
-            "artifact_revision": None,
-        }
+        assert not kwargs
        return "adapter"

    def parse(transformer, path):
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 """Unit tests for the shared LTX pipeline runtime and public contracts."""

@@ -8,7 +8,6 @@ import os
 import tempfile
 from dataclasses import replace
 from types import SimpleNamespace
-from typing import Any

 import pytest
 import torch
@@ -166,19 +165,7 @@ def test_ltx_checkpoint_version_detection_uses_metadata(tmp_path):
    assert detect_ltx_model_version(str(tmp_path)) == "2"


-@pytest.mark.parametrize(
-    ("model", "repo_id", "expected_revision"),
-    [
-        ("Lightricks/test", "Lightricks/test", "model-revision"),
-        ("Lightricks/test-Diffusers", "Lightricks/test", "artifact-revision"),
-    ],
-)
-def test_ltx_artifact_uses_source_revision_and_hub_fallback(
-    monkeypatch,
-    model,
-    repo_id,
-    expected_revision,
-):
+def test_ltx_artifact_falls_back_to_unpinned_hub_download(tmp_path, monkeypatch):
    filename = "ltx-sidecar.safetensors"
    calls = []

@@ -189,22 +176,9 @@ def test_ltx_artifact_uses_source_revision_and_hub_fallback(
    monkeypatch.setattr(ltx2_components, "hf_hub_download", fake_download)

    assert (
-        resolve_ltx_artifact(
-            model,
-            repo_id,
-            filename,
-            model_revision="model-revision",
-            artifact_revision="artifact-revision",
-        )
-        == "/cache/ltx-sidecar.safetensors"
+        resolve_ltx_artifact(str(tmp_path / "model"), "Lightricks/test", filename) == "/cache/ltx-sidecar.safetensors"
    )
-    assert calls == [
-        {
-            "repo_id": repo_id,
-            "filename": filename,
-            "revision": expected_revision,
-        }
-    ]
+    assert calls == [{"repo_id": "Lightricks/test", "filename": filename}]


 def test_ltx_artifact_prefers_model_root(tmp_path, monkeypatch):
@@ -213,41 +187,7 @@ def test_ltx_artifact_prefers_model_root(tmp_path, monkeypatch):
    expected.write_bytes(b"sidecar")
    monkeypatch.setattr(ltx2_components, "hf_hub_download", lambda **_kwargs: pytest.fail("unexpected Hub lookup"))

-    assert resolve_ltx_artifact(
-        str(tmp_path),
-        "Lightricks/test",
-        filename,
-        model_revision="unused-model-revision",
-        artifact_revision="unused-artifact-revision",
-    ) == str(expected)
-
-
-def test_ltx_artifact_local_model_missing_sidecar_falls_back_to_hub(tmp_path, monkeypatch):
-    calls = []
-
-    def fake_download(**kwargs):
-        calls.append(kwargs)
-        return "/cache/ltx-sidecar.safetensors"
-
-    monkeypatch.setattr(ltx2_components, "hf_hub_download", fake_download)
-
-    assert (
-        resolve_ltx_artifact(
-            str(tmp_path),
-            "Lightricks/test",
-            "ltx-sidecar.safetensors",
-            model_revision="local-model-revision",
-            artifact_revision="artifact-revision",
-        )
-        == "/cache/ltx-sidecar.safetensors"
-    )
-    assert calls == [
-        {
-            "repo_id": "Lightricks/test",
-            "filename": "ltx-sidecar.safetensors",
-            "revision": "artifact-revision",
-        }
-    ]
+    assert resolve_ltx_artifact(str(tmp_path), "Lightricks/test", filename) == str(expected)


@pytest.mark.parametrize(
@@ -960,7 +900,7 @@ def test_ltx_two_stage_executes_declarative_i2v_phase_plan(pipeline_cls):
        max_sequence_length=16,
    )
    prompt_context = object()
-    phase_calls: list[Any] = []
+    phase_calls = []

    def resolve_request_inputs(req, **kwargs):
        return request_inputs
@@ -1019,7 +959,7 @@ def test_ltx_two_stage_executes_declarative_i2v_phase_plan(pipeline_cls):
    pipeline.vae_spatial_compression_ratio = 32
    pipeline.vae_temporal_compression_ratio = 8
    pipeline.latent_upsampler = FakeUpsampler()
-    activated_slots: list[Any] = []
+    activated_slots = []
    if pipeline_cls is LTX2TwoStagePipeline:
        pipeline._phase_adapter = SimpleNamespace(activate=activated_slots.append)
    object.__setattr__(pipeline, "_resolve_request_inputs", resolve_request_inputs)
@@ -1193,59 +1133,6 @@ def test_ltx_two_stage_recipe_rejects_sigmas():
        )


-def test_ltx_request_applies_diffvae_minimum_spatial_size_only_when_requested():
-    request = LTXRequestInputs(
-        prompt="prompt",
-        negative_prompt="",
-        height=128,
-        width=128,
-        num_frames=9,
-        frame_rate=24.0,
-        num_inference_steps=30,
-        guidance=LTX2_ONE_STAGE_RECIPE.request_guidance,
-        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=16,
-    )
-
-    with pytest.raises(ValueError, match="DiffVAE requires video latent spatial dimensions of at least 7x7"):
-        validate_pipeline_request(
-            request,
-            pipeline_recipe=LTX2_ONE_STAGE_RECIPE,
-            vae_spatial_compression_ratio=32,
-            vae_temporal_compression_ratio=8,
-            pipeline_name="LTX2Pipeline",
-            min_video_latent_spatial_size=(7, 7),
-        )
-
-    # ConvVAE does not use NATTEN, so its existing 128x128 boundary remains valid.
-    validate_pipeline_request(
-        request,
-        pipeline_recipe=LTX2_ONE_STAGE_RECIPE,
-        vae_spatial_compression_ratio=32,
-        vae_temporal_compression_ratio=8,
-        pipeline_name="LTX2Pipeline",
-    )
-
-    validate_pipeline_request(
-        replace(request, height=256, width=256),
-        pipeline_recipe=LTX2_ONE_STAGE_RECIPE,
-        vae_spatial_compression_ratio=32,
-        vae_temporal_compression_ratio=8,
-        pipeline_name="LTX2Pipeline",
-        min_video_latent_spatial_size=(7, 7),
-    )
-
-
@pytest.mark.parametrize(
    ("direct_kwargs", "sampling_kwargs", "error"),
    [
@@ -1397,7 +1284,7 @@ def test_ltx_official_two_stage_recipes_only_vary_by_model_defaults(recipe, step


 def test_ltx_two_stage_entries_select_the_official_adapter_slots():
-    phase_switches: list[Any] = []
+    phase_switches = []
    ordinary_pipeline = object.__new__(LTX2TwoStagePipeline)
    ordinary_pipeline._phase_adapter = SimpleNamespace(activate=phase_switches.append)
    for phase in LTX2_TWO_STAGE_RECIPE.phases:
@@ -1,10 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

-"""Unit tests for LTX video VAE tiling and distributed decode behavior."""
+"""Unit tests for LTX-2.3 VAE tiling and distributed decode behavior."""

 from types import SimpleNamespace
-from typing import Any

 import pytest
 import torch
@@ -37,441 +36,6 @@ def test_ltx_base_vocoder_keeps_native_dtype(monkeypatch):
    assert output.dtype == torch.bfloat16


-class TestLTXDiffusionDecoder:
-    @pytest.mark.parametrize("mode", ["spatial_shard_height", "spatial_shard_width"])
-    def test_distributed_diffusion_decoder_rejects_non_tile_parallel_modes(self, mode):
-        from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder_distributed import (
-            DistributedLTX2VideoDiffusionDecoderModel,
-        )
-
-        model = object.__new__(DistributedLTX2VideoDiffusionDecoderModel)
-        torch.nn.Module.__init__(model)
-        model.distributed_executor = SimpleNamespace(set_parallel_size=lambda *_args, **_kwargs: None)
-
-        with pytest.raises(ValueError, match="only supports vae_parallel_mode='tile'"):
-            model.set_parallel_size(2, mode=mode)
-
-    def test_distributed_diffusion_decoder_accepts_tile_parallel_mode(self):
-        from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder_distributed import (
-            DistributedLTX2VideoDiffusionDecoderModel,
-        )
-
-        calls = []
-        model = object.__new__(DistributedLTX2VideoDiffusionDecoderModel)
-        torch.nn.Module.__init__(model)
-        model.distributed_executor = SimpleNamespace(
-            set_parallel_size=lambda parallel_size, mode: calls.append((parallel_size, mode))
-        )
-
-        model.set_parallel_size(4, mode="tile")
-
-        assert calls == [(4, "tile")]
-
-    def test_short_clip_keeps_stage5_temporal_context_then_crops_output(self):
-        from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder import (
-            LTX2VideoDiffusionDecoder3d,
-            LTX2VideoDiffusionDecoderModel,
-        )
-
-        class IdentityAttention:
-            def build_block_mask(self, _hidden_states):
-                return None
-
-        class IdentityBlock(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.attn = IdentityAttention()
-
-            def forward(self, hidden_states, _block_mask):
-                return hidden_states
-
-        class IdentityUpsample(torch.nn.Module):
-            def forward(self, hidden_states, *, drop_leading_frame):
-                assert drop_leading_frame
-                return hidden_states
-
-        decoder = object.__new__(LTX2VideoDiffusionDecoder3d)
-        torch.nn.Module.__init__(decoder)
-        decoder.det_stages = torch.nn.ModuleList([torch.nn.ModuleList([IdentityBlock()])])
-        decoder.upsamples = torch.nn.ModuleList([IdentityUpsample()])
-        decoder.trailing_pad_latent_frames = 2
-        decoder.temporal_compression_ratio = 8
-        decoder.stage5_kernel = (11, 3, 3)
-
-        # 17 frames contain one real frame followed by 16 propagated ghost
-        # frames. The reference keeps 11 for stage-5 NATTEN instead of
-        # cropping the tensor immediately back to one.
-        context = decoder.forward_stage_4(torch.zeros(1, 17, 3, 3, 4))
-        assert context.shape == (1, 11, 3, 3, 4)
-
-        class FakeDecoder(torch.nn.Module):
-            def forward(self, z, *, generator, num_inference_steps):
-                assert generator is None
-                assert num_inference_steps is None
-                return torch.zeros(z.shape[0], 3, 11, z.shape[3] * 2, z.shape[4] * 2)
-
-        model = object.__new__(LTX2VideoDiffusionDecoderModel)
-        torch.nn.Module.__init__(model)
-        model.decoder = FakeDecoder()
-        model.use_tiling = False
-        model.tile_sample_min_num_frames = 80
-        model.tile_sample_min_height = 768
-        model.tile_sample_min_width = 768
-        model.temporal_compression_ratio = 8
-        model.spatial_compression_ratio = 2
-
-        output = model.decode(torch.zeros(1, 4, 1, 2, 3), generator=None, return_dict=False)[0]
-        assert output.shape == (1, 3, 1, 4, 6)
-
-    @pytest.mark.parametrize(
-        ("model_version", "extras", "expected"),
-        [
-            ("2.5", {}, True),
-            ("2.5", {"ltx2_use_conv_vae": True}, False),
-            ("2.3", {}, False),
-        ],
-    )
-    def test_ltx25_diffusion_decoder_is_default_with_conv_vae_opt_in(self, model_version, extras, expected):
-        from vllm_omni.diffusion.models.ltx2.ltx2_components import _ltx2_use_diffusion_decoder
-
-        assert _ltx2_use_diffusion_decoder(SimpleNamespace(extras=extras), model_version) is expected
-
-    def test_conv_vae_opt_in_rejects_non_boolean_model_extra(self):
-        from vllm_omni.diffusion.models.ltx2.ltx2_components import _ltx2_use_diffusion_decoder
-
-        with pytest.raises(TypeError, match="ltx2_use_conv_vae"):
-            _ltx2_use_diffusion_decoder(SimpleNamespace(extras={"ltx2_use_conv_vae": "true"}), "2.5")
-
-    def test_native_diffusion_decoder_conversion_splits_qkv_and_folds_gates(self):
-        from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder import (
-            convert_ltx25_native_diffusion_decoder_state_dict,
-        )
-
-        qkv_weight = torch.arange(24, dtype=torch.float32).reshape(6, 4)
-        qkv_bias = torch.arange(6, dtype=torch.float32)
-        projection_weight = torch.arange(4, dtype=torch.float32).reshape(2, 2)
-        projection_bias = torch.ones(2)
-        gate = torch.tensor([2.0, 3.0])
-        mean = torch.tensor([0.1, 0.2])
-        std = torch.tensor([0.3, 0.4])
-        native = {
-            "encoder.ignored": torch.ones(1),
-            "per_channel_statistics.mean-of-means": mean,
-            "vae.per_channel_statistics.std-of-means": std,
-            "decoder.type_emb": torch.ones(2),
-            "decoder.coarse_head.weight": torch.ones(1),
-            "decoder.det_stages.0.0.attn.qkv.weight": qkv_weight,
-            "decoder.det_stages.0.0.attn.qkv.bias": qkv_bias,
-            "decoder.det_stages.0.0.attn.q_norm.weight": torch.tensor([5.0, 6.0]),
-            "decoder.det_stages.0.0.attn.k_norm.weight": torch.tensor([7.0, 8.0]),
-            "decoder.diff_blocks.0.attn.proj.weight": projection_weight,
-            "decoder.diff_blocks.0.attn.proj.bias": projection_bias,
-            "decoder.diff_blocks.0.gate_msa": gate,
-            "vae.decoder.t_embedder.mlp.0.weight": torch.ones(2, 2),
-        }
-
-        converted = convert_ltx25_native_diffusion_decoder_state_dict(native)
-
-        torch.testing.assert_close(converted["latents_mean"], mean)
-        torch.testing.assert_close(converted["latents_std"], std)
-        torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_q.weight"], qkv_weight[:2])
-        torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_k.weight"], qkv_weight[2:4])
-        torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_v.weight"], qkv_weight[4:])
-        torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_q.bias"], qkv_bias[:2])
-        torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_k.bias"], qkv_bias[2:4])
-        torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_v.bias"], qkv_bias[4:])
-        torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.norm_q.weight"], torch.tensor([5.0, 6.0]))
-        torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.norm_k.weight"], torch.tensor([7.0, 8.0]))
-        torch.testing.assert_close(
-            converted["decoder.diff_blocks.0.attn.to_out.0.weight"], gate.unsqueeze(1) * projection_weight
-        )
-        torch.testing.assert_close(converted["decoder.diff_blocks.0.attn.to_out.0.bias"], gate * projection_bias)
-        assert "decoder.t_embedder.timestep_embedder.linear_1.weight" in converted
-        assert not any("type_emb" in key or "coarse" in key or "gate_msa" in key for key in converted)
-
-    def test_native_diffusion_decoder_conversion_rejects_invalid_fused_qkv(self):
-        from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder import (
-            convert_ltx25_native_diffusion_decoder_state_dict,
-        )
-
-        with pytest.raises(ValueError, match="not divisible by 3"):
-            convert_ltx25_native_diffusion_decoder_state_dict(
-                {"decoder.det_stages.0.0.attn.qkv.weight": torch.ones(5, 2)}
-            )
-
-    def test_decode_uses_diffusion_decoder_without_conv_vae_conditioning(self):
-        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
-
-        seen = {}
-
-        class ConvVae:
-            dtype = torch.float32
-            config = SimpleNamespace(timestep_conditioning=True)
-
-            def decode(self, *_args, **_kwargs):
-                raise AssertionError("the convolutional VAE decoder must not run")
-
-        class DiffusionDecoder:
-            dtype = torch.float32
-
-            def decode(self, latents, *, generator, return_dict):
-                seen["latents"] = latents
-                seen["generator"] = generator
-                seen["return_dict"] = return_dict
-                return (latents + 1,)
-
-        class AudioVae:
-            dtype = torch.float32
-
-            def decode(self, audio_latents, *, return_dict):
-                assert return_dict is False
-                return (audio_latents + 2,)
-
-        class VideoProcessor:
-            def postprocess_video(self, video, *, output_type):
-                seen["output_type"] = output_type
-                return video
-
-        pipe = object.__new__(LTX2Pipeline)
-        torch.nn.Module.__init__(pipe)
-        pipe.use_diffusion_decoder = True
-        pipe.vae = ConvVae()
-        pipe.diffusion_decoder = DiffusionDecoder()
-        pipe.audio_vae = AudioVae()
-        pipe.vocoder = torch.nn.Identity()
-        pipe.video_processor = VideoProcessor()
-
-        generator = torch.Generator().manual_seed(123)
-        latents = torch.ones(1, 1)
-        output = pipe._decode_output(
-            latents=latents,
-            audio_latents=torch.ones(1, 1),
-            output_type="pt",
-            connector_prompt_embeds=torch.ones(1, 1),
-            generator=generator,
-            device=torch.device("cpu"),
-            decode_timestep=0.7,
-            decode_noise_scale=0.5,
-            prompt_batch_size=1,
-        )
-
-        torch.testing.assert_close(seen["latents"], latents)
-        assert seen["generator"] is generator
-        assert seen["return_dict"] is False
-        assert seen["output_type"] == "pt"
-        torch.testing.assert_close(output.output[0], latents + 1)
-        torch.testing.assert_close(output.output[1], torch.full((1, 1), 3.0))
-
-    def test_diffusion_decode_denormalizes_with_decoder_statistics(self):
-        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
-
-        pipe = object.__new__(LTX2Pipeline)
-        torch.nn.Module.__init__(pipe)
-        pipe.use_diffusion_decoder = True
-        pipe.transformer_spatial_patch_size = 1
-        pipe.transformer_temporal_patch_size = 1
-        pipe.vae = SimpleNamespace(
-            latents_mean=torch.full((2,), 100.0),
-            latents_std=torch.full((2,), 10.0),
-            config=SimpleNamespace(scaling_factor=1.0),
-        )
-        pipe.diffusion_decoder = SimpleNamespace(
-            latents_mean=torch.tensor([10.0, 20.0]),
-            latents_std=torch.tensor([2.0, 4.0]),
-            config=SimpleNamespace(scaling_factor=2.0),
-        )
-        pipe.audio_vae = SimpleNamespace(
-            latents_mean=torch.tensor(0.0),
-            latents_std=torch.tensor(1.0),
-        )
-        forward_ctx = SimpleNamespace(
-            latent_num_frames=1,
-            latent_height=1,
-            latent_width=1,
-            original_audio_num_frames=1,
-            latent_mel_bins=2,
-        )
-
-        video, audio = pipe._unpack_and_denormalize_stage(
-            forward_ctx,
-            torch.tensor([[[1.0, 2.0]]]),
-            torch.zeros(1, 1, 2),
-        )
-
-        torch.testing.assert_close(video.flatten(), torch.tensor([11.0, 24.0]))
-        torch.testing.assert_close(audio, torch.zeros(1, 1, 1, 2))
-
-    def test_non_output_rank_does_not_enter_diffusion_decoder(self, monkeypatch):
-        import vllm_omni.diffusion.models.ltx2.ltx2_runtime as ltx_runtime
-        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
-
-        class ConvVae:
-            config = SimpleNamespace(timestep_conditioning=False)
-
-            def is_distributed_enabled(self):
-                raise AssertionError("DiffVAE decode must not inspect ConvVAE collectives")
-
-        class DiffusionDecoder:
-            dtype = torch.float32
-
-            def decode(self, *_args, **_kwargs):
-                raise AssertionError("only the output rank may run DiffVAE decode")
-
-        monkeypatch.setattr(ltx_runtime.torch.distributed, "is_initialized", lambda: True)
-        monkeypatch.setattr(ltx_runtime.torch.distributed, "get_rank", lambda: 1)
-        pipe = object.__new__(LTX2Pipeline)
-        torch.nn.Module.__init__(pipe)
-        pipe.use_diffusion_decoder = True
-        pipe.vae = ConvVae()
-        pipe.diffusion_decoder = DiffusionDecoder()
-
-        output = pipe._decode_output(
-            latents=torch.ones(1, 1),
-            audio_latents=torch.ones(1, 1),
-            output_type="pt",
-            connector_prompt_embeds=torch.ones(1, 1),
-            generator=None,
-            device=torch.device("cpu"),
-            decode_timestep=0.0,
-            decode_noise_scale=None,
-            prompt_batch_size=1,
-        )
-
-        assert output.output[0].numel() == 0
-        assert output.output[1].numel() == 0
-
-    def test_non_output_rank_enters_distributed_diffusion_decoder(self, monkeypatch):
-        import vllm_omni.diffusion.models.ltx2.ltx2_runtime as ltx_runtime
-        from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
-
-        class ConvVae:
-            config = SimpleNamespace(timestep_conditioning=False)
-
-            def is_distributed_enabled(self):
-                raise AssertionError("DiffVAE decode must query the active decoder")
-
-        class DiffusionDecoder:
-            dtype = torch.float32
-
-            def __init__(self):
-                self.decode_calls = 0
-
-            def is_distributed_enabled(self):
-                return True
-
-            def decode(self, *_args, **_kwargs):
-                self.decode_calls += 1
-                return (torch.empty(0, 3, 0, 0, 0),)
-
-        monkeypatch.setattr(ltx_runtime.torch.distributed, "is_initialized", lambda: True)
-        monkeypatch.setattr(ltx_runtime.torch.distributed, "get_rank", lambda: 1)
-        pipe = object.__new__(LTX2Pipeline)
-        torch.nn.Module.__init__(pipe)
-        pipe.use_diffusion_decoder = True
-        pipe.vae = ConvVae()
-        pipe.diffusion_decoder = DiffusionDecoder()
-
-        output = pipe._decode_output(
-            latents=torch.ones(1, 1),
-            audio_latents=torch.ones(1, 1),
-            output_type="pt",
-            connector_prompt_embeds=torch.ones(1, 1),
-            generator=None,
-            device=torch.device("cpu"),
-            decode_timestep=0.0,
-            decode_noise_scale=None,
-            prompt_batch_size=1,
-        )
-
-        assert pipe.diffusion_decoder.decode_calls == 1
-        assert output.output[0].numel() == 0
-        assert output.output[1].numel() == 0
-
-    def test_diffusion_decoder_patch_parallel_size_one_uses_native_tiling(self, monkeypatch):
-        from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder import LTX2VideoDiffusionDecoderModel
-        from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder_distributed import (
-            DistributedLTX2VideoDiffusionDecoderModel,
-        )
-
-        expected = torch.ones(1, 3, 1, 2, 2)
-        seen = {}
-
-        def native_tiled_decode(self, z, generator=None, num_inference_steps=None):
-            seen["z"] = z
-            seen["generator"] = generator
-            seen["num_inference_steps"] = num_inference_steps
-            return expected
-
-        monkeypatch.setattr(LTX2VideoDiffusionDecoderModel, "tiled_decode", native_tiled_decode)
-        model = object.__new__(DistributedLTX2VideoDiffusionDecoderModel)
-        torch.nn.Module.__init__(model)
-        model.is_distributed_enabled = lambda: False
-        generator = torch.Generator().manual_seed(17)
-        z = torch.zeros(1, 1, 1, 1, 1)
-
-        result = DistributedLTX2VideoDiffusionDecoderModel.tiled_decode(
-            model,
-            z,
-            generator=generator,
-            num_inference_steps=1,
-        )
-
-        assert result is expected
-        assert seen["z"] is z
-        assert seen["generator"] is generator
-        assert seen["num_inference_steps"] == 1
-
-    def test_distributed_diffusion_tiles_preserve_serial_noise_order(self, monkeypatch):
-        from diffusers.utils.torch_utils import randn_tensor
-
-        from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder_distributed import (
-            DistributedLTX2VideoDiffusionDecoderModel,
-        )
-
-        model = DistributedLTX2VideoDiffusionDecoderModel(
-            out_channels=1,
-            latent_channels=1,
-            patch_size=1,
-            decoder_head_dim=8,
-            decoder_stage_channels=(8, 8, 8, 8, 8),
-            decoder_stage_depths=(1, 1, 1, 1, 1),
-            decoder_stage_kernels=((1, 1, 1),) * 4,
-            decoder_upsample_strides=((1, 1, 1),) * 4,
-            decoder_upsample_channel_reductions=(1, 1, 1, 1),
-            decoder_stage5_kernel=(1, 1, 1),
-            spatial_compression_ratio=1,
-            temporal_compression_ratio=1,
-        )
-        model.tile_sample_min_num_frames = 2
-        model.tile_sample_stride_num_frames = 1
-        model.tile_sample_min_height = 2
-        model.tile_sample_stride_height = 1
-        model.tile_sample_min_width = 2
-        model.tile_sample_stride_width = 1
-        model.distributed_executor = SimpleNamespace(group=None)
-        model.decoder.forward_stages_1_to_3 = lambda z: z.permute(0, 2, 3, 4, 1)
-        monkeypatch.setattr(torch.distributed, "broadcast", lambda *_args, **_kwargs: None)
-
-        z = torch.zeros(1, 1, 2, 3, 3)
-        distributed_generator = torch.Generator().manual_seed(123)
-        reference_generator = torch.Generator().manual_seed(123)
-        tasks, grid_spec = model._distributed_tile_split(z, distributed_generator, num_inference_steps=1)
-
-        assert grid_spec.grid_shape == (1, 3, 3)
-        assert grid_spec.split_dims == (2, 3, 4)
-        for task in tasks:
-            tile_shape = model._tiled_pixel_shape_from_features(
-                task.tensor,
-                drop_leading_frame=task.drop_leading_frame,
-                crop_trailing_ghost=task.crop_trailing_ghost,
-            )
-            expected = randn_tensor(tile_shape, generator=reference_generator, device=z.device, dtype=z.dtype)
-            actual = randn_tensor(tile_shape, generator=task.noise_generator, device=z.device, dtype=z.dtype)
-            torch.testing.assert_close(actual, expected)
-
-        assert torch.equal(distributed_generator.get_state(), reference_generator.get_state())
-
-
 class TestLTXOutputRank:
    @pytest.mark.parametrize(
        ("distributed_vae_state", "expected_decode_calls"),
@@ -574,7 +138,7 @@ class TestLTX23VaeDistributedDecode:
            },
            output_dtype=torch.float32,
        )
-        seen: dict[str, Any] = {}
+        seen = {}

        def exec_tile(task):
            return torch.full(tile_output_shapes[task.tile_id], float(task.tile_id + 1))
@@ -38,7 +38,6 @@ def test_h3_prepares_resolved_cache_state_immediately_before_denoise():
    pipeline.default_audio_shift = 3.0
    pipeline.device = torch.device("cpu")
    pipeline.od_config = SimpleNamespace()
-    pipeline.text_encoder = object()
    cache_spec = object()
    quality_plan = SimpleNamespace(cache_dit=cache_spec)
    pipeline._quality_policy = Mock()
@@ -111,40 +110,6 @@ def test_pipeline_import_registry_and_component_discovery():
    assert MiniMaxH3Pipeline._vae_modules == ["video_vae", "audio_vae"]


-def test_encoder_free_stage_skips_text_encoder_during_dlo_discovery():
-    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
-    from vllm_omni.diffusion.offloader.module_collector import ModuleDiscovery
-
-    pipeline = object.__new__(MiniMaxH3Pipeline)
-    torch.nn.Module.__init__(pipeline)
-    pipeline.transformer = torch.nn.Linear(1, 1)
-    pipeline.video_vae = torch.nn.Linear(1, 1)
-    pipeline.audio_vae = torch.nn.Linear(1, 1)
-    pipeline.text_encoder = None
-    pipeline._dit_modules = ["transformer"]
-    pipeline._encoder_modules = []
-    pipeline._vae_modules = ["video_vae", "audio_vae"]
-
-    discovered = ModuleDiscovery.discover(pipeline)
-
-    assert discovered.dit_names == ["transformer"]
-    assert discovered.encoder_names == []
-    assert discovered.vae_names == ["video_vae", "audio_vae"]
-
-
-def test_encoder_free_stage_skips_missing_component_during_dlo_registration():
-    from vllm_omni.diffusion.models.minimax_h3 import pipeline_minimax_h3 as pipeline_module
-
-    cache = Mock()
-    video_vae = Mock()
-    audio_vae = Mock()
-
-    pipeline_module._register_dlo_component_cache(cache, None, video_vae, audio_vae)
-
-    video_vae.set_omni_component_cache.assert_called_once_with(cache)
-    audio_vae.set_omni_component_cache.assert_called_once_with(cache)
-
-
 def _write_partition_index(path, *, partition, tasks):
    path.mkdir(parents=True)
    (path / "model_index.json").write_text(
@@ -610,7 +575,6 @@ def _distilled_pipeline(diffuse_calls, base_schedule_by_partition):
    pipeline.default_audio_shift = 3.0
    pipeline.device = torch.device("cpu")
    pipeline.od_config = SimpleNamespace()
-    pipeline.text_encoder = Mock()
    pipeline._base_schedule_by_partition = schedules
    pipeline._quality_policy = Mock()
    pipeline._quality_policy.resolve.return_value = SimpleNamespace(cache_dit=None)
@@ -1034,7 +998,7 @@ def test_text_attention_routes_local_gqa_heads_through_sdpa_helper(monkeypatch):
        config.hidden_size,
        bias=False,
    )
-    attn_call: dict[str, object] = {}
+    attn_call = {}

    def fake_attention(query, key, value):
        attn_call.update(query_shape=query.shape, key_shape=key.shape, value_shape=value.shape)
@@ -1056,7 +1020,7 @@ def test_text_attention_routes_local_gqa_heads_through_sdpa_helper(monkeypatch):
 def test_text_attention_sdpa_helper_preserves_expanded_kv_fallback(monkeypatch):
    from vllm_omni.diffusion.models.minimax_h3 import encoder as encoder_module

-    sdpa_call: dict[str, object] = {}
+    sdpa_call = {}

    def fake_sdpa(query, key, value, **kwargs):
        sdpa_call.update(query_shape=query.shape, key_shape=key.shape, value_shape=value.shape, kwargs=kwargs)
@@ -1131,7 +1095,7 @@ def test_encoder_forward_forwards_video_inputs():


 def test_reference_video_shape_uses_h3_adapt_shape_policy():
-    from vllm_omni.model_executor.models.minimax_h3.reference_video import (
+    from vllm_omni.diffusion.models.minimax_h3.reference_video import (
        _reference_video_shape,
    )

@@ -1717,85 +1681,6 @@ def test_r7_r8_ref2va_video_segment_matrix(monkeypatch, tmp_path, case, start_ti
    assert transcode_calls[0][1]["target_frame_count"] == 209


-def test_ref2va_transcode_zero_frame_count_keeps_video_stream(monkeypatch, tmp_path):
-    from vllm_omni.diffusion.models.minimax_h3 import reference_video as reference_video_module
-
-    commands: list[list[str]] = []
-    monkeypatch.setattr(
-        reference_video_module.subprocess,
-        "run",
-        lambda command, **_kwargs: commands.append(command),
-    )
-
-    reference_video_module._transcode_reference_video(
-        "input.mp4",
-        target_width=768,
-        target_height=448,
-        target_frame_count=0,
-        workdir=str(tmp_path),
-        duration_seconds=4.0,
-    )
-
-    assert "-frames:v" not in commands[0]
-
-
-def test_prepared_reference_video_descriptor_round_trip(tmp_path):
-    from vllm_omni.model_executor.models.minimax_h3.reference_video import (
-        deserialize_prepared_reference_videos,
-        serialize_prepared_reference_videos,
-    )
-
-    item = {
-        "original_path": str(tmp_path / "source.mp4"),
-        "prepared_path": str(tmp_path / "prepared.mp4"),
-        "input_has_audio": True,
-        "width": 1344,
-        "height": 768,
-        "start_time_seconds": 0.0,
-        "duration_seconds": 5.2,
-        "audio_duration_seconds": 5.2,
-    }
-
-    descriptor = serialize_prepared_reference_videos([item], str(tmp_path))
-
-    assert deserialize_prepared_reference_videos(descriptor) == (str(tmp_path), [item])
-
-
-def test_prepared_reference_video_descriptor_rejects_unknown_fields():
-    from vllm_omni.model_executor.models.minimax_h3.reference_video import (
-        deserialize_prepared_reference_videos,
-    )
-
-    with pytest.raises(ValueError, match="invalid MiniMax H3 prepared-reference-video descriptor"):
-        deserialize_prepared_reference_videos('{"artifact_dir":"/tmp","videos":[{"path":"x"}]}')
-
-
-def test_stage_one_reuses_existing_prepared_reference_video(tmp_path):
-    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
-        _reuse_prepared_reference_videos,
-    )
-
-    prepared_path = tmp_path / "prepared.mp4"
-    prepared_path.touch()
-    prepared = [{"prepared_path": str(prepared_path)}]
-
-    assert _reuse_prepared_reference_videos(prepared, expected_count=1) is prepared
-
-
-def test_stage_one_rejects_invalid_prepared_reference_video(tmp_path):
-    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
-        _reuse_prepared_reference_videos,
-    )
-
-    with pytest.raises(ValueError, match="count does not match"):
-        _reuse_prepared_reference_videos([], expected_count=1)
-    with pytest.raises(ValueError, match="prepared reference video is unavailable"):
-        _reuse_prepared_reference_videos(
-            [{"prepared_path": str(tmp_path / "missing.mp4")}],
-            expected_count=1,
-        )
-
-
 def test_ref2va_qwen_sampling_uses_one_selective_decode(monkeypatch):
    from vllm_omni.diffusion.models.minimax_h3 import reference_video as reference_video_module

@@ -2235,7 +2120,7 @@ def test_g4_reference_image_file_format_and_size_contract(tmp_path):


 def test_g4_standalone_audio_duration_and_total_duration_contract():
-    from vllm_omni.model_executor.models.minimax_h3.reference_video import (
+    from vllm_omni.diffusion.models.minimax_h3.reference_video import (
        validate_reference_audio_waveforms,
    )

@@ -2310,7 +2195,7 @@ def test_ref2va_standalone_audio_condition_is_bounded_to_output_duration():
    ],
 )
 def test_g4_reference_video_metadata_validation(field, value, message, tmp_path):
-    from vllm_omni.model_executor.models.minimax_h3.reference_video import (
+    from vllm_omni.diffusion.models.minimax_h3.reference_video import (
        _validate_reference_video_metadata,
    )

@@ -1,62 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
-from collections.abc import Iterator
-
-import pytest
-import torch
-import torch.nn as nn
-
-from vllm_omni.diffusion.models.minimax_h3.encoder import MiniMaxH3Qwen3VLEncoder
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
-
-
-@pytest.fixture(autouse=True)
-def restore_cudnn_sdp_state() -> Iterator[None]:
-    original = torch.backends.cuda.cudnn_sdp_enabled()
-    try:
-        yield
-    finally:
-        torch.backends.cuda.enable_cudnn_sdp(original)
-
-
-def _encoder_without_weights() -> MiniMaxH3Qwen3VLEncoder:
-    encoder = MiniMaxH3Qwen3VLEncoder("unused", device=torch.device("cpu"), load_model=False)
-    encoder.text_model = nn.Linear(1, 1)
-    return encoder
-
-
-@pytest.mark.parametrize("initial_state", [False, True])
-def test_encode_ids_restores_cudnn_sdp_after_success(monkeypatch: pytest.MonkeyPatch, initial_state: bool) -> None:
-    encoder = _encoder_without_weights()
-    expected = torch.tensor([1.0])
-
-    def encode(*args, **kwargs):
-        assert torch.backends.cuda.cudnn_sdp_enabled()
-        return expected
-
-    monkeypatch.setattr(encoder, "_encode", encode)
-    torch.backends.cuda.enable_cudnn_sdp(initial_state)
-
-    actual = encoder.encode_ids(torch.tensor([1]))
-
-    torch.testing.assert_close(actual, expected)
-    assert torch.backends.cuda.cudnn_sdp_enabled() is initial_state
-
-
-@pytest.mark.parametrize("initial_state", [False, True])
-def test_encode_ids_restores_cudnn_sdp_after_failure(monkeypatch: pytest.MonkeyPatch, initial_state: bool) -> None:
-    encoder = _encoder_without_weights()
-
-    def fail(*args, **kwargs):
-        assert torch.backends.cuda.cudnn_sdp_enabled()
-        raise RuntimeError("encode failed")
-
-    monkeypatch.setattr(encoder, "_encode", fail)
-    torch.backends.cuda.enable_cudnn_sdp(initial_state)
-
-    with pytest.raises(RuntimeError, match="encode failed"):
-        encoder.encode_ids(torch.tensor([1]))
-
-    assert torch.backends.cuda.cudnn_sdp_enabled() is initial_state
@@ -223,8 +223,6 @@ def test_h3_turbo_allows_distributed_layerwise_offload(monkeypatch):
    torch.nn.Module.__init__(pipeline)
    pipeline.partition = "fl2va"
    pipeline._turbo_lora_adapter_ids = set()
-    pipeline._native_lora_adapter_ids = set()
-    pipeline._lora_sigma_schedules = {}
    pipeline.od_config = SimpleNamespace(
        enable_cpu_offload=False,
        enable_layerwise_offload=False,
@@ -323,8 +321,6 @@ def test_only_an_active_recognized_turbo_adapter_restricts_ref2va(monkeypatch):
    pipeline.partition = "combined"
    pipeline.supported_tasks = frozenset({"t2va", "fl2va", "ref2va"})
    pipeline._turbo_lora_adapter_ids = set()
-    pipeline._native_lora_adapter_ids = set()
-    pipeline._lora_sigma_schedules = {}
    request = _request("generic-peft")

    def load():
@@ -1,860 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
-from __future__ import annotations
-
-from types import SimpleNamespace
-
-import pytest
-import torch
-from safetensors.torch import save_file
-from vllm.lora.lora_weights import PackedLoRALayerWeights
-
-from vllm_omni.diffusion.lora.manager import DiffusionLoRAManager
-from vllm_omni.diffusion.models.minimax_h3.npu import lora as lora_module
-from vllm_omni.diffusion.models.minimax_h3.npu.lora import load_minimax_h3_native_lora
-from vllm_omni.diffusion.sched.sigma_schedule import DMD2SigmaSchedule
-from vllm_omni.errors import OmniClientError
-from vllm_omni.lora.request import LoRARequest
-
-pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
-
-_TINY_HIDDEN = 2
-_TINY_FFN = 2
-_TINY_TIME = 2
-_TINY_BLOCK_ADALN = 4
-_TINY_FINAL_ADALN = 3
-_TINY_RANK = 4
-_TINY_NUM_QUERY_GROUPS = 2
-_TINY_HEADS_PER_GROUP = 1
-_TINY_HEAD_DIM = 1
-_TINY_QKV_SLICE = _TINY_NUM_QUERY_GROUPS * _TINY_HEADS_PER_GROUP * _TINY_HEAD_DIM
-# Grouped qkv stores (heads_per_group + 2) * head_dim rows per query group, so
-# the attention inner size follows from the head layout and cannot be picked
-# independently: 3 * inner must equal the grouped row count.
-_TINY_INNER = _TINY_QKV_SLICE
-
-
-@pytest.fixture(autouse=True)
-def _use_tiny_native_dimensions(monkeypatch):
-    monkeypatch.setattr(lora_module, "_NATIVE_RANK", _TINY_RANK)
-    monkeypatch.setattr(lora_module, "_NATIVE_ALPHA", _TINY_RANK)
-    monkeypatch.setattr(lora_module, "_NATIVE_HIDDEN_SIZE", _TINY_HIDDEN)
-    monkeypatch.setattr(lora_module, "_NATIVE_ATTENTION_INNER_SIZE", _TINY_INNER)
-    monkeypatch.setattr(lora_module, "_NATIVE_FFN_HIDDEN_SIZE", _TINY_FFN)
-    monkeypatch.setattr(lora_module, "_NATIVE_TIME_EMBED_DIM", _TINY_TIME)
-    monkeypatch.setattr(lora_module, "_NATIVE_BLOCK_ADALN_OUT", _TINY_BLOCK_ADALN)
-    monkeypatch.setattr(lora_module, "_NATIVE_FINAL_ADALN_OUT", _TINY_FINAL_ADALN)
-    monkeypatch.setattr(lora_module, "_NATIVE_NUM_QUERY_GROUPS", _TINY_NUM_QUERY_GROUPS)
-    monkeypatch.setattr(lora_module, "_NATIVE_HEADS_PER_GROUP", _TINY_HEADS_PER_GROUP)
-    monkeypatch.setattr(lora_module, "_NATIVE_HEAD_DIM", _TINY_HEAD_DIM)
-    monkeypatch.setattr(lora_module, "_NATIVE_QKV_SLICE", _TINY_QKV_SLICE)
-    monkeypatch.setattr(
-        lora_module,
-        "_NATIVE_TARGET_DIMS",
-        {
-            "attn.qkv_proj": (_TINY_HIDDEN, 3 * _TINY_INNER),
-            "attn.out_proj": (_TINY_INNER, _TINY_HIDDEN),
-            "mlp.fc1": (_TINY_HIDDEN, 2 * _TINY_FFN),
-            "mlp.fc2": (_TINY_FFN, _TINY_HIDDEN),
-            "adaln_proj.linear": (_TINY_TIME, _TINY_BLOCK_ADALN),
-        },
-    )
-    monkeypatch.setattr(lora_module, "_NATIVE_FINAL_ADALN_DIMS", (_TINY_TIME, _TINY_FINAL_ADALN))
-    monkeypatch.setattr(
-        lora_module,
-        "_NATIVE_EXPECTED_TARGETS",
-        frozenset(
-            [
-                *(
-                    f"blocks.{block_index}.{suffix}"
-                    for block_index in range(50)
-                    for suffix in lora_module._NATIVE_TARGET_SUFFIXES
-                )
-            ]
-            + [
-                *(
-                    f"token_refiner.blocks.{block_index}.{suffix}"
-                    for block_index in range(2)
-                    for suffix in lora_module._NATIVE_TOKEN_REFINER_SUFFIXES
-                )
-            ]
-            + ["final_layer.adaln_proj.linear"]
-        ),
-    )
-
-
-def _request(path) -> LoRARequest:
-    return LoRARequest(
-        lora_name="flashgen",
-        lora_int_id=7,
-        lora_path=str(path),
-    )
-
-
-def _write_tiny_native(
-    path,
-    *,
-    omit_target: str | None = None,
-    shape_overrides: dict[str, tuple[int, int]] | None = None,
-    metadata: dict[str, str] | None = None,
-) -> None:
-    rank = _TINY_RANK
-    tensors = {}
-    overrides = shape_overrides or {}
-    for target in sorted(lora_module._NATIVE_EXPECTED_TARGETS):
-        if target == omit_target:
-            continue
-        input_dim, output_dim = lora_module._native_target_dims(target)
-        raw_target = f"transformer.{target}"
-        a_name = f"{raw_target}.lora_A.default.weight"
-        b_name = f"{raw_target}.lora_B.default.weight"
-        tensors[a_name] = torch.ones(overrides.get(a_name, (rank, input_dim)))
-        if target.endswith(".attn.qkv_proj"):
-            grouped_rows = []
-            for group in range(_TINY_NUM_QUERY_GROUPS):
-                grouped_rows.extend(
-                    [
-                        torch.full((1, rank), float(group * 10 + 1)),
-                        torch.full((1, rank), float(group * 10 + 2)),
-                        torch.full((1, rank), float(group * 10 + 3)),
-                    ]
-                )
-            tensors[b_name] = torch.cat(grouped_rows, dim=0)
-        elif target.endswith(".mlp.fc1"):
-            tensors[b_name] = torch.cat(
-                (
-                    torch.full((output_dim // 2, rank), 2.0),
-                    torch.full((output_dim // 2, rank), 1.0),
-                ),
-                dim=0,
-            )
-        else:
-            tensors[b_name] = torch.ones(overrides.get(b_name, (output_dim, rank)))
-    save_file(
-        tensors,
-        str(path),
-        metadata=metadata
-        or {
-            "format": "pt",
-            "key_format": "minimax-h3-native",
-            "qkv_layout": "grouped",
-            "lora_rank": str(rank),
-            "lora_alpha": str(rank),
-            "base_schedule": "1.0,0.7,0.4,0.15,0.0",
-            "tasks": "t2va",
-        },
-    )
-
-
-def test_h3_native_loads_and_packs_qkv_and_fc1(tmp_path):
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path)
-
-    loaded = load_minimax_h3_native_lora(
-        partition="fl2va",
-        lora_request=_request(path),
-        lora_path=path,
-        dtype=torch.float32,
-    )
-
-    assert loaded is not None
-    lora_model, peft_helper, schedule = loaded
-    assert peft_helper.r == _TINY_RANK
-    assert schedule.num_inference_steps == 4
-    assert len(lora_model.loras) == 259
-    assert "blocks.0.adaln_proj.linear" in lora_model.loras
-    assert "final_layer.adaln_proj.linear" in lora_model.loras
-
-    # The writer tags group g with q=g*10+1, k=g*10+2, v=g*10+3, so a correct
-    # grouped -> q/k/v permutation yields slices that are uniform per role.
-    qkv = lora_model.get_lora("blocks.0.attn.qkv_proj")
-    assert isinstance(qkv, PackedLoRALayerWeights)
-    torch.testing.assert_close(qkv.lora_b[0], torch.tensor([[1.0] * _TINY_RANK, [11.0] * _TINY_RANK]))
-    torch.testing.assert_close(qkv.lora_b[1], torch.tensor([[2.0] * _TINY_RANK, [12.0] * _TINY_RANK]))
-    torch.testing.assert_close(qkv.lora_b[2], torch.tensor([[3.0] * _TINY_RANK, [13.0] * _TINY_RANK]))
-
-    fc1 = lora_model.get_lora("blocks.0.mlp.fc1")
-    assert isinstance(fc1, PackedLoRALayerWeights)
-    torch.testing.assert_close(fc1.lora_b[0], torch.full((_TINY_FFN, _TINY_RANK), 2.0))
-    torch.testing.assert_close(fc1.lora_b[1], torch.full((_TINY_FFN, _TINY_RANK), 1.0))
-
-
-def test_h3_native_rejects_bad_metadata_and_ref2va(tmp_path):
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path, metadata={"key_format": "minimax-h3-native", "qkv_layout": "runtime"})
-    with pytest.raises(ValueError, match="qkv_layout='grouped'"):
-        load_minimax_h3_native_lora(
-            partition="fl2va",
-            lora_request=_request(path),
-            lora_path=path,
-            dtype=torch.float32,
-        )
-
-    # The exact-filename guard runs before the partition check, so the artifact
-    # has to keep the published name for this to reach the Ref2VA rejection.
-    valid_dir = tmp_path / "valid"
-    valid_dir.mkdir()
-    valid = valid_dir / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(valid)
-    with pytest.raises(ValueError, match="supports T2VA only"):
-        load_minimax_h3_native_lora(
-            partition="ref2va",
-            lora_request=_request(valid),
-            lora_path=valid,
-            dtype=torch.float32,
-        )
-
-    renamed = tmp_path / "renamed.safetensors"
-    _write_tiny_native(renamed)
-    with pytest.raises(ValueError, match="supports only"):
-        load_minimax_h3_native_lora(
-            partition="fl2va",
-            lora_request=_request(renamed),
-            lora_path=renamed,
-            dtype=torch.float32,
-        )
-
-
-def test_h3_native_rejects_truncated_artifact(tmp_path):
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path, omit_target="blocks.49.mlp.fc2")
-    with pytest.raises(ValueError, match="target set does not match"):
-        load_minimax_h3_native_lora(
-            partition="fl2va",
-            lora_request=_request(path),
-            lora_path=path,
-            dtype=torch.float32,
-        )
-
-
-def test_h3_native_rejects_schedule_with_wrong_interval_count(tmp_path):
-    """A mislabeled schedule must fail closed instead of silently changing steps."""
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(
-        path,
-        metadata={
-            "format": "pt",
-            "key_format": "minimax-h3-native",
-            "qkv_layout": "grouped",
-            "lora_rank": str(_TINY_RANK),
-            "lora_alpha": str(_TINY_RANK),
-            "base_schedule": "1.0,0.5,0.0",
-            "tasks": "t2va",
-        },
-    )
-    with pytest.raises(ValueError, match="4-interval base_schedule"):
-        load_minimax_h3_native_lora(
-            partition="fl2va",
-            lora_request=_request(path),
-            lora_path=path,
-            dtype=torch.float32,
-        )
-
-
-@pytest.mark.parametrize(
-    ("raw_schedule", "message"),
-    [
-        (None, "requires safetensors metadata base_schedule"),
-        ("", "must not be empty"),
-        (",,", "must not be empty"),
-        ("1.0,oops,0.15,0.0", "is malformed"),
-        ("0.9,0.7,0.4,0.15,0.0", "is malformed"),
-    ],
-)
-def test_h3_native_rejects_malformed_schedule_metadata(tmp_path, raw_schedule, message):
-    """safetensors metadata is string-only, so the loader owns the parse contract."""
-    metadata = {
-        "format": "pt",
-        "key_format": "minimax-h3-native",
-        "qkv_layout": "grouped",
-        "lora_rank": str(_TINY_RANK),
-        "lora_alpha": str(_TINY_RANK),
-        "tasks": "t2va",
-    }
-    if raw_schedule is not None:
-        metadata["base_schedule"] = raw_schedule
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path, metadata=metadata)
-
-    with pytest.raises(ValueError, match=message):
-        load_minimax_h3_native_lora(
-            partition="fl2va",
-            lora_request=_request(path),
-            lora_path=path,
-            dtype=torch.float32,
-        )
-
-
-def test_h3_native_declared_file_fails_closed_on_invalid_metadata(tmp_path):
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path, metadata={"key_format": "other"})
-    with pytest.raises(ValueError, match="requires safetensors metadata"):
-        load_minimax_h3_native_lora(
-            partition="fl2va",
-            lora_request=_request(path),
-            lora_path=path,
-            dtype=torch.float32,
-        )
-
-
-def test_pipeline_native_schedule_and_task_validation(monkeypatch):
-    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
-
-    pipeline = object.__new__(MiniMaxH3Pipeline)
-    torch.nn.Module.__init__(pipeline)
-    pipeline.partition = "fl2va"
-    pipeline.supported_tasks = frozenset({"t2va", "fl2va", "ref2va"})
-    pipeline._turbo_lora_adapter_ids = set()
-    pipeline._native_lora_adapter_ids = {7}
-    pipeline._lora_sigma_schedules = {7: DMD2SigmaSchedule.from_positions([1.0, 0.7, 0.4, 0.15, 0.0])}
-    pipeline._base_schedule_by_partition = {"fl2va": None}
-
-    sampling = SimpleNamespace(
-        lora_request=LoRARequest("flashgen", 7, "/tmp/native.safetensors"),
-        lora_scale=1.0,
-        num_inference_steps=4,
-        extra_args={},
-    )
-    assert pipeline._sigma_schedule_for_request(sampling, "t2va").num_inference_steps == 4
-    with pytest.raises(OmniClientError, match="num_inference_steps must be 4"):
-        pipeline._validate_native_sampling(
-            SimpleNamespace(lora_request=sampling.lora_request, num_inference_steps=5),
-            task="t2va",
-        )
-    with pytest.raises(OmniClientError, match="supports T2VA requests only"):
-        pipeline._resolve_task(
-            "fl2va",
-            {},
-            has_turbo_lora=False,
-            has_native_lora=True,
-        )
-
-    pipeline._base_schedule_by_partition = {"fl2va": DMD2SigmaSchedule.from_positions([1.0, 0.5, 0.0])}
-    with pytest.raises(OmniClientError, match="already pins base_schedule"):
-        pipeline._sigma_schedule_for_request(sampling, "t2va")
-
-
-def test_pipeline_native_step_count_follows_adapter_schedule():
-    """The accepted step count is the adapter's interval count, never a literal."""
-    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
-
-    pipeline = object.__new__(MiniMaxH3Pipeline)
-    torch.nn.Module.__init__(pipeline)
-    pipeline.partition = "fl2va"
-    pipeline._native_lora_adapter_ids = {7}
-    pipeline._lora_sigma_schedules = {7: DMD2SigmaSchedule.from_positions([1.0, 0.5, 0.0])}
-    request = LoRARequest("flashgen", 7, "/tmp/native.safetensors")
-
-    pipeline._validate_native_sampling(
-        SimpleNamespace(lora_request=request, num_inference_steps=2),
-        task="t2va",
-    )
-    with pytest.raises(OmniClientError, match="num_inference_steps must be 2 or omitted, not 3"):
-        pipeline._validate_native_sampling(
-            SimpleNamespace(lora_request=request, num_inference_steps=3),
-            task="t2va",
-        )
-    with pytest.raises(OmniClientError, match="requires num_inference_steps=2"):
-        pipeline._validate_native_sampling(
-            SimpleNamespace(lora_request=request, num_inference_steps=4),
-            task="t2va",
-        )
-
-
-@pytest.mark.parametrize("step_execution", [False, True])
-def test_pipeline_native_omitted_step_count_follows_execution_mode(step_execution):
-    """Only request mode can resolve an omitted count from the adapter schedule.
-
-    ``StepScheduler`` reads ``num_inference_steps`` off the request at admission,
-    before this pipeline sees it, so under step execution an omitted value can
-    only disagree with the denoise loop. The advertised contract has to match.
-    """
-    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
-
-    pipeline = object.__new__(MiniMaxH3Pipeline)
-    torch.nn.Module.__init__(pipeline)
-    pipeline.partition = "fl2va"
-    pipeline._native_lora_adapter_ids = {7}
-    pipeline._lora_sigma_schedules = {7: DMD2SigmaSchedule.from_positions([1.0, 0.7, 0.4, 0.15, 0.0])}
-    pipeline.od_config = SimpleNamespace(step_execution=step_execution)
-    sampling = SimpleNamespace(
-        lora_request=LoRARequest("flashgen", 7, "/tmp/native.safetensors"),
-        num_inference_steps=None,
-    )
-
-    if not step_execution:
-        # Request mode defaults to the adapter's own interval count.
-        pipeline._validate_native_sampling(sampling, task="t2va")
-        return
-
-    with pytest.raises(OmniClientError, match="explicit num_inference_steps=4 under step execution"):
-        pipeline._validate_native_sampling(sampling, task="t2va")
-    # The step-mode message must not offer omission it cannot accept.
-    with pytest.raises(OmniClientError, match=r"must be 4, not 5$"):
-        pipeline._validate_native_sampling(
-            SimpleNamespace(lora_request=sampling.lora_request, num_inference_steps=5),
-            task="t2va",
-        )
-
-
-def test_pipeline_replaces_native_classification_after_reload(monkeypatch, tmp_path):
-    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
-    from vllm_omni.diffusion.models.minimax_h3 import pipeline_minimax_h3 as pipeline_module
-
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path)
-    request = _request(path)
-
-    pipeline = object.__new__(MiniMaxH3Pipeline)
-    torch.nn.Module.__init__(pipeline)
-    pipeline.partition = "fl2va"
-    pipeline.od_config = SimpleNamespace(
-        enable_cpu_offload=False,
-        enable_layerwise_offload=False,
-        enable_distributed_layerwise_offload=False,
-    )
-    pipeline._turbo_lora_adapter_ids = set()
-    pipeline._native_lora_adapter_ids = set()
-    pipeline._lora_sigma_schedules = {}
-
-    loaded = pipeline._load_diffusion_lora_adapter(
-        lora_request=request,
-        lora_path=path,
-        dtype=torch.float32,
-    )
-    assert loaded is not None
-    assert request.lora_int_id in pipeline._native_lora_adapter_ids
-    assert request.lora_int_id in pipeline._lora_sigma_schedules
-
-    monkeypatch.setattr(pipeline_module, "load_minimax_h3_turbo_lora", lambda **_: None)
-    monkeypatch.setattr(pipeline_module, "load_minimax_h3_native_lora", lambda **_: None)
-    assert pipeline._load_diffusion_lora_adapter(lora_request=request, lora_path=path, dtype=torch.float32) is None
-    assert request.lora_int_id not in pipeline._native_lora_adapter_ids
-    assert request.lora_int_id not in pipeline._lora_sigma_schedules
-
-
-@pytest.mark.parametrize(
-    "offload_mode",
-    [
-        "model-level CPU offload (--enable-cpu-offload)",
-        "layerwise offload (--enable-layerwise-offload)",
-    ],
-)
-def test_h3_native_rejects_offload_modes(tmp_path, offload_mode):
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path)
-
-    with pytest.raises(ValueError, match="does not support"):
-        load_minimax_h3_native_lora(
-            partition="fl2va",
-            lora_request=_request(path),
-            lora_path=path,
-            dtype=torch.float32,
-            unsupported_offload_mode=offload_mode,
-        )
-
-
-def test_h3_native_allows_distributed_layerwise_offload(monkeypatch):
-    """DLO keeps LoRA A/B buffers resident, so native must not fail closed."""
-    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
-    from vllm_omni.diffusion.models.minimax_h3 import pipeline_minimax_h3 as pipeline_module
-
-    pipeline = object.__new__(MiniMaxH3Pipeline)
-    torch.nn.Module.__init__(pipeline)
-    pipeline.partition = "fl2va"
-    pipeline.od_config = SimpleNamespace(
-        enable_cpu_offload=False,
-        enable_layerwise_offload=False,
-        enable_distributed_layerwise_offload=True,
-    )
-    pipeline._turbo_lora_adapter_ids = set()
-    pipeline._native_lora_adapter_ids = set()
-    pipeline._lora_sigma_schedules = {}
-    captured: dict[str, object] = {}
-    schedule = DMD2SigmaSchedule.from_positions([1.0, 0.7, 0.4, 0.15, 0.0])
-
-    def load_native(**kwargs):
-        captured.update(kwargs)
-        return object(), object(), schedule
-
-    monkeypatch.setattr(pipeline_module, "load_minimax_h3_turbo_lora", lambda **_: None)
-    monkeypatch.setattr(pipeline_module, "load_minimax_h3_native_lora", load_native)
-
-    request = _request("flashgen")
-    loaded = pipeline._load_diffusion_lora_adapter(
-        lora_request=request,
-        lora_path="flashgen",
-        dtype=torch.bfloat16,
-    )
-
-    assert loaded is not None
-    assert captured["unsupported_offload_mode"] is None
-    assert request.lora_int_id in pipeline._native_lora_adapter_ids
-    assert pipeline._lora_sigma_schedules[request.lora_int_id] is schedule
-
-
-def test_h3_native_qkv_reorder_matches_base_loader_contract():
-    from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import _reorder_grouped_qkv_to_qkv
-
-    num_query_groups = 56
-    heads_per_group = 1
-    head_dim = 128
-    grouped = torch.arange(num_query_groups * (heads_per_group + 2) * head_dim, dtype=torch.float32)
-    grouped = grouped.reshape(num_query_groups, (heads_per_group + 2) * head_dim)
-    reordered = _reorder_grouped_qkv_to_qkv(
-        grouped.reshape(-1, 1),
-        num_query_groups=num_query_groups,
-        heads_per_group=heads_per_group,
-        head_dim=head_dim,
-    ).reshape(-1)
-    q_size = num_query_groups * heads_per_group * head_dim
-    k_size = num_query_groups * head_dim
-    q, k, v = torch.split(reordered, [q_size, k_size, k_size])
-    assert q.reshape(num_query_groups, head_dim)[0, 0] == grouped[0, 0]
-    assert k.reshape(num_query_groups, head_dim)[0, 0] == grouped[0, head_dim]
-    assert v.reshape(num_query_groups, head_dim)[0, 0] == grouped[0, 2 * head_dim]
-
-
-def test_native_target_pattern_matches_component_qualified_names():
-    """The manager matches "<component>.<module>", so the pattern needs the prefix."""
-    import regex as re
-
-    pattern = lora_module._NATIVE_TARGET_PATTERN
-    matched = [
-        target
-        for target in lora_module._NATIVE_EXPECTED_TARGETS
-        if re.search(pattern, f"transformer.{target}") is not None
-    ]
-    assert len(matched) == 259
-    assert re.search(pattern, "transformer.blocks.0.attn.q_proj") is None
-    assert re.search(pattern, "transformer.token_refiner.blocks.0.adaln_proj.linear") is None
-
-
-def test_replace_layers_binds_every_native_target(monkeypatch):
-    """Regression for bound=0/259: layer replacement must reach all 259 modules."""
-    from vllm.lora.layers import BaseLayerWithLoRA
-    from vllm.lora.peft_helper import PEFTHelper
-
-    from vllm_omni.diffusion.lora import manager as manager_module
-
-    class _StubLoRALayer(BaseLayerWithLoRA):
-        def __init__(self, base_layer):
-            super().__init__()
-            self.base_layer = base_layer
-
-    def _linear() -> torch.nn.Module:
-        return torch.nn.Linear(_TINY_HIDDEN, _TINY_HIDDEN)
-
-    def _block(*, with_adaln: bool) -> torch.nn.Module:
-        block = torch.nn.Module()
-        block.attn = torch.nn.Module()
-        block.attn.qkv_proj = _linear()
-        block.attn.out_proj = _linear()
-        block.mlp = torch.nn.Module()
-        block.mlp.fc1 = _linear()
-        block.mlp.fc2 = _linear()
-        if with_adaln:
-            block.adaln_proj = torch.nn.Module()
-            block.adaln_proj.linear = _linear()
-        return block
-
-    transformer = torch.nn.Module()
-    transformer.blocks = torch.nn.ModuleList([_block(with_adaln=True) for _ in range(50)])
-    transformer.token_refiner = torch.nn.Module()
-    transformer.token_refiner.blocks = torch.nn.ModuleList([_block(with_adaln=False) for _ in range(2)])
-    transformer.final_layer = torch.nn.Module()
-    transformer.final_layer.adaln_proj = torch.nn.Module()
-    transformer.final_layer.adaln_proj.linear = _linear()
-    pipeline = torch.nn.Module()
-    pipeline.transformer = transformer
-
-    monkeypatch.setattr(manager_module, "from_layer_diffusion", lambda *, layer, **_: _StubLoRALayer(layer))
-
-    manager = object.__new__(DiffusionLoRAManager)
-    manager.pipeline = pipeline
-    manager.dtype = torch.float32
-    manager.max_cached_adapters = 1
-    manager._lora_modules = {}
-    manager._max_lora_rank = 64
-    manager._resident_lora_device = None
-
-    peft_helper = PEFTHelper.from_dict(
-        {
-            "r": _TINY_RANK,
-            "lora_alpha": _TINY_RANK,
-            "target_modules": lora_module._NATIVE_TARGET_PATTERN,
-        }
-    )
-    manager._replace_layers_with_lora(peft_helper)
-
-    assert set(manager._lora_modules) == {f"transformer.{target}" for target in lora_module._NATIVE_EXPECTED_TARGETS}
-
-
-def test_legacy_manager_uses_native_loader(tmp_path):
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path)
-
-    class _Pipeline:
-        def _load_diffusion_lora_adapter(self, **kwargs):
-            from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
-                MiniMaxH3Pipeline,
-            )
-
-            pipeline = object.__new__(MiniMaxH3Pipeline)
-            pipeline.partition = "fl2va"
-            pipeline.od_config = SimpleNamespace(
-                enable_cpu_offload=False,
-                enable_layerwise_offload=False,
-                enable_distributed_layerwise_offload=False,
-            )
-            pipeline._turbo_lora_adapter_ids = set()
-            pipeline._native_lora_adapter_ids = set()
-            pipeline._lora_sigma_schedules = {}
-            return pipeline._load_diffusion_lora_adapter(**kwargs)
-
-    manager = object.__new__(DiffusionLoRAManager)
-    manager.pipeline = _Pipeline()
-    manager.dtype = torch.float32
-    manager._expected_lora_modules = {"qkv_proj", "fc1", "adaln_proj.linear"}
-
-    lora_model, peft_helper = manager._load_adapter(_request(path))
-    assert lora_model.id == 7
-    assert peft_helper.lora_alpha == _TINY_RANK
-    assert len(lora_model.loras) == 259
-
-
-def test_pipeline_schedule_inactive_when_scale_zero():
-    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
-
-    pipeline = object.__new__(MiniMaxH3Pipeline)
-    torch.nn.Module.__init__(pipeline)
-    pipeline.partition = "fl2va"
-    pipeline._turbo_lora_adapter_ids = set()
-    pipeline._native_lora_adapter_ids = {7}
-    pipeline._lora_sigma_schedules = {7: DMD2SigmaSchedule.from_positions([1.0, 0.7, 0.4, 0.15, 0.0])}
-    pipeline._base_schedule_by_partition = {"fl2va": None}
-
-    sampling = SimpleNamespace(
-        lora_request=LoRARequest("flashgen", 7, "/tmp/native.safetensors"),
-        lora_scale=0.0,
-        num_inference_steps=4,
-    )
-    assert pipeline._sigma_schedule_for_request(sampling, "t2va") is None
-    assert not pipeline._has_active_native_lora(sampling)
-
-
-def test_pipeline_schedule_falls_back_after_eviction(monkeypatch, tmp_path):
-    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
-    from vllm_omni.diffusion.models.minimax_h3 import pipeline_minimax_h3 as pipeline_module
-
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path)
-    request = _request(path)
-
-    pipeline = object.__new__(MiniMaxH3Pipeline)
-    torch.nn.Module.__init__(pipeline)
-    pipeline.partition = "fl2va"
-    pipeline.od_config = SimpleNamespace(
-        enable_cpu_offload=False,
-        enable_layerwise_offload=False,
-        enable_distributed_layerwise_offload=False,
-    )
-    pipeline._turbo_lora_adapter_ids = set()
-    pipeline._native_lora_adapter_ids = set()
-    pipeline._lora_sigma_schedules = {}
-    pipeline._base_schedule_by_partition = {"fl2va": None}
-
-    loaded = pipeline._load_diffusion_lora_adapter(
-        lora_request=request,
-        lora_path=path,
-        dtype=torch.float32,
-    )
-    assert loaded is not None
-    assert request.lora_int_id in pipeline._lora_sigma_schedules
-
-    monkeypatch.setattr(pipeline_module, "load_minimax_h3_turbo_lora", lambda **_: None)
-    monkeypatch.setattr(pipeline_module, "load_minimax_h3_native_lora", lambda **_: None)
-    assert pipeline._load_diffusion_lora_adapter(lora_request=request, lora_path=path, dtype=torch.float32) is None
-    assert request.lora_int_id not in pipeline._lora_sigma_schedules
-
-    sampling = SimpleNamespace(
-        lora_request=request,
-        lora_scale=1.0,
-        num_inference_steps=4,
-    )
-    assert pipeline._sigma_schedule_for_request(sampling, "t2va") is None
-
-
-def test_native_packed_qkv_slices_are_tp2_divisible():
-    assert lora_module._NATIVE_QKV_SLICE % 2 == 0
-    assert lora_module._NATIVE_FFN_HIDDEN_SIZE % 2 == 0
-
-
-def test_lora_manager_activates_native_packed_qkv(tmp_path):
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path)
-    loaded = load_minimax_h3_native_lora(
-        partition="fl2va",
-        lora_request=_request(path),
-        lora_path=path,
-        dtype=torch.float32,
-    )
-    assert loaded is not None
-    lora_model, _, _ = loaded
-    packed = lora_model.get_lora("blocks.0.attn.qkv_proj")
-    assert isinstance(packed, PackedLoRALayerWeights)
-    assert len(packed.lora_b) == 3
-    assert all(b.shape[0] == _TINY_QKV_SLICE for b in packed.lora_b)
-
-    class _DummyLoRALayer:
-        def __init__(self):
-            self.n_slices = 3
-            self.output_slices = (_TINY_QKV_SLICE, _TINY_QKV_SLICE, _TINY_QKV_SLICE)
-            self.set_calls: list[tuple[list, list]] = []
-            self.reset_calls = 0
-
-        def reset_lora(self, index: int):
-            self.reset_calls += 1
-
-        def set_lora(self, index: int, lora_a, lora_b):
-            self.set_calls.append((lora_a, lora_b))
-
-    layer = _DummyLoRALayer()
-    manager = object.__new__(DiffusionLoRAManager)
-    manager.pipeline = torch.nn.Module()
-    manager._lora_modules = {"blocks.0.attn.qkv_proj": layer}
-    manager._registered_adapters = {
-        lora_model.id: lora_model,
-    }
-    manager._active_adapter_id = None
-    manager._adapter_scales = {}
-    manager._activate_adapter(lora_model.id, scale=0.5)
-
-    assert manager._active_adapter_id == lora_model.id
-
-    assert len(layer.set_calls) == 1
-    lora_a_list, lora_b_list = layer.set_calls[0]
-    assert len(lora_a_list) == 3
-    assert len(lora_b_list) == 3
-    torch.testing.assert_close(lora_b_list[0], packed.lora_b[0] * 0.5)
-    torch.testing.assert_close(lora_b_list[1], packed.lora_b[1] * 0.5)
-    torch.testing.assert_close(lora_b_list[2], packed.lora_b[2] * 0.5)
-
-
-def test_lora_manager_tp2_splits_native_packed_qkv_per_rank():
-    """Each Q/K/V slice must be divisible by TP2 so rank-local layers can shard rows."""
-
-    tp_size = 2
-    slice_rows = lora_module._NATIVE_QKV_SLICE
-    assert slice_rows % tp_size == 0
-    local_rows = slice_rows // tp_size
-
-    class _TpQkvLoRALayer:
-        def __init__(self, tp_rank: int):
-            self.tp_rank = tp_rank
-            self.n_slices = 3
-            self.output_slices = (local_rows, local_rows, local_rows)
-            self.set_calls: list[tuple[list, list]] = []
-
-        def reset_lora(self, index: int):
-            return
-
-        def set_lora(self, index: int, lora_a, lora_b):
-            self.set_calls.append((lora_a, lora_b))
-
-    for tp_rank in range(tp_size):
-        layer = _TpQkvLoRALayer(tp_rank)
-        start = tp_rank * local_rows
-        end = start + local_rows
-        full_packed = PackedLoRALayerWeights(
-            module_name="blocks.0.attn.qkv_proj",
-            rank=lora_module._NATIVE_RANK,
-            lora_alphas=[64, 64, 64],
-            lora_a=[torch.ones(lora_module._NATIVE_RANK, lora_module._NATIVE_HIDDEN_SIZE)] * 3,
-            lora_b=[torch.full((slice_rows, lora_module._NATIVE_RANK), float(i + 1)) for i in range(3)],
-            scaling=[1.0, 1.0, 1.0],
-        )
-        local_b = [b[start:end].contiguous() for b in full_packed.lora_b]
-        layer.set_lora(0, full_packed.lora_a, local_b)
-        assert len(layer.set_calls) == 1
-        _, lora_b_list = layer.set_calls[0]
-        assert all(b.shape[0] == local_rows for b in lora_b_list)
-        assert lora_b_list[0][0, 0].item() == 1.0
-        assert lora_b_list[1][0, 0].item() == 2.0
-        assert lora_b_list[2][0, 0].item() == 3.0
-
-
-# 259 modules, of which 52 qkv expand to 3 slices and 52 fc1 expand to 2.
-_EXPECTED_NATIVE_SLICE_PAIRS = 415
-
-
-def _iter_native_slice_pairs(lora_model):
-    """Yield ``(module_name, lora_a, lora_b)`` per slice, flattening packed weights."""
-    for module_name, weights in lora_model.loras.items():
-        lora_a = weights.lora_a if isinstance(weights.lora_a, list) else [weights.lora_a]
-        lora_b = weights.lora_b if isinstance(weights.lora_b, list) else [weights.lora_b]
-        for a, b in zip(lora_a, lora_b, strict=True):
-            yield module_name, a, b
-
-
-@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
-def test_native_lora_load_is_platform_agnostic(tmp_path, dtype):
-    """The artifact is trained on Ascend, but loading it is not Ascend-only.
-
-    This whole module runs in the CPU job with no ``torch_npu`` present, so a
-    passing run is itself the evidence. The assertions pin the property that
-    makes it portable: tensors land on CPU in exactly the requested dtype and
-    the loader never picks a device, leaving placement to the pipeline.
-    """
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path)
-
-    loaded = load_minimax_h3_native_lora(
-        partition="fl2va",
-        lora_request=_request(path),
-        lora_path=path,
-        dtype=dtype,
-    )
-
-    assert loaded is not None
-    lora_model, _, _ = loaded
-    tensors = {
-        f"{module_name}.{side}": tensor
-        for module_name, a, b in _iter_native_slice_pairs(lora_model)
-        for side, tensor in (("lora_a", a), ("lora_b", b))
-    }
-    assert sum(1 for _ in _iter_native_slice_pairs(lora_model)) == _EXPECTED_NATIVE_SLICE_PAIRS
-    assert {name: tensor.device.type for name, tensor in tensors.items() if tensor.device.type != "cpu"} == {}
-    assert {name: tensor.dtype for name, tensor in tensors.items() if tensor.dtype != dtype} == {}
-
-
-@pytest.mark.cuda
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires a CUDA device")
-def test_native_lora_delta_matches_between_cpu_and_cuda(tmp_path):
-    """The packed adapter produces the same delta weight on CUDA as on CPU.
-
-    Covers the GPU side of the same artifact: every ``lora_b @ lora_a`` product,
-    including the reordered q/k/v slices, is recomputed on CUDA and compared
-    against the CPU result, so a GPU deployment cannot silently diverge.
-    """
-    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
-    _write_tiny_native(path)
-
-    loaded = load_minimax_h3_native_lora(
-        partition="fl2va",
-        lora_request=_request(path),
-        lora_path=path,
-        dtype=torch.float32,
-    )
-
-    assert loaded is not None
-    lora_model, _, _ = loaded
-    checked = 0
-    for module_name, a, b in _iter_native_slice_pairs(lora_model):
-        cpu_delta = b @ a
-        cuda_delta = b.cuda() @ a.cuda()
-        assert cuda_delta.device.type == "cuda"
-        torch.testing.assert_close(cuda_delta.cpu(), cpu_delta, msg=f"delta mismatch for {module_name}")
-        checked += 1
-    assert checked == _EXPECTED_NATIVE_SLICE_PAIRS
@@ -74,8 +74,6 @@ def test_t2va_and_fl2va_packing_keep_update_rows_separate():
    assert fl2va["update_mask"].sum().item() == 12
    assert (~fl2va["update_mask"]).sum().item() == 6
    assert t2va["audio_pos"].numel() == fl2va["audio_pos"].numel() == 6
-    assert t2va["video_spans"] == ({"start": 10, "latent_grid": (2, 2, 3), "role": "target"},)
-    assert fl2va["video_spans"] == ({"start": 16, "latent_grid": (2, 2, 3), "role": "target"},)


 def test_ref2va_packing_tracks_video_and_audio_update_masks():
@@ -103,33 +101,6 @@ def test_ref2va_packing_tracks_video_and_audio_update_masks():
    assert packed["cu_seqlens"].tolist() == [0, 30, 64]


-def test_ref2va_packing_publishes_physical_video_spans_only():
-    from vllm_omni.diffusion.models.minimax_h3.packed_sequence import (
-        minimax_h3_packed_sequence_ref2va_blocks,
-    )
-
-    packed = minimax_h3_packed_sequence_ref2va_blocks(
-        text_len=4,
-        latent_t=2,
-        latent_h=4,
-        latent_w=6,
-        audio_t=3,
-        ref_blocks=[
-            {"kind": "image", "latent_h": 4, "latent_w": 4},
-            {"kind": "video_audio", "ref_audio_t": 2, "latent_t": 2, "latent_h": 4, "latent_w": 6},
-            {"kind": "audio", "ref_audio_t": 1},
-            {"kind": "video", "ref_audio_t": 0, "latent_t": 1, "latent_h": 4, "latent_w": 4},
-        ],
-    )
-
-    # Image and all audio rows intentionally stay out of the sparse spans.
-    assert packed["video_spans"] == (
-        {"start": 12, "latent_grid": (2, 2, 3), "role": "reference"},
-        {"start": 26, "latent_grid": (1, 2, 2), "role": "reference"},
-        {"start": 36, "latent_grid": (2, 2, 3), "role": "target"},
-    )
-
-
 def test_condition_noise_is_seeded_and_keeps_clean_anchor_at_timestep_one():
    from vllm_omni.diffusion.models.minimax_h3.condition_noise import (
        minimax_h3_audio_cond_noise_aug_rows,
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 import pytest
 import torch
@@ -113,146 +113,6 @@ def test_h3_rope_table_materializes_local_rows_in_fused_kernel_layout():
    torch.testing.assert_close(actual, expected, atol=0, rtol=0)


-def test_h3_prepared_rope_table_is_rank_local_and_validated(monkeypatch):
-    from vllm_omni.diffusion.models.minimax_h3 import minimax_h3_transformer as h3
-
-    class Rope(nn.Module):
-        def forward(self, position_ids):
-            freqs_half = position_ids[0].float()
-            return torch.cat((freqs_half, freqs_half), dim=-1)
-
-    model = object.__new__(h3.MiniMaxH3DiTModel)
-    nn.Module.__init__(model)
-    model.arch = h3.MiniMaxH3DiTArchConfig(rope_inv_freq_len=1)
-    model.rope = Rope()
-    model.local_sp_prepare = nn.Identity()
-    monkeypatch.setattr(h3, "_sequence_parallel_local_span", lambda *args, **kwargs: (1, 2))
-
-    position_ids = torch.arange(12, dtype=torch.long).reshape(1, 4, 3)
-    actual = model.prepare_rope_table(position_ids, seq_len=4)
-    expected = h3._build_rope_table(model.rope(position_ids[:, 1:3]))
-
-    torch.testing.assert_close(actual, expected, atol=0, rtol=0)
-    model._validate_prepared_rope_table(actual, local_len=2, device=torch.device("cpu"))
-    with pytest.raises(ValueError, match="local_seq_len"):
-        model._validate_prepared_rope_table(actual[:1], local_len=2, device=torch.device("cpu"))
-
-
-def test_denoise_branch_prepares_rope_table_once_per_npu_run(monkeypatch: pytest.MonkeyPatch):
-    from vllm_omni.diffusion.models.minimax_h3 import denoise_loop
-    from vllm_omni.diffusion.models.minimax_h3.denoise_loop import (
-        MiniMaxH3DenoiseBranch,
-        minimax_h3_denoise_loop,
-    )
-
-    monkeypatch.setattr(denoise_loop.current_omni_platform, "is_npu", lambda: True)
-
-    packed = {
-        "seq_len": torch.tensor(3),
-        "img_pos": torch.tensor([1]),
-        "audio_pos": torch.tensor([2]),
-        "text_pos": torch.tensor([0]),
-        "update_mask": torch.tensor([True]),
-        "cu_seqlens": torch.tensor([0, 3, 3], dtype=torch.int32),
-        "img_position_ids": torch.zeros(3, 3, dtype=torch.long),
-        "latent_grid": torch.tensor([1, 1, 1]),
-        "video_row_start": torch.tensor(1),
-    }
-    branch = MiniMaxH3DenoiseBranch(
-        packed=packed,
-        text_embeddings=torch.zeros(1, 2),
-        token_tags=torch.zeros(3, dtype=torch.long),
-        device=torch.device("cpu"),
-    )
-
-    class Model:
-        def __init__(self):
-            self.rope_table = torch.zeros(3, 6, dtype=torch.bfloat16)
-            self.prepare_calls = 0
-            self.forward_calls = 0
-
-        def prepare_rope_table(self, img_position_ids, *, seq_len):
-            assert img_position_ids is branch.static_kwargs["img_position_ids"]
-            assert seq_len == 3
-            self.prepare_calls += 1
-            return self.rope_table
-
-        def __call__(self, **kwargs):
-            assert kwargs["rope_table"] is self.rope_table
-            self.forward_calls += 1
-            return torch.zeros(1, 96), torch.zeros(1, 32)
-
-    model = Model()
-    minimax_h3_denoise_loop(
-        model=model,
-        positive=branch,
-        initial_video_rows=torch.zeros(1, 96),
-        initial_audio_rows=torch.zeros(1, 32),
-        keyframe_cond_rows=None,
-        sigmas_video=[1.0, 0.5, 0.0],
-        sigmas_audio=[1.0, 0.5, 0.0],
-        device=torch.device("cpu"),
-    )
-
-    assert model.prepare_calls == 1
-    assert model.forward_calls == 2
-
-
-def test_denoise_branch_keeps_rope_construction_on_the_reference_path_off_npu(
-    monkeypatch: pytest.MonkeyPatch,
-):
-    from vllm_omni.diffusion.models.minimax_h3 import denoise_loop
-    from vllm_omni.diffusion.models.minimax_h3.denoise_loop import (
-        MiniMaxH3DenoiseBranch,
-        minimax_h3_denoise_loop,
-    )
-
-    monkeypatch.setattr(denoise_loop.current_omni_platform, "is_npu", lambda: False)
-    packed = {
-        "seq_len": torch.tensor(3),
-        "img_pos": torch.tensor([1]),
-        "audio_pos": torch.tensor([2]),
-        "text_pos": torch.tensor([0]),
-        "update_mask": torch.tensor([True]),
-        "cu_seqlens": torch.tensor([0, 3, 3], dtype=torch.int32),
-        "img_position_ids": torch.zeros(3, 3, dtype=torch.long),
-        "latent_grid": torch.tensor([1, 1, 1]),
-        "video_row_start": torch.tensor(1),
-    }
-    branch = MiniMaxH3DenoiseBranch(
-        packed=packed,
-        text_embeddings=torch.zeros(1, 2),
-        token_tags=torch.zeros(3, dtype=torch.long),
-        device=torch.device("cpu"),
-    )
-
-    class Model:
-        def __init__(self):
-            self.forward_calls = 0
-
-        def prepare_rope_table(self, *_args, **_kwargs):
-            raise AssertionError("the reference path must not pre-build a rope_table")
-
-        def __call__(self, **kwargs):
-            assert "rope_table" not in kwargs
-            self.forward_calls += 1
-            return torch.zeros(1, 96), torch.zeros(1, 32)
-
-    model = Model()
-    minimax_h3_denoise_loop(
-        model=model,
-        positive=branch,
-        initial_video_rows=torch.zeros(1, 96),
-        initial_audio_rows=torch.zeros(1, 32),
-        keyframe_cond_rows=None,
-        sigmas_video=[1.0, 0.5, 0.0],
-        sigmas_audio=[1.0, 0.5, 0.0],
-        device=torch.device("cpu"),
-    )
-
-    assert model.forward_calls == 2
-
-
 def test_strict_sp_local_span_uses_rank_owned_rows(monkeypatch):
    from vllm_omni.diffusion import forward_context
    from vllm_omni.diffusion.distributed import parallel_state
@@ -254,3 +254,18 @@ def test_pipeline_resolves_transformer_component_quant_config():
    assert transformer_config.ignored_layers == ignored_layers
    assert _resolve_component_quant_config(component_config, "transformer") is transformer_config
    assert _resolve_component_quant_config(transformer_config, "transformer") is transformer_config
+
+
+def test_pipeline_strips_prequantized_text_encoder_config():
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import _resolve_minimax_h3_text_encoder_quant_config
+    from vllm_omni.quantization import ComponentQuantizationConfig
+
+    prequantized = Mock()
+    prequantized.get_name.return_value = "modelopt"
+    online_fp8 = Mock()
+    online_fp8.get_name.return_value = "fp8"
+    component_config = ComponentQuantizationConfig({"text_encoder": prequantized})
+
+    assert _resolve_minimax_h3_text_encoder_quant_config(online_fp8) is online_fp8
+    assert _resolve_minimax_h3_text_encoder_quant_config(prequantized) is None
+    assert _resolve_minimax_h3_text_encoder_quant_config(component_config) is None
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 from pathlib import Path

@@ -187,7 +187,6 @@ def test_minimax_h3_quantization_quality(config: QualityTestConfig):
        "model": model_ref,
        "enforce_eager": True,
        "tensor_parallel_size": 2,
-        # The fused BF16 baseline only fits on H100-80GB with encoder TP.
        "text_encoder_tp_size": 2,
        "vae_use_tiling": True,
    }
@@ -1,18 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 import pytest
 import torch
 import torch.nn.functional as F

-from tests.helpers.mark import hardware_marks
 from vllm_omni.platforms import current_omni_platform

-pytestmark = [
-    pytest.mark.core_model,
-    pytest.mark.diffusion,
-    *hardware_marks(res={"npu": "A3"}, num_cards=1),
-]
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion]


@pytest.mark.skipif(not current_omni_platform.is_npu(), reason="requires Ascend NPU")
@@ -1,385 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
-from unittest.mock import Mock
-
-import pytest
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from vllm_omni.platforms import current_omni_platform
-
-pytestmark = [pytest.mark.core_model, pytest.mark.gpu, pytest.mark.diffusion]
-
-
-def _selected_operators():
-    from vllm_omni.diffusion.models.minimax_h3.ops.vae.dispatch import (
-        resolve_h3_vae_operators,
-    )
-
-    if not current_omni_platform.is_available():
-        pytest.skip("No accelerator is available")
-    device = current_omni_platform.get_torch_device()
-    operators = resolve_h3_vae_operators(device)
-    if operators is None:
-        pytest.skip("No H3 VAE operator implementation is registered for this target")
-    return device, operators
-
-
-def _operator_set(*, supports=lambda _device: True):
-    from vllm_omni.diffusion.models.minimax_h3.ops.vae.dispatch import H3VAEOperatorSet
-
-    return H3VAEOperatorSet(
-        supports=supports,
-        qk_norm_rope=lambda *_args: None,
-        scaled_residual=lambda *_args: None,
-    )
-
-
-def _qk_reference(x, cos, sin):
-    normalized = F.rms_norm(x.float(), (64,), None, 1e-5).to(x.dtype)
-    rotary = normalized[..., :48]
-    first, second = rotary.chunk(2, dim=-1)
-    rotated = torch.cat((-second, first), dim=-1)
-    return torch.cat(
-        (rotary * cos + rotated * sin, normalized[..., 48:]),
-        dim=-1,
-    )
-
-
-def _vit_norm_input(_module, hidden_states):
-    return hidden_states.float()
-
-
-def _same_dtype_norm_input(_module, hidden_states):
-    return hidden_states
-
-
-def _failing_norm_input(_module, _hidden_states):
-    raise RuntimeError("unsupported remote normalization semantics")
-
-
-@pytest.mark.parametrize(("batch", "sequence"), [(1, 1), (1, 195), (2, 1797)])
-def test_h3_vae_qk_norm_rope_is_bit_exact(batch, sequence):
-    device, operators = _selected_operators()
-
-    torch.manual_seed(17)
-    qkv = torch.randn(
-        batch,
-        sequence,
-        32,
-        192,
-        device=device,
-        dtype=torch.float16,
-    )
-    q, k, _ = qkv.chunk(3, dim=-1)
-    cos = torch.randn(batch, sequence, 1, 48, device=device, dtype=torch.float16)
-    sin = torch.randn_like(cos)
-
-    expected_q = _qk_reference(q, cos, sin)
-    expected_k = _qk_reference(k, cos, sin)
-    with torch.inference_mode():
-        actual = operators.qk_norm_rope(q, k, (cos, sin), 1e-5)
-
-    assert actual is not None
-    actual_q, actual_k = actual
-    assert torch.equal(actual_q, expected_q)
-    assert torch.equal(actual_k, expected_k)
-
-
-def test_h3_vae_scaled_residual_is_bit_exact():
-    device, operators = _selected_operators()
-
-    torch.manual_seed(29)
-    residual = torch.randn(195, 2048, device=device, dtype=torch.float32)
-    branch = torch.randn(195, 2048, device=device, dtype=torch.float16)
-    scale = torch.randn(2048, device=device, dtype=torch.float32)
-    expected = residual + branch * scale
-
-    with torch.inference_mode():
-        actual = operators.scaled_residual(residual, branch, scale)
-
-    assert actual is not None
-    assert torch.equal(actual, expected)
-
-
-def test_h3_vae_exact_ops_reject_unsupported_inputs():
-    from vllm_omni.diffusion.models.minimax_h3.ops.vae.qk_norm_rope import (
-        try_qk_norm_rope_exact,
-    )
-    from vllm_omni.diffusion.models.minimax_h3.ops.vae.scaled_residual import try_scaled_residual_exact
-
-    q = torch.randn(1, 2, 32, 64, dtype=torch.float32)
-    cos = torch.randn(1, 2, 1, 48, dtype=torch.float32)
-    residual = torch.randn(2, 2048)
-    branch = torch.randn(2, 2048, dtype=torch.float16)
-    scale = torch.randn(2048)
-    with torch.inference_mode():
-        assert try_qk_norm_rope_exact(q, q, (cos, cos), 1e-5) is None
-        assert try_scaled_residual_exact(residual, branch, scale) is None
-
-
-def _make_decoder():
-    class Attention(nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.to_qkv = nn.Linear(8, 24)
-            self.to_out = nn.Linear(8, 8)
-            self.norm_q = nn.RMSNorm(8, elementwise_affine=False)
-            self.norm_k = nn.RMSNorm(8, elementwise_affine=False)
-            self.spatial_parallel = False
-            self.dim_head = 8
-
-        def perform_attention(self, query, _key, _value, _pack_info):
-            return query
-
-        def forward(self, hidden_states, rotary_pos_emb=None, pack_info=None):
-            return hidden_states
-
-    class FeedForward(nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.w1 = nn.Linear(8, 32)
-            self.w2 = nn.Linear(16, 8)
-            self.use_gated = True
-            self.act_fn = nn.SiLU()
-            self._compile_forward_enabled = False
-            self._compile_forward_fatal = False
-            self._compiled_forward = None
-
-        def forward(self, hidden_states):
-            hidden_states = self.w1(hidden_states)
-            gate, hidden_states = hidden_states.chunk(2, dim=-1)
-            return self.w2(self.act_fn(gate) * hidden_states)
-
-    class Block(nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.attn = Attention()
-            self.ff = FeedForward()
-            self.norm1 = nn.RMSNorm(8)
-            self.norm2 = nn.RMSNorm(8)
-            self.scale1 = nn.Parameter(torch.zeros(8))
-            self.scale2 = nn.Parameter(torch.zeros(8))
-            self.use_scale = True
-
-        def forward(self, hidden_states, rotary_pos_emb=None, pack_info=None):
-            return hidden_states
-
-    decoder = nn.Module()
-    decoder.transformer_blocks = nn.ModuleList([Block(), Block()])
-    decoder.proj_out = nn.Linear(8, 8)
-    return decoder
-
-
-def test_video_vae_installs_exact_optimizations(monkeypatch):
-    from vllm_omni.diffusion.models.minimax_h3 import vae as vae_module
-
-    class FakeModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.decoder = torch.nn.Module()
-
-    class FakeRemote(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.model = FakeModel()
-
-    monkeypatch.setattr(
-        vae_module,
-        "_load_component_config",
-        lambda _path: {
-            "latent_channels": 1,
-            "latents_mean": [0.0],
-            "latents_std": [1.0],
-        },
-    )
-    monkeypatch.setattr(
-        vae_module,
-        "_load_remote_component",
-        lambda _path, _config: FakeRemote(),
-    )
-    install = Mock(return_value=True)
-    monkeypatch.setattr(vae_module, "install_h3_vae_optimizations", install)
-    monkeypatch.setattr(vae_module, "PinnedModuleStager", Mock())
-
-    video_vae = vae_module.MiniMaxH3VideoVAE(
-        "unused",
-        device=torch.device("cuda"),
-        load_device=torch.device("cpu"),
-    )
-
-    install.assert_called_once_with(
-        video_vae.model.decoder,
-        device=torch.device("cuda"),
-    )
-
-
-def test_h3_vae_install_precasts_only_block_linears(monkeypatch):
-    from vllm_omni.diffusion.models.minimax_h3.ops import vae as vae_ops
-
-    operators = _operator_set()
-    monkeypatch.setattr(vae_ops, "resolve_h3_vae_operators", lambda _device: operators)
-    decoder = _make_decoder()
-
-    assert vae_ops.install_h3_vae_optimizations(
-        decoder,
-        device=torch.device("meta"),
-    )
-
-    for block in decoder.transformer_blocks:
-        assert block.attn.to_qkv.weight.dtype == torch.float16
-        assert block.attn.to_out.weight.dtype == torch.float16
-        assert block.ff.w1.weight.dtype == torch.float16
-        assert block.ff.w2.weight.dtype == torch.float16
-        assert block.forward.__func__.__name__ == "_optimized_transformer_block"
-        assert block.attn.forward.__func__.__name__ == "_optimized_attention"
-        assert block.ff.forward.__func__.__name__ == "_optimized_feed_forward"
-        assert block.attn._omni_qk_norm_rope is operators.qk_norm_rope
-        assert block._omni_scaled_residual is operators.scaled_residual
-    assert decoder.proj_out.weight.dtype == torch.float32
-
-    # Repeated installation is idempotent.
-    assert vae_ops.install_h3_vae_optimizations(
-        decoder,
-        device=torch.device("meta"),
-    )
-
-
-def test_h3_vae_install_accepts_remote_integer_parallel_flag(monkeypatch):
-    from vllm_omni.diffusion.models.minimax_h3.ops import vae as vae_ops
-
-    monkeypatch.setattr(
-        vae_ops,
-        "resolve_h3_vae_operators",
-        lambda _device: _operator_set(),
-    )
-    decoder = _make_decoder()
-    for block in decoder.transformer_blocks:
-        # The official checkpoint loads this JSON boolean-like field as int 0.
-        block.attn.spatial_parallel = 0
-
-    assert vae_ops.install_h3_vae_optimizations(
-        decoder,
-        device=torch.device("meta"),
-    )
-    assert getattr(decoder, "_omni_h3_vae_optimizations_installed", False)
-
-
-def test_h3_vae_swiglu_uses_post_linear_fp16_output(monkeypatch):
-    from vllm_omni.diffusion.models.minimax_h3.ops import vae as vae_ops
-
-    device, operators = _selected_operators()
-    monkeypatch.setattr(vae_ops, "resolve_h3_vae_operators", lambda _device: operators)
-    decoder = _make_decoder().to(device)
-    feed_forward = decoder.transformer_blocks[0].ff
-    reference_forward = type(feed_forward).forward
-    assert vae_ops.install_h3_vae_optimizations(
-        decoder,
-        device=device,
-    )
-
-    hidden_states = torch.randn(4, 8, device=device, dtype=torch.float32)
-    with torch.inference_mode(), torch.autocast(device.type, dtype=torch.float16):
-        expected = reference_forward(feed_forward, hidden_states)
-        actual = feed_forward(hidden_states)
-
-    assert expected.dtype == torch.float16
-    assert torch.equal(actual, expected)
-
-
-def test_h3_vae_install_leaves_unsupported_target_untouched(monkeypatch):
-    from vllm_omni.diffusion.models.minimax_h3.ops import vae as vae_ops
-
-    monkeypatch.setattr(vae_ops, "resolve_h3_vae_operators", lambda _device: None)
-    decoder = _make_decoder()
-
-    assert not vae_ops.install_h3_vae_optimizations(
-        decoder,
-        device=torch.device("meta"),
-    )
-    assert not hasattr(decoder, "_omni_h3_vae_optimizations_installed")
-    for block in decoder.transformer_blocks:
-        assert block.attn.to_qkv.weight.dtype == torch.float32
-        assert block.ff.w1.weight.dtype == torch.float32
-        assert block.forward.__func__.__name__ == "forward"
-        assert block.attn.forward.__func__.__name__ == "forward"
-
-
-@pytest.mark.parametrize("norm_input", [None, _same_dtype_norm_input, _failing_norm_input])
-def test_h3_vae_install_requires_fp32_attention_norm_semantics(monkeypatch, norm_input):
-    from vllm_omni.diffusion.models.minimax_h3.ops import vae as vae_ops
-
-    monkeypatch.setattr(
-        vae_ops,
-        "resolve_h3_vae_operators",
-        lambda _device: _operator_set(),
-    )
-    decoder = _make_decoder()
-    forward_globals = type(decoder.transformer_blocks[0].attn).forward.__globals__
-    monkeypatch.setitem(forward_globals, "_vit_norm_input", norm_input)
-
-    assert not vae_ops.install_h3_vae_optimizations(
-        decoder,
-        device=torch.device("meta"),
-    )
-    assert not hasattr(decoder, "_omni_h3_vae_optimizations_installed")
-    assert decoder.transformer_blocks[0].attn.to_qkv.weight.dtype == torch.float32
-
-
-@pytest.mark.parametrize(
-    "break_contract",
-    [
-        lambda block: delattr(block.attn, "spatial_parallel"),
-        lambda block: setattr(block.attn, "spatial_parallel", 2),
-        lambda block: setattr(block.attn, "perform_attention", None),
-        lambda block: setattr(block, "scale1", nn.Parameter(torch.zeros(7))),
-        lambda block: setattr(block.ff, "_compile_forward_enabled", True),
-    ],
-)
-def test_h3_vae_install_rejects_incompatible_remote_contract(monkeypatch, break_contract):
-    from vllm_omni.diffusion.models.minimax_h3.ops import vae as vae_ops
-
-    monkeypatch.setattr(
-        vae_ops,
-        "resolve_h3_vae_operators",
-        lambda _device: _operator_set(),
-    )
-    decoder = _make_decoder()
-    break_contract(decoder.transformer_blocks[0])
-
-    assert not vae_ops.install_h3_vae_optimizations(
-        decoder,
-        device=torch.device("meta"),
-    )
-    assert decoder.transformer_blocks[0].attn.to_qkv.weight.dtype == torch.float32
-
-
-def test_h3_vae_dispatch_is_extended_by_adding_an_operator_set(monkeypatch):
-    from vllm_omni.diffusion.models.minimax_h3.ops.vae import dispatch
-
-    first = _operator_set(supports=lambda _device: False)
-    added = _operator_set(supports=lambda device: device.type == "meta")
-    monkeypatch.setattr(dispatch, "H3_VAE_OPERATOR_TABLE", (first, added))
-
-    assert dispatch.resolve_h3_vae_operators(torch.device("meta")) is added
-    assert dispatch.resolve_h3_vae_operators(torch.device("cpu")) is None
-
-
-def test_h3_vae_dispatch_selects_supported_cuda_capabilities(monkeypatch):
-    from vllm_omni.diffusion.models.minimax_h3.ops.vae import dispatch
-
-    platform = Mock()
-    platform.is_cuda.return_value = True
-    platform.is_available.return_value = True
-    monkeypatch.setattr(dispatch, "HAS_TRITON", True)
-    monkeypatch.setattr(dispatch, "current_omni_platform", platform)
-
-    for capability in (90, 100, 103):
-        platform.get_device_capability.return_value.to_int.return_value = capability
-        assert dispatch.resolve_h3_vae_operators(torch.device("cuda:0")) is not None
-
-    for capability in (89, 101, 110):
-        platform.get_device_capability.return_value.to_int.return_value = capability
-        assert dispatch.resolve_h3_vae_operators(torch.device("cuda:0")) is None
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import json
 from types import SimpleNamespace

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project

-import gc
 from typing import Any

 import numpy as np
@@ -16,11 +15,7 @@ from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.platforms import current_omni_platform

 AUDIO_MODEL: dict[str, dict[str, int | None]] = {
-    # The inference peak includes backend workspaces as well as model weights.
-    # On ROCm, large MIOpen workspaces and allocator fragmentation can mask the
-    # resident-weight reduction, so use a conservative floor that still catches
-    # a disabled/no-op layerwise offloader.
-    "stabilityai/stable-audio-open-1.0": {"cuda": 1500, "rocm": 512},
+    "stabilityai/stable-audio-open-1.0": {"cuda": 1500, "rocm": 1500},
 }

 IMAGE_VIDEO_MODELS: dict[str, dict[str, int | None]] = {
@@ -68,9 +63,8 @@ def run_inference(
 ) -> tuple[float, Any]:
    current_omni_platform.empty_cache()
    device_index = current_omni_platform.current_device()
-    with current_omni_platform.device(device_index):
-        free_bytes, total_bytes = current_omni_platform.mem_get_info()
-    initial_used_mb = (total_bytes - free_bytes) / (1024**2)
+    monitor = DeviceMemoryMonitor(device_index=device_index, interval=0.02)
+    monitor.start()

    if model_name in AUDIO_MODEL:
        params = AUDIO_MODEL_PARAMS
@@ -84,37 +78,22 @@ def run_inference(
        # cache_backend="cache_dit",
        **params["runner_params"],
    ) as runner:
-        # Measure steady-state inference memory, not model construction. Enabling
-        # layerwise offload first loads the model and then replaces each block's
-        # device storage with CPU-backed weights.  Monitoring that transition
-        # captures both the original model and temporary staging allocations,
-        # which is not representative of layerwise-offloaded inference.
-        monitor = DeviceMemoryMonitor(device_index=device_index, interval=0.02)
        current_omni_platform.reset_peak_memory_stats()
-        monitor.start()

-        try:
-            # Refer to tests/e2e/offline_inference/test_wan22.py
-            # Use minimal settings for testing
-            output = runner.omni.generate(
-                "A cat sitting on a table",
-                OmniDiffusionSamplingParams(
-                    generator=torch.Generator(device=current_omni_platform.device_type).manual_seed(42),
-                    guidance_scale=1.0,
-                    num_inference_steps=num_inference_steps,
-                    **params["sampler_params"],
-                ),
-            )
-        finally:
-            monitor.stop()
+        # Refer to tests/e2e/offline_inference/test_wan22.py
+        # Use minimal settings for testing
+        output = runner.omni.generate(
+            "A cat sitting on a table",
+            OmniDiffusionSamplingParams(
+                generator=torch.Generator(device=current_omni_platform.device_type).manual_seed(42),
+                guidance_scale=1.0,
+                num_inference_steps=num_inference_steps,
+                **params["sampler_params"],
+            ),
+        )

-    # DeviceMemoryMonitor reports absolute device usage. Subtract this run's
-    # starting usage so process-wide compiler/workspace caches retained from a
-    # previous run do not make the second measurement order-dependent.
-    peak = max(0.0, monitor.peak_used_mb - initial_used_mb)
-
-    gc.collect()
-    current_omni_platform.empty_cache()
+    peak = monitor.peak_used_mb
+    monitor.stop()

    return peak, output

@@ -169,10 +148,8 @@ def test_layerwise_offload_diffusion_model(model_name: str):
    assert expected_saved_memory is not None

    # Verify that layerwise offloading significantly reduces memory usage
-    # Passes only if the actual savings meets the expected savings
-    actual_saved_memory = no_offload_peak_memory - layerwise_offload_peak_memory
-    assert layerwise_offload_peak_memory + expected_saved_memory <= no_offload_peak_memory, (
+    # Passes only if the actual savings exceeds the expected savings
+    assert layerwise_offload_peak_memory + expected_saved_memory < no_offload_peak_memory, (
        f"Layerwise offload peak memory {layerwise_offload_peak_memory} MB "
-        f"should be at least {expected_saved_memory} MB less than no offload peak memory "
-        f"{no_offload_peak_memory} MB (actual savings: {actual_saved_memory} MB)"
+        f"should be significantly less than no offload peak memory {no_offload_peak_memory} MB"
    )
@@ -245,160 +245,6 @@ class TestDistributedLayerwiseOffloadHook:
        )
        torch.testing.assert_close(rank0_block.weight_scale, expected_scale)

-    def test_allgather_reconstructs_online_int8_weight_and_scale(
-        self,
-        patched_offload_runtime,
-        monkeypatch,
-    ):
-        """DLO must reconstruct finalized online-INT8 tensors byte-for-byte.
-
-        One block carries both finalized online-INT8 layouts: the NPU method
-        leaves a contiguous pre-transposed (K, N) int8 weight with a 1-D fp32
-        per-channel scale, while the CUDA method leaves a transposed-view
-        weight (stride (1, K)) with a scalar fp32 scale.  The int8 and fp32
-        dtype groups must survive the flat-shard AllGather independently.
-        """
-
-        class OnlineInt8Block(nn.Module):
-            def __init__(self):
-                super().__init__()
-                logical = torch.arange(1.0, 13.0).reshape(3, 4)
-                # NPU online int8: qweight.t().contiguous() -> (K, N), 1-D scale
-                self.npu_weight = nn.Parameter(logical.t().contiguous().to(torch.int8), requires_grad=False)
-                self.npu_weight_scale = nn.Parameter(torch.tensor([2.0, 3.0, 4.0]), requires_grad=False)
-                # CUDA online int8: Parameter(qweight.t().data) -> transposed
-                # view (stride (1, K)) over the (N, K) storage, scalar scale
-                self.cuda_weight = nn.Parameter((logical + 100).to(torch.int8).t(), requires_grad=False)
-                self.cuda_weight_scale = nn.Parameter(torch.tensor([0.5]), requires_grad=False)
-
-        current_block = OnlineInt8Block()
-        rank0_block = OnlineInt8Block()
-        rank1_block = OnlineInt8Block()
-        expected = {name: tensor.detach().clone() for name, tensor in rank0_block.named_parameters()}
-        expected_strides = {name: tensor.stride() for name, tensor in rank0_block.named_parameters()}
-
-        def make_hook(block: OnlineInt8Block, rank: int) -> DistributedLayerwiseOffloadHook:
-            return DistributedLayerwiseOffloadHook(
-                next_block=block,
-                device=torch.device("cpu"),
-                dp_group=object(),
-                dp_size=2,
-                rank=rank,
-                copy_stream=DummyStream(),
-                comm_stream=DummyStream(),
-                pin_memory=False,
-            )
-
-        rank0_hook = make_hook(rank0_block, 0)
-        rank1_hook = make_hook(rank1_block, 1)
-        rank0_hook.initialize_hook(current_block)
-        rank1_hook.initialize_hook(OnlineInt8Block())
-        rank0_hook.gpu_shard_buffers = [
-            {dtype: torch.empty_like(shard) for dtype, shard in rank0_hook.cpu_shards.items()} for _ in range(2)
-        ]
-
-        def fake_allgather(output, local_shard, *, group):
-            del group
-            remote_shard = rank1_hook.cpu_shards[local_shard.dtype]
-            shard_size = local_shard.numel()
-            output[:shard_size].copy_(local_shard)
-            output[shard_size : 2 * shard_size].copy_(remote_shard)
-
-        monkeypatch.setattr(torch.distributed, "all_gather_into_tensor", fake_allgather)
-        rank0_hook.prefetch_layer(slot=0, non_blocking=False)
-
-        for name, tensor in rank0_block.named_parameters():
-            assert tensor.stride() == expected_strides[name]
-            torch.testing.assert_close(
-                tensor.float(),
-                expected[name].float(),
-                rtol=0,
-                atol=0,
-            )
-
-    def test_allgather_reconstructs_online_mxfp8_weight_and_scale(
-        self,
-        patched_offload_runtime,
-        monkeypatch,
-    ):
-        """DLO must reconstruct finalized online-MXFP8 tensors byte-for-byte.
-
-        One block carries both finalized online-MXFP8 layouts: the NPU method
-        leaves a contiguous pre-transposed (K, N) fp8 weight with a contiguous
-        (K_groups/2, N, 2) e8m0 block scale, while the vLLM-kernel method
-        (XPU) leaves an (N, K) fp8 weight whose e8m0 scale is a transposed
-        view over a contiguous (K/32, N) buffer.  The fp8 and e8m0 dtype
-        groups must survive the flat-shard AllGather independently.
-        """
-
-        class OnlineMxfp8Block(nn.Module):
-            def __init__(self):
-                super().__init__()
-                logical = torch.arange(1.0, 13.0).reshape(3, 4)
-                # Build scales from raw e8m0 bit patterns (1 byte/elem).
-                npu_scale_bits = torch.arange(1, 13, dtype=torch.uint8).reshape(2, 3, 2)
-                kernel_scale_bits = torch.arange(1, 7, dtype=torch.uint8).reshape(2, 3)
-                # NPU online mxfp8: (N, K).t().contiguous() -> (K, N) fp8
-                # weight, contiguous (K_groups/2, N, 2) e8m0 scale
-                self.npu_weight = nn.Parameter(logical.t().contiguous().to(torch.float8_e4m3fn), requires_grad=False)
-                self.npu_weight_scale = nn.Parameter(npu_scale_bits.view(torch.float8_e8m0fnu), requires_grad=False)
-                # vLLM-kernel online mxfp8 (XPU): (N, K) fp8 weight, e8m0
-                # scale kept as a transposed view (stride (1, K/32)) over the
-                # contiguous (K/32, N) storage
-                self.kernel_weight = nn.Parameter((logical + 100).to(torch.float8_e4m3fn), requires_grad=False)
-                self.kernel_weight_scale = nn.Parameter(
-                    kernel_scale_bits.view(torch.float8_e8m0fnu).t(), requires_grad=False
-                )
-
-        current_block = OnlineMxfp8Block()
-        rank0_block = OnlineMxfp8Block()
-        rank1_block = OnlineMxfp8Block()
-        expected = {name: tensor.detach().clone() for name, tensor in rank0_block.named_parameters()}
-        expected_strides = {name: tensor.stride() for name, tensor in rank0_block.named_parameters()}
-
-        def make_hook(block: OnlineMxfp8Block, rank: int) -> DistributedLayerwiseOffloadHook:
-            return DistributedLayerwiseOffloadHook(
-                next_block=block,
-                device=torch.device("cpu"),
-                dp_group=object(),
-                dp_size=2,
-                rank=rank,
-                copy_stream=DummyStream(),
-                comm_stream=DummyStream(),
-                pin_memory=False,
-            )
-
-        rank0_hook = make_hook(rank0_block, 0)
-        rank1_hook = make_hook(rank1_block, 1)
-        rank0_hook.initialize_hook(current_block)
-        rank1_hook.initialize_hook(OnlineMxfp8Block())
-        rank0_hook.gpu_shard_buffers = [
-            {dtype: torch.empty_like(shard) for dtype, shard in rank0_hook.cpu_shards.items()} for _ in range(2)
-        ]
-
-        def fake_allgather(output, local_shard, *, group):
-            del group
-            remote_shard = rank1_hook.cpu_shards[local_shard.dtype]
-            shard_size = local_shard.numel()
-            output[:shard_size].copy_(local_shard)
-            output[shard_size : 2 * shard_size].copy_(remote_shard)
-
-        monkeypatch.setattr(torch.distributed, "all_gather_into_tensor", fake_allgather)
-        rank0_hook.prefetch_layer(slot=0, non_blocking=False)
-
-        for name, tensor in rank0_block.named_parameters():
-            assert tensor.stride() == expected_strides[name]
-            # Compare raw bytes in logical order: float8/e8m0 CPU upcasts are
-            # version-sensitive, while contiguous().view(uint8) is an exact
-            # byte-level comparison for both contiguous and transposed-view
-            # parameters.
-            torch.testing.assert_close(
-                tensor.detach().contiguous().view(torch.uint8),
-                expected[name].contiguous().view(torch.uint8),
-                rtol=0,
-                atol=0,
-            )
-
    def test_dtensor_wrapper_preserved_across_prefetch_and_offload(self, dist_group, patched_offload_runtime):
        """DTensor wrapper should be preserved through prefetch/offload cycle."""
        current_block = TinyBlock(_make_values(1.0))
@@ -81,7 +81,7 @@ class QualityTestConfig:
    num_frames: int = 5  # only for t2v
    seed: int = 42
    gpu: str = "H100"  # minimum GPU requirement
-    negative_prompt: str | None = ""
+    negative_prompt: str = ""
    guidance_scale: float | None = None
    sigmas: list[float] | None = None
    enable_cpu_offload: bool = False
@@ -212,14 +212,24 @@ QUALITY_CONFIGS = [
        quantization="fp8",
        task="t2v",
        prompt="A serene lakeside sunrise with mist over the water",
-        max_lpips=0.20,
-        height=384,
-        width=512,
-        num_frames=73,
-        num_inference_steps=10,
-        # Do not override the recipe's negative conditioning, guidance, or
-        # scheduler trajectory: this gate follows the supported LTX default.
-        negative_prompt=None,
+        max_lpips=0.10,
+        height=256,
+        width=256,
+        num_frames=25,
+        num_inference_steps=8,
+        # Preserve the final scheduler trajectory used when this FP8 quality
+        # gate was established. Explicit LTX sigmas bypass dynamic shifting.
+        sigmas=[
+            1.0,
+            0.92185378074646,
+            0.8327768445014954,
+            0.7303033471107483,
+            0.6111654043197632,
+            0.4709382653236389,
+            0.30347853899002075,
+            0.10000002384185791,
+            0.0,
+        ],
    ),
 ]

@@ -286,11 +296,8 @@ def _generate_image(omni, config: QualityTestConfig):
    ).manual_seed(config.seed)
    torch.accelerator.reset_peak_memory_stats()

-    prompt = {"prompt": config.prompt}
-    if config.negative_prompt is not None:
-        prompt["negative_prompt"] = config.negative_prompt
    outputs = omni.generate(
-        prompt,
+        {"prompt": config.prompt, "negative_prompt": config.negative_prompt},
        OmniDiffusionSamplingParams(
            height=config.height,
            width=config.width,
@@ -321,11 +328,8 @@ def _generate_video(omni, config: QualityTestConfig):
    ).manual_seed(config.seed)
    torch.accelerator.reset_peak_memory_stats()

-    prompt = {"prompt": config.prompt}
-    if config.negative_prompt is not None:
-        prompt["negative_prompt"] = config.negative_prompt
    outputs = omni.generate(
-        prompt,
+        {"prompt": config.prompt, "negative_prompt": config.negative_prompt},
        OmniDiffusionSamplingParams(
            height=config.height,
            width=config.width,
@@ -461,7 +465,6 @@ def test_benchmark_generate_image_unwraps_nested_omni_request_output(monkeypatch


 def test_generate_video_forwards_sigmas(monkeypatch):
-    from vllm_omni.outputs import OmniRequestOutput
    from vllm_omni.platforms import current_omni_platform

    monkeypatch.setattr(current_omni_platform, "device_type", "cpu", raising=False)
@@ -472,12 +475,7 @@ def test_generate_video_forwards_sigmas(monkeypatch):
    class DummyOmni:
        def generate(self, _prompt, sampling):
            captured.sampling = sampling
-            return [
-                OmniRequestOutput.from_diffusion(
-                    request_id="req",
-                    images=[np.zeros((1, 2, 2, 3), dtype=np.float32)],
-                )
-            ]
+            return [SimpleNamespace(images=[np.zeros((1, 2, 2, 3), dtype=np.float32)])]

    config = QualityTestConfig(
        id="ltx-sigmas",
@@ -493,41 +491,6 @@ def test_generate_video_forwards_sigmas(monkeypatch):
    assert captured.sampling.sigmas == [1.0, 0.5]


-@pytest.mark.core_model
-@pytest.mark.cpu
-def test_ltx_quality_gate_uses_official_eager_defaults(monkeypatch):
-    from vllm_omni.outputs import OmniRequestOutput
-    from vllm_omni.platforms import current_omni_platform
-
-    monkeypatch.setattr(current_omni_platform, "device_type", "cpu", raising=False)
-    monkeypatch.setattr(torch.accelerator, "reset_peak_memory_stats", lambda: None, raising=False)
-    monkeypatch.setattr(torch.accelerator, "max_memory_allocated", lambda: 0, raising=False)
-    captured = SimpleNamespace(prompt=None, sampling=None)
-
-    class DummyOmni:
-        def generate(self, prompt, sampling):
-            captured.prompt = prompt
-            captured.sampling = sampling
-            return [
-                OmniRequestOutput.from_diffusion(
-                    request_id="req",
-                    images=[np.zeros((1, 2, 2, 3), dtype=np.float32)],
-                )
-            ]
-
-    config = next(config for config in QUALITY_CONFIGS if config.id == "fp8_ltx2")
-    _generate_video(DummyOmni(), config)
-
-    assert (config.width, config.height, config.num_frames, config.num_inference_steps) == (512, 384, 73, 10)
-    assert config.max_lpips == 0.20
-    assert config.sigmas is None
-    assert config.guidance_scale is None
-    assert config.negative_prompt is None
-    assert "negative_prompt" not in captured.prompt
-    assert captured.sampling.sigmas is None
-    assert captured.sampling.guidance_scale is None
-
-
 _marks = hardware_marks(res={"cuda": "H100"})
 _OUTPUT_DIR = Path(os.environ["VLLM_OMNI_QUALITY_OUTPUT_DIR"]) if "VLLM_OMNI_QUALITY_OUTPUT_DIR" in os.environ else None

@@ -538,6 +501,8 @@ def _quality_param(c: QualityTestConfig):
        marks.append(
            pytest.mark.skip(reason="Qwen-Image FP8 quality gate temporarily disabled (see CI / issue tracker).")
        )
+    if c.id == "fp8_ltx2":
+        marks.append(pytest.mark.skip(reason="https://github.com/vllm-project/vllm-omni/issues/6245"))
    return pytest.param(c, id=c.id, marks=marks)


@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 import asyncio
 import queue
@@ -197,59 +197,6 @@ class TestRequestBatchCapability:

        assert diffusion_engine_module.supports_request_batch(od_config) is True

-    def test_engine_rejects_multi_seq_diffusers_using_adapter_capability(
-        self,
-        monkeypatch: pytest.MonkeyPatch,
-        mocker: MockerFixture,
-    ) -> None:
-        od_config = SimpleNamespace(
-            model_class_name="QwenImagePipeline",
-            custom_pipeline_args=None,
-            diffusion_load_format="diffusers",
-            streaming_output=False,
-            max_num_seqs=2,
-        )
-        registry_load = mocker.Mock(
-            side_effect=lambda model_class_name: (
-                _SingleRequestPipeline if model_class_name == "DiffusersAdapterPipeline" else _BatchCapablePipeline
-            )
-        )
-        monkeypatch.setattr(
-            diffusion_engine_module.DiffusionModelRegistry,
-            "_try_load_model_cls",
-            registry_load,
-        )
-
-        engine = DiffusionEngine.__new__(DiffusionEngine)
-        with pytest.raises(ValueError, match="max_num_seqs=1"):
-            engine._resolve_execution_mode(od_config)
-
-        registry_load.assert_called_once_with("DiffusersAdapterPipeline")
-
-    def test_engine_prefers_batch_capable_custom_pipeline_over_diffusers_adapter(
-        self,
-        monkeypatch: pytest.MonkeyPatch,
-        mocker: MockerFixture,
-    ) -> None:
-        od_config = SimpleNamespace(
-            model_class_name="QwenImagePipeline",
-            custom_pipeline_args={"pipeline_class": _BatchCapablePipeline},
-            diffusion_load_format="diffusers",
-            streaming_output=False,
-            max_num_seqs=2,
-        )
-        registry_load = mocker.Mock(return_value=_SingleRequestPipeline)
-        monkeypatch.setattr(
-            diffusion_engine_module.DiffusionModelRegistry,
-            "_try_load_model_cls",
-            registry_load,
-        )
-
-        engine = DiffusionEngine.__new__(DiffusionEngine)
-
-        assert engine._resolve_execution_mode(od_config) is DiffusionExecutionMode.REQUEST_BATCH
-        registry_load.assert_not_called()
-
    def test_supports_request_batch_uses_custom_pipeline_class(self, monkeypatch: pytest.MonkeyPatch) -> None:
        od_config = SimpleNamespace(
            model_class_name="SinglePipeline",