[Dependency] Upgrade Cache-DiT to 1.5.0

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

.buildkite/cuda/test-merge.yml

@@ -139,12 +139,7 @@ steps:
         timeout_in_minutes: 50
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-        # h100_2, not h100_1: advanced_model asserts transcribe every audio
+        mirror_hardwares: h100_1
-        # response with Whisper, and tests/helpers/media.py only uses an
-        # accelerator for Whisper when a spare GPU exists. On a single card it
-        # grades on CPU: ~8-10 min per clip vs ~30 s, which alone overruns the
-        # 50-minute job timeout (build 2956).
-        mirror_hardwares: h100_2
 
       - label: "Omni · MiniCPM-o 4.5 Duplex Test"
         source_file_dependencies:

.buildkite/cuda/test-nightly.yml

@@ -345,7 +345,7 @@ steps:
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - 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
@@ -361,7 +361,7 @@ steps:
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
           - |
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_vllm_omni.json
@@ -377,7 +377,7 @@ steps:
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
           - |
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
@@ -393,7 +393,7 @@ steps:
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
           - |
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json
@@ -409,7 +409,7 @@ steps:
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
           - |
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json

.buildkite/cuda/test-ready.yml

@@ -128,21 +128,6 @@ steps:
               image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
               always-pull: true
               propagate-environment: true
-              # vLLM 0.27 added a pre-flight capacity check in
-              # distributed/device_communicators/shm_broadcast.py. Without this
-              # the container gets Docker's 64 MiB default and every test in
-              # this step dies at engine start with
-              #   RuntimeError: Insufficient space in /dev/shm:
-              #                 240 MiB required, 64 MiB available
-              # (9 failed in 8.7s on build #2949). vLLM 0.26 had no such check
-              # and allocated regardless, so this step passed on main while
-              # being under-provisioned the whole time — 0.27 turned a silent
-              # risk into an explicit error.
-              #
-              # This is the only inline docker plugin block in .buildkite/cuda/;
-              # every other step inherits common/ci_mirror_hardwares.yml, which
-              # already sets 8gb. Matching that rather than inventing a value.
-              shm-size: "8gb"
               environment:
                 - "HF_HOME=/fsx/hf_cache"
               volumes:

.buildkite/intel/pipeline-intel.yml

@@ -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: "v0.27.0"
+          VLLM_VERSION: "v0.26.0"
         priority: 100
         timeout_in_minutes: 60
         soft_fail: false

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

@@ -34,23 +34,8 @@ docker_build() {
     docker build "${out[@]}" "$@" -f docker/Dockerfile.xpu .
 }
 
-build_vllm_base() (
-    local vllm_source_dir
-    vllm_source_dir=$(mktemp -d)
-    trap 'rm -rf "${vllm_source_dir}"' EXIT
-
-    git clone --depth 1 --branch "${VLLM_VERSION}" \
-        https://github.com/vllm-project/vllm "${vllm_source_dir}"
-
-    local out=("${export_args[@]/'{{IMAGE}}'/${base_image_name}}")
-    docker build "${out[@]}" \
-        --target vllm-openai \
-        -f "${vllm_source_dir}/docker/Dockerfile.xpu" \
-        "${vllm_source_dir}"
-)
-
 if [ -z "$(docker images -q "${base_image_name}")" ]; then
-    build_vllm_base
+    docker_build "${base_image_name}" --target vllm-base --build-arg "VLLM_VERSION=${VLLM_VERSION}"
 fi
 
 # Try building the docker image

.buildkite/npu/test-npu-nightly.yml

@@ -115,7 +115,7 @@ steps:
           DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
           - |
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json

benchmarks/tts/README.md

@@ -139,34 +139,6 @@ python benchmarks/tts/bench_tts.py \
     --output-dir ./results
 ```
 
-#### Fixed IndexTTS 2.5 performance sweep
-
-IndexTTS 2.5 is registered in `model_configs.yaml` like the other TTS models.
-Use `--served-model-name` when the server was launched from a local native
-bundle instead of the registry key. The command below reproduces the acceptance
-workload: Seed-TTS Eval EN, `n=500`, concurrency `4/8/16/32`, five warmups, and
-dataset seed 0.
-
-```bash
-python benchmarks/tts/bench_tts.py \
-    --model IndexTeam/IndexTTS-2.5 \
-    --served-model-name /path/to/indextts-2.5 \
-    --task voice_clone --locale en \
-    --dataset-path /path/to/seedtts_testset \
-    --host 127.0.0.1 --port 8092 \
-    --concurrency 4 8 16 32 \
-    --num-prompts 500 --num-warmups 5 \
-    --output-dir ./results/indextts2_5 \
-    -- \
-    --tokenizer /path/to/indextts-2.5/qwen0.6bemo4-merge \
-    --seed 0 --metric-percentiles 50,95,99 --disable-tqdm --save-detailed
-```
-
-The wrapper benchmarks an already-running server. For reproducible quality
-comparisons, add `--request-seed 42`; production-performance runs should omit
-it. The trailing `--seed 0` controls Seed-TTS row selection rather than model
-sampling.
-
 ### 4. Plot a sweep
 
 ```bash

benchmarks/tts/bench_tts.py

@@ -84,17 +84,12 @@ def build_bench_args(
     result_filename: str | None,
     extra_cli_args: list[str],
     output_len: int | None = None,
-    served_model_name: str | None = None,
-    num_warmups: int = 2,
-    request_seed: int | None = None,
 ) -> list[str]:
     """Build the ``vllm bench serve --omni`` command for one (task, concurrency) run."""
     dataset_name = _TASK_TO_DATASET[task]
     backend: str = model_cfg["backend"]
     endpoint: str = model_cfg["endpoint"]
-    task_extra_body: dict[str, Any] = dict((model_cfg.get("task_extra_body") or {}).get(task) or {})
+    task_extra_body: dict[str, Any] = (model_cfg.get("task_extra_body") or {}).get(task) or {}
-    if request_seed is not None:
-        task_extra_body["seed"] = request_seed
 
     # Resolve dataset path
     if dataset_path:
@@ -114,7 +109,7 @@ def build_bench_args(
         "--port",
         str(port),
         "--model",
-        served_model_name or model,
+        model,
         "--backend",
         backend,
         "--endpoint",
@@ -124,7 +119,7 @@ def build_bench_args(
         "--num-prompts",
         str(num_prompts),
         "--num-warmups",
-        str(num_warmups),
+        "2",
         "--percentile-metrics",
         "ttft,e2el,audio_rtf,audio_ttfp,audio_duration,audio_underrun",
     ]
@@ -157,8 +152,7 @@ def build_bench_args(
         if result_filename:
             cmd += ["--result-filename", result_filename]
 
-    passthrough = extra_cli_args[1:] if extra_cli_args[:1] == ["--"] else extra_cli_args
+    cmd += extra_cli_args
-    cmd += passthrough
     return cmd
 
 
@@ -231,12 +225,6 @@ def main() -> None:
     parser.add_argument(
         "--model", required=True, help="HuggingFace model ID (e.g. Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice)"
     )
-    parser.add_argument(
-        "--served-model-name",
-        default=None,
-        help="Model name/path accepted by the running server. Use this when --model selects a registry entry "
-        "but the server was launched from a local bundle path.",
-    )
     parser.add_argument("--task", default="all", help="Task type: voice_clone | default_voice | voice_design | all")
     parser.add_argument("--locale", default="en", choices=["en", "zh"])
     parser.add_argument("--concurrency", type=int, nargs="+", default=[1, 4], metavar="N")
@@ -248,13 +236,6 @@ def main() -> None:
         metavar="N",
         help="Number of prompts per run. If one value, applied to all concurrency levels.",
     )
-    parser.add_argument("--num-warmups", type=int, default=2, help="Warmup requests before each measured run")
-    parser.add_argument(
-        "--request-seed",
-        type=int,
-        default=None,
-        help="Optional model sampling seed added to every request body for reproducible quality runs",
-    )
     parser.add_argument(
         "--dataset-path", default=None, help="Root of seed-tts-eval dataset (required for voice_clone/default_voice)"
     )
@@ -275,9 +256,6 @@ def main() -> None:
     parser.add_argument("extra", nargs=argparse.REMAINDER, help="Extra args passed directly to vllm bench serve")
     args = parser.parse_args()
 
-    if args.num_warmups < 0:
-        parser.error("--num-warmups cannot be negative")
-
     model_configs = load_model_configs(Path(args.model_configs))
     if args.model not in model_configs:
         known = "\n  ".join(model_configs.keys())
@@ -330,9 +308,6 @@ def main() -> None:
                 result_filename=result_filename,
                 extra_cli_args=args.extra or [],
                 output_len=args.output_len,
-                served_model_name=args.served_model_name,
-                num_warmups=args.num_warmups,
-                request_seed=args.request_seed,
             )
             result = run_one_benchmark(cmd)
             if result is not None:

benchmarks/tts/model_configs.yaml

@@ -7,17 +7,6 @@
 # is tracked here.
 
 models:
-  IndexTeam/IndexTTS-2.5:
-    supported_tasks: [voice_clone]
-    backend: openai-audio-speech
-    endpoint: /v1/audio/speech
-    trust_remote_code: true
-    task_extra_body:
-      voice_clone:
-        extra_params:
-          lang: en
-          text_normalization: true
-
   # -CustomVoice checkpoints lack speaker_encoder weights, so voice_clone is
   # NOT supported (an attempt raises ValueError from _extract_speaker_embedding
   # at model runtime).  Use -Base for voice_clone.

docker/Dockerfile.ci

@@ -1,24 +1,7 @@
-# Target vllm version: v0.27.0 (releases/v0.27.0 branch).
+# Target vllm version: v0.26.0 (releases/v0.26.0 branch).
 # Keep VLLM_BASE_TAG aligned with the target vLLM release on every rebase.
-#
-# Release-boundary form: there is no precompiled-wheel pin here, because the
-# base image already ships exactly the target vLLM. Everything the runtime
-# needs — torch, flashinfer, nvJitLink, numpy — comes from that image as one
-# self-consistent set.
-#
-# This block reappears only when the rebase targets a vLLM commit that is NOT a
-# released tag. During the 0.27 rebase, targeting v0.27.0rc1 on top of the
-# v0.26.0 base image required reinstalling vLLM from wheels.vllm.ai and then
-# hand-repairing the fallout: pinning torch ahead of flashinfer (installing it
-# after bound flashinfer's TVM FFI runtime to the old torch and aborted engine
-# core init), registering libnvJitLink.so.13 with ldconfig, and re-pinning numpy
-# under numba's ceiling. All of it was compensation for a base image and a vLLM
-# that disagreed. At a real release boundary they agree, so none of it is
-# needed. If you pin an untagged commit again, restore the reinstall block —
-# agent/tasks/20_update_wheel_url.sh fails the run rather than letting the
-# Dockerfile silently ship the wrong vLLM.
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.27.0
+ARG VLLM_BASE_TAG=v0.26.0
 
 # We use registry-based layer caching for the dependencies,
 # where the tag is determined based on the hash of the contents

docker/Dockerfile.xpu

@@ -88,7 +88,7 @@ ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE="copy"
 
-ARG VLLM_VERSION=v0.27.0
+ARG VLLM_VERSION=v0.26.0
 RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
 WORKDIR /workspace/vllm
 
@@ -108,7 +108,7 @@ 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 triton-xpu==3.7.1 && \
     uv pip install --no-build-isolation --no-deps .
 
 CMD ["/bin/bash"]
@@ -144,13 +144,12 @@ WORKDIR /workspace/vllm-omni
 COPY . .
 
 ENV VLLM_OMNI_TARGET_DEVICE=xpu
-# Keep the upstream oneCCL packages paired with the XPU torch runtime.
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --no-build-isolation \
     --extra-index-url https://mirrors.ustc.edu.cn/pypi/simple/ \
     ".[dev]" && \
-    uv pip uninstall triton && \
+    uv pip uninstall triton oneccl oneccl-devel && \
-    uv pip install triton-xpu==3.7.2 --reinstall
+    uv pip install triton-xpu==3.7.1 --reinstall
 
 FROM vllm-omni AS vllm-omni-openai
 

docs/.nav.yml

@@ -40,30 +40,16 @@ nav:
   - Models:
     - models/supported_models.md
   - Features:
-    - Overview: features/README.md
+    - Sleep Mode: features/sleep_mode.md
-    - Runtime and Stage Execution:
+    - Diffusion Features:
-      - Execution Modes and Streaming: user_guide/diffusion/execution_modes.md
+      - Overview: user_guide/diffusion_features.md
-      - Sleep Mode: features/sleep_mode.md
+      - Feature Compatibility: user_guide/feature_compatibility.md
-    - Quantization:
+      - Regional Compilation: user_guide/diffusion/regional_compilation.md
-      - Overview: user_guide/quantization/overview.md
-      - Online Quantization: user_guide/quantization/online.md
-      - Quantized KV Cache: user_guide/quantization/quantized_kvcache.md
-      - FP8 W8A8: user_guide/quantization/fp8.md
-      - Int8 W8A8: user_guide/quantization/int8.md
-      - BitsAndBytes W4: user_guide/quantization/bitsandbytes.md
-      - MXFP8 W8A8: user_guide/quantization/mxfp8.md
-      - MXFP4 W4A4: user_guide/quantization/mxfp4.md
-      - ModelOpt: user_guide/quantization/modelopt.md
-      - AutoRound: user_guide/quantization/autoround.md
-      - msModelSlim: user_guide/quantization/msmodelslim.md
-    - Diffusion Acceleration:
-      - Overview and Compatibility:
-        - Overview: user_guide/diffusion_features.md
-        - Feature Compatibility: user_guide/feature_compatibility.md
-      - CPU Offloading: user_guide/diffusion/cpu_offload.md
       - Cache Acceleration:
         - TeaCache: user_guide/diffusion/cache_acceleration/teacache.md
         - Cache-DiT: user_guide/diffusion/cache_acceleration/cache_dit.md
+      - Attention Backends: user_guide/diffusion/attention_backends.md
+      - Frame Interpolation: user_guide/diffusion/frame_interpolation.md
       - Parallelism:
         - Overview: user_guide/diffusion/parallelism/overview.md
         - CFG Parallel: user_guide/diffusion/parallelism/cfg_parallel.md
@@ -72,15 +58,23 @@ nav:
         - Sequence Parallel: user_guide/diffusion/parallelism/sequence_parallel.md
         - Tensor Parallel: user_guide/diffusion/parallelism/tensor_parallel.md
         - VAE Parallelism: user_guide/diffusion/parallelism/vae_parallelism.md
-      - Attention Backends: user_guide/diffusion/attention_backends.md
+      - CPU Offloading: user_guide/diffusion/cpu_offload.md
-      - Regional Compilation: user_guide/diffusion/regional_compilation.md
-      - Frame Interpolation: user_guide/diffusion/frame_interpolation.md
-      - Startup and Loading: user_guide/diffusion/startup_and_loading.md
       - LoRA: user_guide/diffusion/lora.md
-    - Experimental:
+      - Custom Pipeline: features/custom_pipeline.md
-      - Session State Manager: features/session_state_manager.md
+      - Execution Modes: user_guide/diffusion/execution_modes.md
-  - Integrations:
+    - Quantization:
-    - Custom Diffusion Pipeline: features/custom_pipeline.md
+      - Quantization: user_guide/quantization/overview.md
+      - Online Quantization: user_guide/quantization/online.md
+      - Quantized KV Cache: user_guide/quantization/quantized_kvcache.md
+      - FP8 W8A8: user_guide/quantization/fp8.md
+      - Int8 W8A8: user_guide/quantization/int8.md
+      - BitsAndBytes W4: user_guide/quantization/bitsandbytes.md
+      - MXFP8 W8A8: user_guide/quantization/mxfp8.md
+      - MXFP4 W4A4: user_guide/quantization/mxfp4.md
+      - ModelOpt: user_guide/quantization/modelopt.md
+      - GGUF: user_guide/quantization/gguf.md
+      - AutoRound: user_guide/quantization/autoround.md
+      - msModelSlim: user_guide/quantization/msmodelslim.md
     - ComfyUI: features/comfyui.md
   - Fault Tolerance:
     - Failure Modes: user_guide/fault_injection_reliability_matrix.md
@@ -114,7 +108,6 @@ nav:
           - Shared Memory Connector: design/feature/omni_connectors/shared_memory_connector.md
           - Yuanrong Store Connector: design/feature/omni_connectors/yuanrong_connector.md
           - Yuanrong Transfer Engine Connector: design/feature/omni_connectors/yuanrong_transfer_engine_connector.md
-      - Quantization: design/feature/quantization.md
       - Diffusion Acceleration:
         - Parallelism:
           - CFG-Parallel: design/feature/cfg_parallel.md
@@ -124,8 +117,9 @@ nav:
           - Sequence Parallel: design/feature/sequence_parallel.md
           - Tensor Parallel: design/feature/tensor_parallel.md
           - VAE Patch Parallelism: design/feature/vae_parallel.md
-        - Attention Optimization:
+        - Attention Backends:
           - Skip-Softmax: design/feature/skip_softmax.md
+        - Quantization: design/feature/quantization.md
         - Cache-DiT: design/feature/cache_dit.md
         - TeaCache: design/feature/teacache.md
         - Diffusion Continuous Batching: design/feature/diffusion_continuous_batching.md

docs/design/index.md

@@ -11,10 +11,6 @@ in the active navigation.
 
 ## Feature Design Documents
 
-For user-facing configuration and current compatibility, see the
-[Features overview](../features/README.md). A design document defines an
-implementation contract; it is not, by itself, a general support claim.
-
 ### Runtime and stage execution
 
 - [Disaggregated Inference](feature/disaggregated_inference.md)
@@ -34,10 +30,6 @@ implementation contract; it is not, by itself, a general support claim.
 - [Yuanrong Store Connector](feature/omni_connectors/yuanrong_connector.md)
 - [Yuanrong Transfer Engine Connector](feature/omni_connectors/yuanrong_transfer_engine_connector.md)
 
-### Quantization
-
-- [Quantization](feature/quantization.md)
-
 ### Diffusion acceleration
 
 #### Parallelism
@@ -50,14 +42,14 @@ implementation contract; it is not, by itself, a general support claim.
 - [Tensor Parallel](feature/tensor_parallel.md)
 - [VAE Patch Parallelism](feature/vae_parallel.md)
 
-#### Attention optimization
+#### Attention Backends
-
-The [Diffusion Attention Backends](../user_guide/diffusion/attention_backends.md)
-guide lists all selectable backends and their platform defaults. Skip-Softmax
-is the backend-related optimization with a standalone feature-design contract:
 
 - [Skip-Softmax](feature/skip_softmax.md)
 
+#### Quantization
+
+- [Quantization](feature/quantization.md)
+
 - [Cache-DiT](feature/cache_dit.md)
 - [TeaCache](feature/teacache.md)
 - [Diffusion Continuous Batching](feature/diffusion_continuous_batching.md)

docs/features/README.md

@@ -1,67 +0,0 @@
-# Features
-
-Use this section to choose and configure vLLM-Omni runtime and optimization
-features. The navigation follows the active
-[Feature Design](../design/index.md#feature-design-documents) taxonomy whenever
-an implementation contract has a user-facing workflow.
-
-!!! note "User guides, design documents, and recipes"
-
-    User guides explain how to enable a feature and where it is supported.
-    Design documents define internal contracts and do not, by themselves,
-    imply general or production support. For model-specific launch flags and
-    hardware requirements, use the linked examples and validated recipes from
-    [Supported Models](../models/supported_models.md).
-
-## Runtime and Stage Execution
-
-| Goal | User guide | Related design contract |
-| --- | --- | --- |
-| Choose serial, batched, step-wise, or streaming diffusion execution | [Execution Modes and Streaming](../user_guide/diffusion/execution_modes.md) | [Diffusion Continuous Batching](../design/feature/diffusion_continuous_batching.md), [Async Diffusion Output](../design/feature/async_diffusion_output.md) |
-| Reclaim stage memory without restarting the server | [Sleep Mode](sleep_mode.md) | Runtime lifecycle behavior is documented in the user guide |
-
-Some runtime designs are deliberately not promoted as standalone User Guide
-features yet:
-
-- [Disaggregated Inference](../design/feature/disaggregated_inference.md) and
-  [OmniConnector implementations](../design/index.md#communication) describe
-  topology and transport contracts. Practical deployment configuration remains
-  under [Pipeline and deploy configurations](../configuration/stage_configs.md).
-- [Async Chunk](../design/feature/async_chunk.md) and
-  [Async Omni Output Materialization](../design/feature/omni_async_output_materialization.md)
-  are model- and pipeline-dependent. Use the selected model's deploy
-  configuration and recipe for the supported settings.
-- [Automatic Prefix Caching](../design/feature/prefix_caching.md) remains a
-  design-level contract until its user-facing configuration and compatibility
-  surface is consolidated.
-
-## Quantization
-
-Quantization is a cross-model feature rather than a diffusion-only
-optimization. The unified [`quantization_config` guide](../user_guide/quantization/overview.md)
-covers diffusion-only models, multi-stage omni/TTS models, and multi-stage
-diffusion models. Its [design contract](../design/feature/quantization.md)
-defines the shared configuration and backend extension points.
-
-## Diffusion Acceleration
-
-| Goal | User guide | Related design contract |
-| --- | --- | --- |
-| Compare acceleration methods and supported combinations | [Overview](../user_guide/diffusion_features.md), [Feature Compatibility](../user_guide/feature_compatibility.md) | [Diffusion acceleration designs](../design/index.md#diffusion-acceleration) |
-| Move weights between host and device memory | [CPU Offloading](../user_guide/diffusion/cpu_offload.md) | [Distributed Layerwise Offload](../design/feature/distributed_layerwise_offload.md) |
-| Reuse denoising computation | [Cache Acceleration](../user_guide/diffusion/cache_acceleration/cache_dit.md) | [Cache-DiT](../design/feature/cache_dit.md), [TeaCache](../design/feature/teacache.md) |
-| Distribute diffusion work across devices | [Parallelism](../user_guide/diffusion/parallelism/overview.md) | [Parallelism designs](../design/index.md#parallelism) |
-| Select dense, sparse, or quantized attention paths | [Attention Backends](../user_guide/diffusion/attention_backends.md) | [Skip-Softmax](../design/feature/skip_softmax.md) |
-| Compile repeated diffusion regions | [Regional Compilation](../user_guide/diffusion/regional_compilation.md) | User-facing optimization guide |
-| Add generated video frames | [Frame Interpolation](../user_guide/diffusion/frame_interpolation.md) | User-facing extension guide |
-| Reduce diffusion model startup time | [Startup and Loading](../user_guide/diffusion/startup_and_loading.md) | User-facing loading guide |
-| Apply diffusion adapters | [LoRA](../user_guide/diffusion/lora.md) | User-facing extension guide |
-
-The [Pipeline Parallelism guide](../user_guide/diffusion/parallelism/pipeline_parallel.md)
-remains available by direct link, but it is not promoted in the primary User
-Guide navigation until its user-facing support and placement are settled.
-
-## Experimental
-
-[Session State Manager](session_state_manager.md) is opt-in and experimental.
-Its APIs and compatibility may change without notice.

docs/models/supported_models.md

@@ -80,7 +80,6 @@ th {
 | `HiggsAudioV2ForConditionalGeneration` | Higgs-Audio v2 | `bosonai/higgs-audio-v2-generation-3B-base` | ✅︎ | | | | — |
 | `HiggsMultimodalQwen3ForConditionalGeneration` | Higgs-Audio v3 (TTS) | `bosonai/higgs-audio-v3-tts-4b` | ✅︎ | | | | [Published](https://recipes.vllm.ai/bosonai/higgs-audio-v3-tts-4b) |
 | `IndexTTS2TalkerForConditionalGeneration` | IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✅︎ | | | | — |
-| `IndexTTS25TalkerForConditionalGeneration` | IndexTTS-2.5 | native `checkpoints/` bundle | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/IndexTeam/IndexTTS-2_5.md) |
 | `NextStep11Pipeline` | NextStep-1.1 | `stepfun-ai/NextStep-1.1` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `MiMoAudioModel` | MiMo-Audio-7B-Instruct | `XiaomiMiMo/MiMo-Audio-7B-Instruct` | ✅︎ | ✅︎ | | | — |
 | `MiMoV2ASRForCausalLM` | MiMo-V2.5-ASR | `XiaomiMiMo/MiMo-V2.5-ASR` | ✅︎ | ✅︎ | | | — |

docs/user_guide/diffusion/startup_and_loading.md

@@ -1,60 +0,0 @@
-# Diffusion Startup and Loading
-
-Large diffusion models can take several minutes to load at startup. vLLM-Omni
-loads safetensors shards in parallel to reduce this initialization time.
-
-Multi-thread weight loading is enabled by default with four threads. No
-configuration is needed for the default behavior.
-
-## Configuration
-
-| Parameter | CLI flag | Default | Description |
-| --- | --- | --- | --- |
-| `enable_multithread_weight_load` | `--disable-multithread-weight-load` | `True` | Pass the flag to disable multi-thread loading |
-| `num_weight_load_threads` | `--num-weight-load-threads` | `4` | Number of parallel weight-loading threads |
-
-!!! tip
-
-    The default balances startup speed and disk I/O contention. Fast NVMe
-    storage may benefit from more threads, while network storage or hard disks
-    may not.
-
-## Online Serving
-
-```bash
-# Default: multi-thread loading with four threads
-vllm serve Qwen/Qwen-Image --omni --port 8091
-
-# Increase the thread count
-vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers --omni \
-  --num-weight-load-threads 8
-
-# Disable multi-thread loading
-vllm serve Qwen/Qwen-Image --omni --disable-multithread-weight-load
-```
-
-## Offline Inference
-
-```python
-from vllm_omni import Omni
-
-# Default: multi-thread loading with four threads
-omni = Omni(model="Qwen/Qwen-Image")
-
-# Increase the thread count
-omni = Omni(
-    model="Wan-AI/Wan2.2-I2V-A14B-Diffusers",
-    num_weight_load_threads=8,
-)
-```
-
-## Reference Benchmarks
-
-The following measurements were collected on NVIDIA H800 hardware. Treat them
-as reference results rather than a guarantee for other storage or hardware
-configurations.
-
-| Model | Sequential loading | Multi-thread loading | Speedup |
-| --- | ---: | ---: | ---: |
-| **Qwen/Qwen-Image** (53.7 GiB) | 168 s | 27 s | **6.2x** |
-| **Wan-AI/Wan2.2-I2V-A14B-Diffusers** (64.5 GiB) | 283 s | 56 s | **5.1x** |

docs/user_guide/diffusion_features.md

@@ -49,7 +49,7 @@ Parallelism methods distribute computation across GPUs without quality loss (mat
 
 | Method | Description | Best For |
 |--------|-------------|----------|
-| **[Multi-Thread Weight Loading](diffusion/startup_and_loading.md)** | Loads safetensors shards in parallel using a thread pool | All diffusion models; reduces startup from minutes to seconds |
+| **[Multi-Thread Weight Loading](#multi-thread-weight-loading)** | Loads safetensors shards in parallel using a thread pool | All diffusion models; reduces startup from minutes to seconds |
 
 **Note:** Some acceleration methods can be combined together for optimized performance. See [Feature Compatibility Table](#feature-compatibility) and [Feature Compatibility Tutorial](feature_compatibility.md) for detailed configuration examples.
 
@@ -100,9 +100,7 @@ The following tables show which models support each feature:
 
 - **🔀SP (Ulysses & Ring)**: Includes both Ulysses-SP and Ring-Attention methods
 - ✅ = Fully supported
-- ✅* = Supported with the constraint listed below the table
 - ❌ = Not supported
-- ❓ = Not verified; not recommended
 
 > Notes:
 
@@ -121,7 +119,7 @@ The following tables show which models support each feature:
 | **FLUX.2-dev**           |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |          ✅           |       ❌        |        ❌         |
 | **GLM-Image**            |     ❌     |     ❌      |           ❌           |       ✅        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Hidream-I1-Full**        |     ❌     |     ❌      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
-| **HunyuanImage3**        |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ✅        |        ✅*        |
+| **HunyuanImage3**        |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ✅        |        ❌         |
 | **Krea 2**               |     ❌     |     ✅      |           ❌           |       ❌        |         ❌         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LongCat-Image**        |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **LongCat-Image-Edit**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
@@ -147,7 +145,6 @@ The following tables show which models support each feature:
 > 2. `Tongyi-MAI/Z-Image-Turbo` and `SII-GAIR/daVinci-MagiHuman-Base-1080p` are distilled models with minimal NFEs; CFG-Parallel is not necessary.
 > 3. Cosmos3 T2I uses `Cosmos3OmniDiffusersPipeline` with `modalities=["image"]`. Model-level CPU offload swaps the nested UND reasoner and GEN generator pathways; layerwise offload remains available for blockwise GEN/UND offload.
 > 4. Krea 2 currently supports single-GPU inference plus LoRA, Cache-DiT, HSDP, CPU/layerwise offload, and VAE-patch-parallel (decode). TP/SP/CFG-Parallel are not yet wired. The few-step distilled (Turbo) checkpoint uses `is_distilled=true` (fixed timestep shift `mu=1.15`); generate at 2048x2048 by default with `num_inference_steps≈8` and `guidance_scale=0`. The Raw checkpoint uses 1024x1024, `num_inference_steps=28`, and `guidance_scale=4.5`.
-> 5. HunyuanImage3 supports step execution. Multi-request step execution requires `TORCH_SDPA`; see [Diffusion Execution Modes](diffusion/execution_modes.md#step-execution).
 
 ### VideoGen
 
@@ -158,16 +155,13 @@ The following tables show which models support each feature:
 | **Wan2.1-VACE**              |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2**                    |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2.3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
-| **Helios**                   |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ✅*        |
+| **Helios**                   |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **HunyuanVideo-1.5 T2V I2V** |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **DreamID-Omni**             |     ❌     |     ❌      |           ❌           |       ✅        |         ❌         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Cosmos3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **LongCat-Video-Avatar-1.5** |     ❌     |     ❌      |           ❌           |       ❌        |         ❌         |         ❌         |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **MiniMax-H3**               | ✅ (FL2VA) |     ✅      |           ✅           |       ❌        |       ✅ (DiT/TE)  |         ❌         |   ✅    |             ✅             |       ✅ (tile)       |      ✅ (DiT)      |        ❌         |
 
-> **Step execution note:** Helios supports single-request step execution only;
-> use `max_num_seqs=1`.
-
 **Frame Interpolation Support**
 
 - **Supported**: Wan2.2 text-to-video, image-to-video, and TI2V pipelines
@@ -196,7 +190,7 @@ The following tables show which models support each feature:
 | **🔀Ring-Attn** | ✅ | ✅ | ✅ | | | | | | | | | | | |
 | **🔀CFG-Parallel** | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | |
 | **🔀Tensor Parallel** | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | |
-| **🔀HSDP** | ❓ | ❓ | ✅ | ❓ | ✅ | ❌ | | | | | | | | |
+| **🔀HSDP** | ❓ | ❓ | ❓ | ❓ | ❓ | ❌ | | | | | | | | |
 | **🔀Expert Parallel** | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | | | | | | | |
 | **💾CPU Offloading (Layerwise)** | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | | | | | |
 | **💾CPU Offloading (Module-wise)** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❓ | ❓ | ❌ | | | | | |
@@ -207,43 +201,97 @@ The following tables show which models support each feature:
 
 !!! info
 
-    1. HSDP can be combined with Ulysses-SP or CFG-Parallel. Tensor Parallel
+    1. Tensor Parallel and HSDP are not compatible.
-       and HSDP are not compatible; other HSDP combinations in the table
-       remain unverified.
     2. TeaCache and Cache-DiT are not compatible.
     3. CPU Offloading (Layerwise) and CPU Offloading (Module-wise) are not compatible.
-    4. The CPU Offloading (Layerwise) row describes local layerwise offload.
+    4. CPU Offloading (Layerwise) supports single-card for now.
-       Multi-device Distributed Layerwise Offload has a separate topology and
+    5. Using FP8-Quant as an example of qunatization methods.
-       compatibility matrix in the [CPU Offloading guide](diffusion/cpu_offload.md#distributed-layerwise-offloading).
-    5. The compatibility matrix uses FP8 as the representative quantization method.
     6. Step Execution is not compatible with any diffusion cache backend. LoRA is supported, but each scheduled batch must use a single adapter (requests with different `lora_request` or `lora_scale` are kept in separate batches).
 
 
 ## Multi-Thread Weight Loading
 
-The loading guide now lives at [Diffusion Startup and
+Large diffusion models can take several minutes to load weights at startup (e.g., ~3 min for Qwen-Image, ~5 min for Wan2.2 I2V 14B). Multi-thread weight loading speeds up this process by loading safetensors shards in parallel using a thread pool instead of sequentially.
-Loading](diffusion/startup_and_loading.md). This heading remains so existing
+
-links to this section continue to work.
+This optimization is **enabled by default** with 4 threads. No configuration is needed for the default behavior.
+
+### Configuration
+
+| Parameter | CLI Flag | Default | Description |
+|-----------|----------|---------|-------------|
+| `enable_multithread_weight_load` | `--disable-multithread-weight-load` | `True` (enabled) | Pass the flag to disable multi-thread loading |
+| `num_weight_load_threads` | `--num-weight-load-threads` | `4` | Number of threads for parallel weight loading |
+
+!!! tip
+    The default of 4 threads balances speed and disk I/O contention. On fast NVMe storage you may benefit from more threads (e.g., 8). On HDD or network storage, the default of 4 avoids saturating I/O bandwidth.
+
+### Online Serving
+
+```bash
+# Default (multi-thread enabled, 4 threads)
+vllm serve Qwen/Qwen-Image --omni --port 8091
+
+# Custom thread count
+vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers --omni --num-weight-load-threads 8
+
+# Disable multi-thread loading
+vllm serve Qwen/Qwen-Image --omni --disable-multithread-weight-load
+```
+
+### Offline Inference
+
+```python
+from vllm_omni import Omni
+
+# Default (multi-thread enabled, 4 threads)
+omni = Omni(model="Qwen/Qwen-Image")
+
+# Custom thread count
+omni = Omni(
+    model="Wan-AI/Wan2.2-I2V-A14B-Diffusers",
+    num_weight_load_threads=8,
+)
+```
+
+### Benchmarks
+
+Measured on NVIDIA H800:
+
+| Model | Before | After | Speedup |
+|-------|--------|-------|---------|
+| **Qwen/Qwen-Image** (53.7 GiB) | 168s | 27s | **6.2x** |
+| **Wan-AI/Wan2.2-I2V-A14B-Diffusers** (64.5 GiB) | 283s | 56s | **5.1x** |
 
 ## Learn More
 
-The Diffusion Acceleration navigation groups the remaining guides as follows:
+**Cache Acceleration:**
 
-| Area | Guide |
+- **[TeaCache Configuration Guide](diffusion/cache_acceleration/teacache.md)** - Parameter tuning, performance tips, troubleshooting
-| --- | --- |
+- **[Cache-DiT Advanced Guide](diffusion/cache_acceleration/cache_dit.md)** - DBCache, TaylorSeer, SCM techniques and optimization
-| Compatibility | [Feature Compatibility](feature_compatibility.md) |
-| CPU offloading | [CPU Offloading](diffusion/cpu_offload.md) |
-| Cache acceleration | [TeaCache](diffusion/cache_acceleration/teacache.md), [Cache-DiT](diffusion/cache_acceleration/cache_dit.md) |
-| Parallelism | [Parallelism Overview](diffusion/parallelism/overview.md) |
-| Attention | [Attention Backends](diffusion/attention_backends.md) |
-| Compilation | [Regional Compilation](diffusion/regional_compilation.md) |
-| Video extension | [Frame Interpolation](diffusion/frame_interpolation.md) |
-| Startup | [Startup and Loading](diffusion/startup_and_loading.md) |
-| Adapters | [LoRA](diffusion/lora.md) |
 
-Related cross-model and runtime features are documented separately:
+**Parallelism Methods:**
 
-- [Quantization](quantization/overview.md) covers diffusion-only models,
+- **[Parallelism Overview](diffusion/parallelism/overview.md)** - Tensor Parallelism, Sequence Parallelism, CFG Parallelism, Pipeline Parallelism, HSDP, and Expert Parallelism
-  multi-stage omni/TTS models, and multi-stage diffusion models.
+
-- [Execution Modes and Streaming](diffusion/execution_modes.md) covers the
+**Memory Optimization:**
-  diffusion runtime, including batching, step execution, and streaming output.
+
+- **[CPU Offload Guide](diffusion/cpu_offload.md)** - Offload model components to CPU, reduce GPU memory usage
+- **[VAE Parallelism Guide](diffusion/parallelism/vae_parallelism.md)** - Distribute VAE decode work across GPUs for high-resolution images and videos
+- **[Quantization](quantization/overview.md)** - Quantization methods for diffusion, multi-stage omni/TTS, and multi-stage diffusion models
+
+**Extensions:**
+
+- **[LoRA Inference Guide](diffusion/lora.md)** - Low-Rank Adaptation for style customization and fine-tuning
+- **[Frame Interpolation Guide](diffusion/frame_interpolation.md)** - Worker-side post-generation video frame interpolation for smoother motion
+
+**Execution Modes:**
+
+- **[Diffusion Execution Modes](diffusion/execution_modes.md)** - Configure request batching, step execution, continuous batching, and streaming output
+
+**Startup Optimization:**
+
+- **[Multi-Thread Weight Loading](#multi-thread-weight-loading)** - Speed up model startup by loading safetensors shards in parallel
+
+**Advanced Topics:**
+
+- **[Feature Compatibility](feature_compatibility.md)** - How to combine multiple features for maximum performance

docs/user_guide/examples/online_serving/diffusers_pipeline_adapter.md

@@ -28,7 +28,6 @@ However, as we strive to ensure output similarity between vLLM-Omni's diffuser b
 - Qwen/Qwen-Image
 - Tongyi-MAI/Z-Image-Turbo
 - Wan2.2-I2V-A14B-Diffusers
-- Lightricks/LTX-2.5-Diffusers (source-preview dependencies)
 
 If you find that a model not listed above also produces different outputs from running diffusers model directly.
 Please consider file an issue.
@@ -117,36 +116,6 @@ The model loading and inference strictly follows the diffusers library, and they
 Users are encouraged to double-check the model pipeline's interface in [diffusers' official documentation](https://huggingface.co/docs/diffusers/api/pipelines/overview).
 Some particular examples are below.
 
-#### LTX-2.5 (source preview)
-
-Use the gated Diffusers checkpoint
-[`Lightricks/LTX-2.5-Diffusers`](https://huggingface.co/Lightricks/LTX-2.5-Diffusers);
-[`Lightricks/LTX-2.5`](https://huggingface.co/Lightricks/LTX-2.5) contains
-component weights and is not directly loadable by this backend. Accept the
-model terms, authenticate, and install the source-preview dependencies:
-
-```bash
-hf auth login
-python -m pip install --upgrade 'transformers>=5.10.1' \
-  'diffusers @ git+https://github.com/huggingface/diffusers.git@7564fb016dabda0c943416190fc92398c50b1b20'
-```
-
-```bash
-vllm serve Lightricks/LTX-2.5-Diffusers \
-  --omni \
-  --diffusion-load-format diffusers \
-  --dtype bfloat16 \
-  --enable-cpu-offload \
-  --vae-use-tiling
-```
-
-The converted repository loads the distilled transformer. When no `sigmas`,
-`timesteps`, or `num_inference_steps` is supplied, the adapter applies the
-official eight-sigma unguided schedule and returns synchronized 48 kHz audio.
-Support is limited to one-stage distilled T2V; `transformer_full`, two-stage
-upsampling, image/video conditioning, and the diffusion video decoder are not
-supported. The general Diffusers backend [limitations](#limitations) apply.
-
 #### Wan Series
 
 The Wan series video generation models takes `boundary_ratio` and `flow_shift` during model initialization ([ref](https://huggingface.co/docs/diffusers/api/pipelines/wan)), not during inference.

examples/offline_inference/image_to_video/image_to_video.py

@@ -63,9 +63,7 @@ from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.lora.request import LoRARequest
 from vllm_omni.lora.utils import stable_lora_int_id
 from vllm_omni.model_extras import (
-    build_image_to_video_prompt as build_model_image_to_video_prompt,
+    build_image_to_video_prompt,
-)
-from vllm_omni.model_extras import (
     get_extra_body_params,
     get_model_class_name,
 )
@@ -87,22 +85,6 @@ def parse_json_object(value: str, flag_name: str = "argument") -> dict[str, Any]
 parse_profiler_config = functools.partial(parse_json_object, flag_name="--profiler-config")
 
 
-def build_image_to_video_prompt(
-    prompt: str,
-    negative_prompt: str | None,
-    media_inputs: dict[str, Any],
-) -> dict[str, Any]:
-    """Build the canonical request envelope for the shared I2V example."""
-    result: dict[str, Any] = {
-        "prompt": prompt,
-        "modalities": ["video"],
-        "multi_modal_data": media_inputs,
-    }
-    if negative_prompt is not None:
-        result["negative_prompt"] = negative_prompt
-    return result
-
-
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
         description=(
@@ -584,13 +566,10 @@ def main():
         # Preserve the historical empty-prompt behavior for non-LTX examples.
         negative_prompt = ""
     prompt_dict = build_image_to_video_prompt(
+        model_class_name=model_class_name,
         prompt=args.prompt,
         negative_prompt=negative_prompt,
         media_inputs=media_inputs,
-    )
-    prompt_dict = build_model_image_to_video_prompt(
-        model_class_name=model_class_name,
-        prompt=prompt_dict,
         height=height,
         width=width,
         num_frames=num_frames,

examples/offline_inference/text_to_image/README.md

@@ -57,9 +57,8 @@ python examples/offline_inference/text_to_image/text_to_image.py \
   --output lingbot_t2i.png
 ```
 
-The shared T2I entry point selects the `image` output modality. The LingBot
+The LingBot prompt builder selects the `image` output modality and requests one
-pipeline validates that T2I requests produce one frame and do not include an
+frame. Passing an input image is not supported in LingBot T2I mode.
-input image.
 
 ## Quick Start
 

examples/offline_inference/text_to_image/text_to_image.py

@@ -21,9 +21,7 @@ from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.lora.request import LoRARequest
 from vllm_omni.lora.utils import stable_lora_int_id
 from vllm_omni.model_extras import (
-    build_text_to_image_prompt as build_model_text_to_image_prompt,
+    build_text_to_image_prompt,
-)
-from vllm_omni.model_extras import (
     get_extra_body_params,
     get_model_class_name,
     should_init_extra_args_for_non_diffusion_stages,
@@ -58,17 +56,6 @@ def parse_json_object(value: str, flag_name: str = "argument") -> dict[str, Any]
 parse_profiler_config = functools.partial(parse_json_object, flag_name="--profiler-config")
 
 
-def build_text_to_image_prompt(prompt: str, negative_prompt: str | None) -> dict[str, Any]:
-    """Build the canonical request envelope for the shared T2I example."""
-    result: dict[str, Any] = {
-        "prompt": prompt,
-        "modalities": ["image"],
-    }
-    if negative_prompt is not None:
-        result["negative_prompt"] = negative_prompt
-    return result
-
-
 def _normalize_images_for_save(images: list[Any]) -> list[Any]:
     """Convert NumPy diffusion outputs to PIL images before saving."""
     normalized = []
@@ -536,12 +523,9 @@ def main():
     generation_start = time.perf_counter()
 
     prompt_dict = build_text_to_image_prompt(
+        model_class_name=model_class_name,
         prompt=args.prompt,
         negative_prompt=args.negative_prompt,
-    )
-    prompt_dict = build_model_text_to_image_prompt(
-        model_class_name=model_class_name,
-        prompt=prompt_dict,
         height=args.height,
         width=args.width,
     )

examples/offline_inference/text_to_speech/README.md

@@ -24,7 +24,6 @@ list of supported architectures across all modalities, see
 | Qwen3-TTS | `Qwen/Qwen3-TTS-12Hz-1.7B-{CustomVoice,VoiceDesign,Base}` | 2 (talker + code2wav) | ✓ (Base) | ✓ | 3 task variants | 24 kHz |
 | VoxCPM2 | `openbmb/VoxCPM2` | single (native AR) | ✓ | ✓ (online) | continuation | 48 kHz |
 | IndexTTS-2 | `IndexTeam/IndexTTS-2` | 2 (AR talker + S2Mel DiT + BigVGAN) | ✓ (required) | — | emotion control (`--emo-audio`, `--emo-text`, `--emo-vector`) | 22.05 kHz |
-| IndexTTS-2.5 | native `checkpoints/` bundle | 2 (AR talker + EnhancedCodec + S2Mel DiT + BigVGAN) | ✓ (required) | — | multilingual (`--lang`) + emotion control | 22.05 kHz |
 | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | varies | ✓ | ✓ | voice presets | 24 kHz |
 
 ## Common Quick Start
@@ -438,14 +437,11 @@ Streaming is exposed through the online OpenAI Speech API (`stream=true`). See [
 
 ---
 
-## IndexTTS-2 and IndexTTS-2.5
+## IndexTTS-2
 
-Both versions use a 2-stage TTS pipeline and produce 22.05 kHz mono audio. Stage 0 is a GPT AR talker. IndexTTS-2 Stage 1 uses RepCodec semantic embeddings, GPT latent, S2Mel CFM/DiT, and BigVGAN. IndexTTS-2.5 replaces RepCodec with EnhancedCodec and uses the official code-only path (`use_gpt_latent=false`). Every request requires reference audio for zero-shot voice cloning.
+2-stage TTS pipeline (GPT AR talker + S2Mel CFM DiT + BigVGAN vocoder) at 22.05 kHz. Every request requires reference audio for zero-shot voice cloning. Supports emotion conditioning via audio, text, or 8-dim vector.
 
 ### Quick start
-
-IndexTTS-2:
-
 ```bash
 python examples/offline_inference/text_to_speech/indextts2/end2end.py \
     --model IndexTeam/IndexTTS-2 \
@@ -453,24 +449,6 @@ python examples/offline_inference/text_to_speech/indextts2/end2end.py \
     --ref-audio /path/to/reference.wav
 ```
 
-IndexTTS-2 uses its existing frontend and does not require the IndexTTS-2.5 text dependencies. Before running IndexTTS-2.5, install its optional frontend dependencies:
-
-```bash
-pip install 'vllm-omni[indextts2]'
-```
-
-IndexTTS-2.5 with a native repository/lab bundle whose model root contains `checkpoints/`:
-
-```bash
-python examples/offline_inference/text_to_speech/indextts2/end2end.py \
-    --model /path/to/indextts-2.5 \
-    --model-version 2.5 \
-    --lang ja \
-    --text "こんにちは、音声合成のテストです。" \
-    --ref-audio /path/to/reference.wav
-
-```
-
 ### Emotion control
 ```bash
 # Emotion from reference audio
@@ -496,13 +474,10 @@ python examples/offline_inference/text_to_speech/indextts2/end2end.py \
 ```
 
 ### Notes
-- `--ref-audio` is **required** — neither version provides text-only synthesis.
+- `--ref-audio` is **required** — IndexTTS-2 does not support text-only synthesis.
-- IndexTTS-2.5 Stage 0 (AR Talker) generates mel codes from text + reference audio using plain vLLM sampling. It does not reproduce the official default `num_beams=3` beam search. For parity comparisons, run upstream with `num_beams=1`; output quality can differ from the official beam-search result.
+- Stage 0 (AR Talker): GPT-2 generates mel codes from text + reference audio.
-- IndexTTS-2.5 accepts language codes such as `zh`, `en`, `zhen` (mixed Chinese/English), `ja`, and `yue`. `Mandarin` is a vLLM-Omni convenience alias for `zh`. Japanese (`ja`) uses `fugashi` tokenization and produces audio, but it does not automatically expand numbers, dates, or percentages; write those inputs as readable Japanese text before calling the model. Use `--no-text-normalization` only when the input is already normalized.
+- Stage 1 (S2Mel + BigVGAN): CFM DiT converts mel codes to waveform at 22.05 kHz.
-- A seed controls Stage 0 AR sampling and per-request CFM noise. Different concurrent batch compositions do not guarantee a bit-identical waveform.
+- Deploy config: `vllm_omni/deploy/indextts2.yaml`. Stage 1 runs with `enforce_eager: true` (DiT has dynamic shapes).
-- Stage 1 (semantic codec + S2Mel + BigVGAN): CFM/DiT converts the complete semantic-code sequence to waveform.
-- Deploy configs: `vllm_omni/deploy/indextts2.yaml` and `indextts2_5.yaml`.
-- `async_chunk=false` is intentional for IndexTTS-2.5 correctness: S2Mel consumes the completed semantic sequence.
 
 ---
 

examples/offline_inference/text_to_speech/indextts2/end2end.py

@@ -1,16 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Offline inference example for IndexTTS 2.0 and 2.5 via vLLM-Omni.
+"""Offline inference example for IndexTTS2 via vLLM-Omni.
 
-Two-stage pipeline: GPT AR (Stage 0) → semantic codec + S2Mel + BigVGAN
+Two-stage pipeline: GPT AR (Stage 0) → S2Mel + BigVGAN (Stage 1).
-(Stage 1).
 Output is 22050 Hz mono WAV.
 
 Usage:
   python end2end.py \
-    --model /path/to/indextts-2.5 \
+    --model /path/to/IndexTeam/IndexTTS-2 \
-    --model-version 2.5 \
-    --lang zh \
     --text "你好，这是一个语音合成测试。" \
     --ref-audio /path/to/ref.wav
 
@@ -50,10 +47,6 @@ def build_request(
     emo_alpha: float | None = None,
     use_emo_text: bool = False,
     use_random: bool = False,
-    model_type: str = "indextts2",
-    lang: str = "zh",
-    text_normalization: bool = True,
-    tokenizer_file: str | None = None,
 ) -> dict:
     additional: dict = {"text": [text]}
     if ref_audio_path:
@@ -70,44 +63,12 @@ def build_request(
         additional["use_emo_text"] = [True]
     if use_random:
         additional["use_random"] = [True]
-    if model_type == "indextts2_5":
-        additional["lang"] = [lang]
-        additional["text_normalization"] = [text_normalization]
-    prompt_kwargs = {
-        "model_type": model_type,
-        "lang": lang,
-        "text_normalization": text_normalization,
-    }
-    if tokenizer_file is not None:
-        prompt_kwargs["tokenizer_file"] = tokenizer_file
     return {
-        "prompt_token_ids": build_indextts2_prefill_prompt_ids(
+        "prompt_token_ids": build_indextts2_prefill_prompt_ids(model, text),
-            model,
-            text,
-            **prompt_kwargs,
-        ),
         "additional_information": additional,
     }
 
 
-def _get_stage0_tokenizer_file(omni: Omni) -> str | None:
-    """Read the effective tokenizer override from the resolved Stage 0 config."""
-    stage_configs = omni.stage_configs
-    if not stage_configs:
-        return None
-    engine_args = getattr(stage_configs[0], "engine_args", None)
-    if engine_args is None:
-        return None
-    hf_overrides = getattr(engine_args, "hf_overrides", None)
-    if hf_overrides is None and isinstance(engine_args, dict):
-        hf_overrides = engine_args.get("hf_overrides")
-    if hf_overrides is None:
-        return None
-    if hasattr(hf_overrides, "get"):
-        return hf_overrides.get("tokenizer_file")
-    return getattr(hf_overrides, "tokenizer_file", None)
-
-
 def save_audio(waveform: torch.Tensor, path: str, sample_rate: int = 22050) -> None:
     audio_np = waveform.float().numpy()
     sf.write(path, audio_np, sample_rate)
@@ -134,14 +95,9 @@ def extract_audio(mm: dict) -> tuple[torch.Tensor | None, int]:
 
 
 def main(args) -> None:
-    model_type = "indextts2_5" if args.model_version == "2.5" else "indextts2"
-    deploy_config = args.deploy_config
-    if deploy_config is None and args.model_version == "2.5":
-        repo_root = Path(__file__).resolve().parents[4]
-        deploy_config = str(repo_root / "vllm_omni" / "deploy" / "indextts2_5.yaml")
     omni = Omni(
         model=args.model,
-        deploy_config=deploy_config,
+        deploy_config=args.deploy_config,
         stage_init_timeout=args.stage_init_timeout,
     )
 
@@ -170,7 +126,7 @@ def main(args) -> None:
     print(f"Synthesizing: {args.text!r}")
     if args.ref_audio:
         print(f"  ref_audio: {args.ref_audio}")
-    request_kwargs = dict(
+    inputs = build_request(
         model=args.model,
         text=args.text,
         ref_audio_path=args.ref_audio,
@@ -180,17 +136,7 @@ def main(args) -> None:
         emo_alpha=args.emo_alpha,
         use_emo_text=args.use_emo_text,
         use_random=args.use_random,
-        model_type=model_type,
-        lang=args.lang,
-        text_normalization=args.text_normalization,
     )
-    if model_type == "indextts2_5":
-        tokenizer_file = _get_stage0_tokenizer_file(omni)
-        if tokenizer_file is not None:
-            request_kwargs["tokenizer_file"] = tokenizer_file
-    inputs = build_request(**request_kwargs)
-    if model_type == "indextts2_5":
-        inputs["additional_information"]["duration_factor"] = [1.0 / getattr(args, "speed", 1.0)]
 
     for i, omni_out in enumerate(omni.generate(inputs, sampling_params_list=sampling_params)):
         mm = omni_out.multimodal_output
@@ -208,37 +154,8 @@ def main(args) -> None:
 
 
 def parse_args():
-    parser = FlexibleArgumentParser(description="IndexTTS 2.0/2.5 offline inference")
+    parser = FlexibleArgumentParser(description="IndexTTS2 offline inference")
-    parser.add_argument(
+    parser.add_argument("--model", required=True, help="HF model path for IndexTTS2.")
-        "--model",
-        required=True,
-        help="HF model path or native bundle containing checkpoints/.",
-    )
-    parser.add_argument(
-        "--model-version",
-        choices=("2.0", "2.5"),
-        default="2.0",
-    )
-    parser.add_argument(
-        "--lang",
-        default="zh",
-        help=(
-            "IndexTTS 2.5 language code, for example zh/en/zhen/ja/yue; "
-            "zhen is mixed Chinese/English and Mandarin is a vLLM-Omni alias for zh."
-        ),
-    )
-    parser.add_argument(
-        "--no-text-normalization",
-        action="store_false",
-        dest="text_normalization",
-        help="Disable IndexTTS 2.5 text normalization.",
-    )
-    parser.add_argument(
-        "--speed",
-        type=float,
-        default=1.0,
-        help="IndexTTS 2.5 native synthesis speed in [0.5, 2.0]; 2.0 is faster.",
-    )
     parser.add_argument("--text", default="你好，这是IndexTTS2语音合成测试。")
     parser.add_argument("--ref-audio", required=True, help="Reference audio for voice cloning.")
     parser.add_argument("--emo-audio", default=None, help="Emotion reference audio.")
@@ -253,15 +170,7 @@ def parse_args():
     parser.add_argument("--emo-alpha", type=float, default=None, help="Emotion weight in [0, 1].")
     parser.add_argument("--use-emo-text", action="store_true", help="Infer emotion vector from emo-text or text.")
     parser.add_argument("--use-random", action="store_true", help="Use random emotion prototypes.")
-    parser.add_argument(
+    parser.add_argument("--seed", type=int, default=None)
-        "--seed",
-        type=int,
-        default=None,
-        help=(
-            "Seed Stage 0 AR sampling and per-request CFM noise; changing the "
-            "concurrent batch composition may still change the final waveform."
-        ),
-    )
     parser.add_argument(
         "--output-dir",
         default=os.path.join(
@@ -271,10 +180,7 @@ def parse_args():
     )
     parser.add_argument("--deploy-config", default=None)
     parser.add_argument("--stage-init-timeout", type=int, default=600)
-    args = parser.parse_args()
+    return parser.parse_args()
-    if args.model_version == "2.5" and not 0.5 <= args.speed <= 2.0:
-        parser.error("IndexTTS 2.5 --speed must be between 0.5 and 2.0")
-    return args
 
 
 if __name__ == "__main__":

examples/online_serving/diffusers_pipeline_adapter/README.md

@@ -25,7 +25,6 @@ However, as we strive to ensure output similarity between vLLM-Omni's diffuser b
 - Qwen/Qwen-Image
 - Tongyi-MAI/Z-Image-Turbo
 - Wan2.2-I2V-A14B-Diffusers
-- Lightricks/LTX-2.5-Diffusers (source-preview dependencies)
 
 If you find that a model not listed above also produces different outputs from running diffusers model directly.
 Please consider file an issue or submit a PR to fix.
@@ -119,36 +118,6 @@ The model loading and inference strictly follows the diffusers library, and they
 Users are encouraged to double-check the model pipeline's interface in [diffusers' official documentation](https://huggingface.co/docs/diffusers/api/pipelines/overview).
 Some particular examples are below.
 
-#### LTX-2.5 (source preview)
-
-Use the gated Diffusers checkpoint
-[`Lightricks/LTX-2.5-Diffusers`](https://huggingface.co/Lightricks/LTX-2.5-Diffusers);
-[`Lightricks/LTX-2.5`](https://huggingface.co/Lightricks/LTX-2.5) contains
-component weights and is not directly loadable by this backend. Accept the
-model terms, authenticate, and install the source-preview dependencies:
-
-```bash
-hf auth login
-python -m pip install --upgrade 'transformers>=5.10.1' \
-  'diffusers @ git+https://github.com/huggingface/diffusers.git@7564fb016dabda0c943416190fc92398c50b1b20'
-```
-
-```bash
-vllm serve Lightricks/LTX-2.5-Diffusers \
-  --omni \
-  --diffusion-load-format diffusers \
-  --dtype bfloat16 \
-  --enable-cpu-offload \
-  --vae-use-tiling
-```
-
-The converted repository loads the distilled transformer. When no `sigmas`,
-`timesteps`, or `num_inference_steps` is supplied, the adapter applies the
-official eight-sigma unguided schedule and returns synchronized 48 kHz audio.
-Support is limited to one-stage distilled T2V; `transformer_full`, two-stage
-upsampling, image/video conditioning, and the diffusion video decoder are not
-supported. The general Diffusers backend [limitations](#limitations) apply.
-
 #### Wan Series
 
 The Wan series video generation models takes `boundary_ratio` and `flow_shift` during model initialization ([ref](https://huggingface.co/docs/diffusers/api/pipelines/wan)), not during inference.

examples/online_serving/image_to_video/README.md

@@ -1,9 +1,6 @@
 # Image-To-Video
 
-This example demonstrates online image-to-video generation with vLLM-Omni.
+This example demonstrates how to deploy the Wan2.2 image-to-video model for online video generation using vLLM-Omni.
-The startup script defaults to Wan2.2, but `MODEL` can select any supported
-image-to-video model. The existing curl helper remains a concrete Wan2.2
-request through the standard Videos API.
 
 ## Start Server
 
@@ -23,7 +20,9 @@ bash run_server.sh
 
 The script allows overriding:
 - `MODEL` (default: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`)
-- `PORT` (default: `8099`)
+- `PORT` (default: `8091`)
+- `BOUNDARY_RATIO` (default: `0.875`)
+- `FLOW_SHIFT` (default: `12.0`)
 - `CACHE_BACKEND` (default: `none`)
 - `ENABLE_CACHE_DIT_SUMMARY` (default: `0`)
 

examples/online_serving/image_to_video/run_server.sh

@@ -1,12 +1,12 @@
 #!/bin/bash
-# Shared image-to-video server start script
+# Wan2.2 image-to-video server start script
 
 MODEL="${MODEL:-Wan-AI/Wan2.2-I2V-A14B-Diffusers}"
 PORT="${PORT:-8099}"
 CACHE_BACKEND="${CACHE_BACKEND:-none}"
 ENABLE_CACHE_DIT_SUMMARY="${ENABLE_CACHE_DIT_SUMMARY:-0}"
 
-echo "Starting image-to-video server..."
+echo "Starting Wan2.2 I2V server..."
 echo "Model: $MODEL"
 echo "Port: $PORT"
 echo "Cache backend: $CACHE_BACKEND"

examples/online_serving/text_to_image/README.md

@@ -1,9 +1,6 @@
 # Text-To-Image
 
-This example demonstrates online text-to-image generation with vLLM-Omni. The
+This example demonstrates how to deploy Qwen-Image model for online image generation service using vLLM-Omni.
-startup script and Python clients default to Qwen-Image, but `MODEL` can select
-any supported text-to-image model. The existing curl helper remains a concrete
-Qwen-Image request through the standard Images API.
 
 ## Start Server
 
@@ -245,8 +242,8 @@ count, use `size` and `n` rather than `height`, `width`, or
 | `height`                 | int   | None    | Image height in pixels         |
 | `width`                  | int   | None    | Image width in pixels          |
 | `size`                   | str   | None    | Image size (e.g., "1024x1024") |
-| `num_inference_steps`    | int   | model   | Number of denoising steps      |
+| `num_inference_steps`    | int   | 50      | Number of denoising steps      |
-| `true_cfg_scale`         | float | model   | True CFG scale when supported  |
+| `true_cfg_scale`         | float | 4.0     | Qwen-Image CFG scale           |
 | `seed`                   | int   | None    | Random seed (reproducible)     |
 | `negative_prompt`        | str   | None    | Negative prompt                |
 | `num_outputs_per_prompt` | int   | 1       | Number of images to generate   |

examples/online_serving/text_to_image/gradio_demo.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Shared text-to-image Gradio demo for online serving.
+Qwen-Image Gradio Demo for online serving.
 
 Usage:
     python gradio_demo.py [--server http://localhost:8091] [--port 7860]
@@ -77,9 +77,9 @@ def generate_image(
 def create_demo(server_url: str):
     """Create Gradio demo interface."""
 
-    with gr.Blocks(title="Text-to-Image Demo") as demo:
+    with gr.Blocks(title="Qwen-Image Demo") as demo:
-        gr.Markdown("# Text-to-Image Online Generation")
+        gr.Markdown("# Qwen-Image Online Generation")
-        gr.Markdown("Generate images using the model configured on the server")
+        gr.Markdown("Generate images using Qwen-Image model")
 
         with gr.Row():
             with gr.Column(scale=1):
@@ -115,6 +115,7 @@ def create_demo(server_url: str):
                         label="Inference Steps",
                         minimum=10,
                         maximum=100,
+                        # Default steps aligned with run_curl_text_to_image.sh to 100
                         value=100,
                         step=5,
                     )
@@ -172,7 +173,7 @@ def create_demo(server_url: str):
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Shared text-to-image Gradio demo")
+    parser = argparse.ArgumentParser(description="Qwen-Image Gradio Demo")
     parser.add_argument("--server", default="http://localhost:8091", help="Server URL")
     parser.add_argument("--port", type=int, default=7860, help="Gradio port")
     parser.add_argument("--share", action="store_true", help="Create public link")

examples/online_serving/text_to_image/openai_chat_client.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Shared OpenAI-compatible text-to-image generation client.
+Qwen-Image OpenAI-compatible image generation client.
 
 Usage:
     python openai_chat_client.py --prompt "A beautiful landscape" --output output.png
@@ -39,7 +39,7 @@ def generate_image(
         height: Image height in pixels
         width: Image width in pixels
         steps: Number of diffusion steps
-        true_cfg_scale: True CFG scale for models that support it
+        true_cfg_scale: Qwen-Image CFG scale
         seed: Random seed
         negative_prompt: Negative prompt
         num_outputs_per_prompt: Number of images to generate
@@ -70,7 +70,7 @@ def generate_image(
         payload["num_inference_steps"] = steps
     if true_cfg_scale is not None:
         payload["true_cfg_scale"] = true_cfg_scale
-    if negative_prompt is not None:
+    if negative_prompt:
         payload["negative_prompt"] = negative_prompt
     if seed is not None:
         payload["seed"] = seed
@@ -115,15 +115,15 @@ def generate_image(
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Shared text-to-image client")
+    parser = argparse.ArgumentParser(description="Qwen-Image chat client")
     parser.add_argument("--prompt", "-p", default="a cup of coffee on the table", help="Text prompt")
-    parser.add_argument("--output", "-o", default="text_to_image_output.png", help="Output file")
+    parser.add_argument("--output", "-o", default="qwen_image_output.png", help="Output file")
     parser.add_argument("--server", "-s", default="http://localhost:8091", help="Server URL")
-    parser.add_argument("--height", type=int, help="Image height; defaults to the model setting")
+    parser.add_argument("--height", type=int, default=1024, help="Image height")
-    parser.add_argument("--width", type=int, help="Image width; defaults to the model setting")
+    parser.add_argument("--width", type=int, default=1024, help="Image width")
-    parser.add_argument("--steps", type=int, help="Inference steps; defaults to the model setting")
+    parser.add_argument("--steps", type=int, default=50, help="Inference steps")
-    parser.add_argument("--cfg-scale", type=float, help="True CFG scale when supported")
+    parser.add_argument("--cfg-scale", type=float, default=4.0, help="True CFG scale")
-    parser.add_argument("--seed", type=int, help="Random seed")
+    parser.add_argument("--seed", type=int, default=0, help="Random seed")
     parser.add_argument("--negative", help="Negative prompt")
 
     parser.add_argument("--lora-path", default=None, help="Server-local LoRA adapter folder (PEFT format)")

examples/online_serving/text_to_image/run_server.sh

@@ -1,10 +1,10 @@
 #!/bin/bash
-# Shared text-to-image online serving startup script
+# Qwen-Image online serving startup script
 
 MODEL="${MODEL:-Qwen/Qwen-Image}"
 PORT="${PORT:-8091}"
 
-echo "Starting text-to-image server..."
+echo "Starting Qwen-Image server..."
 echo "Model: $MODEL"
 echo "Port: $PORT"
 

examples/online_serving/text_to_speech/README.md

@@ -16,8 +16,7 @@ For the full list of supported architectures across all modalities, see
 |---|---|---|---|---|---|
 | Fish Speech S2 Pro | `fishaudio/s2-pro` | ✓ (`ref_audio`+`ref_text`) | ✓ (PCM stream) | — | ✓ |
 | GLM-TTS | `zai-org/GLM-TTS` | ✓ (`ref_audio`+`ref_text`, required) | ✓ (PCM stream) | — | ✓ |
-| IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✓ (`ref_audio` or uploaded `voice`) | `stream=true` response, non-chunk | uploaded audio voice only; no presets | — |
+| IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✓ (`ref_audio` or uploaded `voice`) | compat only, non-chunk | uploaded audio voice only; no presets | — |
-| IndexTTS-2.5 | native `checkpoints/` bundle | ✓ (`ref_audio` or uploaded `voice`) | `stream=true` response, non-chunk | uploaded audio voice only; no presets | — |
 | Ming-omni-tts | `inclusionAI/Ming-omni-tts-0.5B` | ✓ (`ref_audio` / `speaker_embedding`) | ✓ (PCM stream) | IP labels + structured `instructions` | — |
 | Ming-flash-omni-TTS | `Jonathan1909/Ming-flash-omni-2.0` | — (caption-controlled) | — | caption fields (`instructions`) | — |
 | MOSS-TTS-Nano | `OpenMOSS-Team/MOSS-TTS-Nano` | ✓ (`ref_audio` required) | ✓ (PCM stream) | — | ✓ |
@@ -139,29 +138,15 @@ bash examples/online_serving/text_to_speech/glm_tts/run_gradio_demo.sh
 
 ---
 
-## IndexTTS-2 and IndexTTS-2.5
+## IndexTTS-2
 
-2-stage TTS at 22.05 kHz. Requests use `ref_audio` for voice cloning, or an uploaded audio `voice` from `/v1/audio/voices`. IndexTTS-2.5 uses a multilingual tokenizer, CAMPPlus speaker projection, EnhancedCodec, and code-only Stage 0→1 transfer by default. Both versions support emotion conditioning via `emo_audio`, `emo_text`, or `emo_vector` passed in `extra_params`.
+2-stage TTS (GPT AR + S2Mel CFM DiT + BigVGAN) at 22.05 kHz. Requests use `ref_audio` for voice cloning, or an uploaded audio `voice` from `/v1/audio/voices`. Supports emotion conditioning via `emo_audio`, `emo_text`, or `emo_vector` passed in `extra_params`.
-
-### Prerequisites
-
-IndexTTS-2 uses its existing frontend and does not require the IndexTTS-2.5 text dependencies. Before launching IndexTTS-2.5, install its optional frontend dependencies:
-
-```bash
-pip install 'vllm-omni[indextts2]'
-```
 
 ### Launch
 ```bash
 vllm serve IndexTeam/IndexTTS-2 --omni --trust-remote-code --port 8092
 # or, to pass the bundled deploy config explicitly:
 bash examples/online_serving/text_to_speech/indextts2/run_server.sh
-
-# IndexTTS-2.5 default: use_gpt_latent=false
-MODEL_VERSION=2.5 \
-MODEL=/path/to/indextts-2.5 \
-bash examples/online_serving/text_to_speech/indextts2/run_server.sh
-
 ```
 
 ### Sending requests
@@ -176,35 +161,14 @@ python examples/online_serving/text_to_speech/indextts2/speech_client.py \
     --text "今天心情很好！" \
     --ref-audio /path/to/ref.wav \
     --emo-audio /path/to/happy.wav
-
-# IndexTTS-2.5 Japanese request
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-    --model-version 2.5 \
-    --model /path/to/indextts-2.5 \
-    --lang ja \
-    --text "こんにちは、音声合成のテストです。" \
-    --ref-audio /path/to/ref.wav
-
-# IndexTTS-2.5 native speed control
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-    --model-version 2.5 \
-    --model /path/to/indextts-2.5 \
-    --speed 2.0 \
-    --text "这是两倍速度的语音合成测试。" \
-    --ref-audio /path/to/ref.wav
 ```
 
 ### Notes
 - Output: 22.05 kHz mono WAV.
-- Provide `ref_audio` on the documented raw request path, or pass `voice` only when it names an uploaded audio voice; neither version provides a built-in text-only preset voice.
+- Provide `ref_audio` on the documented raw request path, or pass `voice` only when it names an uploaded audio voice; IndexTTS-2 does not provide a built-in text-only preset voice.
-- IndexTTS-2.5 request controls `lang` and `text_normalization` are carried in `extra_params`; the client does this for `--model-version 2.5`.
-- IndexTTS-2.5 accepts the public `speed` request field in `[0.5, 2.0]`: `2.0` generates shorter, faster speech and `0.5` generates longer, slower speech. This is native Stage 1 duration control, so serving does not apply the generic playback-speed adjustment a second time. IndexTTS-2 does not use this control.
-- IndexTTS-2.5 accepts language codes such as `zh`, `en`, `zhen` (mixed Chinese/English), `ja`, and `yue`. `Mandarin` is a vLLM-Omni convenience alias for `zh`. Japanese (`ja`) uses `fugashi` tokenization and produces audio, but it does not automatically expand numbers, dates, or percentages; callers should first write those inputs as readable Japanese text.
-- A request `seed` controls Stage 0 AR sampling and per-request CFM noise. Different concurrent batch compositions do not guarantee a bit-identical waveform.
-- IndexTTS-2.5 Stage 0 uses plain vLLM sampling and does not reproduce the official default `num_beams=3` beam search. For parity comparisons, run upstream with `num_beams=1`; output quality can differ from the official beam-search result.
 - Emotion params (`emo_audio`, `emo_text`, `emo_vector`, `emo_alpha`, `use_emo_text`, `use_random`) are passed via the `extra_params` field. Official precedence is `use_emo_text` > `emo_vector` > `emo_audio` > same emotion as the speaker reference.
-- IndexTTS-2.5 uses `vllm_omni/deploy/indextts2_5.yaml` with the official code-only Stage 0 to Stage 1 contract.
+- `stream=true` is accepted as an OpenAI-compatible response path, but IndexTTS-2 is not async-chunk streaming; audio is produced after S2Mel receives the full mel-code sequence.
-- For IndexTTS-2.5, set `MODEL` to the local native bundle; asset discovery accepts its nested `checkpoints/` layout.
+- Deploy config: `vllm_omni/deploy/indextts2.yaml` (auto-loaded).
 
 ---
 

examples/online_serving/text_to_speech/indextts2/run_server.sh

@@ -1,37 +1,20 @@
 #!/bin/bash
-# Launch vLLM-Omni server for IndexTTS 2.0 or 2.5.
+# Launch vLLM-Omni server for IndexTTS2
 #
 # Usage from repository root:
 #   examples/online_serving/text_to_speech/indextts2/run_server.sh
-#   MODEL_VERSION=2.5 MODEL=/path/to/native/bundle \
+#   CUDA_VISIBLE_DEVICES=0 PORT=8092 MODEL=/path/to/IndexTeam/IndexTTS-2 examples/online_serving/text_to_speech/indextts2/run_server.sh
-#     examples/online_serving/text_to_speech/indextts2/run_server.sh
 
 set -e
 
 SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
 ROOT_DIR="$(cd -- "$SCRIPT_DIR/../../../.." && pwd)"
 
-MODEL_VERSION="${MODEL_VERSION:-2.0}"
+MODEL="${MODEL:-IndexTeam/IndexTTS-2}"
 PORT="${PORT:-8092}"
+DEPLOY_CONFIG="${DEPLOY_CONFIG:-$ROOT_DIR/vllm_omni/deploy/indextts2.yaml}"
 
-if [[ "$MODEL_VERSION" == "2.5" ]]; then
+echo "Starting IndexTTS2 server with model: $MODEL"
-    if [[ -z "${MODEL:-}" ]]; then
-        echo "Usage: MODEL_VERSION=2.5 MODEL=/path/to/native/bundle bash $0" >&2
-        exit 2
-    fi
-    DEFAULT_DEPLOY_CONFIG="$ROOT_DIR/vllm_omni/deploy/indextts2_5.yaml"
-elif [[ "$MODEL_VERSION" == "2.0" ]]; then
-    MODEL="${MODEL:-IndexTeam/IndexTTS-2}"
-    DEFAULT_DEPLOY_CONFIG="$ROOT_DIR/vllm_omni/deploy/indextts2.yaml"
-else
-    echo "MODEL_VERSION must be 2.0 or 2.5" >&2
-    exit 2
-fi
-
-DEPLOY_CONFIG="${DEPLOY_CONFIG:-$DEFAULT_DEPLOY_CONFIG}"
-
-echo "Starting IndexTTS $MODEL_VERSION server with model: $MODEL"
-echo "Deploy config: $DEPLOY_CONFIG"
 
 FLASHINFER_DISABLE_VERSION_CHECK=1 \
 vllm serve "$MODEL" \

examples/online_serving/text_to_speech/indextts2/speech_client.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""OpenAI-compatible client for IndexTTS 2.0/2.5 via /v1/audio/speech.
+"""OpenAI-compatible client for IndexTTS2 TTS via /v1/audio/speech endpoint.
 
 Examples:
     # With reference audio for voice cloning
@@ -54,44 +54,13 @@ def main() -> None:
     parser.add_argument("--emo-alpha", type=float, default=None, help="Emotion weight in [0, 1]")
     parser.add_argument("--use-emo-text", action="store_true", help="Infer emotion vector from emo-text or text")
     parser.add_argument("--use-random", action="store_true", help="Use random emotion prototypes")
-    parser.add_argument(
+    parser.add_argument("--model", type=str, default="IndexTeam/IndexTTS-2")
-        "--model-version",
-        choices=("2.0", "2.5"),
-        default="2.0",
-    )
-    parser.add_argument("--model", type=str, default=None)
-    parser.add_argument(
-        "--lang",
-        default="zh",
-        help=(
-            "IndexTTS 2.5 language code, for example zh/en/zhen/ja/yue; "
-            "zhen is mixed Chinese/English and Mandarin is a vLLM-Omni alias for zh"
-        ),
-    )
-    parser.add_argument(
-        "--no-text-normalization",
-        action="store_false",
-        dest="text_normalization",
-        help="Disable IndexTTS 2.5 text normalization",
-    )
-    parser.add_argument(
-        "--speed",
-        type=float,
-        default=1.0,
-        help="IndexTTS 2.5 native synthesis speed in [0.5, 2.0]; 2.0 is faster",
-    )
     parser.add_argument("--voice", type=str, default=None, help="Uploaded voice name to use instead of --ref-audio")
     parser.add_argument("--output", type=str, default="output.wav")
     parser.add_argument("--api-base", type=str, default=DEFAULT_API_BASE)
     parser.add_argument("--api-key", type=str, default=DEFAULT_API_KEY)
     parser.add_argument("--response-format", type=str, default="wav")
     args = parser.parse_args()
-    if args.model is None:
-        if args.model_version == "2.5":
-            parser.error("--model is required for IndexTTS 2.5")
-        args.model = "IndexTeam/IndexTTS-2"
-    if args.model_version == "2.5" and not 0.5 <= args.speed <= 2.0:
-        parser.error("IndexTTS 2.5 --speed must be between 0.5 and 2.0")
 
     if not args.ref_audio and not args.voice:
         parser.error("IndexTTS2 requires --ref-audio or --voice for voice cloning")
@@ -128,10 +97,6 @@ def main() -> None:
         extra_params["use_emo_text"] = True
     if args.use_random:
         extra_params["use_random"] = True
-    if args.model_version == "2.5":
-        payload["speed"] = args.speed
-        extra_params["lang"] = args.lang
-        extra_params["text_normalization"] = args.text_normalization
     if extra_params:
         payload["extra_params"] = extra_params
 

pyproject.toml

@@ -84,13 +84,6 @@ forced-aligner = [
     "qwen-asr>=0.0.6",  # faithful Japanese/Korean word segmentation for the TTS forced aligner
 ]
 
-indextts2 = [
-    "fugashi>=1.2.0",
-    "unidic-lite>=1.0.0",
-    "wetext>=0.0.9; sys_platform != 'linux'",
-    "WeTextProcessing; sys_platform == 'linux'",
-]
-
 # Local-test-only extras
 local = [
     "FlagEmbedding",
@@ -334,7 +327,3 @@ byt5 = "byt5"
 # "seeked" is the standard HTML media element DOM event name (fires when a
 # seek completes), used by the word-timestamps Gradio demo. Not a typo.
 seeked = "seeked"
-# IndexTTS 2.5 language codes and speech-emotion-recognition event token.
-fo = "fo"
-ba = "ba"
-SER = "SER"

recipes/IndexTeam/IndexTTS-2_5.md

@@ -1,308 +0,0 @@
-# IndexTTS-2.5 for multilingual voice-cloned TTS on 1x GPU
-
-## Summary
-
-- Vendor: IndexTeam
-- Model: `IndexTeam/IndexTTS-2.5`
-- Task: Multilingual text-to-speech with voice cloning, emotion control, and
-  native speed control
-- Mode: Online serving with the OpenAI-compatible `/v1/audio/speech` API, or
-  offline inference through `Omni`
-- Maintainer: Community
-
-## When to use this recipe
-
-Use this recipe to serve IndexTTS-2.5 as a two-stage TTS system. Stage 0 is an
-autoregressive talker; Stage 1 uses EnhancedCodec, S2Mel CFM/DiT, and BigVGAN
-to produce 22.05 kHz mono speech. Each request supplies synthesis text and
-reference audio, or an uploaded audio voice, for zero-shot voice cloning.
-
-IndexTTS-2.5 adds multilingual text processing and model-native speed control
-to the IndexTTS-2 serving contract.
-
-## References
-
-- Upstream model:
-  [IndexTeam/IndexTTS-2.5 on Hugging Face](https://huggingface.co/IndexTeam/IndexTTS-2.5)
-- Online serving example:
-  [`examples/online_serving/text_to_speech/README.md#indextts-2-and-indextts-25`](../../examples/online_serving/text_to_speech/README.md#indextts-2-and-indextts-25)
-- Offline inference example:
-  [`examples/offline_inference/text_to_speech/README.md#indextts-2-and-indextts-25`](../../examples/offline_inference/text_to_speech/README.md#indextts-2-and-indextts-25)
-- OpenAI-compatible client:
-  [`examples/online_serving/text_to_speech/indextts2/speech_client.py`](../../examples/online_serving/text_to_speech/indextts2/speech_client.py)
-- Standard deploy config:
-  [`vllm_omni/deploy/indextts2_5.yaml`](../../vllm_omni/deploy/indextts2_5.yaml)
-
-## Hardware Support
-
-### GPU
-
-### 1x NVIDIA H20 96GB
-
-#### Environment
-
-- OS: Linux
-- Python: 3.10+
-- Driver / runtime: NVIDIA CUDA environment
-- vLLM version: Match the repository requirements for your checkout
-- vLLM-Omni version or commit: Use the commit you are deploying from
-
-Install vLLM-Omni with the IndexTTS text-processing dependencies:
-
-```bash
-pip install 'vllm-omni[indextts2]'
-```
-
-Obtain the native IndexTTS-2.5 bundle and point `MODEL` at its root. The model
-loader accepts the upstream nested `checkpoints/` layout.
-
-#### Command
-
-Start the standard code-only server from the repository root:
-
-```bash
-MODEL_VERSION=2.5 \
-MODEL=/path/to/indextts-2.5 \
-bash examples/online_serving/text_to_speech/indextts2/run_server.sh
-```
-
-This selects `vllm_omni/deploy/indextts2_5.yaml`, which uses
-`use_gpt_latent=false`. To launch directly:
-
-```bash
-vllm serve /path/to/indextts-2.5 \
-  --omni \
-  --trust-remote-code \
-  --port 8092 \
-  --deploy-config vllm_omni/deploy/indextts2_5.yaml
-```
-
-#### Optional: NVIDIA MPS for higher single-GPU throughput
-
-Stage 0 and Stage 1 run in separate processes on the same GPU. Enabling
-NVIDIA Multi-Process Service (MPS) can improve steady-state throughput by
-allowing work from both stages to overlap more effectively. MPS is an opt-in
-deployment optimization: this recipe does not start it automatically, and the
-gain depends on the request mix and concurrency, so benchmark it with the
-intended workload before enabling it in production.
-
-Pay attention to CUDA device renumbering. For example, if the MPS control
-daemon is started with physical `CUDA_VISIBLE_DEVICES=1`, that GPU is exposed
-to MPS clients as logical device `0`; launch the server with
-`CUDA_VISIBLE_DEVICES=0`, not `1`. Use a dedicated MPS pipe/log directory when
-the host is shared with other services, and stop only the MPS daemon owned by
-this deployment.
-
-#### Verification
-
-Send a multilingual voice-cloning request with the bundled client. Local audio
-paths are converted to base64 data URLs before transmission:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --api-base http://localhost:8092 \
-  --model-version 2.5 \
-  --model /path/to/indextts-2.5 \
-  --lang zh \
-  --text "你好，这是 IndexTTS-2.5 语音合成测试。" \
-  --ref-audio /path/to/reference.wav \
-  --output indextts2_5.wav
-```
-
-##### Native speed control
-
-The public `speed` field is handled natively. Its accepted range is
-`[0.5, 2.0]`; values above `1.0` produce shorter, faster speech. The serving
-adapter converts the API convention to the model-native control with
-`duration_factor = 1.0 / speed`, so `speed=1.25` uses
-`duration_factor=0.8`:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --api-base http://localhost:8092 \
-  --model-version 2.5 \
-  --model /path/to/indextts-2.5 \
-  --speed 1.25 \
-  --text "这是使用模型原生语速控制的测试。" \
-  --ref-audio /path/to/reference.wav \
-  --output indextts2_5_speed.wav
-```
-
-##### Upload and reuse a named voice
-
-Upload a reference recording once when callers should reuse the same voice
-without sending `ref_audio` on every request. `consent` is required by the
-voice-storage API:
-
-```bash
-curl -X POST http://localhost:8092/v1/audio/voices \
-  -F "audio_sample=@/path/to/reference.wav" \
-  -F "consent=user-consent-id" \
-  -F "name=indextts_demo_voice" \
-  -F "speaker_description=IndexTTS-2.5 demonstration voice"
-```
-
-Confirm that the voice was stored, then synthesize with its name instead of a
-reference-audio payload:
-
-```bash
-curl http://localhost:8092/v1/audio/voices
-
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --api-base http://localhost:8092 \
-  --model-version 2.5 \
-  --model /path/to/indextts-2.5 \
-  --voice indextts_demo_voice \
-  --lang zh \
-  --text "你好，这是复用已上传音色的语音合成测试。" \
-  --output indextts2_5_named_voice.wav
-```
-
-There are no built-in text-only preset voices. Each named IndexTTS voice must
-first be created from an uploaded reference recording.
-
-##### Multilingual synthesis and text normalization
-
-The client accepts `zh` (Mandarin), `en` (English), `zhen` (mixed Chinese and
-English), `ja` (Japanese), and `yue` (Cantonese). The following commands use
-the same reference voice across all supported language modes:
-
-```bash
-CLIENT=examples/online_serving/text_to_speech/indextts2/speech_client.py
-MODEL=/path/to/indextts-2.5
-REF_AUDIO=/path/to/reference.wav
-
-python "$CLIENT" --model-version 2.5 --model "$MODEL" \
-  --ref-audio "$REF_AUDIO" --lang zh \
-  --text "你好，这是中文语音合成测试。" --output indextts2_5_zh.wav
-
-python "$CLIENT" --model-version 2.5 --model "$MODEL" \
-  --ref-audio "$REF_AUDIO" --lang en \
-  --text "Hello, this is an English speech synthesis test." \
-  --output indextts2_5_en.wav
-
-python "$CLIENT" --model-version 2.5 --model "$MODEL" \
-  --ref-audio "$REF_AUDIO" --lang zhen \
-  --text "Hello，欢迎使用 IndexTTS 二点五。" --output indextts2_5_zhen.wav
-
-python "$CLIENT" --model-version 2.5 --model "$MODEL" \
-  --ref-audio "$REF_AUDIO" --lang ja \
-  --text "こんにちは、これは日本語の音声合成テストです。" \
-  --output indextts2_5_ja.wav
-
-python "$CLIENT" --model-version 2.5 --model "$MODEL" \
-  --ref-audio "$REF_AUDIO" --lang yue \
-  --text "你好，呢段係粵語語音合成測試。" --output indextts2_5_yue.wav
-```
-
-Text normalization is enabled by default. Disable it only when the input is
-already written exactly as it should be spoken:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --model-version 2.5 --model /path/to/indextts-2.5 \
-  --ref-audio /path/to/reference.wav --lang en --no-text-normalization \
-  --text "The temperature is twenty five degrees." \
-  --output indextts2_5_pre_normalized.wav
-```
-
-For raw HTTP requests, put the same language and text-normalization controls
-under `extra_params`:
-
-```bash
-curl http://localhost:8092/v1/audio/speech \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "/path/to/indextts-2.5",
-    "input": "This is an IndexTTS-2.5 test.",
-    "response_format": "wav",
-    "speed": 1.0,
-    "ref_audio": "data:audio/wav;base64,<BASE64_ENCODED_AUDIO>",
-    "extra_params": {
-      "lang": "en",
-      "text_normalization": true
-    }
-  }' \
-  --output indextts2_5_en.wav
-```
-
-##### Emotion control
-
-IndexTTS-2.5 supports an explicit eight-value emotion vector, an emotion text
-description, or a separate emotion-reference recording. Use one source at a
-time. Vector values are ordered as `happy`, `angry`, `sad`, `afraid`,
-`disgusted`, `melancholic`, `surprised`, and `calm`; each value must be in
-`[0, 1.2]`. `emo_alpha` controls the strength in `[0, 1]`.
-
-Use an explicit emotion vector for deterministic control:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --model-version 2.5 --model /path/to/indextts-2.5 \
-  --ref-audio /path/to/reference.wav --lang zh \
-  --text "今天真是令人开心的一天！" \
-  --emo-vector 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \
-  --emo-alpha 0.8 --output indextts2_5_happy_vector.wav
-```
-
-Infer the emotion from a natural-language description with `use_emo_text`:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --model-version 2.5 --model /path/to/indextts-2.5 \
-  --ref-audio /path/to/reference.wav --lang zh \
-  --text "我们终于完成了这项工作！" \
-  --use-emo-text --emo-text "开心、兴奋而且充满活力" \
-  --emo-alpha 0.8 --output indextts2_5_happy_text.wav
-```
-
-Transfer emotion from a recording while retaining the speaker identity from
-`ref_audio`:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --model-version 2.5 --model /path/to/indextts-2.5 \
-  --ref-audio /path/to/reference.wav --lang zh \
-  --text "请用情绪参考音频中的表达方式朗读这句话。" \
-  --emo-audio /path/to/emotion_reference.wav \
-  --emo-alpha 0.8 --output indextts2_5_emotion_audio.wav
-```
-
-If multiple emotion sources are supplied, the official precedence is
-`use_emo_text` > `emo_vector` > `emo_audio` > the emotion in the speaker
-reference. `--use-random` is also available for exploratory random emotion
-prototypes, but should not be used when reproducible conditioning is required.
-
-For offline inference:
-
-```bash
-python examples/offline_inference/text_to_speech/indextts2/end2end.py \
-  --model /path/to/indextts-2.5 \
-  --model-version 2.5 \
-  --lang zh \
-  --speed 1.0 \
-  --text "你好，这是离线语音合成测试。" \
-  --ref-audio /path/to/reference.wav
-```
-
-#### Notes
-
-- Hardware scope: the standard recipe has been exercised on one NVIDIA H200.
-  It does not claim an unverified throughput or quality result.
-- Audio output: 22.05 kHz mono WAV.
-- Voice cloning: reference audio is required on the documented raw request
-  path. Alternatively, `voice` may name an uploaded audio voice; there is no
-  built-in text-only preset voice.
-- Native speed: serving maps `speed` to the model's duration factor and skips
-  generic waveform speed adjustment, so speed is applied exactly once.
-- Languages: common codes include `zh`, `en`, `zhen` (mixed Chinese/English),
-  `ja`, and `yue`; `Mandarin` is accepted as an alias for `zh`.
-- Japanese normalization: `ja` tokenization does not automatically expand
-  numbers, dates, or percentages. Write these inputs as readable Japanese text
-  before inference.
-- Emotion controls: `use_emo_text`, `emo_vector`, and `emo_audio` are
-  alternative conditioning modes. Their precedence is `use_emo_text` >
-  `emo_vector` > `emo_audio` > the speaker-reference emotion.
-- Sampling difference: Stage 0 uses plain vLLM sampling, not the upstream
-  default `num_beams=3` beam search. Use upstream `num_beams=1` for parity
-  comparisons; output may differ from the official beam-search result.

recipes/MiniMaxAI/MiniMax-H3-RTX-PRO-5000.md

@@ -1,198 +0,0 @@
-# MiniMax-H3 on RTX PRO 5000 Blackwell GPUs
-
-This recipe runs MiniMax-H3 in BF16 on 72 GiB RTX PRO 5000 Blackwell GPUs. It
-contains the validated two-GPU DLO configuration and the recommended resident
-configurations: TP1 x Ulysses2 with 20 resident layers on two GPUs, TP2 x
-Ulysses2 on four GPUs, and TP4 x Ulysses2 on eight GPUs. The four- and
-eight-GPU routes do not require offload.
-
-## Capacity requirements
-
-| Resource | Requirement |
-| --- | ---: |
-| GPUs | 2, 4, or 8 x RTX PRO 5000 Blackwell |
-| GPU HBM | 72 GiB per GPU |
-| Checkpoint storage | 135 GiB per partition |
-| Available system RAM | 200 GiB minimum |
-| Recommended system RAM | 384 GiB |
-
-`FL2VA` and `Ref2VA` are separate checkpoint partitions. Start one server at a
-time on a host sized for the minimum system-memory requirement.
-
-## PCIe topology and GPU order
-
-RTX PRO 5000 does not provide NVLink. Before starting the server, identify the
-closest PCIe pairs and their NUMA affinity:
-
-```bash
-nvidia-smi topo -m
-nvidia-smi nvlink -s
-```
-
-For two GPUs, select the closest PCIe pair on one NUMA node. For four GPUs,
-select all cards from one NUMA node. On the validated host,
-physical GPUs `(0,1)` and `(2,3)` are the two `PXB` pairs on NUMA node 0. The
-order `CUDA_VISIBLE_DEVICES=0,2,1,3` maps the Ulysses groups to those local
-pairs. For eight GPUs, the validated order is `0,4,1,5,2,6,3,7`, with host
-memory interleaved across NUMA nodes 0 and 1. Do not copy these IDs blindly:
-reproduce the same PCIe and NUMA relationships on the target host.
-
-## Recommended serving configurations
-
-### Two GPUs
-
-Two 72 GiB cards require distributed layerwise offload. The validated route
-uses TP1 x Ulysses2, keeps 20 leading DiT layers resident, and streams
-rank-local weights without AllGather. Eager execution avoids regional-compile
-instability on this offload path.
-
-```bash
-export MODEL_ROOT=/path/to/MiniMax-H3
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8091
-
-CUDA_VISIBLE_DEVICES=0,1 \
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-numactl --cpunodebind=0 --membind=0 \
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 2 \
-  --tensor-parallel-size 1 \
-  --usp 2 \
-  --ring 1 \
-  --text-encoder-tp-size 2 \
-  --vae-patch-parallel-size 2 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --diffusion-attention-backend CUDNN_ATTN \
-  --enable-distributed-layerwise-offload \
-  --dlo-no-use-allgather \
-  --dlo-resident-layers 20 \
-  --enforce-eager
-```
-
-### Four GPUs
-
-The validated baseline uses TP2 x Ulysses2, text-encoder TP4, VAE patch
-parallelism 4, and explicit cuDNN BF16 attention. Selecting the backend
-explicitly keeps the recipe independent of platform-default backend changes.
-
-```bash
-export MODEL_ROOT=/path/to/MiniMax-H3
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8091
-
-CUDA_VISIBLE_DEVICES=0,2,1,3 \
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-numactl --cpunodebind=0 --membind=0 \
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 4 \
-  --tensor-parallel-size 2 \
-  --usp 2 \
-  --ring 1 \
-  --text-encoder-tp-size 4 \
-  --vae-patch-parallel-size 4 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --diffusion-attention-backend CUDNN_ATTN
-```
-
-### Eight GPUs
-
-The recommended eight-GPU route uses TP4 x Ulysses2, text-encoder TP8, VAE
-patch parallelism 8, and host-memory interleaving across both NUMA nodes.
-
-```bash
-export MODEL_ROOT=/path/to/MiniMax-H3
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8091
-
-CUDA_VISIBLE_DEVICES=0,4,1,5,2,6,3,7 \
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-numactl --interleave=0,1 \
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 8 \
-  --tensor-parallel-size 4 \
-  --usp 2 \
-  --ring 1 \
-  --text-encoder-tp-size 8 \
-  --vae-patch-parallel-size 8 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --diffusion-attention-backend CUDNN_ATTN
-```
-
-For the four- and eight-GPU resident routes, do not add `--enforce-eager`.
-Warm the server once before measuring so regional compilation is outside the
-measured request. The two-GPU DLO route intentionally remains eager.
-
-For Ref2VA, stop the FL2VA server and restart the same command with
-`MODEL="${MODEL_ROOT}/Ref2VA"`.
-
-## Target-hardware validation
-
-All three configurations were exercised on a PCIe-only, dual-socket host with
-eight RTX PRO 5000 GPUs. The run used PyTorch 2.11.0+cu130, CUDA 13.0, driver
-580.95.05, 1344x768 output, 124 frames, and two warmups.
-
-The four-GPU route also received a five-step profiling validation for GPU
-balance:
-
-| Measurement | Result |
-| --- | ---: |
-| Maximum externally sampled peak | 69,219 MiB (67.60 GiB) per GPU |
-| T2VA worker-reported peak | 65,314 MiB |
-| First-frame FL2VA worker-reported peak | 66,780 MiB |
-| T2VA maximum GPU kernel-time deviation | 0.56% |
-| First-frame FL2VA maximum GPU kernel-time deviation | 0.09% |
-
-The recommended routes produced the following 50-step results. MiniMax-H3
-requested 50 denoise steps and executed 49 denoise updates, so per-step latency
-is `denoise / 49`.
-
-| GPUs | Workload | Parallelism | E2E (s) | Text encode (s) | Denoise (s) | VAE decode (s) | Per step (ms) | Peak memory (GiB) |
-| ---: | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
-| 2 | T2VA | TP1 x Ulysses2, DLO resident 20 | 515.57 | 1.19 | 504.69 | 8.79 | 10,300 | 36.38 |
-| 2 | FL2VA first-frame I2VA | TP1 x Ulysses2, DLO resident 20 | 553.45 | 1.27 | 541.90 | 8.76 | 11,059 | 36.38 |
-| 4 | T2VA | TP2 x Ulysses2 | 284.01 | 0.03 | 278.73 | 4.36 | 5,688 | 67.58 |
-| 4 | FL2VA first-frame I2VA | TP2 x Ulysses2 | 305.72 | 0.28 | 299.85 | 4.36 | 6,119 | 67.58 |
-| 8 | T2VA | TP4 x Ulysses2 | 163.20 | 0.04 | 159.40 | 2.58 | 3,253 | 45.83 |
-| 8 | FL2VA first-frame I2VA | TP4 x Ulysses2 | 171.62 | 0.27 | 167.25 | 2.57 | 3,413 | 45.83 |
-
-Peak memory is the maximum per-GPU value sampled externally with
-`nvidia-smi`. The two-GPU route leaves about 34.7 GiB below the reported
-73,415 MiB device capacity, the four-GPU route leaves about 4.1 GiB, and the
-eight-GPU route leaves about 25.9 GiB. Re-measure memory for longer reference
-inputs, concurrency greater than one, or a different output shape.
-
-## T2VA request example
-
-```bash
-export API_URL="http://127.0.0.1:${PORT}/v1/videos/sync"
-
-curl -sS --max-time 1800 -X POST "${API_URL}" \
-  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
-  -F 'width=1344' \
-  -F 'height=768' \
-  -F 'aspect_ratio=16:9' \
-  -F 'fps=24' \
-  -F 'num_inference_steps=50' \
-  -F 'flow_shift=12' \
-  -F 'seed=1101' \
-  -F 'extra_params={"task":"t2va","duration":5.0,"audio_flow_shift":3.0}' \
-  -o t2va.mp4
-```

recipes/README.md

@@ -50,7 +50,6 @@ recipes/
 | [`inclusionAI/Ming-flash-omni-2.0.md`](./inclusionAI/Ming-flash-omni-2.0.md) | Online serving for multimodal chat + standalone TTS | 4x H100 / 1x H100 80GB |
 | [`inclusionAI/Ming-omni-tts.md`](./inclusionAI/Ming-omni-tts.md) | Offline + online dense Ming TTS/audio generation | 1x H100 80GB / 1x AMD MI300X (ROCm 7.2) |
 | [`IndexTeam/IndexTTS-2.md`](./IndexTeam/IndexTTS-2.md) | Online serving for voice-cloned TTS with optional emotion control | 1x L4 24GB or larger CUDA GPU |
-| [`IndexTeam/IndexTTS-2_5.md`](./IndexTeam/IndexTTS-2_5.md) | Online + offline multilingual voice-cloned TTS with native speed and emotion control | 1x NVIDIA H20 96GB |
 | [`MiniMaxAI/MiniMax-H3.md`](./MiniMaxAI/MiniMax-H3.md) | T2VA, FL2VA, image+audio Ref2VA, and multi-video Ref2VA serving | 1x GPU with CPU offload / 4x B300 |
 | [`MiniMaxAI/MiniMax-H3-MUSA.md`](./MiniMaxAI/MiniMax-H3-MUSA.md) | MiniMax H3 T2VA, FL2VA, and Ref2VA on MUSA | 4x MTT S5000 (TP4 Ref2VA profile) |
 | [`krea/Krea-2.md`](./krea/Krea-2.md) | Text-to-image (Turbo + Raw), offline + online, with LoRA | 1x H100 80GB |

recipes/Tencent/HunyuanImage-3.0-Instruct.md

@@ -82,7 +82,7 @@ example and the DiT sections below.
 - Optional environment variables:
 
 ```bash
-export CACHE_DIT_VERSION=1.3.0
+export CACHE_DIT_VERSION=1.5.0
 ```
 
 HunyuanImage-3.0 sets the diffusion attention backend to `TORCH_SDPA`

requirements/common.txt

@@ -16,7 +16,7 @@ diffusers==0.38.0
 safetensors>=0.8.0
 accelerate==1.12.0
 soundfile>=0.13.1
-cache-dit==1.3.0
+cache-dit==1.5.0
 tqdm>=4.66.0
 torchsde>=0.2.6
 openai-whisper>=20250625

tests/benchmarks/test_bench_tts_cli.py

@@ -51,14 +51,6 @@ def test_load_model_configs(model_configs_path: Path) -> None:
     assert configs["test/ModelA"]["supported_tasks"] == ["voice_clone", "default_voice"]
 
 
-def test_indextts25_is_registered_in_shared_model_configs() -> None:
-    config_path = Path(bench_tts.__file__).with_name("model_configs.yaml")
-    config = bench_tts.load_model_configs(config_path)["IndexTeam/IndexTTS-2.5"]
-
-    assert config["supported_tasks"] == ["voice_clone"]
-    assert config["task_extra_body"]["voice_clone"]["extra_params"]["lang"] == "en"
-
-
 def test_build_bench_args_voice_clone(model_configs_path: Path) -> None:
     configs = bench_tts.load_model_configs(model_configs_path)
     cmd = bench_tts.build_bench_args(
@@ -128,34 +120,6 @@ def test_build_bench_args_wer_eval_adds_flag(model_configs_path: Path) -> None:
     assert "--seed-tts-wer-eval" in cmd
 
 
-def test_build_bench_args_supports_local_model_and_shared_sweep_options(model_configs_path: Path) -> None:
-    configs = bench_tts.load_model_configs(model_configs_path)
-    cmd = bench_tts.build_bench_args(
-        host="localhost",
-        port=8092,
-        model="test/ModelA",
-        served_model_name="/models/indextts25",
-        task="voice_clone",
-        model_cfg=configs["test/ModelA"],
-        locale="en",
-        num_prompts=500,
-        num_warmups=5,
-        request_seed=42,
-        concurrency=8,
-        dataset_path="/data/seed-tts",
-        wer_eval=False,
-        output_dir=None,
-        result_filename=None,
-        extra_cli_args=["--", "--tokenizer", "/models/indextts25/qwen0.6bemo4-merge"],
-    )
-
-    assert cmd[cmd.index("--model") + 1] == "/models/indextts25"
-    assert cmd[cmd.index("--num-warmups") + 1] == "5"
-    assert cmd[-2:] == ["--tokenizer", "/models/indextts25/qwen0.6bemo4-merge"]
-    extra_body = json.loads(cmd[cmd.index("--extra-body") + 1])
-    assert extra_body == {"task_type": "Base", "seed": 42}
-
-
 def test_unsupported_task_exits(model_configs_path: Path, capsys: pytest.CaptureFixture, mocker) -> None:
     # ModelB does not support voice_design
     mocker.patch.object(

tests/config/test_config_factory.py

@@ -2276,16 +2276,6 @@ class TestPlatformOverrides:
         # Stage 2 unaffected fields stay at base
         assert deploy.stages[2].enforce_eager is False
 
-    def test_qwen2_5_omni_xpu_uses_eager_ar_stages(self):
-        deploy_path = Path(get_deploy_config_path("qwen2_5_omni.yaml"))
-
-        deploy = load_deploy_config(deploy_path)
-        deploy = _apply_platform_overrides(deploy, platform="xpu")
-
-        assert deploy.stages[0].enforce_eager is True
-        assert deploy.stages[1].enforce_eager is True
-        assert deploy.stages[2].enforce_eager is True
-
     def test_xpu_overrides(self):
         deploy_path = Path(get_deploy_config_path("qwen3_omni_moe.yaml"))
         if not deploy_path.exists():

tests/core/sched/test_omni_ar_scheduler_logprobs.py

@@ -92,9 +92,6 @@ class _Request:
         self.num_computed_tokens = 0
         self.num_in_flight_tokens = 0
         self.num_output_placeholders = 0
-        # vLLM 0.27 (a0c092ee72): Request gained num_stale_output_tokens to
-        # track in-flight outputs discarded at preemption/streaming-stop.
-        self.num_stale_output_tokens = 0
         self.has_encoder_inputs = False
         self.pooling_params = None
         self.resumable = True

tests/core/sched/test_omni_ar_scheduler_stale_drain.py

@@ -1,223 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-"""Investigation tests for the num_stale_output_tokens drain (59b9e719).
-
-The seed-TTS WER regression appeared with the vLLM 0.27 rebase (nightly
-2946 -> 2949: mean WER 0.2695 -> 0.3936, median 0.0 in both), whose prime
-suspect is the reimplemented async-token discard: vLLM 0.26 drained
-``async_tokens_to_discard`` one arriving frame at a time and suppressed
-only the token append; the 0.27 adaptation seeds
-``num_stale_output_tokens`` with the discarded *placeholder count* and
-drains it by each arriving frame's ``num_tokens_scheduled``, dropping the
-whole per-request update. Whenever seed and arrivals disagree, the counter
-outlives the old segment and the drain consumes *valid* frames of the new
-segment — a hole in the talker's codec stream (garbled audio, text/audio
-mismatch) — or underflows its own assert on the new segment's prefill
-frame (engine-core death mid-step).
-
-The fix seeds the counter from ``num_in_flight_tokens`` — the same
-scheduled-token units the drain subtracts — so every pre-discard frame
-drains exactly its own contribution and new-segment frames can never be
-swallowed. These tests pin that contract one frame at a time; the
-delivered / swallowed observable is whether ``_update_request_with_output``
-runs for the frame.
-"""
-
-from __future__ import annotations
-
-from types import SimpleNamespace
-from unittest.mock import MagicMock
-
-import pytest
-
-# Imports must run in this order: vllm_omni applies patches to vllm.v1.request
-# before Request / StreamingUpdate are bound in this module.
-# isort: off
-import vllm_omni  # noqa: F401 - import for side effects (patch vLLM)
-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 OmniARScheduler
-
-# isort: on
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-def _make_session() -> Request:
-    session = Request(
-        request_id="req-stale-drain-test",
-        prompt_token_ids=[1, 2, 3],
-        sampling_params=SamplingParams(max_tokens=32),
-        pooling_params=None,
-        arrival_time=100.0,
-        block_hasher=None,
-    )
-    session.status = RequestStatus.RUNNING
-    if not hasattr(session, "num_stale_output_tokens"):
-        # vLLM 0.27 defines this Request field; 0.26 (local dev) does not.
-        session.num_stale_output_tokens = 0
-    if not hasattr(session, "num_in_flight_tokens"):
-        session.num_in_flight_tokens = 0
-    return session
-
-
-def _replace_streaming_session(session: Request) -> None:
-    """Run the real stage-0 streaming replacement (the discard/seed site)."""
-    sched = OmniARScheduler.__new__(OmniARScheduler)
-    sched._new_prompt_len_snapshot = {}
-    sched.vllm_config = SimpleNamespace(model_config=SimpleNamespace(stage_id=0))
-    sched.num_waiting_for_streaming_input = 0
-    sched.log_stats = False
-    sched.chunk_transfer_adapter = None
-    sched.skipped_waiting = set()
-    session.status = RequestStatus.WAITING_FOR_STREAMING_REQ
-    update = StreamingUpdate(
-        mm_features=None,
-        prompt_token_ids=[10, 20],
-        max_tokens=32,
-        arrival_time=200.0,
-        sampling_params=SamplingParams(max_tokens=16),
-    )
-    sched._update_request_as_session(session, update)
-    session.status = RequestStatus.RUNNING
-
-
-def _make_drain_sched(session: Request) -> MagicMock:
-    sched = MagicMock()
-    sched.requests = {session.request_id: session}
-    sched.perf_metrics = None
-    sched.structured_output_manager.should_advance.return_value = False
-    sched._update_request_with_output.return_value = ([42], False)
-    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.finished_req_ids_dict = {}
-    sched.make_stats.return_value = None
-    return sched
-
-
-def _run_step(sched: MagicMock, session: Request, *, num_scheduled: int, token: int) -> bool:
-    """Feed one model-runner frame through update_from_output.
-
-    Returns True when the frame's tokens were delivered (the append ran),
-    False when the frame was swallowed by the stale drain.
-    """
-    scheduler_output = MagicMock(spec=SchedulerOutput)
-    scheduler_output.num_scheduled_tokens = {session.request_id: num_scheduled}
-    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 = [[token]]
-    model_runner_output.logprobs = None
-    model_runner_output.prompt_logprobs_dict = {}
-    model_runner_output.pooler_output = 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 = {session.request_id: 0}
-    model_runner_output.routed_experts = None
-    model_runner_output.inter_stage_outputs = None
-
-    sched._update_request_with_output.reset_mock()
-    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
-    return sched._update_request_with_output.called
-
-
-def test_exact_drain_delivers_new_segment_frame() -> None:
-    """One in-flight decode frame at replacement: its late output drains the
-    counter exactly and the new segment's first frame is delivered."""
-    session = _make_session()
-    session.append_output_token_ids([7, 8, 9])
-    session.num_computed_tokens = 6
-    session.num_output_placeholders = 1
-    session.num_in_flight_tokens = 1
-
-    _replace_streaming_session(session)
-    assert session.num_stale_output_tokens == 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 _run_step(sched, session, num_scheduled=1, token=43) is True  # new segment survives
-
-
-def test_two_in_flight_frames_drain_without_swallowing() -> None:
-    """Two in-flight decode frames: both late frames drop, the new segment's
-    first valid frame is delivered. A placeholder-based seed swallowed it
-    whenever fewer frames arrived than placeholders counted (the driver of
-    the seed-TTS WER regression, nightly 2946 -> 2949: 0.2695 -> 0.3936)."""
-    session = _make_session()
-    session.append_output_token_ids([7, 8, 9])
-    session.num_computed_tokens = 7
-    session.num_output_placeholders = 2
-    session.num_in_flight_tokens = 2
-
-    _replace_streaming_session(session)
-    assert session.num_stale_output_tokens == 2
-
-    sched = _make_drain_sched(session)
-    assert _run_step(sched, session, num_scheduled=1, token=42) is False  # late frame 1
-    assert _run_step(sched, session, num_scheduled=1, token=44) is False  # late frame 2
-    assert session.num_stale_output_tokens == 0
-
-    delivered = _run_step(sched, session, num_scheduled=1, token=43)  # new segment, frame 1
-    assert delivered is True, (
-        "valid new-segment frame was swallowed by leftover stale count "
-        f"(num_stale_output_tokens left at {session.num_stale_output_tokens})"
-    )
-
-
-def test_spec_placeholder_asymmetry_does_not_swallow_valid_frame() -> None:
-    """Placeholder counts can exceed scheduled counts (e.g. a draft
-    invalidated before execution): 1 in-flight MTP frame scheduled 2 tokens
-    but contributed 3 placeholders. Seeding from num_in_flight_tokens keeps
-    seed == drain, so the new segment's valid frame is delivered."""
-    session = _make_session()
-    session.append_output_token_ids([7])
-    session.num_computed_tokens = 7
-    session.num_output_placeholders = 3  # 1 sampled + 2 spec placeholders
-    session.num_in_flight_tokens = 2  # the frame actually scheduled 1 + 1 tokens
-
-    _replace_streaming_session(session)
-    assert session.num_stale_output_tokens == 2
-
-    sched = _make_drain_sched(session)
-    assert _run_step(sched, session, num_scheduled=2, token=42) is False  # late MTP frame
-    assert session.num_stale_output_tokens == 0
-    delivered = _run_step(sched, session, num_scheduled=1, token=43)  # new segment, frame 1
-    assert delivered is True, (
-        "valid new-segment frame swallowed after spec placeholder asymmetry "
-        f"(num_stale_output_tokens left at {session.num_stale_output_tokens})"
-    )
-
-
-def test_in_flight_prefill_chunk_drains_exactly_without_underflow() -> None:
-    """An in-flight prefill chunk carries no placeholders but 5 scheduled
-    tokens. The placeholder-based seed missed it entirely (its late output
-    was appended to the rolled-back session) and any leftover counter
-    underflowed 'assert num_stale_output_tokens >= 0' on the chunk's arrival,
-    killing the engine-core step. Seeding from num_in_flight_tokens drops the
-    late chunk and drains to exactly zero."""
-    session = _make_session()
-    session.append_output_token_ids([7, 8, 9])
-    session.num_computed_tokens = 7
-    session.num_output_placeholders = 0  # prefill chunk: no placeholders
-    session.num_in_flight_tokens = 5  # the chunk's scheduled tokens
-
-    _replace_streaming_session(session)
-    assert session.num_stale_output_tokens == 5
-
-    sched = _make_drain_sched(session)
-    assert _run_step(sched, session, num_scheduled=5, token=42) is False  # late prefill chunk dropped
-    assert session.num_stale_output_tokens == 0
-    assert _run_step(sched, session, num_scheduled=1, token=43) is True  # new segment survives

tests/core/sched/test_omni_scheduling_coordinator.py

@@ -126,7 +126,6 @@ class TestFullPayloadCoordinatorSelection(unittest.TestCase):
             ("AudexCode2Wav", "audex_code2wav"),
             ("AudexXCodec1", "audex_xcodec"),
             ("IndexTTS2S2MelDecoder", "indextts2_s2mel_decoder"),
-            ("IndexTTS25S2MelDecoder", "indextts2_5_s2mel_decoder"),
             ("DyninOmniForConditionalGeneration", "token2image"),
             ("DyninOmniForConditionalGeneration", "token2audio"),
         }

tests/diffusion/diffusion_backend/test_diffusers_backend.py

@@ -17,10 +17,6 @@ from vllm_omni.diffusion.data import (
     OmniDiffusionConfig,
 )
 from vllm_omni.diffusion.models.diffusers_adapter import DiffusersAdapterPipeline
-from vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils import (
-    LTX25_DISTILLED_SIGMAS,
-    get_pipeline_utils,
-)
 from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
@@ -184,32 +180,6 @@ class TestPipelineArgumentsHandling:
         with pytest.raises(NotImplementedError):
             DiffusersAdapterPipeline(od_config=od_config)
 
-    def test_ltx25_original_repo_rejected_before_loading(self, mocker):
-        mock_from_pretrained = mocker.patch(
-            "vllm_omni.diffusion.models.diffusers_adapter.pipeline_diffusers_adapter.DiffusionPipeline.from_pretrained"
-        )
-        adapter = DiffusersAdapterPipeline(od_config=_make_od_config(model="Lightricks/LTX-2.5"))
-
-        with pytest.raises(ValueError, match="Lightricks/LTX-2.5-Diffusers"):
-            adapter.load_weights()
-
-        mock_from_pretrained.assert_not_called()
-
-    def test_ltx25_requires_upstream_diffusers_feature(self, mocker):
-        mocker.patch(
-            "vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils._supports_ltx25_diffusers",
-            return_value=False,
-        )
-        mock_from_pretrained = mocker.patch(
-            "vllm_omni.diffusion.models.diffusers_adapter.pipeline_diffusers_adapter.DiffusionPipeline.from_pretrained"
-        )
-        adapter = DiffusersAdapterPipeline(od_config=_make_od_config(model="Lightricks/LTX-2.5-Diffusers"))
-
-        with pytest.raises(ImportError, match="7564fb016dabda0c943416190fc92398c50b1b20"):
-            adapter.load_weights()
-
-        mock_from_pretrained.assert_not_called()
-
     def test_adapter_load_weights_injects_supported_quantization_config(self, mocker):
         (
             FakePipelineQuantizationConfig,
@@ -404,28 +374,6 @@ class TestPipelineArgumentsHandling:
         assert isinstance(output, DiffusionOutput)
         assert output.output == raw_output
 
-    def test_ltx25_preserves_video_and_singular_audio_output(self, tmp_path):
-        frames = torch.zeros(1, 2, 3, 4, 3)
-        audio = torch.zeros(1, 48000)
-        (tmp_path / "model_index.json").write_text(
-            '{"_class_name":"LTX2Pipeline","duration_head":["diffusers","LTX2DurationHead"]}',
-            encoding="utf-8",
-        )
-        od_config = _make_od_config(model=str(tmp_path))
-        adapter = DiffusersAdapterPipeline(od_config=od_config)
-        adapter._pipeline = SimpleNamespace(vocoder=SimpleNamespace(config=SimpleNamespace(output_sampling_rate=48000)))
-        adapter._pipeline_utils = get_pipeline_utils("LTX2Pipeline")
-
-        output = adapter._wrap_output(SimpleNamespace(frames=frames, audio=audio))
-
-        assert isinstance(output.output, dict)
-        assert output.output["payload"]["video"] is frames
-        assert output.output["payload"]["audio"] is audio
-        assert output.output["metadata"] == {"audio": {"sample_rate": 48000}}
-
-        frames_only = adapter._wrap_output(SimpleNamespace(frames=frames, audio=None))
-        assert frames_only.output is frames
-
     def test_adapter_build_call_kwargs(self, mocker):
         class MockPipeline:
             def __call__(
@@ -681,97 +629,6 @@ class TestPipelineArgumentsHandling:
         with pytest.raises(ValueError):
             pipeline.forward(DiffusionRequestBatch(requests=[problematic_request]))
 
-    def test_ltx25_uses_distilled_sampling_defaults_from_renamed_local_directory(self, mocker, tmp_path):
-        class LTX2Pipeline:
-            def __call__(
-                self,
-                prompt=None,
-                num_inference_steps=None,
-                sigmas=None,
-                timesteps=None,
-                guidance_scale=None,
-                audio_guidance_scale=None,
-                stg_scale=None,
-                audio_stg_scale=None,
-                modality_scale=None,
-                audio_modality_scale=None,
-                guidance_rescale=None,
-                audio_guidance_rescale=None,
-                num_frames=None,
-                frame_rate=None,
-                output_type=None,
-                generator=None,
-            ):
-                return None
-
-            def to(self, device):
-                return self
-
-        # Local model directories do not retain their Hub repository name.
-        # Detect 2.5 from its pipeline component metadata instead.
-        (tmp_path / "model_index.json").write_text(
-            '{"_class_name":"LTX2Pipeline","duration_head":["diffusers","LTX2DurationHead"]}',
-            encoding="utf-8",
-        )
-        enable_tiling = mocker.Mock()
-        loaded_pipeline = LTX2Pipeline()
-        loaded_pipeline.vae = SimpleNamespace(enable_tiling=enable_tiling)
-
-        mocker.patch(
-            "vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils._supports_ltx25_diffusers",
-            return_value=True,
-        )
-        mocker.patch(
-            "vllm_omni.diffusion.models.diffusers_adapter.pipeline_diffusers_adapter.DiffusionPipeline.from_pretrained",
-            return_value=loaded_pipeline,
-        )
-        adapter = DiffusersAdapterPipeline(
-            od_config=_make_od_config(
-                model=str(tmp_path),
-                vae_use_tiling=True,
-            )
-        )
-        adapter.load_weights()
-        enable_tiling.assert_called_once_with()
-
-        sampling = OmniDiffusionSamplingParams(
-            num_frames=121,
-            frame_rate=24.0,
-            seed=42,
-            generator_device="cpu",
-        )
-        kwargs = adapter._build_call_kwargs(DiffusionRequestBatch(requests=[_make_request(sampling_params=sampling)]))
-
-        assert kwargs["sigmas"] == LTX25_DISTILLED_SIGMAS
-        assert kwargs["num_inference_steps"] == 8
-        assert kwargs["guidance_scale"] == 1.0
-        assert kwargs["audio_guidance_scale"] == 1.0
-        assert kwargs["stg_scale"] == 0.0
-        assert kwargs["audio_stg_scale"] == 0.0
-        assert kwargs["modality_scale"] == 1.0
-        assert kwargs["audio_modality_scale"] == 1.0
-        assert kwargs["guidance_rescale"] == 0.0
-        assert kwargs["audio_guidance_rescale"] == 0.0
-
-        explicit_steps = OmniDiffusionSamplingParams(num_inference_steps=12, generator_device="cpu")
-        step_kwargs = adapter._build_call_kwargs(
-            DiffusionRequestBatch(requests=[_make_request(sampling_params=explicit_steps)])
-        )
-        assert step_kwargs["num_inference_steps"] == 12
-        assert "sigmas" not in step_kwargs
-
-        custom_sigmas = [1.0, 0.5]
-        explicit_schedule = OmniDiffusionSamplingParams(
-            num_inference_steps=2,
-            sigmas=custom_sigmas,
-            generator_device="cpu",
-        )
-        schedule_kwargs = adapter._build_call_kwargs(
-            DiffusionRequestBatch(requests=[_make_request(sampling_params=explicit_schedule)])
-        )
-        assert schedule_kwargs["num_inference_steps"] == 2
-        assert schedule_kwargs["sigmas"] is custom_sigmas
-
 
 @pytest.mark.advanced_model
 @hardware_test(res={"cuda": "L4"}, num_cards=1)

tests/diffusion/distributed/test_expert_parallel_layout.py

@@ -64,7 +64,6 @@ def test_moe_ep_maps_diffusion_sp_cfg_dp_to_vllm_groups(monkeypatch):
         local_rank,
         backend,
         group_name,
-        use_all2all=False,
     ):
         del backend
         group = _FakeGroup(
@@ -74,7 +73,6 @@ def test_moe_ep_maps_diffusion_sp_cfg_dp_to_vllm_groups(monkeypatch):
             device_communicator=object(),
             reduce_scatter=lambda tensor, **kwargs: tensor,
         )
-        group.use_all2all = use_all2all
         created_groups.append(group)
         return group
 
@@ -123,9 +121,6 @@ def test_moe_ep_maps_diffusion_sp_cfg_dp_to_vllm_groups(monkeypatch):
     assert parallel_state.vllm_parallel_state._PCP.device_communicator is not None
     assert parallel_state.vllm_parallel_state._DP.device_communicator is not None
     assert parallel_state.vllm_parallel_state._EP.device_communicator is not None
-    # vLLM 0.27's MoE oracle asserts the EP communicator has an all2all
-    # manager, which vLLM only builds for groups created with use_all2all.
-    assert parallel_state.vllm_parallel_state._EP.use_all2all is True
     assert hasattr(parallel_state.vllm_parallel_state._PCP, "reduce_scatter")
     assert hasattr(parallel_state.vllm_parallel_state._DP, "reduce_scatter")
     assert hasattr(parallel_state.vllm_parallel_state._EP, "reduce_scatter")
@@ -318,35 +313,3 @@ def test_non_moe_ep_fails_before_vllm_ep_remap(monkeypatch):
     assert parallel_state.vllm_parallel_state._PCP is None
     assert parallel_state.vllm_parallel_state._EP is None
     assert parallel_state._EXPERT_PARALLEL_GROUP_RANKS is None
-
-
-@pytest.mark.cpu
-@pytest.mark.core_model
-def test_init_vllm_group_forwards_use_all2all_only_when_supported(monkeypatch):
-    """use_all2all reaches vLLM only on versions whose API accepts it.
-
-    vLLM 0.27 builds the EP all2all manager only for groups created with
-    use_all2all=True (its MoE oracle then asserts the manager exists —
-    build 2953, HunyuanImage3-DIT accuracy). Older vLLM has no such kwarg,
-    so forwarding it unconditionally would TypeError at init.
-    """
-    received = {}
-
-    def modern(group_ranks, local_rank, backend, group_name=None, use_device_communicator=True, use_all2all=False):
-        received["modern"] = use_all2all
-        return object()
-
-    monkeypatch.setattr(parallel_state.vllm_parallel_state, "init_model_parallel_group", modern)
-    parallel_state.init_vllm_model_parallel_group([[0]], 0, "gloo", "ep", use_all2all=True)
-    assert received["modern"] is True
-
-    parallel_state.init_vllm_model_parallel_group([[0]], 0, "gloo", "tp")
-    assert received["modern"] is False
-
-    def legacy(group_ranks, local_rank, backend, group_name=None, use_device_communicator=True):
-        received["legacy"] = True
-        return object()
-
-    monkeypatch.setattr(parallel_state.vllm_parallel_state, "init_model_parallel_group", legacy)
-    parallel_state.init_vllm_model_parallel_group([[0]], 0, "gloo", "ep", use_all2all=True)
-    assert received["legacy"] is True

tests/diffusion/layers/test_fused_moe.py

@@ -110,7 +110,7 @@ class TestFusedMoEFactory:
         monkeypatch.setitem(
             sys.modules,
             "vllm.model_executor.layers.fused_moe",
-            SimpleNamespace(FusedMoEFactory=mock_vllm_fused_moe),
+            SimpleNamespace(FusedMoE=mock_vllm_fused_moe),
         )
 
         result = fused_moe.FusedMoE(prefix="test", a=1, reduce_results=True)
@@ -139,7 +139,7 @@ class TestFusedMoEFactory:
         monkeypatch.setitem(
             sys.modules,
             "vllm.model_executor.layers.fused_moe",
-            SimpleNamespace(FusedMoEFactory=mocker.MagicMock(return_value=mock_runner)),
+            SimpleNamespace(FusedMoE=mocker.MagicMock(return_value=mock_runner)),
         )
         mocker.patch.object(fused_moe, "current_omni_platform", mocker.MagicMock())
         mock_set_num_tokens = mocker.patch.object(fused_moe, "_set_forward_context_num_tokens")
@@ -169,7 +169,7 @@ class TestFusedMoEFactory:
         monkeypatch.setitem(
             sys.modules,
             "vllm.model_executor.layers.fused_moe",
-            SimpleNamespace(FusedMoEFactory=mocker.MagicMock(return_value=mock_runner)),
+            SimpleNamespace(FusedMoE=mocker.MagicMock(return_value=mock_runner)),
         )
         mocker.patch.object(fused_moe, "current_omni_platform", mocker.MagicMock())
         mock_set_num_tokens = mocker.patch.object(fused_moe, "_set_forward_context_num_tokens")

tests/diffusion/models/lingbot_video/test_request_utils.py

@@ -348,53 +348,3 @@ def test_normalize_lingbot_request_model_size_overrides_sampling_dimensions():
         num_frames=81,
     )
     assert (config.height, config.width) == (480, 480)
-
-
-def test_normalize_lingbot_request_reads_size_from_extra_args():
-    """Regression: /v1/videos delivers `extra_params` via sampling.extra_args.
-
-    The nightly TI2V case sends `extra_params={"size": "320x192"}` with no
-    height/width form fields; the size used to be dropped and the video came
-    out at the 480x480 default (build 2952, LingBot Video MoE Function Test).
-    """
-    config = _normalize(
-        {
-            "prompt": "motion",
-            "modalities": ["video"],
-            "multi_modal_data": {"image": Image.new("RGB", (320, 192))},
-        },
-        num_frames=9,
-        extra_args={"size": "320x192"},
-    )
-    assert config.mode is LingBotGenerationMode.TI2V
-    assert (config.height, config.width) == (192, 320)
-
-
-def test_normalize_lingbot_request_extra_args_size_overrides_sampling_dimensions():
-    config = _normalize(
-        {"prompt": "motion", "modalities": ["video"]},
-        width=480,
-        height=480,
-        num_frames=81,
-        extra_args={"size": "320x192"},
-    )
-    assert (config.height, config.width) == (192, 320)
-
-
-def test_normalize_lingbot_request_reads_width_height_from_extra_args():
-    # Parity with the pre-#5311 pipeline, which honored extra_args height/width.
-    config = _normalize(
-        {"prompt": "motion", "modalities": ["video"]},
-        num_frames=81,
-        extra_args={"width": 320, "height": 192},
-    )
-    assert (config.height, config.width) == (192, 320)
-
-
-def test_resolve_lingbot_output_dimensions_reads_extra_fields():
-    assert resolve_lingbot_output_dimensions(extra_fields={"size": "320x192"}) == (320, 192)
-    assert resolve_lingbot_output_dimensions(
-        sampling_width=480,
-        sampling_height=480,
-        extra_fields={"resolution": "720p", "ratio": "16:9"},
-    ) == (736, 1280)

tests/diffusion/quantization/test_quantization_quality.py

@@ -515,14 +515,8 @@ def test_quantization_quality(config: QualityTestConfig):
 
     generate_fn = _generate_video if config.task == "t2v" else _generate_image
 
-    # Run both arms eager: LPIPS must measure quantization only. With compile
-    # on, a compiler failure in one arm (e.g. inductor's CantSplit on fp8 FLUX)
-    # silently drops that arm to eager while the other stays compiled, and the
-    # metric then includes compile-state differences (fp8_ltx2 rose from ~0.09
-    # to 0.1291 that way in build 2954). Mirrors
-    # vllm_omni/quantization/tools/compare_diffusion_trajectory_similarity.py.
     # --- BF16 baseline ---
-    bl_kwargs: dict = {"model": config.baseline_ref(), "enforce_eager": True}
+    bl_kwargs: dict = {"model": config.baseline_ref()}
     if config.enable_cpu_offload:
         bl_kwargs["enable_cpu_offload"] = True
     omni_bl = Omni(**bl_kwargs)
@@ -534,7 +528,7 @@ def test_quantization_quality(config: QualityTestConfig):
 
     # --- Quantized ---
     quantization = config.quantization_ref()
-    qt_kwargs: dict = {"model": config.quantized_ref(), "enforce_eager": True}
+    qt_kwargs: dict = {"model": config.quantized_ref()}
     if config.enable_cpu_offload:
         qt_kwargs["enable_cpu_offload"] = True
     if quantization is None:

tests/diffusion/test_compile.py

@@ -74,37 +74,3 @@ def test_regionally_compile_does_not_partially_mutate_on_setup_failure(monkeypat
         regionally_compile(model, dynamic=True)
 
     assert [block.forward.__func__ for block in model.transformer_blocks] == original_forwards
-
-
-def test_compiled_block_preserves_forward_signature_for_inspection(monkeypatch):
-    """cache-dit matches blocks via inspect.signature(block.forward).
-
-    Whatever regionally_compile installs as the block forward must stay
-    signature-transparent: parameter names and the return annotation drive
-    cache-dit's ForwardPattern match (build 2954, Multi-GPU Layered job
-    failed when a bare *args/**kwargs wrapper hid them; torch.compile's own
-    wrapper preserves the signature).
-    """
-    import inspect
-
-    import torch
-
-    class _SignatureBlock(nn.Module):
-        def forward(self, hidden_states, encoder_hidden_states=None) -> "torch.Tensor":
-            return hidden_states
-
-    class _Model(nn.Module):
-        _repeated_blocks = ["_SignatureBlock"]
-
-        def __init__(self) -> None:
-            super().__init__()
-            self.blocks = nn.ModuleList([_SignatureBlock()])
-
-    model = _Model()
-    monkeypatch.setattr(compile_module.torch, "compile", lambda fn, *a, **k: fn)
-    regionally_compile(model)
-
-    sig = inspect.signature(model.blocks[0].forward)
-    assert set(sig.parameters.keys()) == {"hidden_states", "encoder_hidden_states"}
-    assert "torch.Tensor" in str(sig.return_annotation)
-    assert model.blocks[0].forward("x") == "x"

tests/diffusion/test_inductor_divisibility_patch.py

@@ -1,83 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Regression tests for the inductor factorable-divisibility patch.
-
-torch 2.13 proves symbolic divisibility with torch's own unevaluated Mod,
-so ``15360*s31 + 15360*s87`` is no longer provably divisible by
-``s31 + s87`` and inductor raises CantSplit on FLUX's fp8 graph (nightly
-builds 2953/2954). The patch adds an exact-polynomial-division proof that
-is sound for every symbol assignment; on torch versions whose original
-check already proves the case (<= 2.11, via sympy.Mod), the patch
-self-extinguishes.
-"""
-
-import pytest
-import sympy
-from torch._inductor.sizevars import SizeVarAllocator
-
-import vllm_omni.patch as patch_module
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
-
-_S1, _S2 = sympy.symbols("s31 s87", positive=True, integer=True)
-
-
-@pytest.mark.parametrize(
-    ("numerator", "denominator", "expected"),
-    [
-        (15360 * _S1 + 15360 * _S2, _S1 + _S2, True),  # the CantSplit expression
-        (7 * _S1 + 7 * _S2, _S1 + _S2, True),
-        (_S1 * _S2, _S2, True),
-        (_S1 * _S2 + _S2, _S2, True),  # s2*(s1 + 1)
-        (3 * _S1, 2 * _S1, False),
-        (_S1 + 1, _S1, False),
-        (15360 * _S1 + 15359 * _S2, _S1 + _S2, False),
-        (sympy.Integer(12), sympy.Integer(4), True),
-        (sympy.Integer(12), sympy.Integer(5), False),
-    ],
-)
-def test_provably_factorable_multiple(numerator, denominator, expected):
-    assert patch_module._provably_factorable_multiple(numerator, denominator) is expected
-
-
-def test_non_polynomial_atoms_never_reach_cancel():
-    from torch.utils._sympy.functions import FloorDiv, ModularIndexing
-
-    assert patch_module._provably_factorable_multiple(FloorDiv(_S1, 2) * 4, _S1) is False
-    assert patch_module._provably_factorable_multiple(ModularIndexing(_S1, 1, 8) * _S2, _S2) is False
-
-
-def test_divisibility_holds_after_patch_regardless_of_torch_version():
-    """The end contract on any torch: the CantSplit expression proves divisible.
-
-    On torch <= 2.11 the original method proves it (patch self-extinguished);
-    on torch >= 2.13 the installed wrapper proves it via exact division.
-    """
-    sv = SizeVarAllocator()
-    method = SizeVarAllocator.statically_known_multiple_of
-    patched = getattr(method, "_vllm_omni_factorable_divisibility", False)
-    assert method(sv, 15360 * _S1 + 15360 * _S2, _S1 + _S2) is True
-    # Negative control must stay non-divisible either way.
-    assert method(sv, 3 * _S1, 2 * _S1) is False
-    if patched:
-        # Wrapper installed: the original alone must have failed the probe.
-        assert patch_module._provably_factorable_multiple(15360 * _S1 + 15360 * _S2, _S1 + _S2) is True
-
-
-def test_patch_installs_when_original_cannot_prove(monkeypatch):
-    """Simulate torch 2.13's unevaluated-Mod prover: the wrapper must install."""
-
-    def weak(self, numerator, denominator):
-        return False
-
-    monkeypatch.setattr(SizeVarAllocator, "statically_known_multiple_of", weak)
-    patch_module._patch_inductor_factorable_divisibility()
-    method = SizeVarAllocator.statically_known_multiple_of
-    assert getattr(method, "_vllm_omni_factorable_divisibility", False) is True
-
-    sv = SizeVarAllocator()
-    assert method(sv, 15360 * _S1 + 15360 * _S2, _S1 + _S2) is True
-    assert method(sv, 3 * _S1, 2 * _S1) is False
-    # Idempotency: a second install call must not wrap the wrapper.
-    patch_module._patch_inductor_factorable_divisibility()
-    assert SizeVarAllocator.statically_known_multiple_of is method

tests/distributed/omni_connectors/test_bagel_shared_memory_connector.py

@@ -33,19 +33,16 @@ BAGEL_CI_DEPLOY = get_deploy_config_path("ci/bagel.yaml")
 # Generated with seed=52, num_inference_steps=15,
 # prompt='Change the grass color to red',
 # input image: 2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg
-# Re-goldened from the vLLM-0.27-aligned output (nightly builds 2953/2954
-# produced bit-identical pixels across two different torch/base images; only
-# (512, 336) moved beyond tolerance vs the main-branch goldens from #5898).
 REFERENCE_PIXELS = [
-    {"position": (100, 100), "rgb": (157, 172, 217)},
+    {"position": (100, 100), "rgb": (156, 172, 217)},
     {"position": (400, 50), "rgb": (105, 144, 217)},
     {"position": (700, 100), "rgb": (118, 159, 232)},
-    {"position": (150, 400), "rgb": (185, 21, 55)},
+    {"position": (150, 400), "rgb": (180, 22, 52)},
-    {"position": (512, 336), "rgb": (205, 215, 191)},
+    {"position": (512, 336), "rgb": (221, 211, 194)},
-    {"position": (700, 400), "rgb": (193, 11, 48)},
+    {"position": (700, 400), "rgb": (192, 10, 46)},
-    {"position": (100, 600), "rgb": (103, 14, 25)},
+    {"position": (100, 600), "rgb": (102, 12, 22)},
-    {"position": (400, 600), "rgb": (159, 28, 48)},
+    {"position": (400, 600), "rgb": (161, 28, 47)},
-    {"position": (700, 600), "rgb": (101, 87, 94)},
+    {"position": (700, 600), "rgb": (100, 87, 94)},
     {"position": (256, 256), "rgb": (181, 201, 221)},
 ]
 
@@ -144,24 +141,13 @@ def _validate_pixels(
     Raises:
         AssertionError: If any pixel differs beyond tolerance.
     """
-    mismatches = []
-    probes = []
     for ref in reference_pixels:
         x, y = ref["position"]
         expected = ref["rgb"]
         actual = image.getpixel((x, y))[:3]
-        probes.append(f'    {{"position": ({x}, {y}), "rgb": {actual}}},')
+        assert all(abs(a - e) <= tolerance for a, e in zip(actual, expected)), (
-        if not all(abs(a - e) <= tolerance for a, e in zip(actual, expected)):
+            f"Pixel mismatch at ({x}, {y}): expected {expected}, got {actual}"
-            mismatches.append(f"({x}, {y}): expected {expected}, got {actual}")
+        )
-    # Report every probe, not just the first mismatch: CI does not upload the
-    # generated image, so this failure message is the only place the full
-    # actual-value table exists when the goldens need to be regenerated.
-    assert not mismatches, (
-        f"Pixel mismatch at {len(mismatches)} probe(s) (tolerance ±{tolerance}):\n  "
-        + "\n  ".join(mismatches)
-        + "\nActual values at all probes (paste as the new reference table if re-goldening):\n"
-        + "\n".join(probes)
-    )
 
 
 def _generate_bagel_img2img(

tests/distributed/omni_connectors/test_chunk_transfer_adapter.py

@@ -1094,12 +1094,6 @@ class _HashableRequest(SimpleNamespace):
     # request; real Requests initialise it to 0 (vllm/v1/request.py).
     num_in_flight_tokens = 0
 
-    # vLLM 0.27 (a0c092ee72): the stale-output counter that replaced
-    # async_tokens_to_discard. update_from_output reads it for every scheduled
-    # request, and real Requests initialise it to 0 (vllm/v1/request.py), so the
-    # double needs it too.
-    num_stale_output_tokens = 0
-
     def __hash__(self):
         return hash(self.request_id)
 

tests/e2e/accuracy/test_diffusers_backend_similarity.py

@@ -42,31 +42,10 @@ from tests.e2e.accuracy.helpers import (
 from tests.helpers.env import run_post_test_cleanup, run_pre_test_cleanup
 from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniServer, OpenAIClientHandler
-from vllm_omni.diffusion.models.diffusers_adapter.pipeline_diffusers_adapter import (
-    CUDA_FLASH_ATTENTION_BACKEND_ATTEMPTS,
-)
 
 pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
 
 
-def _set_matched_attention_backend(pipe: DiffusionPipeline) -> None:
-    """Walk the same backend chain the omni server's diffusers adapter walks.
-
-    The server side (--diffusion-load-format diffusers) resolves its attention
-    backend through the adapter's preference chain, skipping backends that are
-    unavailable on the image (e.g. the FA3 hub kernel has no build variant for
-    the image's torch — build 2953). The reference run must resolve to the
-    same backend or the similarity comparison measures kernel differences.
-    """
-    for backend in CUDA_FLASH_ATTENTION_BACKEND_ATTEMPTS:
-        try:
-            pipe.transformer.set_attention_backend(backend)
-            return
-        except Exception:
-            continue
-    pipe.transformer.set_attention_backend("native")
-
-
 PROMPT = "A photo of a cat sitting on a laptop keyboard, digital art style."
 NEGATIVE_PROMPT = "blurry, low quality"
 WIDTH = 512
@@ -256,7 +235,7 @@ def _run_diffusers_qwen_image(*, model: str, output_path: Path) -> tuple[Image.I
             torch_dtype=torch.bfloat16,
             trust_remote_code=True,
         ).to("cuda")
-        _set_matched_attention_backend(pipe)
+        pipe.transformer.set_attention_backend("_flash_3_hub")
         _diffusers_dummy_run(pipe)
 
         latencies: list[float] = []

tests/e2e/accuracy/test_qwen_image.py

@@ -30,12 +30,6 @@ NUM_INFERENCE_STEPS = 20
 TRUE_CFG_SCALE = 4.0
 SEED = 42
 SSIM_THRESHOLD = 0.97
-# The FA3-hub-matched comparison clears 0.97 with margin. The SDPA-matched
-# fallback (hub kernel unavailable for the image's torch) landed at 0.9687 in
-# build 2954 — the attention math matches but the surrounding stacks (vLLM
-# compiled path vs eager diffusers) still differ slightly. Keep the strict
-# gate for the FA3 path and a slightly looser one for the fallback only.
-SSIM_THRESHOLD_SDPA_FALLBACK = 0.96
 PSNR_THRESHOLD = 30.0
 
 MODEL_2512_ID = "Qwen/Qwen-Image-2512"
@@ -62,44 +56,6 @@ MEAN_ABS_DIFF_THRESHOLD_2512 = 3e-2
 P99_ABS_DIFF_THRESHOLD_2512 = 4e-1
 
 
-_FLASH_ATTN3_HUB_AVAILABLE: bool | None = None
-
-
-def _flash_attn3_hub_available() -> bool:
-    """Whether kernels-community/flash-attn3 has a build for this torch/CUDA.
-
-    The hub repo only ships prebuilt variants for specific torch versions;
-    a base-image torch bump can outrun it (build 2952 failed with "Cannot
-    find a build variant for this system"). When it is unavailable, both
-    sides of the comparison fall back to torch SDPA together — the test
-    compares implementations, so the attention math must stay matched.
-    """
-    global _FLASH_ATTN3_HUB_AVAILABLE
-    if _FLASH_ATTN3_HUB_AVAILABLE is None:
-        try:
-            from kernels import get_kernel
-
-            get_kernel("kernels-community/flash-attn3")
-            _FLASH_ATTN3_HUB_AVAILABLE = True
-        except Exception as exc:
-            print(f"kernels-community/flash-attn3 unavailable ({exc}); using matched torch SDPA on both sides.")
-            _FLASH_ATTN3_HUB_AVAILABLE = False
-    return _FLASH_ATTN3_HUB_AVAILABLE
-
-
-def _omni_server_env() -> dict[str, str] | None:
-    if _flash_attn3_hub_available():
-        return None
-    return {"DIFFUSION_ATTENTION_BACKEND": "TORCH_SDPA"}
-
-
-def _set_reference_attention_backend(pipe: DiffusionPipeline) -> None:
-    if _flash_attn3_hub_available():
-        pipe.transformer.set_attention_backend("_flash_3_hub")
-    else:
-        pipe.transformer.set_attention_backend("native")
-
-
 def _model_name() -> str:
     return os.environ.get(MODEL_ENV_VAR, MODEL_ID)
 
@@ -122,7 +78,7 @@ def _run_vllm_omni_qwen_image(*, model: str, output_path: Path) -> Image.Image:
         "900",
         "--fa-deterministic",
     ]
-    with OmniServer(model, server_args, use_omni=True, env_dict=_omni_server_env()) as omni_server:
+    with OmniServer(model, server_args, use_omni=True) as omni_server:
         response = requests.post(
             f"http://{omni_server.host}:{omni_server.port}/v1/images/generations",
             json={
@@ -158,7 +114,7 @@ def _run_diffusers_qwen_image(*, model: str, output_path: Path) -> Image.Image:
             trust_remote_code=True,
             local_files_only=_local_files_only(model),
         ).to("cuda")
-        _set_reference_attention_backend(pipe)
+        pipe.transformer.set_attention_backend("_flash_3_hub")
         generator = torch.Generator(device="cuda").manual_seed(SEED)
         result = pipe(  # pyright: ignore[reportCallIssue]
             prompt=PROMPT,
@@ -184,7 +140,7 @@ def _run_diffusers_qwen_image(*, model: str, output_path: Path) -> Image.Image:
 
 def _run_vllm_omni_qwen_image_2512(*, model: str, output_path: Path) -> Image.Image:
     server_args = ["--num-gpus", "1", "--stage-init-timeout", "300", "--init-timeout", "900"]
-    with OmniServer(model, server_args, use_omni=True, env_dict=_omni_server_env()) as omni_server:
+    with OmniServer(model, server_args, use_omni=True) as omni_server:
         response = requests.post(
             f"http://{omni_server.host}:{omni_server.port}/v1/images/generations",
             json={
@@ -220,7 +176,7 @@ def _run_diffusers_qwen_image_2512(*, model: str, output_path: Path) -> Image.Im
             trust_remote_code=True,
             local_files_only=_local_files_only(model),
         ).to("cuda")
-        _set_reference_attention_backend(pipe)
+        pipe.transformer.set_attention_backend("_flash_3_hub")
         generator = torch.Generator(device="cuda").manual_seed(SEED_2512)
         result = pipe(  # pyright: ignore[reportCallIssue]
             prompt=PROMPT_2512,
@@ -259,7 +215,7 @@ def test_qwen_image_matches_diffusers(accuracy_artifact_root: Path) -> None:
         diffusers_image=diffusers_output,
         width=WIDTH,
         height=HEIGHT,
-        ssim_threshold=SSIM_THRESHOLD if _flash_attn3_hub_available() else SSIM_THRESHOLD_SDPA_FALLBACK,
+        ssim_threshold=SSIM_THRESHOLD,
         psnr_threshold=PSNR_THRESHOLD,
     )
 

tests/e2e/online_serving/test_voxtral_tts_expansion.py

@@ -118,17 +118,6 @@ def test_speech_instructions(omni_server, openai_client) -> None:
                 "response_format": "wav",
                 "timeout": 120.0,
                 "instructions": instruction,
-                # Re-grade a below-threshold result with whisper large-v3 before
-                # failing, so a miss is attributable rather than ambiguous. Same
-                # opt-in as test_higgs_audio_v3.py.
-                #
-                # Note: for this input it does NOT rescue the check — small and
-                # large-v3 both transcribe "The boy was there when sun rose."
-                # (sim=0.87 each) against an input of "...when the sun rose.",
-                # i.e. the TTS really does drop the article. That is the value
-                # here: two independent ASR models agreeing rules out a grader
-                # flake and points at the model.
-                "transcript_escalation_model": "large-v3",
             }
         )
 

tests/engine/test_arg_utils.py

@@ -6,9 +6,6 @@ explicitly patch values that differ from vLLM.
 
 import argparse
 import inspect
-import json
-import os
-import shutil
 from types import SimpleNamespace
 from unittest.mock import Mock
 
@@ -197,155 +194,6 @@ def test_qwen3_tts_code2wav_injects_max_position_embeddings(monkeypatch):
     }
 
 
-def test_patch_missing_local_hf_config(tmp_path):
-    """Native model bundles may only contain a checkpoints/ directory."""
-    (tmp_path / "checkpoints").mkdir()
-    args = object.__new__(OmniEngineArgs)
-    args.model = str(tmp_path)
-    args.model_arch = "IndexTTS25TalkerForConditionalGeneration"
-    args.hf_config_path = None
-
-    try:
-        args._patch_empty_hf_config("indextts2_5")
-
-        assert args.hf_config_path is not None
-        config_path = args.hf_config_path + "/config.json"
-        with open(config_path, encoding="utf-8") as config_file:
-            config = json.load(config_file)
-        assert config == {
-            "model_type": "indextts2_5",
-            "architectures": ["IndexTTS25TalkerForConditionalGeneration"],
-        }
-    finally:
-        if hasattr(args, "_temp_config_dir"):
-            shutil.rmtree(args._temp_config_dir, ignore_errors=True)
-
-
-@pytest.mark.parametrize(
-    "config_entry",
-    ["malformed", "directory", "broken_symlink", "permission_error"],
-)
-def test_non_missing_local_hf_config_error_reaches_parent_loader(tmp_path, monkeypatch, config_entry):
-    """Non-missing config errors must reach vLLM's normal loader."""
-    config_path = tmp_path / "config.json"
-    if config_entry == "malformed":
-        config_path.write_text("{not valid json", encoding="utf-8")
-        loader_error = json.JSONDecodeError("invalid config", "{not valid json", 1)
-    elif config_entry == "directory":
-        config_path.mkdir()
-        loader_error = IsADirectoryError(str(config_path))
-    elif config_entry == "broken_symlink":
-        config_path.symlink_to(tmp_path / "missing-target.json")
-        loader_error = FileNotFoundError(str(config_path))
-    else:
-        config_path.write_text("{}", encoding="utf-8")
-        loader_error = PermissionError(str(config_path))
-
-    parent_inputs = {}
-
-    def controlled_get_config_dict(cls, model, **kwargs):
-        if model == str(tmp_path):
-            raise loader_error
-        with open(os.path.join(model, "config.json"), encoding="utf-8") as config_file:
-            return json.load(config_file), {}
-
-    def fake_parent_create_model_config(self):
-        parent_inputs["called"] = True
-        parent_inputs["model"] = self.model
-        parent_inputs["hf_config_path"] = self.hf_config_path
-        config_root = self.hf_config_path or self.model
-        parent_inputs["config_root"] = config_root
-        return PretrainedConfig.get_config_dict(config_root)[0]
-
-    monkeypatch.setattr(PretrainedConfig, "get_config_dict", classmethod(controlled_get_config_dict))
-    monkeypatch.setattr(OmniEngineArgs, "_ensure_omni_models_registered", lambda self: True)
-    monkeypatch.setattr(EngineArgs, "create_model_config", fake_parent_create_model_config)
-    monkeypatch.setattr(
-        OmniModelConfig,
-        "from_vllm_model_config",
-        classmethod(lambda cls, model_config, **omni_kwargs: model_config),
-    )
-
-    args = OmniEngineArgs(
-        model=str(tmp_path),
-        model_arch="IndexTTS25TalkerForConditionalGeneration",
-    )
-
-    with pytest.raises(type(loader_error)) as exc_info:
-        args.create_model_config()
-
-    assert str(exc_info.value) == str(loader_error)
-    assert parent_inputs["called"] is True
-    assert parent_inputs["model"] == str(tmp_path)
-    assert parent_inputs["config_root"] == str(tmp_path)
-    assert parent_inputs["hf_config_path"] is None
-    assert args.model == str(tmp_path)
-    assert args.hf_config_path is None
-    assert not hasattr(args, "_temp_config_dir")
-
-
-def test_patch_valid_hf_config_without_model_type_preserves_keys(tmp_path):
-    """A valid partial config should be copied before required keys are added."""
-    original_config = {
-        "hidden_size": 1024,
-        "custom_key": {"nested": True},
-    }
-    (tmp_path / "config.json").write_text(json.dumps(original_config), encoding="utf-8")
-    args = object.__new__(OmniEngineArgs)
-    args.model = str(tmp_path)
-    args.model_arch = "IndexTTS25TalkerForConditionalGeneration"
-    args.hf_config_path = None
-
-    try:
-        args._patch_empty_hf_config("indextts2_5")
-
-        assert args.hf_config_path is not None
-        with open(os.path.join(args.hf_config_path, "config.json"), encoding="utf-8") as config_file:
-            patched_config = json.load(config_file)
-        assert patched_config["hidden_size"] == original_config["hidden_size"]
-        assert patched_config["custom_key"] == original_config["custom_key"]
-        assert patched_config["model_type"] == "indextts2_5"
-        assert patched_config["architectures"] == ["IndexTTS25TalkerForConditionalGeneration"]
-    finally:
-        if hasattr(args, "_temp_config_dir"):
-            shutil.rmtree(args._temp_config_dir, ignore_errors=True)
-
-
-def test_remote_hf_config_error_reaches_parent_loader(monkeypatch):
-    """Remote resolution failures must stay on vLLM's normal error path."""
-    loader_error = OSError("remote config resolution failed")
-    parent_inputs = {}
-
-    def controlled_get_config_dict(cls, model, **kwargs):
-        raise loader_error
-
-    def fake_parent_create_model_config(self):
-        parent_inputs["called"] = True
-        parent_inputs["model"] = self.model
-        parent_inputs["hf_config_path"] = self.hf_config_path
-        return PretrainedConfig.get_config_dict(self.model)[0]
-
-    monkeypatch.setattr(PretrainedConfig, "get_config_dict", classmethod(controlled_get_config_dict))
-    monkeypatch.setattr(OmniEngineArgs, "_ensure_omni_models_registered", lambda self: True)
-    monkeypatch.setattr(EngineArgs, "create_model_config", fake_parent_create_model_config)
-
-    args = OmniEngineArgs(
-        model="remote/model",
-        model_arch="IndexTTS25TalkerForConditionalGeneration",
-    )
-
-    with pytest.raises(OSError) as exc_info:
-        args.create_model_config()
-
-    assert str(exc_info.value) == str(loader_error)
-    assert parent_inputs["called"] is True
-    assert parent_inputs["model"] == "remote/model"
-    assert parent_inputs["hf_config_path"] is None
-    assert args.model == "remote/model"
-    assert args.hf_config_path is None
-    assert not hasattr(args, "_temp_config_dir")
-
-
 def test_stage_specific_text_config_override():
     """Stage swap must refresh hf_text_config, dependent attrs, and model_arch_config."""
     vllm_config = EngineArgs().create_model_config()

tests/engine/test_async_omni_engine_stage_init.py

@@ -412,7 +412,7 @@ def test_stage_runtime_passes_log_stats_to_llm_replica_launch(monkeypatch):
     monkeypatch.setattr(
         runtime_mod.StageEngineCoreClientBase,
         "make_async_mp_client",
-        lambda **kwargs: captured.__setitem__("client_log_stats", kwargs["log_stats"]) or stage_client,
+        lambda **kwargs: (captured.__setitem__("client_log_stats", kwargs["log_stats"]) or stage_client),
     )
 
     assert runtime._initialize_local_llm_replica(plan, stage_init_timeout=1) is stage_client
@@ -807,9 +807,9 @@ def test_build_stage0_input_processor_uses_omni_input_preprocessor(monkeypatch):
     import vllm_omni.engine.stage_init_utils as init_mod
 
     class DummyInputProcessor:
-        def __init__(self, vllm_config, renderer=None):
+        def __init__(self, vllm_config):
             self.vllm_config = vllm_config
-            self.renderer = renderer or object()
+            self.renderer = object()
             self.input_preprocessor = None
 
     class DummyOmniInputPreprocessor:
@@ -828,50 +828,6 @@ def test_build_stage0_input_processor_uses_omni_input_preprocessor(monkeypatch):
     assert input_processor.input_preprocessor.renderer is input_processor.renderer
 
 
-def test_build_stage0_input_processor_does_not_resolve_tokenizer_when_skipped(
-    monkeypatch,
-):
-    import vllm_omni.engine.stage_init_utils as init_mod
-
-    token_only_renderer = object()
-    seen = {}
-
-    class DummyInputProcessor:
-        def __init__(self, vllm_config, renderer=None):
-            if renderer is None:
-                raise AssertionError("skip_tokenizer_init must supply a renderer")
-            seen["renderer"] = renderer
-            self.renderer = renderer
-            self.input_preprocessor = None
-
-    class DummyOmniInputPreprocessor:
-        def __init__(self, vllm_config, renderer=None):
-            seen["preprocessor_renderer"] = renderer
-
-    monkeypatch.setattr(init_mod, "InputProcessor", DummyInputProcessor)
-    monkeypatch.setattr(
-        init_mod,
-        "_build_token_only_renderer",
-        lambda _config: token_only_renderer,
-    )
-    monkeypatch.setattr(
-        init_mod,
-        "OmniInputPreprocessor",
-        DummyOmniInputPreprocessor,
-    )
-
-    config = types.SimpleNamespace(
-        model_config=types.SimpleNamespace(
-            skip_tokenizer_init=True,
-            try_get_generation_config=lambda: {},
-        )
-    )
-    build_stage0_input_processor(config)
-
-    assert seen["renderer"] is token_only_renderer
-    assert seen["preprocessor_renderer"] is token_only_renderer
-
-
 def test_inject_kv_stage_info_infers_sender_tp_topology():
     from vllm_omni.engine.stage_init_utils import inject_kv_stage_info
 

tests/engine/test_omni_request.py

@@ -52,7 +52,6 @@ def test_positional_construction_matches_base_request():
     assert req.external_req_id is None
     assert req.additional_information is None
     assert req.prompt_embeds_payload is None
-    assert req.num_stale_output_tokens == 0
 
 
 def test_positional_prompt_embeds_does_not_collide():

tests/engine/test_stage_engine_startup_cache_env.py

@@ -1,47 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Regression tests for per-replica compile-cache isolation at spawn time.
-
-Replicas of the same stage compile the same model concurrently, and vLLM
-0.27's AOT compile (TritonBundler) races on the shared VLLM_CACHE_ROOT —
-a replica then dies with 'Cubin file saved by TritonBundler not found'
-(build 2954, Omni Perf Multi-Replica). Replica 0 must keep the default
-shared cache; additional replicas must spawn with an isolated cache dir,
-and the parent env must be restored afterwards.
-"""
-
-import os
-
-import pytest
-
-from vllm_omni.engine.stage_engine_startup import scoped_spawn_device_env
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-def test_replica_zero_keeps_shared_cache_root(monkeypatch):
-    monkeypatch.delenv("VLLM_CACHE_ROOT", raising=False)
-    with scoped_spawn_device_env(None, None, stage_id=1, replica_id=0):
-        assert "VLLM_CACHE_ROOT" not in os.environ
-    assert "VLLM_CACHE_ROOT" not in os.environ
-
-
-def test_additional_replica_gets_isolated_cache_root(monkeypatch):
-    monkeypatch.setenv("VLLM_CACHE_ROOT", "/tmp/base-cache")
-    with scoped_spawn_device_env(None, None, stage_id=2, replica_id=1):
-        assert os.environ["VLLM_CACHE_ROOT"] == os.path.join("/tmp/base-cache", "omni_stage2_replica1")
-    assert os.environ["VLLM_CACHE_ROOT"] == "/tmp/base-cache"
-
-
-def test_isolated_cache_root_defaults_without_env(monkeypatch):
-    monkeypatch.delenv("VLLM_CACHE_ROOT", raising=False)
-    with scoped_spawn_device_env(None, None, stage_id=0, replica_id=3):
-        assert os.environ["VLLM_CACHE_ROOT"].endswith("omni_stage0_replica3")
-        assert os.environ["VLLM_CACHE_ROOT"].startswith(os.path.expanduser("~/.cache/vllm"))
-    assert "VLLM_CACHE_ROOT" not in os.environ
-
-
-def test_missing_ids_leave_env_untouched(monkeypatch):
-    monkeypatch.delenv("VLLM_CACHE_ROOT", raising=False)
-    with scoped_spawn_device_env(None, None):
-        assert "VLLM_CACHE_ROOT" not in os.environ

tests/entrypoints/openai_api/test_serving_speech.py

@@ -743,48 +743,6 @@ class TestTTSMethods:
         assert speech_server._is_tts is False
         assert speech_server._tts_stage is None
 
-    @pytest.mark.parametrize(
-        ("model_type", "expected_speed"),
-        [("indextts2_5", 1.0), ("qwen3_tts", 2.0)],
-    )
-    def test_audio_encode_speed_respects_adapter_native_control(
-        self,
-        speech_server,
-        model_type,
-        expected_speed,
-    ):
-        speech_server._tts_model_type = model_type
-        request = OpenAICreateSpeechRequest(input="Hello", speed=2.0)
-
-        assert speech_server._audio_encode_speed(request) == expected_speed
-
-    @pytest.mark.asyncio
-    async def test_non_streaming_native_speed_skips_generic_audio_adjustment(
-        self,
-        speech_server,
-        mocker: MockerFixture,
-    ):
-        async def mock_generate():
-            yield create_mock_audio_output_for_test()
-
-        speech_server._tts_model_type = "indextts2_5"
-        mocker.patch.object(
-            speech_server,
-            "_prepare_speech_generation",
-            new=mocker.AsyncMock(return_value=("speech-native-speed", mock_generate(), {})),
-        )
-        create_audio = mocker.patch.object(
-            speech_server,
-            "create_audio",
-            return_value=SimpleNamespace(audio_data=b"dummy", media_type="audio/wav"),
-        )
-
-        await speech_server._generate_audio_bytes(OpenAICreateSpeechRequest(input="Hello", speed=2.0))
-
-        audio_obj = create_audio.call_args.args[0]
-        assert isinstance(audio_obj, CreateAudio)
-        assert audio_obj.speed == 1.0
-
     def test_is_tts_detection_with_tts_stage(self, mocker: MockerFixture):
         """Test TTS model detection when TTS stage exists."""
         mock_engine_client = mocker.MagicMock()
@@ -2220,17 +2178,17 @@ class TestStreamingProtocolValidation:
         assert req.is_streaming() is False
 
     def test_stream_validation_errors(self):
-        """The request schema validates formats; model-aware speed checks happen in serving."""
+        """stream=True requires response_format in ('pcm', 'wav') and speed=1.0."""
         with pytest.raises(ValidationError, match="requires response_format='pcm' or 'wav'"):
             OpenAICreateSpeechRequest(input="Hello", stream=True, response_format="mp3")
-        request = OpenAICreateSpeechRequest(input="Hello", stream=True, response_format="pcm", speed=2.0)
+        with pytest.raises(ValidationError, match="Speed adjustment is not supported"):
-        assert request.speed == 2.0
+            OpenAICreateSpeechRequest(input="Hello", stream=True, response_format="pcm", speed=2.0)
 
     def test_stream_format_audio_validation_errors(self):
         with pytest.raises(ValidationError, match="requires response_format='pcm' or 'wav'"):
             OpenAICreateSpeechRequest(input="Hello", stream_format="audio", response_format="mp3")
-        request = OpenAICreateSpeechRequest(input="Hello", stream_format="audio", response_format="pcm", speed=2.0)
+        with pytest.raises(ValidationError, match="Speed adjustment is not supported"):
-        assert request.speed == 2.0
+            OpenAICreateSpeechRequest(input="Hello", stream_format="audio", response_format="pcm", speed=2.0)
 
     def test_stream_valid(self):
         """stream=True + response_format in ('pcm', 'wav') + speed=1.0 is accepted as SSE."""
@@ -2313,18 +2271,6 @@ class TestStreamingResponse:
             models=mock_models,
             request_logger=mocker.MagicMock(),
         )
-        speech_server._tts_model_type = None
-        speech_server._adapter = None
-        speech_server._diffusion_mode = False
-        mocker.patch.object(speech_server, "_uses_native_speed_control", return_value=False)
-        mocker.patch.object(
-            speech_server,
-            "create_error_response",
-            side_effect=lambda message, **_: JSONResponse(
-                status_code=400,
-                content={"error": {"message": message}},
-            ),
-        )
 
         original_create_speech = speech_server.create_speech
         sig = signature(original_create_speech)
@@ -2339,7 +2285,6 @@ class TestStreamingResponse:
 
         app = FastAPI()
         app.add_api_route("/v1/audio/speech", speech_server.create_speech, methods=["POST"], response_model=None)
-        app.state.speech_server = speech_server
         return app
 
     @staticmethod
@@ -2448,39 +2393,6 @@ class TestStreamingResponse:
         assert response.status_code in (400, 422)
         assert "audio/" not in response.headers.get("content-type", "")
 
-    @pytest.mark.parametrize("stream_format", ["sse", "audio"])
-    def test_native_speed_control_accepts_streaming_speed(
-        self,
-        streaming_app,
-        mocker: MockerFixture,
-        stream_format: str,
-    ):
-        speech_server = streaming_app.state.speech_server
-        speech_server._tts_model_type = "indextts2_5"
-        speech_server._uses_native_speed_control.return_value = True
-
-        async def prepare(request, request_id=None, **kwargs):
-            del kwargs
-            generator = speech_server.engine_client.generate(prompt={}, request_id=request_id)
-            return request_id, generator, {"duration_factor": [1.0 / request.speed]}
-
-        mocker.patch.object(speech_server, "_prepare_speech_generation", side_effect=prepare)
-
-        client = TestClient(streaming_app)
-        response = client.post(
-            "/v1/audio/speech",
-            json={
-                "input": "Hello",
-                "stream_format": stream_format,
-                "response_format": "pcm",
-                "speed": 2.0,
-            },
-        )
-
-        assert response.status_code == 200
-        expected_media_type = "text/event-stream" if stream_format == "sse" else "audio/pcm"
-        assert expected_media_type in response.headers["content-type"]
-
     @pytest.fixture
     def erroring_streaming_app(self, mocker: MockerFixture):
         """Test app whose mock engine raises mid-stream, to exercise the SSE error event."""
@@ -2704,18 +2616,6 @@ def test_streaming_speech_session_config_accepts_non_streaming_mode():
     assert config.non_streaming_mode is True
 
 
-def test_streaming_speech_session_config_scopes_native_speed_to_http():
-    with pytest.raises(
-        ValidationError,
-        match="native speed control is only available through HTTP streaming",
-    ):
-        StreamingSpeechSessionConfig(
-            stream_audio=True,
-            response_format="pcm",
-            speed=1.5,
-        )
-
-
 class TestAsyncOmniSupportedTasks:
     """Test that AsyncOmni reports correct supported tasks based on output modalities."""
 

tests/entrypoints/openai_api/test_tts_adapter.py

@@ -4,9 +4,6 @@
 Pure-Python registry/resolution logic; no model or GPU resources are loaded.
 """
 
-import asyncio
-from types import SimpleNamespace
-
 import pytest
 
 from vllm_omni.entrypoints.openai.tts_adapters import (
@@ -14,19 +11,9 @@ from vllm_omni.entrypoints.openai.tts_adapters import (
     ARTTSAdapter,
     DiffusionTTSAdapter,
     all_tts_model_types,
-    detect_tts_model_type,
     resolve_adapter,
 )
-from vllm_omni.entrypoints.openai.tts_adapters.indextts2 import (
-    IndexTTS2Adapter,
-    IndexTTS25Adapter,
-    indextts2_conditioning_cache_salt,
-)
 from vllm_omni.entrypoints.openai.tts_adapters.qwen3_tts import Qwen3TTSAdapter
-from vllm_omni.model_executor.models.indextts2 import prompt_utils
-from vllm_omni.model_executor.models.indextts2.tokenizer_v2_5 import (
-    INDEXTTS25_TOKENIZER_FILE,
-)
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
@@ -48,8 +35,6 @@ EXPECTED_MODEL_TYPES = {
     "higgs_audio_v3",
     "glm_tts",
     "step_audio2",
-    "indextts2",
-    "indextts2_5",
 }
 
 
@@ -99,113 +84,6 @@ def test_qwen3_tts_metadata():
     assert issubclass(Qwen3TTSAdapter, ARTTSAdapter)
 
 
-def test_indextts_adapters_are_versioned():
-    assert resolve_adapter("indextts2") is IndexTTS2Adapter
-    assert resolve_adapter("indextts2_5") is IndexTTS25Adapter
-    assert IndexTTS25Adapter.stage_keys == frozenset({"indextts2_5_talker"})
-    assert detect_tts_model_type("indextts2_5_talker", None) == "indextts2_5"
-
-
-def test_indextts25_validates_explicit_language():
-    adapter = IndexTTS25Adapter(type("Context", (), {"server": object()})())
-
-    assert adapter._validate_extra_params({"lang": "ja"}) is None
-    assert "Unsupported IndexTTS 2.5 language" in adapter._validate_extra_params({"lang": "xx-invalid"})
-
-
-def _indextts25_adapter_and_request(*, speed: float):
-    server = SimpleNamespace(
-        uploaded_speakers={},
-        _validate_ref_audio_format=lambda ref_audio: None,
-    )
-    adapter = IndexTTS25Adapter(SimpleNamespace(server=server))
-    request = SimpleNamespace(
-        input="hello",
-        voice="alloy",
-        ref_audio=object(),
-        max_new_tokens=None,
-        extra_params=None,
-        speed=speed,
-    )
-    return adapter, request
-
-
-def test_indextts25_uses_native_speed_control_duration_factor():
-    adapter, fast_request = _indextts25_adapter_and_request(speed=2.0)
-    _, slow_request = _indextts25_adapter_and_request(speed=0.5)
-
-    assert adapter.native_speed_control is True
-    assert adapter.validate(fast_request) is None
-    assert adapter.validate(slow_request) is None
-    assert asyncio.run(adapter._build_params(fast_request))["duration_factor"] == [0.5]
-    assert asyncio.run(adapter._build_params(slow_request))["duration_factor"] == [2.0]
-
-
-@pytest.mark.parametrize("speed", [0.49, 2.01])
-def test_indextts25_rejects_out_of_range_native_speed(speed):
-    adapter, request = _indextts25_adapter_and_request(speed=speed)
-
-    assert adapter.validate(request) == "IndexTTS 2.5 speed must be between 0.5 and 2.0"
-
-
-def test_indextts25_speed_does_not_change_conditioning_cache_salt():
-    adapter, fast_request = _indextts25_adapter_and_request(speed=2.0)
-    _, slow_request = _indextts25_adapter_and_request(speed=0.5)
-    slow_request.ref_audio = fast_request.ref_audio
-
-    fast_params = asyncio.run(adapter._build_params(fast_request))
-    slow_params = asyncio.run(adapter._build_params(slow_request))
-
-    assert indextts2_conditioning_cache_salt(
-        fast_request,
-        fast_params,
-    ) == indextts2_conditioning_cache_salt(slow_request, slow_params)
-
-
-@pytest.mark.parametrize(
-    ("hf_config", "expected_tokenizer_file"),
-    [
-        (SimpleNamespace(tokenizer_file="custom-tokenizer.tiktoken"), "custom-tokenizer.tiktoken"),
-        (SimpleNamespace(), INDEXTTS25_TOKENIZER_FILE),
-    ],
-)
-def test_indextts25_build_uses_configured_tokenizer_file(
-    monkeypatch,
-    hf_config,
-    expected_tokenizer_file,
-):
-    captured = {}
-
-    def fake_estimate(*args, **kwargs):
-        captured.update(kwargs)
-        return 4
-
-    async def fake_build_params(request):
-        return {"lang": ["en"], "text_normalization": [True]}
-
-    monkeypatch.setattr(
-        prompt_utils,
-        "estimate_indextts2_prefill_prompt_len",
-        fake_estimate,
-    )
-    server = SimpleNamespace(
-        engine_client=SimpleNamespace(
-            model_config=SimpleNamespace(
-                model="/model",
-                hf_config=hf_config,
-            )
-        )
-    )
-    adapter = IndexTTS25Adapter(SimpleNamespace(server=server))
-    monkeypatch.setattr(adapter, "_build_params", fake_build_params)
-    request = SimpleNamespace(input="hello", ref_audio=None)
-
-    prepared = asyncio.run(adapter.build(request, [], False))
-
-    assert prepared.prompt["prompt_token_ids"] == [1] * 4
-    assert captured["tokenizer_file"] == expected_tokenizer_file
-
-
 def test_diffusion_adapter_extra_body_params_fallback():
     class _DiffAdapter(DiffusionTTSAdapter):
         name = "diff_probe"

tests/entrypoints/openai_api/test_tts_detection.py

@@ -149,7 +149,6 @@ _PIPELINE_STAGES = [
     "glm_tts_dit",
     "higgs_audio_v2",
     "higgs_audio_v3",
-    "indextts2_5_talker",
     "indextts2_s2mel_decoder",
     "indextts2_talker",
     "latent_generator",
@@ -214,23 +213,18 @@ _CLAIMED_ARCH_CASES = [
     ("fused_thinker_talker", "Qwen3TTSForConditionalGeneration"),
 ]
 
-# The frozen ladder can only be compared over stages it knew about. New
+_REACHABLE = list(itertools.product(_STAGES, _UNCLAIMED_ARCHS)) + _CLAIMED_ARCH_CASES
-# adapter-owned stages are covered by their adapter tests and by the registry
-# completeness checks below.
-_NEW_ADAPTER_STAGE_KEYS = all_tts_stage_keys() - _LEGACY_TTS_MODEL_STAGES
-_LEGACY_REACHABLE_STAGES = [stage for stage in _STAGES if stage not in _NEW_ADAPTER_STAGE_KEYS]
-_REACHABLE = list(itertools.product(_LEGACY_REACHABLE_STAGES, _UNCLAIMED_ARCHS)) + _CLAIMED_ARCH_CASES
 
 
 @pytest.mark.parametrize(("model_stage", "model_arch"), _REACHABLE)
 def test_detection_matches_legacy_ladder(model_stage, model_arch):
     """Registry detection is identical to the ladder it replaced.
 
-    Covers every stage the frozen ladder knew about, plus non-TTS pipeline
+    Covers every ``model_stage`` any in-tree pipeline emits (``_PIPELINE_STAGES``,
-    stages and negatives, against every architecture no adapter claims. New
+    plus negatives) against every architecture no adapter claims, and each
-    adapter-owned stages are tested by their adapter suites. Pairing a claimed
+    claimed architecture against the stage keys it really ships with. Pairing a
-    architecture with an unrelated model's stage key is excluded because no
+    claimed architecture with an unrelated model's stage key is excluded because
-    pipeline can produce it, and is covered separately by
+    no pipeline can produce it, and is covered separately by
     ``test_arch_matching_is_a_fallback_not_an_override``.
     """
     assert detect_tts_model_type(model_stage, model_arch) == _legacy_detect(model_stage, model_arch)
@@ -257,7 +251,7 @@ def test_arch_matching_is_a_fallback_not_an_override():
 
 def test_stage_keys_cover_legacy_stage_set():
     """No stage key was dropped when the module constants were deleted."""
-    assert _LEGACY_TTS_MODEL_STAGES <= all_tts_stage_keys()
+    assert all_tts_stage_keys() == frozenset(_LEGACY_TTS_MODEL_STAGES)
 
 
 def test_pipeline_stage_list_is_complete():

tests/examples/offline_inference/test_image_task_prompts.py

@@ -1,47 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-import pytest
-from PIL import Image
-
-from examples.offline_inference.image_to_video.image_to_video import build_image_to_video_prompt
-from examples.offline_inference.text_to_image.text_to_image import build_text_to_image_prompt
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-@pytest.mark.parametrize(
-    ("negative_prompt", "expected_negative_prompt"),
-    [(None, None), ("", ""), ("blurry", "blurry")],
-)
-def test_text_to_image_builds_canonical_prompt(
-    negative_prompt: str | None,
-    expected_negative_prompt: str | None,
-) -> None:
-    result = build_text_to_image_prompt("a red fox", negative_prompt)
-
-    assert result["prompt"] == "a red fox"
-    assert result["modalities"] == ["image"]
-    if expected_negative_prompt is None:
-        assert "negative_prompt" not in result
-    else:
-        assert result["negative_prompt"] == expected_negative_prompt
-
-
-def test_image_to_video_builds_canonical_prompt() -> None:
-    image = Image.new("RGB", (32, 16), "red")
-
-    result = build_image_to_video_prompt(
-        prompt="the fox turns toward the camera",
-        negative_prompt="flicker",
-        media_inputs={"image": image},
-    )
-
-    assert result == {
-        "prompt": "the fox turns toward the camera",
-        "modalities": ["video"],
-        "multi_modal_data": {"image": image},
-        "negative_prompt": "flicker",
-    }

tests/examples/online_serving/test_indextts2_run_server.py

@@ -1,108 +0,0 @@
-import os
-import re
-import subprocess
-from pathlib import Path
-
-import pytest
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-ROOT = Path(__file__).resolve().parents[3]
-SCRIPT = ROOT / "examples/online_serving/text_to_speech/indextts2/run_server.sh"
-README = ROOT / "examples/online_serving/text_to_speech/README.md"
-MACHINE_SPECIFIC_HOME_PATH = re.compile(r"(?:/home|/Users)/[^/\s`]+/")
-
-
-def run_server(
-    tmp_path: Path,
-    *,
-    model_version: str | None = None,
-    model: str | None = None,
-) -> tuple[subprocess.CompletedProcess[str], Path]:
-    argv_file = tmp_path / "vllm.argv"
-    stub = tmp_path / "vllm"
-    stub.write_text(
-        '#!/bin/bash\nprintf "%s\\0" "$@" > "$VLLM_ARGV_FILE"\n',
-        encoding="utf-8",
-    )
-    stub.chmod(0o755)
-
-    env = os.environ.copy()
-    for name in ("MODEL_VERSION", "MODEL", "DEPLOY_CONFIG", "PORT"):
-        env.pop(name, None)
-    env["PATH"] = f"{tmp_path}{os.pathsep}{env['PATH']}"
-    env["VLLM_ARGV_FILE"] = str(argv_file)
-
-    if model_version is not None:
-        env["MODEL_VERSION"] = model_version
-    if model is not None:
-        env["MODEL"] = model
-    result = subprocess.run(
-        ["bash", str(SCRIPT)],
-        cwd=ROOT,
-        env=env,
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    return result, argv_file
-
-
-def read_argv(argv_file: Path) -> list[str]:
-    return argv_file.read_bytes().decode().rstrip("\0").split("\0")
-
-
-@pytest.mark.parametrize("model", [None, ""])
-def test_version_2_5_requires_model_before_invoking_vllm(
-    tmp_path: Path,
-    model: str | None,
-) -> None:
-    result, argv_file = run_server(tmp_path, model_version="2.5", model=model)
-
-    assert result.returncode == 2
-    assert "MODEL=/path/to/native/bundle" in result.stderr
-    assert not argv_file.exists()
-
-
-def test_version_2_5_selects_deploy_config(
-    tmp_path: Path,
-) -> None:
-    model = "/models/indextts-2.5"
-    result, argv_file = run_server(
-        tmp_path,
-        model_version="2.5",
-        model=model,
-    )
-
-    assert result.returncode == 0, result.stderr
-    argv = read_argv(argv_file)
-    assert argv[:2] == ["serve", model]
-    deploy_config_index = argv.index("--deploy-config") + 1
-    assert Path(argv[deploy_config_index]) == ROOT / "vllm_omni/deploy/indextts2_5.yaml"
-
-
-@pytest.mark.parametrize("model_version", [None, "2.0"])
-def test_default_and_version_2_0_use_huggingface_model(
-    tmp_path: Path,
-    model_version: str | None,
-) -> None:
-    result, argv_file = run_server(tmp_path, model_version=model_version)
-
-    assert result.returncode == 0, result.stderr
-    argv = read_argv(argv_file)
-    assert argv[:2] == ["serve", "IndexTeam/IndexTTS-2"]
-    deploy_config_index = argv.index("--deploy-config") + 1
-    assert Path(argv[deploy_config_index]) == ROOT / "vllm_omni/deploy/indextts2.yaml"
-
-
-def test_invalid_model_version_exits_before_invoking_vllm(tmp_path: Path) -> None:
-    result, argv_file = run_server(tmp_path, model_version="3.0")
-
-    assert result.returncode == 2
-    assert "MODEL_VERSION must be 2.0 or 2.5" in result.stderr
-    assert not argv_file.exists()
-
-
-@pytest.mark.parametrize("path", [SCRIPT, README])
-def test_public_example_does_not_contain_machine_specific_home_path(path: Path) -> None:
-    assert MACHINE_SPECIFIC_HOME_PATH.search(path.read_text(encoding="utf-8")) is None

tests/model_executor/models/indextts2/test_configuration_and_pipeline.py

@@ -1,89 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from pathlib import Path
-
-import yaml
-
-from vllm_omni.config.pipeline_registry import OMNI_PIPELINES
-from vllm_omni.model_executor.models.indextts2.configuration_indextts2 import (
-    IndexTTS2Config,
-    IndexTTS25Config,
-)
-from vllm_omni.model_executor.models.indextts2.pipeline import INDEXTTS25_PIPELINE
-
-
-def test_indextts25_defaults_are_distinct_from_v2():
-    v2 = IndexTTS2Config()
-    v25 = IndexTTS25Config()
-
-    assert v2.model_type == "indextts2"
-    assert v2.gpt["number_text_tokens"] == 12000
-    assert v2.gpt["condition_num_latent"] == 32
-    assert v2.use_gpt_latent is True
-    assert v2.semantic_codec_type == "repcodec"
-    assert v2.vocab_size == v2.gpt["number_mel_codes"]
-
-    assert v25.model_type == "indextts2_5"
-    assert v25.gpt["number_text_tokens"] == 60509
-    assert v25.gpt["condition_num_latent"] == 32
-    assert v25.use_gpt_latent is False
-    assert v25.semantic_codec_type == "enhanced"
-    assert v25.semantic_codec_checkpoint == "codec.pth"
-    assert v25.tokenizer_file == "multilingual_zh_ja_yue_char_del.tiktoken"
-    assert v25.vocab_size == v25.gpt["number_mel_codes"]
-
-
-def test_indextts25_explicit_overrides_win_over_defaults():
-    config = IndexTTS25Config(
-        use_gpt_latent=True,
-        gpt={
-            "number_text_tokens": 70000,
-            "condition_num_latent": 2,
-        },
-        vocab_size=9000,
-    )
-
-    assert config.use_gpt_latent is True
-    assert config.gpt["number_text_tokens"] == 70000
-    assert config.gpt["condition_num_latent"] == 2
-    assert config.gpt["model_dim"] == config.hidden_size
-    assert config.vocab_size == 9000
-
-
-def test_indextts25_pipeline_is_registered_with_two_distinct_stages():
-    assert OMNI_PIPELINES["indextts2_5"] is INDEXTTS25_PIPELINE
-    assert INDEXTTS25_PIPELINE.default_deploy_config_name == "indextts2_5.yaml"
-    assert [stage.model_stage for stage in INDEXTTS25_PIPELINE.stages] == [
-        "indextts2_5_talker",
-        "indextts2_5_s2mel_decoder",
-    ]
-    assert [stage.model_arch for stage in INDEXTTS25_PIPELINE.stages] == [
-        None,
-        "IndexTTS25S2MelDecoder",
-    ]
-    assert all("tokenizer" not in stage.extras for stage in INDEXTTS25_PIPELINE.stages)
-
-
-def test_indextts25_default_deploy_selects_validated_triton_backend():
-    deploy_path = Path(__file__).parents[4] / "vllm_omni" / "deploy" / "indextts2_5.yaml"
-    config = yaml.safe_load(deploy_path.read_text(encoding="utf-8"))
-    stage0 = next(stage for stage in config["stages"] if stage["stage_id"] == 0)
-
-    assert stage0["attention_backend"] == "TRITON_ATTN"
-    assert stage0["enable_chunked_prefill"] is False
-
-
-def test_indextts25_deploy_configs_keep_latent_policy_in_sync():
-    deploy_dir = Path(__file__).parents[4] / "vllm_omni" / "deploy"
-    expected = {"indextts2_5.yaml": False}
-    for filename, use_gpt_latent in expected.items():
-        with open(deploy_dir / filename, encoding="utf-8") as deploy_file:
-            config = yaml.safe_load(deploy_file)
-        assert config["pipeline"] == "indextts2_5"
-        assert config["async_chunk"] is False
-        assert all("tokenizer" not in stage for stage in config["stages"])
-        assert [stage["hf_overrides"]["use_gpt_latent"] for stage in config["stages"]] == [
-            use_gpt_latent,
-            use_gpt_latent,
-        ]

tests/model_executor/models/indextts2/test_dit_cuda_graph.py

@@ -1,75 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from types import SimpleNamespace
-
-import pytest
-
-from vllm_omni.model_executor.models.indextts2.dit_cuda_graph import (
-    CUDAGraphDiTRunner,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-class _CopySpy:
-    def __init__(self):
-        self.sources = []
-
-    def copy_(self, source):
-        self.sources.append(source)
-        return self
-
-
-class _GraphSpy:
-    def __init__(self):
-        self.replays = 0
-
-    def replay(self):
-        self.replays += 1
-
-
-def test_replay_only_updates_dynamic_inputs_and_reuses_static_output():
-    runner = CUDAGraphDiTRunner(transformer=object())
-    graph = _GraphSpy()
-    static_x = _CopySpy()
-    static_c = _CopySpy()
-    static_pos = _CopySpy()
-    static_mask = _CopySpy()
-    static_out = object()
-    entry = SimpleNamespace(
-        graph=graph,
-        static_x=static_x,
-        static_c=static_c,
-        static_pos=static_pos,
-        static_mask=static_mask,
-        static_out=static_out,
-    )
-    x_in = object()
-    c = object()
-
-    output = runner._replay(entry, x_in=x_in, c=c)
-
-    assert static_x.sources == [x_in]
-    assert static_c.sources == [c]
-    assert static_pos.sources == []
-    assert static_mask.sources == []
-    assert graph.replays == 1
-    assert output is static_out
-
-
-def test_graph_stats_start_empty_and_remain_bounded():
-    runner = CUDAGraphDiTRunner(transformer=object(), max_graphs=4)
-
-    stats = runner.stats_snapshot()
-    stats["hits"] = 99
-
-    assert runner.stats_snapshot() == {
-        "calls": 0,
-        "hits": 0,
-        "captures": 0,
-        "capture_failures": 0,
-        "evictions": 0,
-        "eager": 0,
-        "cache_size": 0,
-    }

tests/model_executor/models/indextts2/test_enhanced_codec.py

@@ -1,84 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import torch
-
-from vllm_omni.model_executor.models.indextts2 import codec as codec_module
-from vllm_omni.model_executor.models.indextts2 import preprocess_utils
-from vllm_omni.model_executor.models.indextts2.codec import EnhancedCodec
-
-
-def test_enhanced_codec_decode_restores_hidden_size_and_doubles_time():
-    codec = EnhancedCodec(
-        codebook_size=8,
-        hidden_size=4,
-        codebook_dim=2,
-        vocos_dim=4,
-        vocos_intermediate_dim=8,
-        vocos_num_layers=1,
-        downsample_scale=2,
-    ).eval()
-
-    decoded = codec.decode(torch.tensor([[0, 1, 2]]))
-
-    assert decoded.shape == (1, 6, 4)
-
-
-def test_enhanced_codec_inference_module_does_not_allocate_unused_encoder():
-    codec = EnhancedCodec(
-        codebook_size=8,
-        hidden_size=4,
-        codebook_dim=2,
-        vocos_dim=4,
-        vocos_intermediate_dim=8,
-        vocos_num_layers=1,
-    )
-
-    parameter_names = set(dict(codec.named_parameters()))
-    assert not any(name.startswith("encoder.") for name in parameter_names)
-    assert not any(name.startswith("down.") for name in parameter_names)
-    assert any(name.startswith("decoder.") for name in parameter_names)
-    assert any(name.startswith("quantizer.") for name in parameter_names)
-
-
-def test_semantic_codec_loader_selects_enhanced_checkpoint(tmp_path, monkeypatch):
-    checkpoint = tmp_path / "codec.pth"
-    checkpoint.touch()
-    seen = {}
-
-    class FakeCodec:
-        def load_checkpoint(self, path):
-            seen["path"] = path
-
-        def to(self, **kwargs):
-            seen["to"] = kwargs
-            return self
-
-        def eval(self):
-            return self
-
-        def parameters(self):
-            return ()
-
-    monkeypatch.setattr(codec_module, "EnhancedCodec", lambda **kwargs: FakeCodec())
-    preprocess_utils._semantic_codec_cache.clear()
-
-    codec = preprocess_utils.load_semantic_codec(
-        str(tmp_path),
-        {"codebook_size": 8192},
-        torch.device("cpu"),
-        codec_type="enhanced",
-        checkpoint_name="codec.pth",
-    )
-
-    assert isinstance(codec, FakeCodec)
-    assert seen["path"] == str(checkpoint)
-    assert seen["to"] == {"device": torch.device("cpu"), "dtype": torch.float32}
-
-
-def test_model_asset_resolver_supports_native_checkpoints_layout(tmp_path):
-    checkpoint = tmp_path / "checkpoints" / "codec.pth"
-    checkpoint.parent.mkdir()
-    checkpoint.touch()
-
-    assert preprocess_utils.resolve_model_file(str(tmp_path), "codec.pth") == str(checkpoint)

tests/model_executor/models/indextts2/test_prompt_utils.py

@@ -1,202 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import importlib.util
-from pathlib import Path
-from types import SimpleNamespace
-
-import pytest
-
-from vllm_omni.model_executor.models.indextts2 import prompt_utils
-from vllm_omni.model_executor.models.indextts2.tokenizer_v2_5 import (
-    INDEXTTS25_TOKENIZER_FILE,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-def _load_indextts2_offline_example():
-    example_path = (
-        Path(__file__).resolve().parents[4]
-        / "examples"
-        / "offline_inference"
-        / "text_to_speech"
-        / "indextts2"
-        / "end2end.py"
-    )
-    spec = importlib.util.spec_from_file_location(
-        "indextts2_offline_example_test",
-        example_path,
-    )
-    assert spec is not None and spec.loader is not None
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-    return module
-
-
-def test_offline_main_uses_resolved_stage0_tokenizer_file(monkeypatch, tmp_path):
-    monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
-    example = _load_indextts2_offline_example()
-    stage0 = SimpleNamespace(engine_args=SimpleNamespace(hf_overrides={"tokenizer_file": "custom-tokenizer.tiktoken"}))
-    fake_omni = SimpleNamespace(
-        stage_configs=[stage0],
-        generate=lambda *args, **kwargs: [],
-    )
-    captured = {}
-
-    monkeypatch.setattr(example, "Omni", lambda **kwargs: fake_omni)
-    monkeypatch.setattr(example, "SamplingParams", lambda **kwargs: kwargs)
-    monkeypatch.setattr(
-        example,
-        "build_request",
-        lambda **kwargs: captured.update(kwargs) or {"additional_information": {}},
-    )
-
-    example.main(
-        SimpleNamespace(
-            model="/model",
-            model_version="2.5",
-            deploy_config="/deploy/custom.yaml",
-            use_gpt_latent=False,
-            stage_init_timeout=300,
-            seed=42,
-            output_dir=str(tmp_path),
-            text="hello",
-            ref_audio=None,
-            emo_audio=None,
-            emo_text=None,
-            emo_vector=None,
-            emo_alpha=None,
-            use_emo_text=False,
-            use_random=False,
-            lang="en",
-            text_normalization=True,
-        )
-    )
-
-    assert captured["tokenizer_file"] == "custom-tokenizer.tiktoken"
-
-
-def test_indextts25_prompt_len_uses_three_conditioning_tokens(monkeypatch):
-    captured = {}
-
-    def fake_prepare(*args, **kwargs):
-        captured.update(kwargs)
-        return [11, 12, 13, 14], 0
-
-    monkeypatch.setattr(
-        prompt_utils,
-        "prepare_indextts25_text",
-        fake_prepare,
-    )
-
-    prompt_len = prompt_utils.estimate_indextts2_prefill_prompt_len(
-        "/model",
-        "hello",
-        model_type="indextts2_5",
-        lang="en",
-        text_normalization=False,
-    )
-
-    # Official prepare_gpt_inputs wraps the language-prefixed tokenizer IDs
-    # with start_text=0 and stop_text=1 before appending start_mel.
-    assert prompt_len == 3 + (4 + 2) + 1
-    assert captured["text_normalization"] is False
-    assert captured["tokenizer_file"] == INDEXTTS25_TOKENIZER_FILE
-
-
-def test_indextts25_prompt_len_uses_custom_tokenizer_file(monkeypatch):
-    captured = {}
-
-    def fake_prepare(*args, **kwargs):
-        captured.update(kwargs)
-        return [11, 12], 0
-
-    monkeypatch.setattr(
-        prompt_utils,
-        "prepare_indextts25_text",
-        fake_prepare,
-    )
-
-    prompt_utils.estimate_indextts2_prefill_prompt_len(
-        "/model",
-        "hello",
-        model_type="indextts2_5",
-        tokenizer_file="custom-tokenizer.tiktoken",
-    )
-
-    assert captured["tokenizer_file"] == "custom-tokenizer.tiktoken"
-
-
-def test_indextts25_prompt_len_filters_existing_text_wrapper_ids(monkeypatch):
-    monkeypatch.setattr(
-        prompt_utils,
-        "prepare_indextts25_text",
-        lambda *args, **kwargs: ([0, 58838, 1, 42, 0], 0),
-    )
-
-    prompt_len = prompt_utils.estimate_indextts2_prefill_prompt_len(
-        "/model",
-        "hello",
-        model_type="indextts2_5",
-        lang="en",
-    )
-
-    assert prompt_len == 3 + (2 + 2) + 1
-
-
-def test_indextts2_prompt_len_ignores_v25_tokenizer_file(monkeypatch):
-    class FakeTokenizer:
-        def encode(self, text, *, add_special_tokens):
-            assert text == "hello"
-            assert add_special_tokens is False
-            return [11, 12]
-
-    def fail_prepare(*args, **kwargs):
-        raise AssertionError("IndexTTS 2 must not use the IndexTTS 2.5 tokenizer")
-
-    monkeypatch.setattr(
-        prompt_utils,
-        "_get_text_tokenizer",
-        lambda model_id_or_path: FakeTokenizer(),
-    )
-    monkeypatch.setattr(
-        prompt_utils,
-        "prepare_indextts25_text",
-        fail_prepare,
-    )
-
-    prompt_len = prompt_utils.estimate_indextts2_prefill_prompt_len(
-        "/model",
-        "hello",
-        model_type="indextts2",
-        tokenizer_file="custom.tiktoken",
-    )
-
-    assert prompt_len == 34 + (2 + 2) + 1
-
-
-def test_indextts25_prompt_ids_preserve_placeholder_value(monkeypatch):
-    captured = {}
-
-    def fake_estimate(*args, **kwargs):
-        captured.update(kwargs)
-        return 10
-
-    monkeypatch.setattr(
-        prompt_utils,
-        "estimate_indextts2_prefill_prompt_len",
-        fake_estimate,
-    )
-
-    prompt_ids = prompt_utils.build_indextts2_prefill_prompt_ids(
-        "/model",
-        "hello",
-        model_type="indextts2_5",
-        lang="en",
-        tokenizer_file="custom-tokenizer.tiktoken",
-        placeholder_token_id=17,
-    )
-
-    assert prompt_ids == [17] * 10
-    assert captured["tokenizer_file"] == "custom-tokenizer.tiktoken"

tests/model_executor/models/indextts2/test_recipe_defaults.py

@@ -1,14 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from pathlib import Path
-
-import yaml
-
-
-def test_indextts25_default_recipe_enables_validated_cfm_batching():
-    recipe_path = Path(__file__).parents[4] / "vllm_omni" / "deploy" / "indextts2_5.yaml"
-    recipe = yaml.safe_load(recipe_path.read_text(encoding="utf-8"))
-    stage1 = next(stage for stage in recipe["stages"] if stage["stage_id"] == 1)
-
-    assert stage1["hf_overrides"]["s2mel_cfm_batch_size"] == 4

tests/model_executor/models/indextts2/test_stage_input_processor.py

@@ -1,53 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from types import SimpleNamespace
-
-import pytest
-import torch
-
-from vllm_omni.model_executor.stage_input_processors.indextts2 import (
-    STOP_MEL_TOKEN,
-    talker2s2mel_full_payload,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-def test_no_latent_full_payload_uses_sampled_output_ids_and_strips_stop():
-    request = SimpleNamespace(
-        request_id="req-1",
-        output_token_ids=[17, 23, STOP_MEL_TOKEN],
-        sampling_params=SimpleNamespace(seed=42),
-    )
-    payload = {
-        "meta": {
-            "use_gpt_latent": False,
-            "S_ref": torch.ones(1, 4),
-            "ref_mel": torch.ones(1, 80, 3),
-            "style": torch.ones(1, 192),
-        }
-    }
-
-    result = talker2s2mel_full_payload(None, payload, request)
-
-    assert result is not None
-    assert result["mel_codes"].tolist() == [[17, 23]]
-    assert result["code_lens"].tolist() == [2]
-    assert result["use_gpt_latent"] is False
-    assert result["seed"] == 42
-
-
-def test_no_latent_full_payload_rejects_legacy_payload_codes():
-    request = SimpleNamespace(
-        request_id="legacy",
-        output_token_ids=[],
-        sampling_params=SimpleNamespace(seed=None),
-    )
-    payload = {
-        "codes": {"mel": torch.tensor([31, 37, STOP_MEL_TOKEN])},
-        "meta": {"use_gpt_latent": False},
-    }
-
-    with pytest.raises(ValueError, match="no completed output_token_ids"):
-        talker2s2mel_full_payload(None, payload, request)

tests/model_executor/models/indextts2/test_talker_v2_5.py

@@ -1,476 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from types import SimpleNamespace
-
-import pytest
-import torch
-from vllm.config import CompilationMode
-
-from vllm_omni.model_executor.models.indextts2.indextts2_talker import (
-    IndexTTS2TalkerForConditionalGeneration,
-    _normalize_indextts_checkpoint_key,
-    _require_exact_indextts_prefill_span,
-    resolve_indextts_conditioning_policy,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-def test_v25_conditioning_policy():
-    policy = resolve_indextts_conditioning_policy("indextts2_5")
-
-    assert policy.prefix_tokens == 3
-    assert policy.uses_conformer_perceiver is False
-    assert policy.uses_campplus_projection is True
-    assert policy.uses_language_embedding is True
-
-
-def test_v2_conditioning_policy_is_unchanged():
-    policy = resolve_indextts_conditioning_policy("indextts2")
-
-    assert policy.prefix_tokens == 34
-    assert policy.uses_conformer_perceiver is True
-    assert policy.uses_campplus_projection is False
-    assert policy.uses_language_embedding is False
-
-
-def test_v25_text_tokens_wrap_language_prefixed_ids_with_start_and_stop(
-    monkeypatch,
-):
-    import vllm_omni.model_executor.models.indextts2.text_processing_v2_5 as text_processing
-
-    monkeypatch.setattr(
-        text_processing,
-        "prepare_indextts25_text",
-        lambda *args, **kwargs: ([58838, 42], 7),
-    )
-    talker = object.__new__(IndexTTS2TalkerForConditionalGeneration)
-    talker.conditioning_policy = resolve_indextts_conditioning_policy("indextts2_5")
-    talker.model_path = "/model"
-    talker.config = SimpleNamespace(tokenizer_file="tokenizer.tiktoken")
-
-    token_ids, language_id = talker._tokenize_text(
-        "hello",
-        torch.device("cpu"),
-        lang="ja",
-    )
-
-    assert token_ids.tolist() == [[0, 58838, 42, 1]]
-    assert language_id.tolist() == [7]
-
-
-def test_v25_text_tokens_filter_existing_start_and_stop_ids(monkeypatch):
-    import vllm_omni.model_executor.models.indextts2.text_processing_v2_5 as text_processing
-
-    monkeypatch.setattr(
-        text_processing,
-        "prepare_indextts25_text",
-        lambda *args, **kwargs: ([0, 58838, 1, 42, 0], 7),
-    )
-    talker = object.__new__(IndexTTS2TalkerForConditionalGeneration)
-    talker.conditioning_policy = resolve_indextts_conditioning_policy("indextts2_5")
-    talker.model_path = "/model"
-    talker.config = SimpleNamespace(tokenizer_file="tokenizer.tiktoken")
-
-    token_ids, language_id = talker._tokenize_text(
-        "hello",
-        torch.device("cpu"),
-        lang="en",
-    )
-
-    assert token_ids.tolist() == [[0, 58838, 42, 1]]
-    assert language_id.tolist() == [7]
-
-
-def test_native_fsdp_checkpoint_keys_are_normalized_at_every_nesting_level():
-    assert _normalize_indextts_checkpoint_key("_fsdp_wrapped_module.spk_emb_proj.weight") == "spk_emb_proj.weight"
-    assert (
-        _normalize_indextts_checkpoint_key("_fsdp_wrapped_module.mel_embedding._fsdp_wrapped_module.weight")
-        == "mel_embedding.weight"
-    )
-
-
-def test_prefill_span_mismatch_fails_instead_of_padding_or_truncating():
-    for actual_span_len in (9, 11):
-        with pytest.raises(
-            ValueError,
-            match="model_type=indextts2_5.*lang=es.*text_token_count=6",
-        ):
-            _require_exact_indextts_prefill_span(
-                actual_span_len=actual_span_len,
-                scheduled_span_len=10,
-                model_type="indextts2_5",
-                lang="es",
-                text_token_count=6,
-            )
-
-
-def test_matching_prefill_span_is_accepted():
-    _require_exact_indextts_prefill_span(
-        actual_span_len=10,
-        scheduled_span_len=10,
-        model_type="indextts2_5",
-        lang="en",
-        text_token_count=6,
-    )
-
-
-def _make_minimal_talker(
-    monkeypatch,
-    *,
-    model_type,
-    use_gpt_latent,
-):
-    import vllm_omni.model_executor.models.indextts2.indextts2_talker as talker_module
-
-    seen = {}
-
-    def fake_parallel_lm_head(vocab_size, hidden_size, *, bias=False, **_kwargs):
-        seen["bias"] = bias
-        return torch.nn.Linear(hidden_size, vocab_size, bias=bias)
-
-    monkeypatch.setattr(talker_module, "ParallelLMHead", fake_parallel_lm_head)
-    monkeypatch.setattr(
-        talker_module,
-        "make_layers",
-        lambda *_args, **_kwargs: (
-            0,
-            1,
-            torch.nn.ModuleList([torch.nn.Linear(8, 8)]),
-        ),
-    )
-    monkeypatch.setattr(
-        talker_module,
-        "get_pp_group",
-        lambda: SimpleNamespace(is_last_rank=lambda: True),
-    )
-    monkeypatch.setattr(talker_module, "get_speaker_cache", object)
-    monkeypatch.setattr(
-        talker_module,
-        "LogitsProcessor",
-        lambda *_args, **_kwargs: object(),
-    )
-    monkeypatch.setattr(
-        talker_module,
-        "ConformerEncoder",
-        lambda **_kwargs: torch.nn.Linear(1, 1),
-    )
-    monkeypatch.setattr(
-        talker_module,
-        "PerceiverResampler",
-        lambda *_args, **_kwargs: torch.nn.Linear(1, 1),
-    )
-
-    gpt = {
-        "model_dim": 8,
-        "layers": 0,
-        "heads": 1,
-        "max_mel_tokens": 4,
-        "max_text_tokens": 4,
-        "number_mel_codes": 16,
-        "start_mel_token": 14,
-        "stop_mel_token": 15,
-        "number_text_tokens": 32,
-        "condition_num_latent": 1,
-        "condition_module": {},
-        "emo_condition_module": {},
-    }
-    config = SimpleNamespace(
-        gpt=gpt,
-        model_type=model_type,
-        use_gpt_latent=use_gpt_latent,
-        gpt_checkpoint="gpt.pth",
-    )
-    vllm_config = SimpleNamespace(
-        model_config=SimpleNamespace(model="/model", hf_config=config),
-        cache_config=None,
-        quant_config=None,
-        compilation_config=SimpleNamespace(mode=CompilationMode.NONE),
-    )
-
-    return (
-        IndexTTS2TalkerForConditionalGeneration(vllm_config=vllm_config),
-        seen,
-    )
-
-
-@pytest.mark.parametrize(
-    ("model_type", "use_gpt_latent"),
-    [
-        ("indextts2", True),
-        ("indextts2_5", False),
-    ],
-)
-def test_mel_head_preserves_official_checkpoint_bias_and_hidden_payload_policy(
-    monkeypatch,
-    model_type,
-    use_gpt_latent,
-):
-    talker, seen = _make_minimal_talker(
-        monkeypatch,
-        model_type=model_type,
-        use_gpt_latent=use_gpt_latent,
-    )
-
-    assert seen["bias"] is True
-    assert talker.mel_head.bias is not None
-    assert talker.omni_pooler_payload_include_hidden is use_gpt_latent
-    assert talker.omni_payload_at_request_end is (model_type == "indextts2_5")
-
-
-def test_v2_load_weights_loads_exact_mel_head_bias_parameter(monkeypatch):
-    import vllm_omni.model_executor.models.indextts2.indextts2_talker as talker_module
-
-    talker, _ = _make_minimal_talker(
-        monkeypatch,
-        model_type="indextts2",
-        use_gpt_latent=True,
-    )
-    checkpoint = {
-        name: parameter.detach().clone() for name, parameter in talker.named_parameters(remove_duplicate=False)
-    }
-    expected_bias = torch.arange(
-        talker.number_mel_codes,
-        dtype=talker.mel_head.bias.dtype,
-    )
-    checkpoint["mel_head.bias"] = expected_bias
-    with torch.no_grad():
-        talker.mel_head.bias.zero_()
-
-    monkeypatch.setattr(
-        talker_module,
-        "resolve_model_file",
-        lambda *_args, **_kwargs: "/model/gpt.pth",
-    )
-    monkeypatch.setattr(
-        talker_module.torch,
-        "load",
-        lambda *_args, **_kwargs: checkpoint,
-    )
-    monkeypatch.setattr(
-        talker_module,
-        "is_pp_missing_parameter",
-        lambda *_args, **_kwargs: False,
-    )
-    monkeypatch.setattr(
-        talker,
-        "_warmup_external_models",
-        lambda _device: None,
-    )
-
-    loaded = talker.load_weights([])
-
-    assert "mel_head.bias" in loaded
-    torch.testing.assert_close(talker.mel_head.bias, expected_bias)
-
-
-def test_compute_logits_passes_mel_head_bias_to_logits_processor():
-    talker = object.__new__(IndexTTS2TalkerForConditionalGeneration)
-    torch.nn.Module.__init__(talker)
-    talker.mel_head = torch.nn.Linear(3, 4, bias=True)
-    hidden_states = torch.zeros(1, 3)
-    seen = {}
-
-    def fake_logits_processor(head, hidden, **kwargs):
-        seen["head"] = head
-        seen["hidden"] = hidden
-        seen.update(kwargs)
-        return hidden
-
-    talker.logits_processor = fake_logits_processor
-
-    result = talker.compute_logits(hidden_states)
-
-    assert result is hidden_states
-    assert seen["head"] is talker.mel_head
-    assert seen["hidden"] is hidden_states
-    assert seen["embedding_bias"] is talker.mel_head.bias
-
-
-def _make_cached_v25_prefill_talker(monkeypatch):
-    talker, _ = _make_minimal_talker(
-        monkeypatch,
-        model_type="indextts2_5",
-        use_gpt_latent=False,
-    )
-    talker._speaker_cache = SimpleNamespace(
-        make_cache_key=lambda *_args: "cached-speaker",
-        get=lambda _key: {
-            "S_ref": torch.ones(1, 3, 8),
-            "ref_mel": torch.ones(1, 80, 3),
-            "style": torch.ones(1, 192),
-            "spk_cond_emb": torch.ones(1, 3, 8),
-        },
-    )
-    monkeypatch.setattr(
-        talker,
-        "_compute_emotion_vector",
-        lambda **_kwargs: torch.zeros(1, talker.model_dim),
-    )
-    monkeypatch.setattr(
-        talker,
-        "_tokenize_text",
-        lambda *_args, **_kwargs: (torch.tensor([[1]]), None),
-    )
-    return talker
-
-
-def test_prefill_preserves_duration_factor_in_metadata(monkeypatch):
-    talker = _make_cached_v25_prefill_talker(monkeypatch)
-
-    _, _, info_update = talker._preprocess_prefill(
-        torch.zeros(5, dtype=torch.long),
-        None,
-        {
-            "text": ["hello"],
-            "voice": ["/speaker.wav"],
-            "voice_name": ["cached"],
-            "duration_factor": [0.5],
-        },
-        span_len=5,
-    )
-
-    assert info_update["meta"]["duration_factor"] == 0.5
-
-
-@pytest.mark.parametrize("duration_factor", [0.49, 2.01])
-def test_prefill_rejects_duration_factor_outside_supported_range(monkeypatch, duration_factor):
-    talker = _make_cached_v25_prefill_talker(monkeypatch)
-
-    with pytest.raises(
-        ValueError,
-        match="^IndexTTS 2.5 duration_factor must be between 0.5 and 2.0$",
-    ):
-        talker._preprocess_prefill(
-            torch.zeros(5, dtype=torch.long),
-            None,
-            {
-                "text": ["hello"],
-                "voice": ["/speaker.wav"],
-                "voice_name": ["cached"],
-                "duration_factor": [duration_factor],
-            },
-            span_len=5,
-        )
-
-
-def test_no_latent_sampled_token_payload_emits_only_first_step_metadata():
-    talker = object.__new__(IndexTTS2TalkerForConditionalGeneration)
-    talker.use_gpt_latent = False
-    hidden = torch.zeros(1, 8)
-    info = {
-        "codes": {"mel": torch.tensor([17])},
-        "meta": {
-            "mel_code_count": 1,
-            "S_ref": torch.ones(1, 4),
-            "ref_mel": torch.ones(1, 80, 3),
-            "style": torch.ones(1, 192),
-            "duration_factor": 0.5,
-        },
-    }
-
-    first_decode_output = talker.make_omni_output(
-        hidden,
-        model_intermediate_buffer=[info],
-    )
-
-    assert "codes" not in first_decode_output.multimodal_outputs
-    assert "hidden_states" not in first_decode_output.multimodal_outputs
-    assert first_decode_output.multimodal_outputs["meta"][0]["S_ref"] is info["meta"]["S_ref"]
-    assert first_decode_output.multimodal_outputs["meta"][0]["duration_factor"] == 0.5
-
-    later = talker.make_omni_output(
-        hidden,
-        model_intermediate_buffer=[
-            {
-                "codes": {"mel": torch.tensor([23])},
-                "meta": {"mel_code_count": 2},
-            }
-        ],
-    )
-    assert later.multimodal_outputs == {"meta": [{"use_gpt_latent": False}]}
-
-
-def test_first_decode_payload_defaults_missing_duration_factor_to_one():
-    talker = object.__new__(IndexTTS2TalkerForConditionalGeneration)
-    talker.use_gpt_latent = False
-    hidden = torch.zeros(1, 8)
-    info = {
-        "codes": {"mel": torch.tensor([17])},
-        "meta": {
-            "mel_code_count": 1,
-            "S_ref": torch.ones(1, 4),
-            "ref_mel": torch.ones(1, 80, 3),
-            "style": torch.ones(1, 192),
-        },
-    }
-
-    output = talker.make_omni_output(
-        hidden,
-        model_intermediate_buffer=[info],
-    )
-
-    assert output.multimodal_outputs["meta"][0]["duration_factor"] == 1.0
-
-
-def test_latent_sampled_token_payload_keeps_aligned_gpu_deltas():
-    talker = object.__new__(IndexTTS2TalkerForConditionalGeneration)
-    talker.use_gpt_latent = True
-    hidden = torch.zeros(1, 8)
-    info = {
-        "codes": {"mel": torch.tensor([17])},
-        "meta": {
-            "mel_code_count": 1,
-            "latent_acc": torch.arange(8, dtype=torch.float32).reshape(1, 8),
-            "duration_factor": 1.0,
-        },
-    }
-
-    output = talker.make_omni_output(
-        hidden,
-        model_intermediate_buffer=[info],
-    )
-
-    assert output.multimodal_outputs["codes"]["mel"][0].tolist() == [[17]]
-    assert output.multimodal_outputs["hidden_states"]["latent"][0].tolist() == [list(range(8))]
-
-
-def test_v25_default_emotion_reuses_speaker_w2v_features(monkeypatch):
-    talker = object.__new__(IndexTTS2TalkerForConditionalGeneration)
-    speaker_features = torch.zeros(1, 4, 1024)
-    expected = torch.ones(1, 1280)
-    seen = {}
-
-    def fake_merge(spk_cond_emb, emo_cond_emb, alpha):
-        seen["spk"] = spk_cond_emb
-        seen["emo"] = emo_cond_emb
-        seen["alpha"] = alpha
-        return expected
-
-    monkeypatch.setattr(talker, "_merge_emovec", fake_merge)
-
-    result = talker._compute_emotion_vector(
-        wav_16k=None,
-        speaker_audio_path="/speaker.wav",
-        emo_audio_path=None,
-        emo_voice_name=None,
-        main_text="hello",
-        use_emo_text=False,
-        emo_text=None,
-        emo_vector=None,
-        emo_alpha=0.25,
-        use_random=False,
-        style=torch.zeros(1, 192),
-        spk_cond_emb=speaker_features,
-        w2v_model=None,
-        w2v_proc=None,
-        device=torch.device("cpu"),
-    )
-
-    assert result is expected
-    assert seen == {
-        "spk": speaker_features,
-        "emo": speaker_features,
-        "alpha": 1.0,
-    }

tests/model_executor/models/indextts2/test_tokenizer_v2_5.py

@@ -1,244 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import builtins
-
-import pytest
-
-from vllm_omni.model_executor.models.indextts2 import text_processing_v2_5
-from vllm_omni.model_executor.models.indextts2.text_processing_v2_5 import (
-    apply_pronunciation_annotations,
-    clean_indextts25_text,
-    prepare_indextts25_text,
-)
-from vllm_omni.model_executor.models.indextts2.tokenizer_v2_5 import (
-    INDEXTTS25_SPECIAL_TOKENS,
-    INDEXTTS25_VOCAB_SIZE,
-    LANGUAGE_DICT,
-    lang_to_token,
-    normalize_language_code,
-    resolve_indextts25_tokenizer_file,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-def test_special_token_layout_matches_official_checkpoint_vocab():
-    assert len(INDEXTTS25_SPECIAL_TOKENS) == 1673
-    assert len(set(INDEXTTS25_SPECIAL_TOKENS)) == 1673
-    assert 58836 + len(INDEXTTS25_SPECIAL_TOKENS) == INDEXTTS25_VOCAB_SIZE
-    assert INDEXTTS25_VOCAB_SIZE == 60509
-
-
-def test_pronunciation_annotations_select_english_and_chinese_markers():
-    assert apply_pronunciation_annotations("<going|G OW1 . IH0 NG>") == (
-        "<|SPECIAL_TOKEN_1|>G OW1 . IH0 NG<|SPECIAL_TOKEN_1|>"
-    )
-    assert apply_pronunciation_annotations("<行|xing2>") == ("<|SPECIAL_TOKEN_2|>XING2<|SPECIAL_TOKEN_2|>")
-
-
-def test_prepare_text_applies_language_prefix_case_and_annotation(monkeypatch):
-    captured = {}
-
-    def fake_encode(text, *, model_dir, tokenizer_file):
-        captured["text"] = text
-        captured["model_dir"] = model_dir
-        captured["tokenizer_file"] = tokenizer_file
-        return [7, 8, 9]
-
-    monkeypatch.setattr(text_processing_v2_5, "encode_indextts25_text", fake_encode)
-
-    token_ids, lang_id = prepare_indextts25_text(
-        "HELLO <行|xing2>",
-        lang="zh",
-        model_dir="/model",
-        text_normalization=False,
-    )
-
-    assert token_ids == [7, 8, 9]
-    assert lang_id == LANGUAGE_DICT["zh"]
-    assert captured == {
-        "text": "<|zh|> hello <|SPECIAL_TOKEN_2|>XING2<|SPECIAL_TOKEN_2|>",
-        "model_dir": "/model",
-        "tokenizer_file": "multilingual_zh_ja_yue_char_del.tiktoken",
-    }
-
-
-def test_mandarin_alias_is_an_intentional_vllm_omni_convenience():
-    assert normalize_language_code("Mandarin") == "zh"
-    assert lang_to_token("mandarin") == LANGUAGE_DICT["zh"]
-
-
-def test_invalid_language_does_not_fall_back_to_common():
-    with pytest.raises(ValueError, match="Unsupported IndexTTS 2.5 language"):
-        lang_to_token("xx-invalid")
-
-
-def test_zhen_uses_mixed_normalization():
-    calls = []
-
-    def fake_mixed_normalizer(text):
-        calls.append(text)
-        return "MIXED"
-
-    original = text_processing_v2_5._normalize_zh_or_en
-    text_processing_v2_5._normalize_zh_or_en = fake_mixed_normalizer
-    try:
-        result = text_processing_v2_5._normalize_with_official_backend(
-            "中文 and English",
-            "zhen",
-        )
-    finally:
-        text_processing_v2_5._normalize_zh_or_en = original
-
-    assert calls == ["中文 and English"]
-    assert result == "MIXED"
-
-
-def test_spanish_normalization_does_not_hide_broken_dependencies(monkeypatch):
-    original_import = builtins.__import__
-
-    def controlled_import(name, *args, **kwargs):
-        if name == "nemo_text_processing.text_normalization.normalize":
-            raise RuntimeError("broken NeMo grammar")
-        return original_import(name, *args, **kwargs)
-
-    monkeypatch.setattr(builtins, "__import__", controlled_import)
-
-    with pytest.raises(RuntimeError, match="broken NeMo grammar"):
-        text_processing_v2_5._normalize_with_official_backend("hola", "es")
-
-
-def test_zhen_keeps_literal_prefix_and_uses_common_embedding(monkeypatch):
-    captured = {}
-
-    def fake_encode(text, *, model_dir, tokenizer_file):
-        captured["text"] = text
-        return [7, 8, 9]
-
-    monkeypatch.setattr(text_processing_v2_5, "encode_indextts25_text", fake_encode)
-
-    token_ids, lang_id = prepare_indextts25_text(
-        "HELLO 中文",
-        lang="zhen",
-        model_dir="/model",
-        text_normalization=False,
-    )
-
-    assert token_ids == [7, 8, 9]
-    assert captured["text"] == "<|zhen|> hello 中文"
-    assert lang_id == LANGUAGE_DICT["common"]
-    assert "<|zhen|>" not in INDEXTTS25_SPECIAL_TOKENS
-    assert "<|common|>" not in INDEXTTS25_SPECIAL_TOKENS
-
-
-@pytest.mark.parametrize(
-    ("text", "expected"),
-    [
-        ("，，，", "…"),
-        ("测试，，，结束", "测试…结束"),
-    ],
-)
-def test_repeated_full_width_commas_use_longest_first_cleanup(text, expected):
-    assert clean_indextts25_text(text) == expected
-
-
-def test_default_text_normalizer_is_shared_by_prompt_and_talker(monkeypatch):
-    calls = []
-
-    def fake_normalizer(text, lang):
-        calls.append((text, lang))
-        return "twenty five percent"
-
-    monkeypatch.setattr(
-        text_processing_v2_5,
-        "_normalize_with_official_backend",
-        fake_normalizer,
-    )
-
-    result = text_processing_v2_5.normalize_indextts25_text(
-        "25%",
-        lang="en",
-        text_normalization=True,
-    )
-
-    assert calls == [("25%", "en")]
-    assert result == "twenty five percent"
-
-
-def test_mixed_text_uses_official_content_routing_and_protection(monkeypatch):
-    calls = []
-
-    class FakeNormalizer:
-        def __init__(self, lang):
-            self.lang = lang
-
-        def normalize(self, text):
-            calls.append((self.lang, text))
-            return text.replace("<H>", " <H> ").replace("5", "five")
-
-    monkeypatch.setattr(
-        text_processing_v2_5,
-        "_load_wetext_normalizer",
-        lambda lang: FakeNormalizer(lang),
-    )
-
-    result = text_processing_v2_5._normalize_with_official_backend(
-        "GPT-5-nano 中文 xuan4",
-        "en",
-    )
-
-    assert calls[0][0] == "zh"
-    assert result == "GPT-five-nano 中文 XVAN4"
-
-
-def test_tokenizer_resolves_native_checkpoints_subdirectory(tmp_path):
-    tokenizer = tmp_path / "checkpoints" / "multilingual_zh_ja_yue_char_del.tiktoken"
-    tokenizer.parent.mkdir()
-    tokenizer.touch()
-
-    assert resolve_indextts25_tokenizer_file(str(tmp_path)) == str(tokenizer)
-
-
-@pytest.mark.parametrize(
-    ("lang", "text", "expected"),
-    [
-        (
-            "es",
-            "En la actualidad se continúa utilizando una corona abierta.",
-            "<|es|> EN LA ACTUALIDAD SE CONTINÚA UTILIZANDO UNA CORONA ABIERTA.",
-        ),
-        (
-            "ar",
-            "فكلُّها خياراتٌ اتَّخذتَها بنفسِك.",
-            "<|ar|> فكلُّها خياراتٌ اتَّخذتَها بنفسِك.",
-        ),
-    ],
-)
-def test_official_spanish_and_arabic_cases_preserve_expected_text_contract(
-    monkeypatch,
-    lang,
-    text,
-    expected,
-):
-    captured = {}
-
-    def fake_encode(value, *, model_dir, tokenizer_file):
-        captured["text"] = value
-        return [1]
-
-    monkeypatch.setattr(
-        text_processing_v2_5,
-        "encode_indextts25_text",
-        fake_encode,
-    )
-
-    _, lang_id = prepare_indextts25_text(
-        text,
-        lang=lang,
-        model_dir="/model",
-        text_normalization=False,
-    )
-
-    assert captured["text"] == expected
-    assert lang_id == LANGUAGE_DICT[lang]

tests/model_executor/models/indextts2/test_wavenet_ragged.py

@@ -1,196 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from types import SimpleNamespace
-
-import pytest
-import torch
-
-from vllm_omni.model_executor.models.indextts2.indextts2_s2mel_decoder import (
-    IndexTTS2S2MelDecoder,
-)
-from vllm_omni.model_executor.models.indextts2.s2mel.modules.wavenet import WN
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-def _make_wavenet() -> WN:
-    torch.manual_seed(0)
-    return WN(
-        hidden_channels=2,
-        kernel_size=5,
-        dilation_rate=1,
-        n_layers=8,
-        gin_channels=2,
-        p_dropout=0.0,
-        causal=False,
-    ).eval()
-
-
-def _sequence_mask(lengths: torch.Tensor, max_length: int) -> torch.Tensor:
-    positions = torch.arange(max_length, device=lengths.device)
-    return (positions.unsqueeze(0) < lengths.unsqueeze(1)).unsqueeze(1)
-
-
-def _singleton_outputs(
-    wavenet: WN,
-    inputs: torch.Tensor,
-    lengths: list[int],
-    conditioning: torch.Tensor,
-) -> list[torch.Tensor]:
-    # This is the independent oracle: every request ends at the physical tensor
-    # boundary and therefore uses SConv1d's original reflect-padding behavior.
-    wavenet._assume_full_mask = True
-    return [
-        wavenet(
-            inputs[row : row + 1, :, :length],
-            torch.ones(1, 1, length, dtype=torch.bool),
-            g=conditioning[row : row + 1],
-        )
-        for row, length in enumerate(lengths)
-    ]
-
-
-def test_ragged_wavenet_matches_original_singleton_reflect_boundaries():
-    wavenet = _make_wavenet()
-    lengths = [6, 5, 4, 3]
-    inputs = torch.randn(len(lengths), 2, max(lengths))
-    conditioning = torch.randn(len(lengths), 2, 1)
-    expected = _singleton_outputs(wavenet, inputs, lengths, conditioning)
-
-    mask = _sequence_mask(torch.tensor(lengths), inputs.shape[-1])
-    wavenet._assume_full_mask = False
-    # Keep deliberately non-zero invalid tails. The first WaveNet layer sees
-    # transformer output before the recurrent mask has cleared those values.
-    actual = wavenet(inputs, mask, g=conditioning)
-
-    for row, (length, singleton) in enumerate(zip(lengths, expected)):
-        torch.testing.assert_close(
-            actual[row : row + 1, :, :length],
-            singleton,
-            rtol=1e-5,
-            atol=1e-6,
-        )
-
-
-def test_wavenet_exposes_minimum_safe_ragged_length():
-    # kernel=5, dilation=1 has two right-reflect ghost positions, so a
-    # logical row must contain at least three frames before it can use the
-    # vectorized ragged path. Shorter rows stay on original singleton SConv1d.
-    assert _make_wavenet().ragged_min_length == 3
-
-
-def test_cfm_grouping_isolates_rows_too_short_for_logical_reflect():
-    groups = IndexTTS2S2MelDecoder._cfm_group_indices(
-        sorted_indices=[0, 1, 2, 3, 4],
-        total_lengths=[0, 1, 2, 3, 6],
-        max_group_size=2,
-        min_ragged_length=3,
-    )
-
-    assert groups == [[0], [1], [2], [3, 4]]
-
-
-def test_ragged_wavenet_preserves_cfg_cond_uncond_row_order():
-    wavenet = _make_wavenet()
-    base_lengths = [3, 6]
-    conditional = torch.randn(2, 2, 6)
-    unconditional = torch.randn(2, 2, 6)
-    cfg_inputs = torch.cat([conditional, unconditional], dim=0)
-    cfg_lengths = base_lengths + base_lengths
-    cfg_conditioning = torch.randn(4, 2, 1)
-    expected = _singleton_outputs(
-        wavenet,
-        cfg_inputs,
-        cfg_lengths,
-        cfg_conditioning,
-    )
-
-    mask = _sequence_mask(torch.tensor(cfg_lengths), cfg_inputs.shape[-1])
-    wavenet._assume_full_mask = False
-    actual = wavenet(cfg_inputs, mask, g=cfg_conditioning)
-
-    for row, (length, singleton) in enumerate(zip(cfg_lengths, expected)):
-        torch.testing.assert_close(
-            actual[row : row + 1, :, :length],
-            singleton,
-            rtol=1e-5,
-            atol=1e-6,
-        )
-
-
-def test_full_mask_wavenet_path_is_bit_exact_with_original_path():
-    wavenet = _make_wavenet()
-    inputs = torch.randn(3, 2, 6)
-    mask = torch.ones(3, 1, 6, dtype=torch.bool)
-    conditioning = torch.randn(3, 2, 1)
-
-    if hasattr(wavenet, "_assume_full_mask"):
-        del wavenet._assume_full_mask
-    expected = wavenet(inputs, mask, g=conditioning)
-
-    wavenet._assume_full_mask = True
-    actual = wavenet(inputs, mask, g=conditioning)
-
-    assert torch.equal(actual, expected)
-
-
-@pytest.mark.skipif(not hasattr(torch, "compile"), reason="torch.compile unavailable")
-def test_ragged_wavenet_is_one_dynamic_fullgraph():
-    wavenet = _make_wavenet()
-    lengths = [3, 6]
-    inputs = torch.randn(2, 2, 6)
-    conditioning = torch.randn(2, 2, 1)
-    mask = _sequence_mask(torch.tensor(lengths), inputs.shape[-1])
-    expected = _singleton_outputs(wavenet, inputs, lengths, conditioning)
-    wavenet._assume_full_mask = False
-
-    compiled = torch.compile(
-        wavenet,
-        backend="eager",
-        fullgraph=True,
-        dynamic=True,
-    )
-    actual = compiled(inputs, mask, g=conditioning)
-
-    for row, (length, singleton) in enumerate(zip(lengths, expected)):
-        torch.testing.assert_close(
-            actual[row : row + 1, :, :length],
-            singleton,
-            rtol=1e-5,
-            atol=1e-6,
-        )
-
-
-def test_decoder_full_mask_switch_also_controls_wavenet_boundary_path():
-    decoder = object.__new__(IndexTTS2S2MelDecoder)
-    attention_layers = [SimpleNamespace(attention=SimpleNamespace()) for _ in range(2)]
-    estimator = SimpleNamespace(
-        transformer=SimpleNamespace(layers=attention_layers),
-        wavenet=SimpleNamespace(),
-    )
-
-    decoder._set_dit_full_mask_fast_path(estimator, enabled=False)
-
-    assert all(layer.attention._assume_full_mask is False for layer in attention_layers)
-    assert estimator.wavenet._assume_full_mask is False
-
-    decoder._set_dit_full_mask_fast_path(estimator, enabled=True)
-
-    assert all(layer.attention._assume_full_mask is True for layer in attention_layers)
-    assert estimator.wavenet._assume_full_mask is True
-
-
-def test_decoder_full_mask_switch_unwraps_compiled_estimator():
-    decoder = object.__new__(IndexTTS2S2MelDecoder)
-    attention_layers = [SimpleNamespace(attention=SimpleNamespace()) for _ in range(2)]
-    eager_estimator = SimpleNamespace(
-        transformer=SimpleNamespace(layers=attention_layers),
-        wavenet=SimpleNamespace(),
-    )
-    compiled_estimator = SimpleNamespace(_orig_mod=eager_estimator)
-
-    decoder._set_dit_full_mask_fast_path(compiled_estimator, enabled=False)
-
-    assert all(layer.attention._assume_full_mask is False for layer in attention_layers)
-    assert eager_estimator.wavenet._assume_full_mask is False

tests/model_executor/models/qwen2_5_omni/test_per_video_audio_mask.py

@@ -285,17 +285,7 @@ def test_cached_apply_hf_processor_expands_pair_miss_for_audio_hit_video_miss():
         captured["mm_cache_keys"] = kwargs["mm_hashes"]
         return MagicMock(name="mm_kwargs"), {"video": [[]], "audio": [[]]}
 
-    fake_self = SimpleNamespace(
+    fake_self = SimpleNamespace(cache=cache, info=SimpleNamespace(model_id="m"))
-        cache=cache,
-        info=SimpleNamespace(
-            model_id="m",
-            ctx=SimpleNamespace(
-                get_mm_config=lambda: SimpleNamespace(
-                    mm_hasher_algorithm="sha256",
-                )
-            ),
-        ),
-    )
     fake_self._get_hf_mm_data = MagicMock(return_value=({}, {}))
     # Bound methods called via self inside _cached_apply_hf_processor.
     fake_self._get_audio_in_video_pairs = lambda mm_data_items, hf_processor_mm_kwargs: (

tests/model_executor/models/qwen3_omni/test_qwen3_omni_forward_contract.py

@@ -1,175 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Regression tests for the Qwen3-Omni thinker forward return contract.
-
-Background
-----------
-Since omni architectures unconditionally override upstream's registry entries,
-``Qwen3OmniMoeForConditionalGeneration`` (and the standalone thinker) are also
-constructed by plain, non-staged vLLM runs — e.g. ``vllm serve
-Qwen/Qwen3-Omni-30B-A3B-Instruct`` in the nightly vLLM-text perf job. Stock
-vLLM's ``GPUModelRunner`` indexes the forward output directly
-(``hidden_states[logit_indices]``), so a thinker forward that always returns a
-``(hidden_states, captured_hidden_states)`` tuple kills the engine during
-``profile_run`` with ``TypeError: only integer tensors of a single element can
-be converted to an index`` (Buildkite build 2952).
-
-Contract under test:
-
-- No capture requested → the forward returns a **bare tensor** (stock-runner
-  compatible).
-- Capture requested (``capture_layer_indices`` + ``return_hidden_states``,
-  staged runs with a talker downstream) → the forward returns the
-  ``(hidden_states, captured_layer_dict)`` tuple omni's runner consumes via
-  ``make_omni_output``.
-- The combined model requests capture only in staged runs
-  (``model_config.model_stage`` injected), never in plain vLLM runs.
-- ``make_omni_output`` tolerates both return shapes.
-
-Pure shape/contract tests: CPU-only, no weights, no GPU.
-"""
-
-from types import SimpleNamespace
-
-import pytest
-import torch
-from torch import nn
-from vllm.sequence import IntermediateTensors
-
-from vllm_omni.model_executor.models.output_templates import OmniOutput
-from vllm_omni.model_executor.models.qwen3_omni.qwen3_omni import (
-    Qwen3OmniMoeForConditionalGeneration,
-)
-from vllm_omni.model_executor.models.qwen3_omni.qwen3_omni_moe_thinker import (
-    Qwen3OmniMoeThinkerForConditionalGeneration,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-_HIDDEN = 8
-_TOKENS = 4
-
-
-def _hidden_states() -> torch.Tensor:
-    return torch.zeros(_TOKENS, _HIDDEN)
-
-
-class _InnerModelStub:
-    """Stands in for ``Qwen3MoeLLMModel``: always returns the internal 2-tuple."""
-
-    def __init__(self, output=None):
-        self.calls: list[dict] = []
-        self._output = output
-
-    def __call__(self, input_ids, positions, intermediate_tensors, **kwargs):
-        self.calls.append(kwargs)
-        if self._output is not None:
-            return self._output
-        if kwargs.get("return_hidden_states"):
-            return _hidden_states(), {"hidden_states": {"layers": {0: _hidden_states()}}}
-        return _hidden_states(), None
-
-
-def _make_thinker(inner_output=None) -> tuple[Qwen3OmniMoeThinkerForConditionalGeneration, _InnerModelStub]:
-    thinker = object.__new__(Qwen3OmniMoeThinkerForConditionalGeneration)
-    nn.Module.__init__(thinker)
-    inner = _InnerModelStub(output=inner_output)
-    thinker.use_deepstack = False
-    thinker.language_model = SimpleNamespace(model=inner)
-    return thinker, inner
-
-
-def _forward_args():
-    return {
-        "input_ids": torch.zeros(_TOKENS, dtype=torch.long),
-        "positions": torch.arange(_TOKENS),
-    }
-
-
-def test_thinker_forward_returns_bare_tensor_without_capture():
-    thinker, _ = _make_thinker()
-    out = thinker.forward(**_forward_args())
-    assert isinstance(out, torch.Tensor)
-    assert out.shape == (_TOKENS, _HIDDEN)
-
-
-def test_thinker_forward_returns_tuple_when_capturing():
-    thinker, _ = _make_thinker()
-    out = thinker.forward(
-        **_forward_args(),
-        capture_layer_indices=[0, 2],
-        return_hidden_states=True,
-    )
-    assert isinstance(out, tuple)
-    hidden, captured = out
-    assert isinstance(hidden, torch.Tensor)
-    assert "hidden_states" in captured
-
-
-def test_thinker_forward_passes_through_intermediate_tensors():
-    it = IntermediateTensors({"hidden_states": _hidden_states(), "residual": _hidden_states()})
-    thinker, _ = _make_thinker(inner_output=it)
-    out = thinker.forward(**_forward_args())
-    assert out is it
-
-
-class _ThinkerStub(nn.Module):
-    """Records capture kwargs; mirrors the fixed thinker return contract."""
-
-    def __init__(self):
-        super().__init__()
-        self.received: dict = {}
-
-    def forward(self, **kwargs):
-        self.received = kwargs
-        if kwargs.get("return_hidden_states"):
-            return _hidden_states(), {"hidden_states": {"layers": {}}}
-        return _hidden_states()
-
-
-def _make_combined(*, is_staged_run: bool, accept_hidden_layer=6) -> Qwen3OmniMoeForConditionalGeneration:
-    model = object.__new__(Qwen3OmniMoeForConditionalGeneration)
-    nn.Module.__init__(model)
-    model.model_stage = "thinker"
-    model.is_staged_run = is_staged_run
-    model.talker_config = SimpleNamespace(accept_hidden_layer=accept_hidden_layer)
-    model.thinker = _ThinkerStub()
-    return model
-
-
-def test_combined_forward_plain_run_skips_capture_and_returns_tensor():
-    model = _make_combined(is_staged_run=False)
-    out = model.forward(**_forward_args())
-    assert "capture_layer_indices" not in model.thinker.received
-    assert isinstance(out, torch.Tensor)
-
-
-def test_combined_forward_staged_run_captures_and_returns_tuple():
-    model = _make_combined(is_staged_run=True)
-    out = model.forward(**_forward_args())
-    assert model.thinker.received["capture_layer_indices"] == [0, 6]
-    assert model.thinker.received["return_hidden_states"] is True
-    assert isinstance(out, tuple)
-
-
-def test_combined_forward_staged_run_without_accept_layer_returns_tensor():
-    model = _make_combined(is_staged_run=True, accept_hidden_layer=None)
-    out = model.forward(**_forward_args())
-    assert "capture_layer_indices" not in model.thinker.received
-    assert isinstance(out, torch.Tensor)
-
-
-def test_make_omni_output_accepts_bare_tensor():
-    model = _make_combined(is_staged_run=False)
-    out = model.make_omni_output(_hidden_states())
-    assert isinstance(out, OmniOutput)
-    assert out.text_hidden_states.shape == (_TOKENS, _HIDDEN)
-    assert out.multimodal_outputs == {}
-
-
-def test_make_omni_output_accepts_capture_tuple():
-    model = _make_combined(is_staged_run=True)
-    captured = {"hidden_states": {"layers": {}}}
-    out = model.make_omni_output((_hidden_states(), captured))
-    assert isinstance(out, OmniOutput)
-    assert out.multimodal_outputs == captured

tests/model_executor/models/qwen3_tts/test_code_predictor_dtype.py

@@ -57,7 +57,6 @@ def _build_mock_modules(mocker: MockerFixture) -> dict[str, object]:
     platforms_mock = mocker.MagicMock()
     platforms_mock.current_omni_platform.supports_torch_inductor.return_value = False
     platforms_mock.current_omni_platform.is_npu.return_value = False
-    platforms_mock.current_omni_platform.is_xpu.return_value = False
 
     logger_mock = mocker.MagicMock()
     logger_mock.init_logger = lambda name: mocker.MagicMock()
@@ -285,28 +284,6 @@ class TestCodePredictorDtypeAlignment:
         predictor._setup_compile()
         assert predictor._model_dtype == torch.float16
 
-    def test_setup_compile_uses_eager_on_xpu(self, mocker: MockerFixture, loaded_target_classes) -> None:
-        """XPU should avoid the torch.compile path used by CUDA."""
-        _, _, code_predictor_wrapper, _, _ = loaded_target_classes
-        cp_config, talker_config = _make_tiny_config(loaded_target_classes)
-        vllm_config = _make_vllm_config(mocker, max_num_seqs=2)
-
-        predictor = code_predictor_wrapper(
-            vllm_config=vllm_config,
-            config=cp_config,
-            talker_config=talker_config,
-        )
-
-        common_mod = sys.modules["vllm_omni.model_executor.models.common.qwen3_code_predictor"]
-        mocker.patch.object(common_mod.current_omni_platform, "supports_torch_inductor", return_value=True)
-        mocker.patch.object(common_mod.current_omni_platform, "is_xpu", return_value=True)
-        compile_mock = mocker.patch.object(common_mod.torch, "compile")
-
-        predictor._setup_compile()
-
-        compile_mock.assert_not_called()
-        assert predictor._compiled_model_fwd == predictor.model.forward
-
     def test_forward_with_mismatched_input_dtype(self, mocker: MockerFixture, loaded_target_classes) -> None:
         """forward() should not crash when inputs are float32 but model is float16."""
         _, _, code_predictor_wrapper, _, _ = loaded_target_classes

tests/model_executor/stage_input_processors/test_indextts2.py

@@ -116,7 +116,6 @@ def test_strip_stop_token_all_stop():
 
 def _conditioning_tensors():
     return {
-        "use_gpt_latent": True,
         "S_ref": torch.randn(1, 3, LATENT_DIM),
         "ref_mel": torch.randn(1, 80, 5),
         "style": torch.randn(1, 4),
@@ -144,8 +143,6 @@ def test_talker2s2mel_full_payload_flat_keys_builds_s2mel_contract():
         "meta.S_ref": cond["S_ref"],
         "meta.ref_mel": cond["ref_mel"],
         "meta.style": cond["style"],
-        "meta.use_gpt_latent": True,
-        "meta.duration_factor": 0.5,
     }
 
     result = talker2s2mel_full_payload(None, payload, SimpleNamespace(request_id="r-flat"))
@@ -159,45 +156,6 @@ def test_talker2s2mel_full_payload_flat_keys_builds_s2mel_contract():
     assert result["ref_mel"].device.type == "cpu"
     assert result["style"].device.type == "cpu"
     assert result["S_ref"].data_ptr() != cond["S_ref"].data_ptr()
-    assert result["duration_factor"] == 0.5
-
-
-def test_talker2s2mel_full_payload_defaults_missing_duration_factor_to_one():
-    cond = _conditioning_tensors()
-    payload = {
-        "codes.mel": torch.tensor([[10], [20]]),
-        "hidden_states.latent": torch.randn(2, LATENT_DIM),
-        "meta.S_ref": cond["S_ref"],
-        "meta.ref_mel": cond["ref_mel"],
-        "meta.style": cond["style"],
-        "meta.use_gpt_latent": True,
-    }
-
-    result = talker2s2mel_full_payload(None, payload, SimpleNamespace(request_id="r-default-duration"))
-
-    assert result is not None
-    assert result["duration_factor"] == 1.0
-
-
-def test_talker2s2mel_full_payload_preserves_explicit_request_seed():
-    cond = _conditioning_tensors()
-    payload = {
-        "codes.mel": torch.tensor([[10], [20]]),
-        "hidden_states.latent": torch.randn(2, LATENT_DIM),
-        "meta.S_ref": cond["S_ref"],
-        "meta.ref_mel": cond["ref_mel"],
-        "meta.style": cond["style"],
-        "meta.use_gpt_latent": True,
-    }
-    request = SimpleNamespace(
-        request_id="r-seeded",
-        sampling_params=SimpleNamespace(seed=0),
-    )
-
-    result = talker2s2mel_full_payload(None, payload, request)
-
-    assert result is not None
-    assert result["seed"] == 0
 
 
 def test_talker2s2mel_full_payload_nested_fallback_input():
@@ -248,7 +206,7 @@ def test_talker2s2mel_full_payload_trims_stop_token_and_matching_latent():
     assert result["code_lens"].tolist() == [2]
 
 
-def test_talker2s2mel_full_payload_rejects_mel_latent_length_mismatch():
+def test_talker2s2mel_full_payload_crops_mel_latent_to_common_length():
     cond = _conditioning_tensors()
     payload = {
         "codes.mel": torch.tensor([[1], [2], [3], [4]]),
@@ -256,44 +214,14 @@ def test_talker2s2mel_full_payload_rejects_mel_latent_length_mismatch():
         "meta": cond,
     }
 
-    with pytest.raises(ValueError, match="mel/latent length mismatch"):
+    result = talker2s2mel_full_payload(None, payload, SimpleNamespace(request_id="r-crop"))
-        talker2s2mel_full_payload(None, payload, SimpleNamespace(request_id="r-crop"))
-
-
-def test_talker2s2mel_full_payload_v25_code_only_does_not_invent_latent():
-    cond = _conditioning_tensors()
-    payload = {
-        "meta.use_gpt_latent": False,
-        "meta.S_ref": cond["S_ref"],
-        "meta.ref_mel": cond["ref_mel"],
-        "meta.style": cond["style"],
-    }
-
-    request = SimpleNamespace(
-        request_id="r-v25",
-        output_token_ids=[3, 4, STOP_MEL_TOKEN],
-    )
-    result = talker2s2mel_full_payload(None, payload, request)
 
     assert result is not None
-    assert result["mel_codes"].tolist() == [[3, 4]]
+    assert result["mel_codes"].tolist() == [[1, 2]]
+    assert result["latent"].shape == (1, 2, LATENT_DIM)
     assert result["code_lens"].tolist() == [2]
-    assert result["use_gpt_latent"] is False
-    assert "latent" not in result
 
 
-def test_talker2s2mel_full_payload_latent_mode_rejects_missing_latent():
+def test_talker2s2mel_full_payload_missing_required_fields_returns_none():
-    payload = {
+    assert talker2s2mel_full_payload(None, {"hidden_states.latent": torch.randn(1, LATENT_DIM)}, None) is None
-        "codes.mel": torch.tensor([[3], [4], [STOP_MEL_TOKEN]]),
+    assert talker2s2mel_full_payload(None, {"codes.mel": torch.tensor([[1]])}, None) is None
-        "meta.use_gpt_latent": True,
-    }
-
-    with pytest.raises(ValueError, match="missing hidden_states.latent"):
-        talker2s2mel_full_payload(None, payload, SimpleNamespace(request_id="r-latent"))
-
-
-def test_talker2s2mel_full_payload_missing_required_fields_raises():
-    with pytest.raises(ValueError, match="missing meta.use_gpt_latent"):
-        talker2s2mel_full_payload(None, {"hidden_states.latent": torch.randn(1, LATENT_DIM)}, None)
-    with pytest.raises(ValueError, match="missing meta.use_gpt_latent"):
-        talker2s2mel_full_payload(None, {"codes.mel": torch.tensor([[1]])}, None)

tests/model_extras/test_model_extras.py

@@ -3,8 +3,6 @@
 
 from __future__ import annotations
 
-from typing import Any
-
 import pytest
 from PIL import Image
 
@@ -137,6 +135,67 @@ def test_lingbot_extra_registry_declares_request_params() -> None:
     assert should_init_extra_args_for_non_diffusion_stages("LingBotVideoPipeline") is False
 
 
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_lingbot_text_to_image_prompt_builder_preserves_empty_negative_prompt() -> None:
+    assert build_text_to_image_prompt(
+        "LingBotVideoPipeline",
+        prompt="a red fox in fresh snow",
+        negative_prompt="",
+        height=192,
+        width=320,
+    ) == {
+        "prompt": "a red fox in fresh snow",
+        "modalities": ["image"],
+        "negative_prompt": "",
+    }
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_lingbot_image_to_video_prompt_builder() -> None:
+    image = Image.new("RGB", (320, 192), "red")
+    result = build_image_to_video_prompt(
+        "LingBotVideoPipeline",
+        prompt="the fox looks toward the camera",
+        negative_prompt=None,
+        media_inputs={"image": image},
+        height=192,
+        width=320,
+        num_frames=9,
+    )
+    assert result == {
+        "prompt": "the fox looks toward the camera",
+        "modalities": ["video"],
+        "multi_modal_data": {"image": image},
+        "num_frames": 9,
+    }
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+@pytest.mark.parametrize(
+    "media_inputs",
+    [
+        {},
+        {"image": "input.png"},
+        {"image": [Image.new("RGB", (16, 16))]},
+        {"image": Image.new("RGB", (16, 16)), "video": b"video"},
+    ],
+    ids=["missing", "path", "multiple", "extra-modality"],
+)
+def test_lingbot_image_to_video_prompt_builder_rejects_invalid_media(
+    media_inputs: dict[str, object],
+) -> None:
+    with pytest.raises(ValueError, match="exactly one PIL image"):
+        build_image_to_video_prompt(
+            "LingBotVideoPipeline",
+            prompt="move",
+            negative_prompt=None,
+            media_inputs=media_inputs,
+        )
+
+
 @pytest.mark.core_model
 @pytest.mark.cpu
 @pytest.mark.parametrize("pipeline_name", ["Cosmos3OmniDiffusersPipeline", "Cosmos3OmniPipeline"])
@@ -283,11 +342,8 @@ def test_ming_flash_omni_declared_extra_args_route_into_sampling_params() -> Non
 def test_ming_flash_omni_text_to_image_prompt_builder() -> None:
     assert build_text_to_image_prompt(
         "MingImagePipeline",
-        {
+        prompt="Please draw a cute cat.",
-            "prompt": "Please draw a cute cat.",
+        negative_prompt="ugly, blurry",
-            "modalities": ["image"],
-            "negative_prompt": "ugly, blurry",
-        },
         height=512,
         width=768,
     ) == {
@@ -304,7 +360,8 @@ def test_ming_flash_omni_text_to_image_prompt_builder() -> None:
     # pipeline's 1024x1024 default still applies.
     assert build_text_to_image_prompt(
         "MingImagePipeline",
-        {"prompt": "Draw a poster.", "modalities": ["image"]},
+        prompt="Draw a poster.",
+        negative_prompt=None,
     ) == {
         "prompt": "Draw a poster.",
         "modalities": ["image"],
@@ -334,6 +391,28 @@ def test_ming_flash_omni_image_to_image_prompt_builder() -> None:
     assert "negative_prompt" not in result
 
 
+@pytest.mark.core_model
+@pytest.mark.cpu
+@pytest.mark.parametrize("pipeline_name", ["Cosmos3OmniDiffusersPipeline", "Cosmos3OmniPipeline"])
+def test_cosmos3_text_to_image_prompt_builder_selects_image_modality(pipeline_name: str) -> None:
+    assert build_text_to_image_prompt(
+        pipeline_name,
+        prompt="a red sports car at golden hour",
+        negative_prompt="blurry, distorted",
+        height=1024,
+        width=1024,
+    ) == {
+        "prompt": "a red sports car at golden hour",
+        "modalities": ["image"],
+        "negative_prompt": "blurry, distorted",
+    }
+    assert build_text_to_image_prompt(
+        pipeline_name,
+        prompt="a red sports car",
+        negative_prompt=None,
+    ) == {"prompt": "a red sports car", "modalities": ["image"]}
+
+
 @pytest.mark.core_model
 @pytest.mark.cpu
 def test_audiox_extra_registry_declares_request_and_response_params() -> None:
@@ -426,11 +505,8 @@ def test_unknown_pipeline_has_empty_extra_registry() -> None:
 def test_bagel_text_to_image_prompt_builder() -> None:
     assert build_text_to_image_prompt(
         "BagelPipeline",
-        {
+        prompt="a cat",
-            "prompt": "a cat",
+        negative_prompt="blurry",
-            "modalities": ["image"],
-            "negative_prompt": "blurry",
-        },
         height=512,
         width=768,
     ) == {
@@ -467,17 +543,14 @@ def test_bagel_image_to_image_prompt_builder() -> None:
 
 @pytest.mark.core_model
 @pytest.mark.cpu
-def test_unknown_pipeline_preserves_canonical_text_to_image_prompt() -> None:
+def test_unknown_pipeline_uses_default_text_to_image_prompt() -> None:
-    canonical_prompt = {"prompt": "a cat", "modalities": ["image"]}
+    assert build_text_to_image_prompt(
-    assert (
+        "UnknownPipeline",
-        build_text_to_image_prompt(
+        prompt="a cat",
-            "UnknownPipeline",
+        negative_prompt=None,
-            canonical_prompt,
+        height=512,
-            height=512,
+        width=512,
-            width=512,
+    ) == {"prompt": "a cat"}
-        )
-        is canonical_prompt
-    )
 
 
 @pytest.mark.core_model
@@ -496,14 +569,12 @@ def test_unknown_pipeline_uses_default_image_to_image_prompt() -> None:
     }
 
 
-def _build_vace_prompt(media_inputs: dict[str, Any], *, num_frames: int = 5) -> dict:
+def _build_vace_prompt(media_inputs: dict[str, object], *, num_frames: int = 5) -> dict:
     return build_image_to_video_prompt(
         "WanVACEPipeline",
-        {
+        prompt="a bird flying",
-            "prompt": "a bird flying",
+        negative_prompt=None,
-            "modalities": ["video"],
+        media_inputs=media_inputs,
-            "multi_modal_data": media_inputs,
-        },
         height=16,
         width=320,
         num_frames=num_frames,
@@ -526,7 +597,7 @@ def _build_vace_prompt(media_inputs: dict[str, Any], *, num_frames: int = 5) ->
     ],
     ids=["i2v", "v2lf", "flf2v", "inpaint", "r2v"],
 )
-def test_vace_image_to_video_prompt_builder(media_inputs: dict[str, Any]) -> None:
+def test_vace_image_to_video_prompt_builder(media_inputs: dict[str, object]) -> None:
     result = _build_vace_prompt(media_inputs)
     mmd = result["multi_modal_data"]
     if "reference_images" in mmd:
@@ -545,7 +616,7 @@ def test_vace_image_to_video_prompt_builder(media_inputs: dict[str, Any]) -> Non
         ({"control_image": Image.new("RGB", (320, 16))}, "Unsupported VACE media input"),
     ],
 )
-def test_vace_rejects_invalid_media_combinations(media_inputs: dict[str, Any], message: str) -> None:
+def test_vace_rejects_invalid_media_combinations(media_inputs: dict[str, object], message: str) -> None:
     with pytest.raises(ValueError, match=message):
         _build_vace_prompt(media_inputs)
 
@@ -593,7 +664,8 @@ def test_mammothmoda2_extra_registry_declares_request_and_response_params() -> N
 
     wrapper_prompt = build_text_to_image_prompt(
         "MammothModa2ForConditionalGeneration",
-        {"prompt": "a cat", "modalities": ["image"]},
+        prompt="a cat",
+        negative_prompt=None,
         height=256,
         width=256,
     )
@@ -607,11 +679,8 @@ def test_mammothmoda2_text_to_image_prompt_builder() -> None:
     # prompt is ignored (MammothModa2 t2i uses CFG, not an explicit negative path).
     assert build_text_to_image_prompt(
         "MammothModa2DiTPipeline",
-        {
+        prompt="a cat",
-            "prompt": "a cat",
+        negative_prompt="blurry",
-            "modalities": ["image"],
-            "negative_prompt": "blurry",
-        },
         height=512,
         width=768,
     ) == {

tests/test_indextts2_optional_dependencies.py

@@ -1,155 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import builtins
-import importlib.util
-import platform
-import sys
-import types
-from pathlib import Path
-
-import pytest
-import tomllib
-from packaging.requirements import Requirement
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-ROOT = Path(__file__).parents[1]
-TEXT_PROCESSING_PATH = ROOT / "vllm_omni" / "model_executor" / "models" / "indextts2" / "text_processing_v2_5.py"
-INDEXTTS2_FRONTEND_PACKAGES = {
-    "fugashi",
-    "unidic-lite",
-    "wetext",
-    "wetextprocessing",
-}
-INSTALL_COMMAND = "pip install 'vllm-omni[indextts2]'"
-
-
-def _load_text_processing_module(monkeypatch: pytest.MonkeyPatch):
-    package_name = "_indextts2_optional_dependencies_test"
-    package = types.ModuleType(package_name)
-    package.__path__ = [str(TEXT_PROCESSING_PATH.parent)]
-    tokenizer = types.ModuleType(f"{package_name}.tokenizer_v2_5")
-    tokenizer.INDEXTTS25_TOKENIZER_FILE = "tokenizer.tiktoken"
-    tokenizer.encode_indextts25_text = lambda *args, **kwargs: []
-    tokenizer.lang_to_token = lambda lang: 0
-    tokenizer.normalize_language_code = lambda lang: lang
-    monkeypatch.setitem(sys.modules, package_name, package)
-    monkeypatch.setitem(sys.modules, tokenizer.__name__, tokenizer)
-
-    module_name = f"{package_name}.text_processing_v2_5"
-    spec = importlib.util.spec_from_file_location(module_name, TEXT_PROCESSING_PATH)
-    assert spec is not None and spec.loader is not None
-    module = importlib.util.module_from_spec(spec)
-    monkeypatch.setitem(sys.modules, module_name, module)
-    spec.loader.exec_module(module)
-    return module
-
-
-def _requirements_from_file(path: Path) -> list[Requirement]:
-    requirements = []
-    for raw_line in path.read_text().splitlines():
-        line = raw_line.split("#", 1)[0].strip()
-        if not line:
-            continue
-        if line.startswith(("-r ", "--requirement ")):
-            included = line.split(maxsplit=1)[1]
-            requirements.extend(_requirements_from_file(path.parent / included))
-            continue
-        requirements.append(Requirement(line))
-    return requirements
-
-
-def _block_import(monkeypatch: pytest.MonkeyPatch, package: str) -> None:
-    original_import = builtins.__import__
-
-    def controlled_import(name, *args, **kwargs):
-        if name == package or name.startswith(f"{package}."):
-            raise ModuleNotFoundError(f"No module named {package!r}", name=package)
-        return original_import(name, *args, **kwargs)
-
-    monkeypatch.setattr(builtins, "__import__", controlled_import)
-
-
-def test_indextts2_extra_declares_platform_specific_frontend_dependencies():
-    project = tomllib.loads((ROOT / "pyproject.toml").read_text())
-    requirements = [Requirement(value) for value in project["project"]["optional-dependencies"]["indextts2"]]
-
-    assert {
-        (requirement.name.lower(), str(requirement.specifier), str(requirement.marker) if requirement.marker else None)
-        for requirement in requirements
-    } == {
-        ("fugashi", ">=1.2.0", None),
-        ("unidic-lite", ">=1.0.0", None),
-        ("wetext", ">=0.0.9", 'sys_platform != "linux"'),
-        ("wetextprocessing", "", 'sys_platform == "linux"'),
-    }
-
-
-def test_common_requirements_exclude_indextts2_frontend_dependencies():
-    requirements = _requirements_from_file(ROOT / "requirements" / "common.txt")
-
-    assert not ({requirement.name.lower() for requirement in requirements} & INDEXTTS2_FRONTEND_PACKAGES)
-
-
-def test_wetext_normalizer_constructor_error_propagates(monkeypatch):
-    text_processing_v2_5 = _load_text_processing_module(monkeypatch)
-    sentinel = RuntimeError("wetext constructor sentinel")
-
-    class FailingNormalizer:
-        def __init__(self, *args, **kwargs):
-            raise sentinel
-
-    wetext = types.ModuleType("wetext")
-    wetext.Normalizer = FailingNormalizer
-    monkeypatch.setitem(sys.modules, "wetext", wetext)
-    monkeypatch.setattr(platform, "system", lambda: "Darwin")
-
-    with pytest.raises(RuntimeError) as exc_info:
-        text_processing_v2_5._load_wetext_normalizer("zh")
-
-    assert exc_info.value is sentinel
-
-
-def test_japanese_tagger_constructor_error_propagates(monkeypatch):
-    text_processing_v2_5 = _load_text_processing_module(monkeypatch)
-    sentinel = RuntimeError("fugashi constructor sentinel")
-
-    fugashi = types.ModuleType("fugashi")
-
-    def failing_tagger():
-        raise sentinel
-
-    fugashi.Tagger = failing_tagger
-    monkeypatch.setitem(sys.modules, "fugashi", fugashi)
-    monkeypatch.setitem(sys.modules, "unidic_lite", types.ModuleType("unidic_lite"))
-
-    with pytest.raises(RuntimeError) as exc_info:
-        text_processing_v2_5._load_japanese_tagger()
-
-    assert exc_info.value is sentinel
-
-
-def test_missing_wetext_backend_reports_indextts2_extra(monkeypatch):
-    text_processing_v2_5 = _load_text_processing_module(monkeypatch)
-    text_processing_v2_5._load_wetext_normalizer.cache_clear()
-    monkeypatch.setattr(platform, "system", lambda: "Darwin")
-    _block_import(monkeypatch, "wetext")
-
-    with pytest.raises(RuntimeError, match="IndexTTS 2.5 text normalization") as exc_info:
-        text_processing_v2_5._load_wetext_normalizer("zh")
-
-    assert INSTALL_COMMAND in str(exc_info.value)
-    text_processing_v2_5._load_wetext_normalizer.cache_clear()
-
-
-def test_missing_japanese_tagger_reports_indextts2_extra(monkeypatch):
-    text_processing_v2_5 = _load_text_processing_module(monkeypatch)
-    text_processing_v2_5._load_japanese_tagger.cache_clear()
-    _block_import(monkeypatch, "fugashi")
-
-    with pytest.raises(RuntimeError, match="IndexTTS 2.5 Japanese preprocessing") as exc_info:
-        text_processing_v2_5._load_japanese_tagger()
-
-    assert INSTALL_COMMAND in str(exc_info.value)
-    text_processing_v2_5._load_japanese_tagger.cache_clear()

tests/utils/test_mm_outputs_partition.py

@@ -1,7 +1,6 @@
 import pytest
 import torch
 
-import vllm_omni.utils.mm_outputs as mm_outputs
 from vllm_omni.utils.mm_outputs import partition_flat_payload, partition_payload_list
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
@@ -70,33 +69,3 @@ def test_partition_duplex_audio_transcript_metadata_to_client_mm():
     assert "meta.turn_end" in client
     assert "meta.native_duplex_segment_text" not in client
     assert "meta.native_duplex_segment_text" in inter
-
-
-def test_snapshot_mm_payload_coalesces_compatible_tensor_list(monkeypatch):
-    chunks = [
-        torch.tensor([[7]], dtype=torch.long),
-        torch.empty(0, 1, dtype=torch.long),
-        torch.tensor([[9], [10]], dtype=torch.long),
-    ]
-    real_cat = torch.cat
-    cat_shapes = []
-
-    def recording_cat(tensors, *args, **kwargs):
-        cat_shapes.append([tuple(tensor.shape) for tensor in tensors])
-        return real_cat(tensors, *args, **kwargs)
-
-    monkeypatch.setattr(torch, "cat", recording_cat)
-
-    result = mm_outputs.snapshot_mm_payload({"codes.mel": chunks})
-
-    assert cat_shapes == [[(1, 1), (0, 1), (2, 1)]]
-    output_chunks = result["codes.mel"]
-    assert [tuple(chunk.shape) for chunk in output_chunks] == [
-        (1, 1),
-        (0, 1),
-        (2, 1),
-    ]
-    torch.testing.assert_close(output_chunks[0], chunks[0])
-    torch.testing.assert_close(output_chunks[1], chunks[1])
-    torch.testing.assert_close(output_chunks[2], chunks[2])
-    assert output_chunks[0].untyped_storage().data_ptr() == output_chunks[2].untyped_storage().data_ptr()

tests/worker/test_omni_gpu_model_runner.py

@@ -5,7 +5,6 @@ import pytest
 import torch
 from vllm.v1.cudagraph_dispatcher import CUDAGraphMode
 
-from vllm_omni.worker.gpu_ar_model_runner import GPUARModelRunner
 from vllm_omni.worker.gpu_model_runner import (
     OmniGPUModelRunner,
     _filter_mrope_kwargs_for_model,
@@ -692,49 +691,6 @@ def test_full_payload_output_accumulation_hook_matrix():
         assert not runner._should_accumulate_full_payload_output()
 
 
-def _make_request_end_payload_runner(*, enabled=True, prefix_cache=None):
-    runner = object.__new__(GPUARModelRunner)
-    runner.model = SimpleNamespace(omni_payload_at_request_end=enabled)
-    runner.omni_prefix_cache = prefix_cache
-    runner.model_config = SimpleNamespace(
-        model_arch="IndexTTS25TalkerForConditionalGeneration",
-        model_stage="indextts2_5_talker",
-        async_chunk=False,
-        final_output=False,
-        custom_process_next_stage_input_func="module.full_payload",
-    )
-    runner._custom_process_func = object()
-    runner._pending_full_payload_send = {}
-    runner._stage_id = 0
-    runner._omni_connector = object()
-    return runner
-
-
-def test_request_end_payload_d2h_gate_requires_opt_in_and_no_prefix_cache():
-    assert _make_request_end_payload_runner()._should_defer_full_payload_d2h()
-    assert not _make_request_end_payload_runner(enabled=False)._should_defer_full_payload_d2h()
-    assert not _make_request_end_payload_runner(prefix_cache=object())._should_defer_full_payload_d2h()
-
-
-def test_request_end_payload_suppresses_per_step_multimodal_outputs():
-    runner = _make_request_end_payload_runner()
-
-    def unexpected_build(_payload):
-        raise AssertionError("request-end payloads must stay inside the GPU accumulator")
-
-    runner._build_multimodal_outputs = unexpected_build
-    pooler_inter = [{"codes.mel": torch.tensor([[7]])}]
-
-    inter_stage, client = runner._build_omni_step_outputs(
-        pooler_inter,
-        pooler_inter,
-        defer_full_payload_d2h=True,
-    )
-
-    assert inter_stage is None
-    assert client is None
-
-
 def test_sync_local_stage_payloads_retains_payload_until_request_is_active():
     runner = object.__new__(OmniGPUModelRunner)
     payload = {"codes": {"audio": [1, 2, 3]}}

tests/worker/test_worker_base.py

@@ -30,12 +30,9 @@ def _fake_memory_profiling(*, non_torch: int, torch_peak: int):
 
     @contextmanager
     def _mp(snapshot, weights_memory):  # noqa: ARG001 - signature parity only
-        # total_consumed mirrors upstream vllm.utils.mem_utils.MemoryProfilingResult
-        # (added by upstream 58b2012aa2) and is read by OmniGPUWorkerBase.
         yield SimpleNamespace(
             non_torch_increase=non_torch,
             torch_peak_increase=torch_peak,
-            total_consumed=torch_peak + non_torch,
         )
 
     return _mp

vllm_omni/benchmarks/patch/patch.py

@@ -12,7 +12,7 @@ import traceback
 import uuid
 import wave
 from collections.abc import Iterable
-from dataclasses import dataclass, replace
+from dataclasses import dataclass
 from datetime import datetime
 from typing import Any, Literal
 from urllib.parse import parse_qsl, urlencode, urlsplit, urlunsplit
@@ -1542,7 +1542,6 @@ async def benchmark(
     ready_check_timeout_sec: int = 600,
     ssl_context: ssl.SSLContext | bool | None = None,
     self_timed: bool = False,
-    probe_request_rate: float = 0.0,
 ):
     try:
         request_func = ASYNC_REQUEST_FUNCS[endpoint_type]
@@ -1688,31 +1687,6 @@ async def benchmark(
         async with semaphore:
             return await request_func(request_func_input=request_func_input, session=session, pbar=pbar)
 
-    # Ported from upstream vLLM v0.27.0 (vllm/benchmarks/serve.py), which added
-    # the probe_request_rate background probe; this file is the patched copy of
-    # that module and tracks its upstream base.
-    probe_outputs: list[MixRequestFuncOutput] = []
-    probe_stop = asyncio.Event()
-
-    async def probe_loop():
-        probe_input = replace(
-            test_input,
-            prompt="Hi",
-            prompt_len=1,
-            output_len=1,
-            multi_modal_content=None,
-            chat_messages=None,
-        )
-        interval = 1 / probe_request_rate
-        while not probe_stop.is_set():
-            probe_outputs.append(await request_func(request_func_input=probe_input, session=session))
-            await asyncio.sleep(interval)
-
-    probe_task: asyncio.Task | None = None
-    if probe_request_rate > 0:
-        print(f"Probe request rate: {probe_request_rate} req/s")
-        probe_task = asyncio.create_task(probe_loop())
-
     benchmark_start_time = time.perf_counter()
     tasks: list[asyncio.Task] = []
 
@@ -1778,10 +1752,6 @@ async def benchmark(
         )
     outputs: list[MixRequestFuncOutput] = await asyncio.gather(*tasks)
 
-    if probe_task is not None:
-        probe_stop.set()
-        await probe_task
-
     if pbar is not None:
         pbar.close()
 
@@ -1849,19 +1819,11 @@ async def benchmark(
             "input_lens": [output.prompt_len for output in outputs],
             "errors": [output.error for output in outputs],
         }
-    # Plain-vLLM backends (e.g. the vLLM-text perf config) return upstream
+    duplex_request_metrics = [metric for output in outputs for metric in (output.duplex_request_metrics or [])]
-    # RequestFuncOutput objects without the Mix duplex fields; read them
-    # tolerantly or the whole benchmark result is discarded ("fallback to
-    # template", completed=0) after every request already succeeded.
-    duplex_request_metrics = [
-        metric for output in outputs for metric in (getattr(output, "duplex_request_metrics", None) or [])
-    ]
     if duplex_request_metrics:
         result["duplex_request_metrics"] = duplex_request_metrics
     duplex_session_metrics = [
-        session_metrics
+        output.duplex_session_metrics for output in outputs if output.duplex_session_metrics is not None
-        for output in outputs
-        if (session_metrics := getattr(output, "duplex_session_metrics", None)) is not None
     ]
     if duplex_session_metrics:
         result["duplex_session_metrics"] = duplex_session_metrics

vllm_omni/config/pipeline_registry.py

@@ -61,10 +61,7 @@ from vllm_omni.model_executor.models.hunyuan_image3.pipeline import (
     HUNYUAN_IMAGE3_PIPELINE,
 )
 from vllm_omni.model_executor.models.hunyuan_video.pipeline import HUNYUAN_VIDEO_15_PIPELINE
-from vllm_omni.model_executor.models.indextts2.pipeline import (
+from vllm_omni.model_executor.models.indextts2.pipeline import INDEXTTS2_PIPELINE
-    INDEXTTS2_PIPELINE,
-    INDEXTTS25_PIPELINE,
-)
 from vllm_omni.model_executor.models.lance.pipeline import LANCE_PIPELINE
 from vllm_omni.model_executor.models.mammoth_moda2.pipeline import (
     MAMMOTH_MODA2_AR_PIPELINE,
@@ -167,7 +164,6 @@ OMNI_PIPELINES: dict[str, PipelineConfig | PipelineResolverFunc] = {
     "higgs_multimodal_qwen3": HIGGS_AUDIO_V3_PIPELINE,
     "dynin_omni": DYNIN_OMNI_PIPELINE,
     "indextts2": INDEXTTS2_PIPELINE,
-    "indextts2_5": INDEXTTS25_PIPELINE,
     "soulxsinger_svc": SOULXSINGER_SVC_PIPELINE,
     "soulxsinger_svs": SOULXSINGER_SVS_PIPELINE,
 }

vllm_omni/core/sched/omni_ar_scheduler.py

@@ -325,18 +325,6 @@ class OmniARScheduler(OmniSchedulerMixin, VLLMScheduler):
                 # max(0, computed - in_flight), so a leaked counter silently
                 # freezes sliding-window block freeing.
                 request.num_in_flight_tokens -= num_tokens_scheduled
-            # vLLM 0.27 (a0c092ee72) removed the async_tokens_to_discard
-            # handling from the upstream scheduler and replaced it with the
-            # num_stale_output_tokens/is_stale mechanism. Omni's discard
-            # sites (segment stop, streaming-session replacement) record the
-            # in-flight share here; the delayed outputs are dropped below
-            # instead of decrementing num_output_placeholders (which the
-            # discard zeroed) and underflowing the upstream assert.
-            output_is_stale = False
-            if request is not None and request.num_stale_output_tokens > 0:
-                output_is_stale = True
-                request.num_stale_output_tokens -= num_tokens_scheduled
-                assert request.num_stale_output_tokens >= 0
             if failed_kv_load_req_ids and req_id in failed_kv_load_req_ids:
                 # Skip requests that were recovered from KV load failure
                 continue
@@ -345,12 +333,6 @@ class OmniARScheduler(OmniSchedulerMixin, VLLMScheduler):
                 # request is aborted while the model is executing it (e.g.,
                 # in pipeline parallelism or async scheduling).
                 continue
-            if output_is_stale:
-                # Output of a step scheduled before the request's in-flight
-                # tokens were discarded (segment stop / session replacement).
-                # num_computed_tokens was rolled back at the discard site, so
-                # this output must not be appended or emitted.
-                continue
 
             req_index = model_runner_output.req_id_to_index[req_id]
             generated_token_ids = sampled_token_ids[req_index] if sampled_token_ids else []
@@ -470,28 +452,10 @@ class OmniARScheduler(OmniSchedulerMixin, VLLMScheduler):
                             # connector. This update only resumes polling for the next segment.
                             self.chunk_transfer_adapter.segment_finished_requests.discard(request.request_id)
                     outstanding_async_tokens = request.num_output_placeholders
-                    # Always record the discard signal (0 when nothing is in
-                    # flight). Upstream a0c092ee72 removed the
-                    # `async_tokens_to_discard` default from `Request`; it
-                    # remains an omni-only signal set here on every segment
-                    # stop, so a stop with no outstanding placeholders
-                    # explicitly records 0.
-                    request.async_tokens_to_discard = outstanding_async_tokens
-                    # Seed the stale share in SCHEDULED-token units:
-                    # num_in_flight_tokens is exactly the unreported steps'
-                    # num_tokens_scheduled sum (settled per frame at the top
-                    # of this loop), and the drain subtracts each arriving
-                    # frame's num_tokens_scheduled — commensurable by
-                    # construction, so pre-discard frames drain to exactly
-                    # zero. Seeding from num_output_placeholders swallowed
-                    # valid new-segment frames or underflowed the drain
-                    # assert whenever placeholder counts diverged from
-                    # scheduled counts (spec drafts, in-flight prefill).
-                    if request.num_in_flight_tokens > 0:
-                        request.num_stale_output_tokens += request.num_in_flight_tokens
                     if outstanding_async_tokens > 0:
                         # Discard only outputs that are already in flight and
                         # roll back their optimistic computed-token accounting.
+                        request.async_tokens_to_discard = outstanding_async_tokens
                         request.num_computed_tokens -= outstanding_async_tokens
                         request.num_output_placeholders = 0
                     request.spec_token_ids = []
@@ -618,15 +582,6 @@ class OmniARScheduler(OmniSchedulerMixin, VLLMScheduler):
         req_id = session.request_id
         self._new_prompt_len_snapshot[req_id] = len(update.prompt_token_ids)
         outstanding_async_tokens = getattr(session, "num_output_placeholders", 0)
-        # Seed the stale share in SCHEDULED-token units (see the segment-stop
-        # site in update_from_output): num_in_flight_tokens matches what each
-        # pre-replacement frame will drain, so the counter reaches exactly
-        # zero and the new segment's frames are never swallowed. This also
-        # covers an in-flight prefill chunk, which carries no placeholders
-        # but must still have its late output dropped.
-        in_flight_tokens = int(getattr(session, "num_in_flight_tokens", 0) or 0)
-        if in_flight_tokens > 0:
-            session.num_stale_output_tokens += in_flight_tokens
         if outstanding_async_tokens > 0:
             # Async scheduling may already have sampled the previous
             # segment's next token. Drop that late token instead of

vllm_omni/core/sched/omni_generation_scheduler.py

@@ -374,18 +374,6 @@ class OmniGenerationScheduler(OmniSchedulerMixin, VLLMScheduler):
                 # max(0, computed - in_flight), so a leaked counter silently
                 # freezes sliding-window block freeing.
                 request.num_in_flight_tokens -= num_tokens_scheduled
-            # vLLM 0.27 (a0c092ee72) removed the async_tokens_to_discard
-            # handling from the upstream scheduler and replaced it with the
-            # num_stale_output_tokens/is_stale mechanism. Omni's discard
-            # sites (segment stop, streaming-session replacement) record the
-            # in-flight share here; the delayed outputs are dropped below
-            # instead of decrementing num_output_placeholders (which the
-            # discard zeroed) and underflowing the upstream assert.
-            output_is_stale = False
-            if request is not None and request.num_stale_output_tokens > 0:
-                output_is_stale = True
-                request.num_stale_output_tokens -= num_tokens_scheduled
-                assert request.num_stale_output_tokens >= 0
             if failed_kv_load_req_ids and req_id in failed_kv_load_req_ids:
                 # Skip requests that were recovered from KV load failure
                 continue
@@ -393,12 +381,6 @@ class OmniGenerationScheduler(OmniSchedulerMixin, VLLMScheduler):
                 # Request may already be finished (e.g., aborted during
                 # execution / pipeline parallelism / async scheduling).
                 continue
-            if output_is_stale:
-                # Output of a step scheduled before the request's in-flight
-                # tokens were discarded (segment stop / session replacement).
-                # num_computed_tokens was rolled back at the discard site, so
-                # this output must not be appended or emitted.
-                continue
 
             req_index = model_runner_output.req_id_to_index[req_id]
             generated_token_ids = sampled_token_ids[req_index] if sampled_token_ids else []

vllm_omni/core/sched/omni_scheduler_mixin.py

@@ -94,17 +94,6 @@ class OmniSchedulerMixin:
             adapter.segment_finished_requests.discard(session.request_id)
         session._output_token_ids.clear()
         session._all_token_ids.clear()
-        # In-flight outputs from the previous segment were optimistically
-        # scheduled (async lookahead). Mark them stale so update_from_output
-        # drops them instead of underflowing num_output_placeholders
-        # (vLLM 0.27 a0c092ee72 removed async_tokens_to_discard). Seed in
-        # SCHEDULED-token units — num_in_flight_tokens matches what each
-        # pre-replacement frame will drain, so the counter reaches exactly
-        # zero; a placeholder-based seed swallowed valid new-segment frames
-        # whenever placeholder counts diverged from scheduled counts.
-        session.num_stale_output_tokens += int(getattr(session, "num_in_flight_tokens", 0) or 0)
-        session.num_output_placeholders = 0
-        session.spec_token_ids = []
         new_prompt = update.prompt_token_ids or ()
         session._all_token_ids.extend(new_prompt)
         session.num_computed_tokens = 0

vllm_omni/core/sched/omni_scheduling_coordinator.py

@@ -57,10 +57,9 @@ _FULL_PAYLOAD_INPUT_STAGES: frozenset[tuple[str, str]] = frozenset(
         # audex TTA: tta thinker (Stage 0) -> XCodec1 (Stage 1, always sync
         # full-payload — CNN codec decoded over the full sequence).
         ("AudexXCodec1", "audex_xcodec"),
-        # indextts2 / indextts2_5: talker (Stage 0) -> s2mel decoder
+        # indextts2: indextts2_talker (Stage 0) -> indextts2_s2mel_decoder
-        # (Stage 1). Stage 1 consumes the complete mel/optional-latent payload.
+        # (Stage 1). Stage 1 consumes the complete mel/latent payload.
         ("IndexTTS2S2MelDecoder", "indextts2_s2mel_decoder"),
-        ("IndexTTS25S2MelDecoder", "indextts2_5_s2mel_decoder"),
         # dynin: token2text (Stage 0) -> token2image (Stage 1) ->
         # token2audio (Stage 2).  Producer wires via
         # custom_process_next_stage_input_func: *_full_payload in deploy yaml.

vllm_omni/deploy/indextts2_5.yaml

@@ -1,64 +0,0 @@
-async_chunk: false
-trust_remote_code: true
-pipeline: indextts2_5
-connectors:
-  connector_of_shared_memory:
-    name: SharedMemoryConnector
-    extra:
-      codec_streaming: false
-      connector_get_sleep_s: 0.01
-      connector_get_max_wait_first_chunk: 5000
-      connector_get_max_wait: 500
-stages:
-  - stage_id: 0
-    max_num_seqs: 4
-    # Triton matches the native GPT-2 logits for this non-RoPE model.
-    # FlashInfer produces materially different semantic-token logits.
-    attention_backend: TRITON_ATTN
-    gpu_memory_utilization: 0.4
-    enable_prefix_caching: false
-    enable_chunked_prefill: false
-    skip_tokenizer_init: true
-    async_scheduling: true
-    max_num_batched_tokens: 2560
-    max_model_len: 2560
-    devices: "0"
-    output_connectors:
-      to_stage_1: connector_of_shared_memory
-    hf_overrides:
-      use_gpt_latent: false
-    # Stage 0 uses plain vLLM sampling, not the upstream default
-    # num_beams=3 beam search. Use upstream num_beams=1 for parity comparisons.
-    default_sampling_params:
-      temperature: 0.8
-      top_p: 0.8
-      top_k: 30
-      max_tokens: 1500
-      repetition_penalty: 10.0
-      stop_token_ids: [8193]
-      detokenize: false
-  - stage_id: 1
-    max_num_seqs: 4
-    gpu_memory_utilization: 0.4
-    enable_prefix_caching: false
-    skip_tokenizer_init: true
-    enforce_eager: true
-    async_scheduling: true
-    max_num_batched_tokens: 8192
-    max_model_len: 32768
-    devices: "0"
-    input_connectors:
-      from_stage_0: connector_of_shared_memory
-    hf_overrides:
-      use_gpt_latent: false
-      diffusion_steps: 25
-      s2mel_dit_bf16: true
-      s2mel_cfm_batch_size: 4
-      inference_cfg_rate: 0.7
-      s2mel_dit_cuda_graph: false
-      s2mel_dit_torch_compile: true
-      s2mel_dit_torch_compile_mode: default
-      s2mel_vocoder_torch_compile: false
-    default_sampling_params:
-      temperature: 0.0
-      max_tokens: 32767

vllm_omni/deploy/qwen2_5_omni.yaml

@@ -13,8 +13,8 @@
 #     the custom generation loop — set explicitly everywhere).
 #   * AR stages (thinker, talker):   model-dependent. Qwen2.5-Omni runs eager
 #     on CUDA (thinker uses custom ops that don't trace cleanly); NPU / XPU
-#     overrides are documented below. Qwen3-Omni / Qwen3-TTS AR stages use the
+#     platform overrides flip back to false where cudagraph is verified.
-#     default (false = cudagraph on).
+#     Qwen3-Omni / Qwen3-TTS AR stages use the default (false = cudagraph on).
 async_chunk: false
 
 stages:
@@ -82,15 +82,13 @@ platforms:
 
   xpu:
     stages:
-      # PyTorch 2.13 XPU Dynamo raises duplicate-handler assertions while
+      # Verified on 2x Intel Arc Pro B60. Both AR stages use cudagraphs.
-      # compiling both Qwen2.5 AR stages. Keep them eager until that toolchain
-      # issue is fixed; stage 2 already inherits enforce_eager: true above.
       - stage_id: 0
         gpu_memory_utilization: 0.9
-        enforce_eager: true
+        enforce_eager: false
       - stage_id: 1
         gpu_memory_utilization: 0.5
-        enforce_eager: true
+        enforce_eager: false
       - stage_id: 2
         gpu_memory_utilization: 0.3
         # Stage 2 colocates with stage 1's device on XPU.

vllm_omni/diffusion/compile.py

@@ -60,10 +60,7 @@ def regionally_compile(
     for name, submod in model.named_modules():
         if _matches_repeated_block(name, submod, repeated_blocks, repeated_block_attrs):
             # Compile the block compute while keeping nn.Module.__call__ hooks
-            # outside the compiled graph. NOTE: anything that wraps this
+            # outside the compiled graph.
-            # callable must stay signature-transparent — cache-dit's
-            # BlockAdapter matches blocks by inspecting forward's parameter
-            # names and return annotation, which torch.compile preserves.
             compiled_forwards.append(
                 (
                     submod,

vllm_omni/diffusion/distributed/parallel_state.py

@@ -28,8 +28,6 @@ If you only need to use the distributed environment without model parallelism,
  you can skip the model parallel initialization and destruction steps.
 """
 
-import inspect
-
 import torch
 import torch.distributed
 import vllm.distributed.parallel_state as vllm_parallel_state
@@ -526,23 +524,13 @@ def init_vllm_model_parallel_group(
     local_rank: int,
     backend: str,
     group_name: str,
-    use_all2all: bool = False,
 ) -> vllm_parallel_state.GroupCoordinator:
-    # vLLM 0.27's MoE oracle (make_unquantized_moe_kernel ->
-    # get_ep_all2all_manager) asserts that the EP group's device communicator
-    # carries an all2all_manager, and vLLM only builds one when the group is
-    # created with use_all2all=True. Older vLLM has neither the kwarg nor the
-    # requirement, so forward it only when supported.
-    kwargs = {}
-    if use_all2all and "use_all2all" in inspect.signature(vllm_parallel_state.init_model_parallel_group).parameters:
-        kwargs["use_all2all"] = True
     return vllm_parallel_state.init_model_parallel_group(
         group_ranks=group_ranks,
         local_rank=local_rank,
         backend=backend,
         group_name=group_name,
         use_device_communicator=True,
-        **kwargs,
     )
 
 
@@ -966,7 +954,6 @@ def initialize_model_parallel(
             local_rank=get_world_group().local_rank,
             backend=backend,
             group_name="ep",
-            use_all2all=True,
         )
 
     init_dit_group(dit_parallel_size, backend)

vllm_omni/diffusion/layers/fused_moe.py

@@ -75,7 +75,7 @@ class FusedMoE:
         # Current vLLM FusedMoE handles output reduction internally.
         kwargs.pop("reduce_results", None)
         # FusedMoE is now a factory function; call it to get a MoERunner.
-        from vllm.model_executor.layers.fused_moe import FusedMoEFactory as _FusedMoE
+        from vllm.model_executor.layers.fused_moe import FusedMoE as _FusedMoE
 
         moe_runner = _FusedMoE(prefix=prefix, **kwargs)
 

vllm_omni/diffusion/models/diffusers_adapter/pipeline_diffusers_adapter.py

@@ -23,11 +23,7 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from torch import nn
 
 from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
-from vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils import (
+from vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils import BasePipelineUtils, get_pipeline_utils
-    BasePipelineUtils,
-    get_pipeline_utils,
-    validate_model_compatibility,
-)
 from vllm_omni.diffusion.models.diffusers_adapter.quantization_utils import (
     apply_diffusers_quantization_config,
     convert_diffusers_quantization_config,
@@ -40,22 +36,6 @@ from vllm_omni.platforms import current_omni_platform
 
 logger = logging.getLogger(__name__)
 
-# Preference chain for CUDA flash attention in diffusers, best first. Shared
-# with the accuracy tests' diffusers reference runs so both sides of a
-# similarity comparison resolve to the same backend on any given image
-# (hub kernels may lack a build variant for the image's torch).
-CUDA_FLASH_ATTENTION_BACKEND_ATTEMPTS: list[str] = [
-    "_flash_3_hub",
-    "_flash_3_varlen_hub",
-    "_flash_3",
-    "_flash_varlen_3",
-    "flash_hub",
-    "flash_varlen_hub",
-    "flash",
-    "flash_varlen",
-    "_native_flash",
-]
-
 _DIFFUSERS_CONFIG_LOAD_KWARGS = {
     "cache_dir",
     "dduf_entries",
@@ -114,7 +94,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
 
         model_id = self.od_config.model
         dtype = self.od_config.dtype
-        validate_model_compatibility(self.od_config)
 
         load_kwargs = {
             "torch_dtype": dtype,
@@ -135,8 +114,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
         logger.debug(f"Loading diffusers pipeline with kwargs: {load_kwargs}")
 
         self._pipeline = DiffusionPipeline.from_pretrained(model_id, **load_kwargs)
-        if pipeline_class_name is None:
-            self._pipeline_utils = get_pipeline_utils(self._pipeline.__class__.__name__)
         self._pipeline_utils.apply_post_load_updates(self._pipeline, self.od_config)
 
         self._pipeline.to(self.device)
@@ -153,14 +130,14 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
         # VAE slicing and tiling: try-catch because not all models have VAE
         if self.od_config.vae_use_slicing:
             try:
-                self._pipeline_utils.enable_vae_optimization(self._pipeline, "slicing")
+                self._pipeline.enable_vae_slicing()
             except Exception as e:
                 logger.warning(
                     f"Failed to enable VAE slicing for diffusers pipeline {self._pipeline.__class__.__name__}: {e}"
                 )
         if self.od_config.vae_use_tiling:
             try:
-                self._pipeline_utils.enable_vae_optimization(self._pipeline, "tiling")
+                self._pipeline.enable_vae_tiling()
             except Exception as e:
                 logger.warning(
                     f"Failed to enable VAE tiling for diffusers pipeline {self._pipeline.__class__.__name__}: {e}"
@@ -302,7 +279,19 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
                     )
                     attention_backend_attempts.append("native")
                 else:
-                    attention_backend_attempts.extend(CUDA_FLASH_ATTENTION_BACKEND_ATTEMPTS)
+                    attention_backend_attempts.extend(
+                        [
+                            "_flash_3_hub",
+                            "_flash_3_varlen_hub",
+                            "_flash_3",
+                            "_flash_varlen_3",
+                            "flash_hub",
+                            "flash_varlen_hub",
+                            "flash",
+                            "flash_varlen",
+                            "_native_flash",
+                        ]
+                    )
             case "SAGE_ATTN":
                 attention_backend_attempts.extend(["sage_hub", "sage", "sage", "sage_varlen"])
             case "ASCEND":
@@ -400,8 +389,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
         else:
             kwargs["generator"] = torch.Generator(device=sampling.generator_device)
 
-        self._pipeline_utils.update_call_kwargs(self.od_config, kwargs)
-
         logger.info(
             "Calling diffusers pipeline with kwargs: %s", DiffusersAdapterPipeline._summarize_call_kwargs_value(kwargs)
         )
@@ -506,8 +493,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
         """
         from vllm_omni.diffusion.data import DiffusionOutput
 
-        output = self._pipeline_utils.normalize_output(self._pipeline, self.od_config, output)
-
         if hasattr(output, "images"):
             # Preserve diffusers image format (`output_type`)
             return DiffusionOutput(output=output.images)

vllm_omni/diffusion/models/diffusers_adapter/pipeline_utils.py

@@ -1,10 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import inspect
-import json
-import logging
-from pathlib import Path
 from typing import Any
 
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
@@ -12,74 +8,6 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from vllm_omni.diffusion.data import OmniDiffusionConfig
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
-logger = logging.getLogger(__name__)
-
-LTX25_ORIGINAL_MODEL_ID = "Lightricks/LTX-2.5"
-LTX25_DIFFUSERS_MODEL_ID = "Lightricks/LTX-2.5-Diffusers"
-LTX25_DIFFUSERS_COMMIT = "7564fb016dabda0c943416190fc92398c50b1b20"
-
-# Diffusers' public LTX-2.5 schedule intentionally excludes the scheduler's
-# terminal zero. FlowMatchEulerDiscreteScheduler appends that internally.
-LTX25_DISTILLED_SIGMAS = [1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875]
-
-_LTX25_DISTILLED_GUIDANCE_DEFAULTS: dict[str, float] = {
-    "guidance_scale": 1.0,
-    "audio_guidance_scale": 1.0,
-    "stg_scale": 0.0,
-    "audio_stg_scale": 0.0,
-    "modality_scale": 1.0,
-    "audio_modality_scale": 1.0,
-    "guidance_rescale": 0.0,
-    "audio_guidance_rescale": 0.0,
-}
-
-
-def _supports_ltx25_diffusers() -> bool:
-    """Check the standard-pipeline capabilities added with LTX-2.5."""
-    try:
-        import diffusers
-        import transformers
-
-        pipeline_class = diffusers.LTX2Pipeline
-        init_parameters = inspect.signature(pipeline_class.__init__).parameters
-        return (
-            "duration_head" in init_parameters
-            and "prompt_enhancer" in init_parameters
-            and hasattr(transformers, "Gemma4UnifiedForConditionalGeneration")
-        )
-    except (AttributeError, ImportError, RuntimeError):
-        return False
-
-
-def is_ltx25_diffusers_model(od_config: OmniDiffusionConfig) -> bool:
-    """Return whether config targets the official converted LTX-2.5 repo."""
-    normalized_model = str(od_config.model).rstrip("/\\").replace("\\", "/").lower()
-    if normalized_model == LTX25_DIFFUSERS_MODEL_ID.lower():
-        return True
-
-    # A downloaded checkpoint may be served from any directory name. The
-    # duration-head component is the pipeline-level marker introduced for
-    # LTX-2.5; older LTX-2.0/2.3 indexes do not contain it.
-    model_path = Path(str(od_config.model)).expanduser()
-    subfolder = od_config.diffusers_load_kwargs.get("subfolder")
-    if subfolder is not None:
-        model_path /= str(subfolder)
-    try:
-        with (model_path / "model_index.json").open(encoding="utf-8") as model_index_file:
-            model_index = json.load(model_index_file)
-    except (OSError, TypeError, ValueError):
-        return False
-    if not isinstance(model_index, dict):
-        return False
-
-    duration_head = model_index.get("duration_head")
-    return (
-        model_index.get("_class_name") == "LTX2Pipeline"
-        and isinstance(duration_head, list)
-        and bool(duration_head)
-        and duration_head[0] is not None
-    )
-
 
 class BasePipelineUtils:
     """No-op hooks for pipeline-specific diffusers adapter behavior."""
@@ -90,112 +18,9 @@ class BasePipelineUtils:
     def apply_post_load_updates(self, pipeline: DiffusionPipeline, od_config: OmniDiffusionConfig) -> None:
         pass
 
-    def enable_vae_optimization(self, pipeline: DiffusionPipeline, optimization: str) -> None:
-        getattr(pipeline, f"enable_vae_{optimization}")()
-
     def validate_runtime_sampling_params(self, sampling: OmniDiffusionSamplingParams) -> None:
         pass
 
-    def update_call_kwargs(
-        self,
-        od_config: OmniDiffusionConfig,
-        call_kwargs: dict[str, Any],
-    ) -> None:
-        pass
-
-    def normalize_output(
-        self,
-        pipeline: DiffusionPipeline,
-        od_config: OmniDiffusionConfig,
-        output: Any,
-    ) -> Any:
-        return output
-
-
-def validate_model_compatibility(od_config: OmniDiffusionConfig) -> None:
-    """Reject known model/dependency combinations before loading weights."""
-    normalized_model = str(od_config.model).rstrip("/\\").replace("\\", "/").lower()
-    if normalized_model == LTX25_ORIGINAL_MODEL_ID.lower():
-        raise ValueError(
-            f"{LTX25_ORIGINAL_MODEL_ID} is a component-weight repository without a Diffusers "
-            f"model_index.json. Use the official converted repository {LTX25_DIFFUSERS_MODEL_ID} "
-            "with --diffusion-load-format diffusers."
-        )
-
-    if is_ltx25_diffusers_model(od_config) and not _supports_ltx25_diffusers():
-        raise ImportError(
-            f"{LTX25_DIFFUSERS_MODEL_ID} requires unreleased LTX-2.5 support from Diffusers and "
-            "Gemma 4 Unified support from Transformers. Install the source-preview dependencies with "
-            "`python -m pip install --upgrade 'transformers>=5.10.1' "
-            f"'diffusers @ git+https://github.com/huggingface/diffusers.git@{LTX25_DIFFUSERS_COMMIT}'`."
-        )
-
-
-class LTX2PipelineUtils(BasePipelineUtils):
-    """Diffusers call policy for the official default LTX-2.5 checkpoint."""
-
-    def enable_vae_optimization(self, pipeline: DiffusionPipeline, optimization: str) -> None:
-        pipeline_method = getattr(pipeline, f"enable_vae_{optimization}", None)
-        if callable(pipeline_method):
-            pipeline_method()
-        else:
-            getattr(pipeline.vae, f"enable_{optimization}")()
-
-    def update_call_kwargs(
-        self,
-        od_config: OmniDiffusionConfig,
-        call_kwargs: dict[str, Any],
-    ) -> None:
-        if not is_ltx25_diffusers_model(od_config):
-            return
-
-        has_sigmas = call_kwargs.get("sigmas") is not None
-        has_timesteps = call_kwargs.get("timesteps") is not None
-        has_step_count = call_kwargs.get("num_inference_steps") is not None
-        if not has_sigmas and not has_timesteps:
-            if has_step_count:
-                logger.warning(
-                    "LTX-2.5 distilled inference received num_inference_steps without sigmas. "
-                    "Diffusers will use a generic schedule; omit num_inference_steps to use the "
-                    "official distilled schedule."
-                )
-            else:
-                call_kwargs["sigmas"] = list(LTX25_DISTILLED_SIGMAS)
-                call_kwargs["num_inference_steps"] = len(LTX25_DISTILLED_SIGMAS)
-
-        for key, value in _LTX25_DISTILLED_GUIDANCE_DEFAULTS.items():
-            if call_kwargs.get(key) is None:
-                call_kwargs[key] = value
-
-    def normalize_output(
-        self,
-        pipeline: DiffusionPipeline,
-        od_config: OmniDiffusionConfig,
-        output: Any,
-    ) -> Any:
-        if not is_ltx25_diffusers_model(od_config):
-            return output
-
-        frames = getattr(output, "frames", None)
-        audio = getattr(output, "audio", None)
-        if frames is None or audio is None:
-            return output
-
-        envelope: dict[str, Any] = {"payload": {"video": frames, "audio": audio}}
-        config = getattr(getattr(pipeline, "vocoder", None), "config", None)
-        value = (
-            config.get("output_sampling_rate")
-            if hasattr(config, "get")
-            else getattr(config, "output_sampling_rate", None)
-        )
-        try:
-            sample_rate = None if isinstance(value, bool) else int(value)
-        except (TypeError, ValueError):
-            sample_rate = None
-        if sample_rate is not None and sample_rate > 0:
-            envelope["metadata"] = {"audio": {"sample_rate": sample_rate}}
-        return envelope
-
 
 class WanPipelineUtils(BasePipelineUtils):
     def update_load_kwargs(self, od_config: OmniDiffusionConfig, load_kwargs: dict[str, Any]) -> None:
@@ -224,7 +49,6 @@ class WanPipelineUtils(BasePipelineUtils):
 
 
 PIPELINE_UTILS_REGISTRY: dict[str, type[BasePipelineUtils]] = {
-    "LTX2Pipeline": LTX2PipelineUtils,
     "WanPipeline": WanPipelineUtils,
     "WanImageToVideoPipeline": WanPipelineUtils,
     "WanVACEPipeline": WanPipelineUtils,

vllm_omni/diffusion/models/lingbot_video/request_utils.py

@@ -299,21 +299,16 @@ def resolve_lingbot_output_dimensions(
     sampling_width: Any = None,
     sampling_height: Any = None,
     prompt_fields: Mapping[str, Any] | None = None,
-    extra_fields: Mapping[str, Any] | None = None,
     default_width: int = 480,
     default_height: int = 480,
 ) -> tuple[int, int]:
     """Resolve the ``(width, height)`` that LingBot will use for generation."""
     prompt_fields = prompt_fields or {}
-    extra_fields = extra_fields or {}
+    requested_width = _pick(prompt_fields, "width")
-    # Online requests deliver `extra_params` through sampling.extra_args; the
+    requested_height = _pick(prompt_fields, "height")
-    # offline path embeds the same fields in the prompt dict. Consult both,
+    size = _pick(prompt_fields, "size")
-    # extra_args first — the same precedence fps and num_frames use.
+    resolution = _pick(prompt_fields, "resolution")
-    requested_width = _first_not_none(_pick(extra_fields, "width"), _pick(prompt_fields, "width"))
+    ratio = _pick(prompt_fields, "ratio")
-    requested_height = _first_not_none(_pick(extra_fields, "height"), _pick(prompt_fields, "height"))
-    size = _first_not_none(_pick(extra_fields, "size"), _pick(prompt_fields, "size"))
-    resolution = _first_not_none(_pick(extra_fields, "resolution"), _pick(prompt_fields, "resolution"))
-    ratio = _first_not_none(_pick(extra_fields, "ratio"), _pick(prompt_fields, "ratio"))
     if requested_width is not None or requested_height is not None:
         width, height = requested_width, requested_height
     elif size is not None or resolution is not None or ratio is not None:
@@ -419,7 +414,6 @@ def normalize_lingbot_request(
         sampling_width=sampling.width,
         sampling_height=sampling.height,
         prompt_fields=prompt_fields,
-        extra_fields=extra_args,
         default_width=default_width,
         default_height=default_height,
     )

vllm_omni/engine/arg_utils.py

@@ -22,8 +22,6 @@ _ARCH_TO_MODEL_TYPE: dict[str, str] = {
     "GLMTTSForConditionalGeneration": "glm_tts",
     "IndexTTS2S2MelDecoder": "indextts2",
     "IndexTTS2TalkerForConditionalGeneration": "indextts2",
-    "IndexTTS25S2MelDecoder": "indextts2_5",
-    "IndexTTS25TalkerForConditionalGeneration": "indextts2_5",
     "OmniVoiceModel": "omnivoice",
     # PersonaPlex ships an empty config.json, so create_model_config() must patch
     # model_type=personaplex from the arch name or the staged pipeline can't load
@@ -46,7 +44,6 @@ def _register_omni_hf_configs() -> None:
 
         from vllm_omni.model_executor.models.indextts2.configuration_indextts2 import (
             IndexTTS2Config,
-            IndexTTS25Config,
         )
         from vllm_omni.model_executor.models.ming_tts.config_ming_tts import (
             MingDenseConfig,
@@ -82,7 +79,6 @@ def _register_omni_hf_configs() -> None:
         ("dense", MingDenseConfig),
         ("bailingmm", MingMoeConfig),
         ("indextts2", IndexTTS2Config),
-        ("indextts2_5", IndexTTS25Config),
         ("moss_tts_local", MossTTSLocalConfig),
         ("moss_tts_realtime", MossTTSRealtimeConfig),
         ("qwen3_tts", Qwen3TTSConfig),
@@ -108,16 +104,10 @@ def register_omni_models_to_vllm():
 
     _register_omni_hf_configs()
 
-    # Unconditionally (re)register every omni arch into the upstream global
+    supported_archs = ModelRegistry.get_supported_archs()
-    # ModelRegistry: vLLM 0.27 added some omni archs (e.g.
-    # Qwen3OmniMoeForConditionalGeneration) to its own registry, but resolves
-    # them to upstream classes that lack omni features (e.g.
-    # buffer_realtime_audio for the realtime endpoint). Registering here
-    # overwrites those entries with the vllm-omni classes, which is the
-    # behavior entrypoints resolving through the global registry rely on
-    # (and matches OmniModelRegistry's omni-wins ordering).
     for arch, (mod_folder, mod_relname, cls_name) in _OMNI_MODELS.items():
-        ModelRegistry.register_model(arch, f"vllm_omni.model_executor.models.{mod_folder}.{mod_relname}:{cls_name}")
+        if arch not in supported_archs:
+            ModelRegistry.register_model(arch, f"vllm_omni.model_executor.models.{mod_folder}.{mod_relname}:{cls_name}")
 
     # Register omni-specific reasoning parsers (e.g., step_audio).
     import vllm_omni.reasoning  # noqa: F401
@@ -254,15 +244,7 @@ class OmniEngineArgs(EngineArgs):
             if config_dict.get("model_type"):
                 return  # config.json already has model_type, no patching needed
         except Exception:
-            # The official IndexTTS 2.5 bundle has no HuggingFace config.json.
+            return  # can't load config, let vLLM handle the error
-            # Keep this exception model-scoped so other loader failures retain
-            # vLLM's normal error path.
-            if model_type != "indextts2_5" or not os.path.isdir(self.model):
-                return
-            config_path = os.path.join(self.model, "config.json")
-            if os.path.lexists(config_path):
-                return
-            config_dict = {}
 
         # Create a temp dir with a patched config.json
         temp_dir = tempfile.mkdtemp(prefix="omni_hf_config_")