comment

Signed-off-by: Roger Wang <hey@rogerw.io>

.buildkite/pipeline.yml

@@ -1,6 +1,87 @@
 steps:
+  - label: ":docker: Build image"
+    key: image-build
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "docker build --file docker/Dockerfile.ci -t vllm-omni-ci ."
+      - "docker tag vllm-omni-ci public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
+    agents:
+      queue: "cpu_queue_premerge_us_east_1"
+
   - label: "Simple Unit Test"
     commands:
       - ".buildkite/scripts/simple_test.sh"
     agents:
       queue: "cpu_queue_premerge"
+
+  - label: "Diffusion Model Test"
+    timeout_in_minutes: 15
+    depends_on: image-build
+    commands:
+      - pytest -s -v tests/e2e/offline_inference/test_t2i_model.py
+    agents:
+      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
+  - label: "Omni Model Test"
+    timeout_in_minutes: 15
+    depends_on: image-build
+    commands:
+      - export VLLM_LOGGING_LEVEL=DEBUG
+      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+      - pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
+    agents:
+      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
+  - label: "Omni Model Test with H100"
+    timeout_in_minutes: 20
+    depends_on: image-build
+    commands:
+      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+      - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py
+    agents:
+      queue: "mithril-h100-pool"
+    plugins:
+      - kubernetes:
+          podSpec:
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 2
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /root/.cache/huggingface
+                env:
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+            nodeSelector:
+              node.kubernetes.io/instance-type: gpu-h100-sxm
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: hf-cache
+                hostPath:
+                  path: /mnt/hf-cache
+                  type: DirectoryOrCreate

.github/ISSUE_TEMPLATE/config.yml

@@ -1,5 +1,5 @@
 blank_issues_enabled: false
 contact_links:
   - name: Questions
-    url: https://github.com/vllm-project/vllm-omni/discussions
-    about: Ask questions and discuss with other vLLM-Omni community members
+    url: https://slack.vllm.ai
+    about: Ask questions and discuss with other vLLM-Omni community members in our #sig-omni slack channel.

.gitignore

@@ -156,9 +156,6 @@ cython_debug/
 # cursor
 .cursor/
 
-# docker
-docker/
-
 # scripts
 /scripts/
 
@@ -182,6 +179,7 @@ checkpoints/
 
 # Cache directories
 cache/
+!vllm_omni/diffusion/cache/
 .cache/
 diffusion_cache/
 kv_cache/
@@ -232,3 +230,5 @@ configs/development.yaml
 # Docker
 .dockerignore
 Dockerfile.dev
+discussion
+tmp_test

CONTRIBUTING.md

@@ -1,3 +1,3 @@
 # Contributing to vLLM-Omni
 
-You may find information about contributing to vLLM-Omni on [Contributing](docs/contributing/)
+You may find information about contributing to vLLM-Omni on [Contributing](https://vllm-omni.readthedocs.io/en/latest/contributing/)

README.md

@@ -1,7 +1,7 @@
 <p align="center">
   <picture>
-    <source media="(prefers-color-scheme: dark)" src="./docs/source/logos/vllm-omni-logo-text-dark.png">
-    <img alt="vllm-omni" src="./docs/source/logos/vllm-omni-logo-text-dark.png" width=55%>
+    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/logos/vllm-omni-logo.png">
+    <img alt="vllm-omni" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/logos/vllm-omni-logo.png" width=55%>
   </picture>
 </p>
 <h3 align="center">
@@ -9,7 +9,7 @@ Easy, fast, and cheap omni-modality model serving for everyone
 </h3>
 
 <p align="center">
-| <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://tinyurl.com/vllm-omni-meeting"><b>Weekly Meeting</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
+| <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>
 
 ---
@@ -30,7 +30,7 @@ Easy, fast, and cheap omni-modality model serving for everyone
 
 <p align="center">
   <picture>
-    <img alt="vllm-omni" src="./docs/source/architecture/omni-modality-model-architecture.png" width=55%>
+    <img alt="vllm-omni" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/omni-modality-model-architecture.png" width=55%>
   </picture>
 </p>
 
@@ -66,10 +66,8 @@ Visit our [documentation](https://vllm-omni.readthedocs.io/en/latest/) to learn
 We welcome and value any contributions and collaborations.
 Please check out [Contributing to vLLM-Omni](https://vllm-omni.readthedocs.io/en/latest/contributing/) for how to get involved.
 
-## Weekly Meeting
-
-- vLLM Omni Weekly Meeting: https://tinyurl.com/vllm-omni-meeting
-- Wednesday, 11:30 - 12:30 (UTC+8, [Convert to your timezone](https://dateful.com/convert/gmt8?t=15))
+## Join the Community
+Feel free to ask questions, provide feedbacks and discuss with fellow users of vLLM-Omni in `#sig-omni` slack channel at [slack.vllm.ai](https://slack.vllm.ai) or vLLM user forum at [discuss.vllm.ai](https://discuss.vllm.ai).
 
 ## License
 

docker/Dockerfile.ci

@@ -0,0 +1,14 @@
+ARG VLLM_BASE_IMAGE=vllm/vllm-openai
+ARG VLLM_BASE_TAG=v0.11.0
+FROM ${VLLM_BASE_IMAGE}:${VLLM_BASE_TAG}
+ARG APP_DIR=/workspace/vllm-omni
+WORKDIR ${APP_DIR}
+
+COPY . .
+
+# Install vllm-omni into the same uv-managed Python environment used by the base image.
+RUN uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" --no-cache-dir ".[dev]"
+
+RUN ln -sf /usr/bin/python3 /usr/bin/python
+
+ENTRYPOINT []

docs/.nav.yml

@@ -7,17 +7,21 @@ nav:
   - Examples:
     - examples/README.md
     - Offline Inference:
-      - Offline Example of vLLM-Omni for Qwen2.5-omni: user_guide/examples/offline_inference/qwen2_5_omni.md
-      - Offline Example of vLLM-Omni for Qwen3-omni: user_guide/examples/offline_inference/qwen3_omni.md
-      - Qwen-Image Offline Inference: user_guide/examples/offline_inference/qwen_image.md
+      - Qwen2.5-Omni: user_guide/examples/offline_inference/qwen2_5_omni.md
+      - Qwen3-Omni: user_guide/examples/offline_inference/qwen3_omni.md
+      - Text-To-Image: user_guide/examples/offline_inference/text_to_image.md
+      - Image-To-Image: user_guide/examples/offline_inference/image_to_image.md
+      - Text-To-Video: user_guide/examples/offline_inference/text_to_video.md
     - Online Serving:
-      - Online serving Example of vLLM-Omni for Qwen2.5-omni: user_guide/examples/online_serving/qwen2_5_omni.md
-      - Online serving Example of vLLM-Omni for Qwen3-omni: user_guide/examples/online_serving/qwen3_omni.md
+      - Qwen2.5-Omni: user_guide/examples/online_serving/qwen2_5_omni.md
+      - Qwen3-Omni: user_guide/examples/online_serving/qwen3_omni.md
   - General:
     - usage/*
   - Configuration:
     - configuration/README.md
     - configuration/*
+  - Diffusion Acceleration:
+     - TeaCache: user_guide/teacache.md
   - Models:
     - models/supported_models.md
 - Developer Guide:
@@ -31,9 +35,14 @@ nav:
   - Design Documents:
     - design/index.md
     - design/architecture_overview.md
-    - design/vllm_omni_design.md
-    - design/mrs_design.md
-    - design/api_design_doc.md
+    - Feature Design:
+      - design/feature/multi_request_streaming_design.md
+      - design/feature/omni_connector_design.md
+      - design/feature/ray_based_execution.md
+    - Module Design:
+      - design/module/ar_module_design.md
+      - design/module/dit_module_design.md
+      - design/module/entrypoint_module_design.md
   - Docs Guide: contributing/DOCS_GUIDE.md
 - API Reference:
   - api/README.md

docs/README.md

@@ -8,8 +8,8 @@ hide:
 
 <p align="center">
   <picture>
-    <source media="(prefers-color-scheme: dark)" src="./source/logos/vllm-omni-logo-text-dark.png">
-    <img alt="vllm-omni" src="./source/logos/vllm-omni-logo-text-dark.png" width=55%>
+    <source media="(prefers-color-scheme: dark)" src="./source/logos/vllm-omni-logo.png">
+    <img alt="vllm-omni" src="./source/logos/vllm-omni-logo.png" width=55%>
   </picture>
 </p>
 <h3 align="center">
@@ -34,7 +34,8 @@ Easy, fast, and cheap omni-modality model serving for everyone
 
 <p align="center">
   <picture>
-    <img alt="vllm-omni" src="./source/architecture/omni-modality model architecture.png" width=55%>
+    <source media="(prefers-color-scheme: dark)" src="./source/architecture/omni-modality-model-architecture.png">
+    <img alt="vllm-omni-arch" src="./source/architecture/omni-modality-model-architecture.png" width=55%>
   </picture>
 </p>
 
@@ -58,4 +59,6 @@ vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, in
 - Multi-modality generation models (e.g. Qwen-Image)
 
 For more information, checkout the following:
-- [Placeholder for vllm-omni blogpost]()
+
+- [vllm-omni architecture design and recent roadmaps](https://docs.google.com/presentation/d/1Y7t2Zm3BIISPN-_X_sDpxZMNCWir2rAx/edit?usp=drive_link&ouid=110473603432222024453&rtpof=true&sd=true)
+- [vllm-omni announcement blogpost](https://blog.vllm.ai/2025/11/30/vllm-omni.html)

docs/api/README.md

@@ -49,10 +49,9 @@ Engine classes for offline and online inference.
 Core scheduling and caching components.
 
 - [vllm_omni.core.dit_cache_manager.DiTCacheManager][]
-- [vllm_omni.core.sched.diffusion_scheduler.DiffusionScheduler][]
-- [vllm_omni.core.sched.generation_scheduler.GenerationScheduler][]
+- [vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler][]
+- [vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler][]
 - [vllm_omni.core.sched.output.OmniNewRequestData][]
-- [vllm_omni.core.sched.scheduler.OmniScheduler][]
 
 ## Model Executor
 
@@ -97,20 +96,23 @@ Model execution components.
 Configuration classes.
 
 - [vllm_omni.config.model.OmniModelConfig][]
+- [vllm_omni.diffusion.cache.teacache.config.TeaCacheConfig][]
 
 ## Workers
 
 Worker classes and model runners for distributed inference.
 
+- [vllm_omni.diffusion.worker.gpu_worker.GPUWorker][]
+- [vllm_omni.diffusion.worker.gpu_worker.WorkerProc][]
+- [vllm_omni.diffusion.worker.npu.npu_worker.NPUWorker][]
+- [vllm_omni.diffusion.worker.npu.npu_worker.NPUWorkerProc][]
 - [vllm_omni.worker.gpu_ar_model_runner.GPUARModelRunner][]
 - [vllm_omni.worker.gpu_ar_worker.GPUARWorker][]
-- [vllm_omni.worker.gpu_diffusion_model_runner.GPUDiffusionModelRunner][]
-- [vllm_omni.worker.gpu_diffusion_worker.GPUDiffusionWorker][]
 - [vllm_omni.worker.gpu_generation_model_runner.GPUGenerationModelRunner][]
 - [vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker][]
 - [vllm_omni.worker.gpu_model_runner.OmniGPUModelRunner][]
 - [vllm_omni.worker.npu.npu_ar_model_runner.NPUARModelRunner][]
 - [vllm_omni.worker.npu.npu_ar_worker.NPUARWorker][]
-- [vllm_omni.worker.npu.npu_diffusion_model_runner.NPUDiffusionModelRunner][]
-- [vllm_omni.worker.npu.npu_diffusion_worker.NPUDiffusionWorker][]
+- [vllm_omni.worker.npu.npu_generation_model_runner.NPUGenerationModelRunner][]
+- [vllm_omni.worker.npu.npu_generation_worker.NPUGenerationWorker][]
 - [vllm_omni.worker.npu.npu_model_runner.OmniNPUModelRunner][]

docs/community/contact_us.md

@@ -2,4 +2,4 @@
 
 - For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm-omni/issues)
 - For coordinating contributions and development and discussing with other users and developers, please join `sig-omni` channel in our [Slack](https://slack.vllm.ai/) or use the [vLLM Forum](https://discuss.vllm.ai/)
-- For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature
+- For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm-omni/security/advisories) feature

docs/community/volunteers.md

@@ -0,0 +1,12 @@
+# Volunteers for Bugfix and CI
+
+We encourage you to check current docs and [issues](https://github.com/vllm-project/vllm-omni/issues) to find possible solutions for your questions. If non of these can solve it, please propose an issue to describe your questions about bug or CI problems for developing.
+
+If you have urgent need for locating and solving bugfix or CI problems, please find community volunteers below.
+
+| Dec 4-Dec 12 | Dec 15-Dec 19 | Dec 22-Dec 26 | Dec 29- Jan 2, 2026| Jan 5-Jan 9 | Jan 12-Jan 16 |
+|----------|----------|----------|----------|----------|----------|
+| <a href="https://github.com/congw729">Conw729</a> | <a href="https://github.com/yinpeiqi">yinpeiqi</a> | <a href="https://github.com/tzhouam">tzhouam</a> | <a href="https://github.com/SamitHuang">SamitHuang</a> | <a href="https://github.com/gcanlin">gcanlin</a> | <a href="https://github.com/natureofnature">natureofnature</a> |
+| <a href="https://github.com/david6666666">david6666666</a> | <a href="https://github.com/R2-Y">R2-Y</a> | <a href="https://github.com/hsliuustc0106">hsliuustc0106</a> | <a href="https://github.com/Gaohan123">Gaohan123</a> | <a href="https://github.com/ZJY0516">ZJY0516</a> | <a href="https://github.com/qibaoyuan">qibaoyuan</a> |
+
+We kindly welcome more contributors to fix bugs and contribute new features!

docs/configuration/README.md

@@ -9,3 +9,7 @@ Currently, the main options are maintained by stage configs for each model.
 For specific example, please refer to [Qwen2.5-omni stage config](stage_configs/qwen2_5_omni.yaml)
 
 For introduction, please check [Introduction for stage config](./stage_configs.md)
+
+## Optimization Features
+
+- **[TeaCache Configuration](../user_guide/teacache.md)** - Enable TeaCache adaptive caching for DiT models to achieve 1.5x-2.0x speedup with minimal quality loss

docs/configuration/stage_configs.md

@@ -2,6 +2,9 @@
 
 In vLLM-Omni, the target model is separated into multiple stages, which are processed by different LLMEngines, DiffusionEngines or other types of engines. Depending on different types of stages, such as Autoregressive (AR) stage or Diffusion transformer (DiT) stage, each can choose corresponding schedulers, model workers to load with the Engines in a plug-in fashion.
 
+!!! note
+    Default stage config YAMLs (for example, `vllm_omni/model_executor/stage_configs/qwen2_5_omni.yaml` and `vllm_omni/model_executor/stage_configs/qwen3_omni_moe.yaml`) are bundled and loaded automatically when `stage_configs_path` is not provided. They have been verified to work on 1xH100 for Qwen2.5-Omni and 2xH100 for Qwen3-Omni.
+
 Therefore, as a core part of vLLM-Omni, the stage configs for a model have several main functions:
 
 - Claim partition of stages and their corresponding class implementation in `model_executor/models`.

docs/configuration/stage_configs/qwen2_5_omni.yaml

@@ -9,7 +9,7 @@ stage_args:
       model_stage: thinker
       model_arch: Qwen2_5OmniForConditionalGeneration
       worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
       gpu_memory_utilization: 0.8
       enforce_eager: true  # Now we only support eager mode
       trust_remote_code: true
@@ -35,7 +35,7 @@ stage_args:
       model_stage: talker
       model_arch: Qwen2_5OmniForConditionalGeneration
       worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
       gpu_memory_utilization: 0.8
       enforce_eager: true
       trust_remote_code: true

docs/contributing/README.md

@@ -2,6 +2,9 @@
 
 Thank you for your interest in contributing to vLLM-Omni! This document provides guidelines and instructions for contributing.
 
+!!! note
+    We host weekly developer-facing online meetings to discuss milestones and updates **every Tuesday at 19:30 PDT**. Meeting link as well as the past meeting notes can be found [here](https://tinyurl.com/vllm-omni-meeting).
+
 ## Getting Started
 
 vLLM-Omni uses `uv` as the environment manager, to create and manage Python environments. Please follow the documentation to install `uv`. After installing `uv`, you can create a new Python environment using the following commands:
@@ -20,7 +23,7 @@ Install vLLM-Omni from source with development dependencies:
 
 ```bash
 git clone https://github.com/vllm-project/vllm-omni.git
-cd vllm_omni
+cd vllm-omni
 uv pip install -e ".[dev]"
 ```
 

docs/design/architecture_overview.md

@@ -1,113 +1,195 @@
 # Architecture Overview
 
-# Introduction
+This document outlines the architectural design for vLLM-Omni.
 
-This document outlines the architectural design for vLLM-Omni, a new repository developed under the vLLM project. vLLM-Omni supports multi-modality models with non-autoregressive structures and non-textual outputs, extending vLLM beyond traditional text-based, autoregressive generation.
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/omni-modality-model-architecture.png">
+    <img alt="Omni-Modality Model Architecture" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/omni-modality-model-architecture.png" width=55%>
+  </picture>
+</p>
 
 # Goals
 
-The primary goals for vLLM-Omni are:
+The primary goal of the vLLM-Omni project is to build the fastest and easiest-to-use open-source Omni-Modality model inference & serving engine. vLLM-Omni extends the original vLLM, which was created to support large language models for text-based autoregressive (AR) generation tasks. vLLM-Omni is designed to support:
 
-* **Support for Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, and video, alongside text.  
-* **Non-Autoregressive Structure:** Support model structure beyond autoregressive, especially Diffusion Transformer (DiT), which is widely used in visual and audio generation.  
-* **Integration with vLLM Core:** Maintain compatibility and leverage existing vLLM key modules and optimizations where applicable.  
+* **Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, and video, alongside text.
+* **Non-Autoregressive Structure:** Support model structure beyond autoregressive, especially Diffusion Transformer (DiT), which is widely used in visual and audio generation.
+* **Integration with vLLM Core:** Maintain compatibility and leverage existing vLLM key modules and optimizations where applicable.
 * **Extensibility:** Design a modular and flexible architecture that can easily accommodate new modalities, model architectures, and output formats.
 
-# Expected Gains
 
-* It breaks through the limitation of vLLM that only supports autoregressive text generation models, and provides architectural support for popular model structures, especially multimodal generation.  
-* By supporting new output formats and new model architectures, developers can easily connect to mature ecosystems such as Higress Gateway and Ray, improving the ease of deployment and efficiency of corresponding models.  
-* Integrating efficient cache management, multiple parallel strategies, disaggregation from vLLM, to improve inference efficiency of corresponding models.
-
-# Non-Autoregressive Structure
-
-* **Diffusion Transformer:** initialized with a random latent noise, the transformer model iteratively predicts latent output for multiple steps, under control of sampling timestamps. Finally, it is decoded by the decoder of Variational AutoEncoder (VAE). It is not supported by vLLM now.  
-* **Autoregressive:** dominant generation paradigm for text, now supported by vLLM. It generates tokens conditioned on previous tokens one by one. The efficient KV Cache management provided by vLLM can accelerate inference effectively.  
-  ![][image1]                       ![][image2]
-
-
-|  | Autoregressive | DiT |
-| :---- | ----- | ----- |
-| Scenarios | Text generation | Multimodal generation |
-| Generation | Token-by-token, left to right | Continuous vector, iteratively refine |
-| Key advantages | Smooth, good local consistency | Global consistency, diversity and controlability |
-| Efficiency | More efficient with KV cache | Need generation repeatedly, slow |
-| Error accumulation | Easy to accumulate | Can fix possibly |
-| Key challenges | Limited global planning, long context generation | Large global computational cost |
-| Sequence length | Varied length | Fixed length |
-| Attention mask | Lower triangular | Full bi-directional matrix |
-| Parallel strategy | TP, DP, PP, .etc | DP, CP, .etc |
-| vLLM support | Supported | Unsupported |
-
-# Targeted models to support
+# Representative omni-modality models
 
 According to analysis for current popular open-source models, most of them have the combination of AR+DiT. Specifically, they can be further categorized into 3 types below:
 
-* **DiT as a main structure, with AR as text encoder (e.g.: Qwen-Image)**  
-  A popular model for image generation and editing. Main visual generation models are similar to the structure, such as Flux.  
-  ![][image3]  
-* **AR as a main structure, with DiT as multi-modal generator (e.g. BAGEL)**  
-  A unified multimodal comprehension and generation model. Visual generation can utilize CoT text generation output. Main unified multimodal models are similar to the structure.  
-      ![][image4]  
-* **Extension, multiple AR+DiT (e.g. Qwen-omni)**  
-  A new model for multimodal input and output. It designs the Thinker-Talker-Codec structure, which is a double-AR+DiT format.  
-  ![][image5]  
+**DiT as a main structure, with AR as text encoder (e.g.: Qwen-Image)**
+  A powerful image generation foundation model capable of complex text rendering and precise image editing.
 
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/ar-main-architecture.png">
+    <img alt="Qwen-Image" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/ar-main-architecture.png" width=30%>
+  </picture>
+</p>
 
+**AR as a main structure, with DiT as multi-modal generator (e.g. BAGEL)**
+  A unified multimodal comprehension and generation model, with cot text output and visual generation.
 
-# Architectural Overview
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/dit-main-architecture.png">
+    <img alt="Bagel" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/dit-main-architecture.png" width=30%>
+  </picture>
+</p>
 
-vLLM-Omni will import core modules from existing vLLM framework and introduce several new components and modifications to achieve its goals. The core architecture will be based on a modular design, allowing for independent development and integration of different modality handlers and output generators.  
-![][image6]
+**AR+DiT (e.g. Qwen-Omni)**
+  A natively end-to-end omni-modal LLM for multimodal inputs (text/image/audio/video...) and outputs (text/audio...).
 
-## General ideas
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/ar-dit-main-architecture.png">
+    <img alt="Qwen-Omni" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/ar-dit-main-architecture.png" width=30%>
+  </picture>
+</p>
 
-* It divides models in vLLM-Omni into multiple stages, which are processed by different Engine Cores. Each stage can choose AR Engine Core for AR model part, and Diffusion Engine Core for diffusion model part.  
-* Designed a specialized diffusion engine core for DiT or dLLM.  
-* Plug-in with external DiT acceleration modules such as xDiT, Cache-DiT.  
-* Output linked with Higress API, Gradio for online demo, ComfyUI for less-code process.
+# vLLM-Omni main architecture
+
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/vllm-omni-main-architecture.png">
+    <img alt="vLLM-Omni Main Architecture" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/vllm-omni-main-architecture.png" width=55%>
+  </picture>
+</p>
 
 ## Key Components
 
-* **Processor:** Input processing, tokenization or encoding  
-* **Scheduler:** for order and queue of incoming requests  
-* **Executor:**  Prepare model input and run  
-* **Multimodal output processor:** support multimodal outputs  
-* **Hidden State Output processor:** support intermediate hidden vectors as request output  
-* **Engine Core:** accelerate AR modules, imported from vLLM main repo  
-* **Diffusion Engine Core:** Wrap and accelerate DiT modules.  
-* **Diffusion model:** Wrap up diffusion models with vLLM format
+| Component | Description |
+|-----------|-------------|
+| **OmniRouter** | provide an intelligent router for Omni-modality requests dispatch |
+| **EntryPoints** | define the APIs for offline/online serving (APIServer, Omni/AsyncOmni) and provide the OmniStage abstraction for different AR/DiT stages |
+| **AR** | adapted for omni-modality models while inheriting efficient features from vLLM, such as cache management |
+| **Diffusion** | natively implemented and optimized using acceleration components |
+| **OmniConnector** | supports fully disaggregation based on E/P/D/G (Encoding/Processing/Decoding/Generation) disaggregation across stages |
 
-# Examples data flow of specific models
+Disaggregated stages are managed through configuration, such as in the Qwen3-Omni example, where stages like Thinker, Talker, and Code2wav are defined as separate OmniStage instances with specific resources and input/output type.
 
-1. Qwen-omni  
-   ![][image7]![][image8]  
-2. Qwen-Image  
-   ![][image9]      ![][image10]
+## Main features
 
-# Overview of function call dependency
+vLLM-Omni aims to be fast, flexible, and easy to use with the following features:
 
-![][image11]
+### Performance and Acceleration
 
+The framework achieves high performance through several optimization techniques:
 
-[image1]: 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truncated
+* **Efficient AR Support:** Leverages efficient KV cache management inherited from vLLM.
+* **Pipelined Execution:** Uses pipelined stage execution overlapping to ensure high throughput.
+* **Full Disaggregation:** Relies on the OmniConnector and dynamic resource allocation across stages.
+* **Diffusion Acceleration:** Includes integrated support for diffusion acceleration. This is managed by the acceleration layer, which handles:
+    * **Cache:** Includes DBCache, TeaCache and third-party integration(e.g., [cache-dit](https://github.com/vipshop/cache-dit)).
+    * **Parallelism:** Supports TP, CP, USP, and CFG.
+    * **Attention:** Provides an interface for third-party integration (e.g., FA3, SAGE, MindIE-SD).
+    * **Quantization:** Supports various quantization implementations including FP8 and AWQ.
+    * **FusedOps:** Allows for custom and third-party integration.
 
-[image2]: <data:image/png;base64,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 truncated
+### Flexibility and Usability
 
-[image3]: <data:image/png;base64,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 truncated
+vLLM-Omni is designed to be flexible and straightforward for users:
 
-[image4]: 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+* **Heterogeneous Pipeline Abstraction:** Manages complex model workflows effectively.
+* **Hugging Face Integration:** Offers seamless integration with popular Hugging Face models.
+* **Distributed Inference:** Supports tensor, pipeline, data, and expert parallelism.
+* **Streaming Outputs:** Supports streaming outputs.
+* **Unified API:** Provides a consistent and unified API interface compatible with vLLM.
+* **OpenAI-compatible API Server:** Includes a FastAPI-based server for online serving that is compatible with the OpenAI API.
 
-[image5]: <data:image/png;base64,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+# Interface design
 
-[image6]: <data:image/png;base64,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 truncated
+If you use vLLM, then you know how to use vLLM-Omni from Day 0:
 
-[image7]: <data:image/png;base64,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 truncated
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/vllm-omni-user-interface.png">
+    <img alt="vLLM-Omni interface design" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/vllm-omni-user-interface.png" width=55%>
+  </picture>
+</p>
 
-[image8]: <data:image/png;base64,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+Taking **Qwen3-Omni** as an example:
 
-[image9]: <data:image/png;base64,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+## Offline Inference
+The **Omni** class provides a Python interface for offline batched inference. Users initialize the Omni class with a Hugging Face model name and use the generate method, passing inputs that include both text prompts and multi-modal data:
 
-[image10]: <data:image/png;base64,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 truncated
+```
+# Create an omni_lm with HF model name.
+from vllm_omni.entrypoints.omni import Omni
 
-[image11]: <data:image/png;base64,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 truncated
+omni_lm = Omni(model="Qwen/Qwen3-Omni-30B-A3B-Instruct")
+
+# Example prompts.
+om_inputs = {"prompt": prompt,
+             "multi_modal_data": {
+                 "video": video_frames,
+                 "audio": audio_signal,
+             }}
+
+# Generate texts and audio from the multi-modality inputs.
+outputs = omni_lm.generate(om_inputs, sampling_params_list)
+```
+
+## Online Serving
+Similar to vLLM, vLLM-Omni also provides a FastAPI-based server for online serving. Users can launch the server using the vllm serve command with the `--omni` flag:
+
+```
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
+```
+
+Users can send requests to the server using curl:
+
+```
+# prepare user content
+user_content='[
+        {
+          "type": "video_url",
+          "video_url": {
+            "url": "'"$SAMPLE_VIDEO_URL"'"
+          }
+        },
+        {
+          "type": "text",
+          "text": "Why is this video funny?"
+        }
+      ]'
+    sampling_params_list='[
+      '"$thinker_sampling_params"',
+      '"$talker_sampling_params"',
+      '"$code2wav_sampling_params"'
+    ]'
+    mm_processor_kwargs="{}"
+
+# send the request
+curl -sS -X POST http://localhost:8091/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d @- <<EOF
+{
+  "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+  "sampling_params_list": $sampling_params_list,
+  "mm_processor_kwargs": $mm_processor_kwargs,
+  "messages": [
+    {
+      "role": "system",
+      "content": [
+        {
+          "type": "text",
+          "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."
+        }
+      ]
+    },
+    {
+      "role": "user",
+      "content": $user_content
+    }
+  ]
+}
+```
+
+For more usages, please refer to [examples](../user_guide/examples/).

docs/design/feature/omni_connector_design.md

@@ -0,0 +1,200 @@
+# OmniConnector: a unified connector interface of disaggregated inference for omni-modality models
+
+This guide explains how to configure and use distributed connectors (vllm_omni/distributed/connectors) in vllm-omni for multi-stage pipelines.
+
+## 1. Overview
+
+Connectors enable data transfer between pipeline stages (e.g., Thinker -> Talker).
+Currently supported connectors operate in **D2H2D (Device-to-Host-to-Device)** mode:
+1. **SharedMemoryConnector**: Uses system shared memory.
+2. **MooncakeConnector**: Uses [Mooncake](https://github.com/kvcache-ai/Mooncake).
+
+*   **SharedMemoryConnector (Default)**: Zero-copy (on host), lowest latency. Best for **single-node** deployments. Auto-configured if no connectors are specified.
+*   **MooncakeConnector**: TCP/RDMA based. Best for **multi-node** distributed deployments. Requires a Mooncake Master service.
+
+## 2. API Design
+
+The connector system is built around the `OmniConnectorBase` abstraction, which decouples data transport from stage logic.
+
+### Core Interface
+
+```python
+class OmniConnectorBase(ABC):
+    @abstractmethod
+    def put(self, from_stage: str, to_stage: str, request_id: str, data: Any) -> tuple[bool, int, Optional[dict]]:
+        """
+        Store data.
+        Returns: (success, serialized_size, metadata)
+        """
+        pass
+
+    @abstractmethod
+    def get(self, from_stage: str, to_stage: str, request_id: str, metadata: Optional[dict] = None) -> Optional[tuple[Any, int]]:
+        """
+        Retrieve data.
+        Args: metadata - Transport-specific handles returned by put() (e.g., SHM name).
+        Returns: (object, serialized_size)
+        """
+        pass
+```
+
+### Key Concept: Metadata Passing
+Unlike a pure key-value store, some connectors (like `SharedMemoryConnector`) generate transient resources (e.g., a shared memory block name) during `put()`. This `metadata` **must be passed via the control plane** (e.g., HTTP headers, queue messages) from the producer stage to the consumer stage so `get()` can locate the data.
+
+## 3. Backends & Use Cases
+
+### 3.1 SharedMemoryConnector
+**Best for:** Single-node, high-performance IPC.
+
+*   **Mechanism:**
+    *   **Small Payloads (< Threshold)**: Data is serialized and passed directly "inline" within the `metadata` dictionary. This avoids the overhead of creating SHM blocks for tiny messages.
+    *   **Large Payloads (>= Threshold)**: Data is written to a named System Shared Memory block. The block name is returned in `metadata`.
+*   **Configuration:**
+    *   `shm_threshold_bytes`: Size in bytes to switch from inline to SHM (default: 64KB).
+
+### 3.2 MooncakeConnector
+**Best for:** Multi-node distributed inference.
+
+*   **Mechanism:** Uses Mooncake's distributed KVCache store.
+    *   **Data Plane**: TCP or RDMA for high-bandwidth transfer.
+    *   **Control Plane**: Uses a centralized Mooncake Master and Metadata Server.
+    *   **Keying**: Deterministic keys based on `request_id/from_stage_to_stage`.
+*   **Requirements**: Requires a running Mooncake Master service.
+
+## 4. Relationship with vLLM
+
+vLLM provides specialized distributed mechanisms for specific artifacts:
+*   **KV Transfer** (`vllm.distributed.kv_transfer`): Optimized for transferring KV caches between prefill and decode instances (using NCCL, Mooncake, etc.).
+*   **EC Transfer** (`vllm.distributed.ec_transfer`): Optimized for sharing encoder embeddings.
+*   **Device Communicators** (`vllm.distributed.device_communicators`): Low-level primitives (NCCL, SHM) for Tensor/Pipeline Parallelism.
+
+`vllm-omni` complements this by introducing a **Generalized Connector Abstraction** (`OmniConnector`) for multimodal pipelines. While vLLM's connectors are artifact-specific, `vllm-omni`:
+
+1.  **Unifies Transport**: Provides a single API (`put`/`get`) to transport *any* stage artifact (Input Embeddings, Hidden States, Audio/Image Tensors, KV Cache, Final Output) between arbitrary pipeline stages (e.g., AudioEncoder -> LLM -> AudioGenerator).
+2.  **Extends Connectivity**: Enables flexible construction of complex DAGs (Directed Acyclic Graphs) where stages can run in the same process, same node, or across nodes, using the most appropriate backend (SHM, Mooncake, etc.) for each edge.
+3.  **Wraps & Adapts**: Can internally utilize vLLM's specialized `kv_transfer` for KV paths while using generic transports (SHM/Mooncake) for other data types, presenting a consistent interface to the application layer.
+
+## 5. Installation (Mooncake)
+
+If using `MooncakeConnector`, install the library first:
+
+```bash
+# For CUDA-enabled systems (Recommended)
+pip install mooncake-transfer-engine
+
+# For non-CUDA systems
+pip install mooncake-transfer-engine-non-cuda
+```
+
+## 6. Using MooncakeConnector
+
+### 6.1 Start Mooncake Master
+
+Start the master service on your primary node:
+
+```bash
+# if you use mooncake SSD storage
+mkdir -p ./mc_storage
+
+mooncake_master \
+  --rpc_port=50051 \
+  --enable_http_metadata_server=true \
+  --http_metadata_server_host=0.0.0.0 \
+  --http_metadata_server_port=8080 \
+  --metrics_port=9003 \
+  --root_fs_dir=./mc_storage/ \
+  --cluster_id=mc-local-1 &
+```
+
+### 6.2 Configuration (YAML)
+
+Edit your stage config (e.g., `qwen2_5_omni.yaml`).
+
+**Step 1: Define Connector in Global Runtime**
+
+```yaml
+runtime:
+  connectors:
+    connector_of_mooncake:
+      name: MooncakeConnector
+      extra:
+        host: "127.0.0.1"           # Local Worker IP
+        metadata_server: "http://<MASTER_IP>:8080/metadata"
+        master: "<MASTER_IP>:50051"
+        segment: 512000000          # 512MB segment
+        localbuf: 64000000          # 64MB buffer
+        proto: "tcp"                # "tcp" or "rdma"
+    ```
+
+**Mooncake Configuration Parameters:**
+
+*   **host**: The hostname or IP address of the local machine (worker). Mooncake uses this to register itself in the metadata server so other nodes can find it.
+*   **metadata_server**: The URL of the metadata server. This is used for service discovery and connection establishment (e.g., exchanging QP information for RDMA).
+*   **master**: The address of the Mooncake Master Server (e.g., `<MASTER_IP>:50051`). This is used for global state management and control plane operations.
+*   **segment**: The size of the global memory segment in bytes (default: ~512MB). This defines the shared memory region accessible by Mooncake for data transfer.
+*   **localbuf**: The size of the local buffer in bytes (default: ~64MB). Used for local data buffering during transfer operations.
+*   **proto**: The transport protocol to use. Options:
+    *   `tcp`: Standard TCP/IP (easier setup, universal compatibility).
+    *   `rdma`: Remote Direct Memory Access (higher performance, requires RDMA-capable hardware).
+
+For more details, refer to the [Mooncake Repository](https://github.com/kvcache-ai/Mooncake).
+
+    **Step 2: Reference in Stages**
+
+Explicitly link stages using `input_connectors` and `output_connectors`:
+
+```yaml
+stage_args:
+  - stage_id: 0
+    # ...
+    output_connectors:
+      to_stage_1: connector_of_mooncake
+
+  - stage_id: 1
+    # ...
+    input_connectors:
+      from_stage_0: connector_of_mooncake
+```
+
+## 7. Using SharedMemoryConnector (Auto-Mode)
+
+**Best for single-node.**
+
+The system will automatically create `SharedMemoryConnector`s for any pipeline edge that does not have an explicit connector defined. This is inferred from:
+1.  `runtime.edges` list in the config.
+2.  `engine_input_source` dependencies defined in `stage_args`.
+
+### Threshold Configuration
+By default, payloads larger than **64KB** (default threshold) are transferred via shared memory, while smaller ones use the control queue (inline).
+
+To adjust this threshold (e.g., to 1GB), add the following to your `runtime.connectors`:
+
+```yaml
+runtime:
+  connectors:
+    connector_of_shared_memory:
+      name: SharedMemoryConnector
+      extra:
+        shm_threshold_bytes: 1024 # 1KB threshold
+```
+
+## 8. Summary
+
+| Use Case | Recommended Connector | Configuration |
+| :--- | :--- | :--- |
+| **Single Node** | `SharedMemoryConnector` | **None** (Automatic) or Custom Threshold |
+| **Multi Node** | `MooncakeConnector` | Explicit YAML + Mooncake Master |
+
+## 9. Operational Notes (important)
+
+- **Fail-fast config validation**: the loader raises if any expected edge is missing a connector. Define `input_connectors`/`output_connectors` or rely on auto-SHM filling; otherwise startup aborts.
+- **Missing payloads halt the stage**: workers expect connector payloads; if metadata or connector config is missing, the stage raises and stops. Verify connector wiring and metadata propagation before production.
+
+## 10. Future Roadmap: Device-to-Device (D2D) Transport
+
+The current implementations (`SharedMemoryConnector`, `MooncakeConnector`) utilize a **D2H2D (Device-to-Host-to-Device)** data path. Tensors are moved to CPU memory (Host) for transport, which incurs PCIe overhead.
+
+As outlined in the design RFC, future versions will introduce **D2D (Device-to-Device)** connectors:
+
+*   **Goal**: Direct GPU-to-GPU transfer (via NCCL, UCX, or IPC) to minimize latency for large tensor payloads.
+*   **Mechanism**: The `OmniConnector` API allows `put()` to initiate a transfer and return a lightweight handle (metadata) via the control plane, while the heavy payload flows directly between devices.

docs/design/feature/ray_based_execution.md

@@ -0,0 +1,61 @@
+# VLLM-Omni Distributed Utilities
+
+This directory (vllm_omni/distributed/ray_utils) contains utilities for distributed execution in vllm-omni, supporting both **Ray** and **Multiprocessing** backends.
+
+## 1. Ray Utils
+
+The `ray_utils` module provides helper functions for managing Ray clusters and actors, which is essential for:
+*   **Multi-node deployment**: Running pipeline stages across different physical machines.
+*   **Resource management**: Efficient GPU/CPU allocation.
+
+### 1.1 Basic Usage
+
+To use the Ray backend, specify `worker_backend="ray"` when initializing the engine.
+
+**Command Line Example:**
+```bash
+vllm serve Qwen/Qwen2.5-Omni-7B \
+  --omni \
+  --port 8091 \
+  --worker-backend ray \
+  --ray-address auto
+```
+
+### 1.2 Cluster Setup
+
+**Step 1: Start Head Node**
+Run this on your primary machine:
+```bash
+ray start --head --port=6399
+```
+
+**Step 2: Connect Worker Nodes**
+Run this on each worker machine:
+```bash
+ray start --address=<HEAD_NODE_IP>:6399
+```
+
+> **Tip**: For a complete cluster setup script, refer to the vLLM example:
+> [run_cluster.sh](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/run_cluster.sh)
+
+### 1.3 Distributed Connector Support
+
+When running on Ray, the system automatically adapts its communication strategy:
+
+*   **Cross-Node**: Recommended to use `MooncakeConnector` (requires separate configuration).
+*   **Same-Node**: Can still use `SharedMemoryConnector` for efficiency, or Ray's native object store (plasma).
+*   **SHM threshold default differs**: when `worker_backend="ray"`, the SharedMemoryConnector default threshold is set to `sys.maxsize`, which forces payloads to go inline (no SHM). Override `shm_threshold_bytes` in the connector config if you want SHM for Ray runs.
+
+### 1.4 Internal Helpers
+
+*   **`initialize_ray_cluster`**: Connects to an existing Ray cluster or starts a local one.
+
+## 2. Troubleshooting
+
+*   **Connection Issues**: Ensure the Ray head node is accessible and ports (default 6399 in this example) are open.
+*   **Version Mismatch**: Ensure all nodes run the same version of Ray and Python.
+
+### Installation
+```bash
+pip install "ray[default]"
+```

docs/design/index.md

@@ -5,9 +5,15 @@ This section contains design documents and architecture specifications for vLLM-
 ## Architecture Documents
 
 - [Architecture Overview](architecture_overview.md)
-- [vLLM-Omni Software Design Document](vllm_omni_design.md)
-- [Multi-Request Streaming](mrs_design.md)
 
-## API Design Documentation
+## Feature Design Documents
 
-- [vLLM-Omni API Design Documentation](api_design_doc.md)
+- [Multi-Request Streaming](feature/multi_request_streaming_design.md)
+- [Omni Connector Design](feature/omni_connector_design.md)
+- [Ray-based Execution](feature/ray_based_execution.md)
+
+## Module Design Documents
+
+- [AR Module Design](module/ar_module_design.md)
+- [DIT Module Design](module/dit_module_design.md)
+- [Entrypoint Module Design](module/entrypoint_module_design.md)

docs/design/module/ar_module_design.md

@@ -0,0 +1 @@
+Architecture design of the AR module (update soon)

docs/design/module/dit_module_design.md

@@ -0,0 +1 @@
+Architecture design of the DiT module (update soon)

docs/design/module/entrypoint_module_design.md

@@ -0,0 +1 @@
+Architecture design of the entrypoint (update soon)

docs/design/vllm_omni_design.md

@@ -1,509 +0,0 @@
-# Architecture Design Principles
-
-## Overview
-
-vLLM-Omni is a multi-modality extension for vLLM that supports non-autoregressive structures and non-textual outputs. This document outlines the key software abstractions, APIs, and dependencies for the system, designed to maximize reuse of vLLM's proven architecture.
-
-## Architecture Principles
-
-1. **vLLM V1 Compatibility**: Built on vLLM's Engine V1 architecture with AsyncLLM and EngineCore patterns
-2. **Stage-based Processing**: Models are divided into multiple stages, each processed by different Engine Cores
-3. **Multiple Engine Core Support**: Each stage can use either AR Engine Core (reusing vLLM) or Diffusion Engine Core (new DiT support), or other new Cores
-4. **Worker Process Pattern**: Follows vLLM's multiprocess worker architecture for scalability
-5. **Extensibility**: Easy integration of new modalities, model architectures, and output formats
-
-## Key Data Flow
-
-API Server --> OmniLLM/AsyncOmni (New, including multi engines) --> LLMEngine/AsyncLLM --> Engine Core
- --> Scheduler (New one for DiT) --> Executor (New one for diffusers) --> Worker (New one for DiT)
- --> ModelRunner (New one for AR hiddenstate, New one for DiT) --> RequestState --> OutputProcessoer (New one for final multimodal output)
-
-## Core Components (Ordered by Data Flow)
-
-### 1. Installation
-```bash
-pip install vllm
-pip install vllm-omni
-```
-
-### 2. Online inference launch (Entry Point)
-
-Keep consistency with the vllm main branch
-```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8000
-```
-
-#### Design Architecture
-```mermaid
-graph TD
-    A[vllm serve model --omni] --> B[vLLM-Omni CLI Wrapper]
-    B --> C{Detect --omni flag}
-    C -->|Yes| D[Parse OmniConfig]
-    C -->|No| E[Forward to vLLM CLI]
-    D --> F[Initialize AsyncOmni]
-    F --> G[Start omni Server]
-    G --> H[Multi-stage Processing]
-    E --> I[Standard vLLM Pipeline]
-
-    subgraph "vLLM-Omni Components"
-        F
-        G
-        H
-    end
-
-    subgraph "vLLM Components"
-        I
-    end
-```
-
-#### omni Serve Command Implementation
-File: vllm_omni/entrypoints/cli/main.py
-```python
-import sys
-import argparse
-from typing import List, Optional
-from vllm_omni.entrypoints.omni import OmniServeCommand
-
-def main():
-    """Main CLI entry point that intercepts vLLM commands"""
-    # Check if --omni flag is present
-    if "--omni" in sys.argv:
-        # Remove --omni flag and process with vLLM-Omni
-        omni_args = [arg for arg in sys.argv[1:] if arg != "--omni"]
-        omni_serve = OmniServeCommand()
-        omni_serve.run(omni_args)
-    else:
-        # Forward to original vLLM CLI
-        from vllm.entrypoints.cli.main import main as vllm_main
-        vllm_main()
-
-if __name__ == "__main__":
-    main()
-```
-
-#### vLLM Plugin Integration
-File: vllm_omni/pyproject.toml
-```python
-[project.scripts]
-# Override vLLM's CLI entry point
-vllm = "vllm_omni.cli:main"
-
-# Add entry point for vLLM integration
-[project.entry-points."vllm.plugins"]
-omni = "vllm_omni.plugin:OmniPlugin"
-```
-
-### 3. Offline inference launch (Entry Point)
-Design an upper level class to incorporate multi Engines, each engine has a engine call.
-```python
-from vllm.entrypoints.llm import LLM
-from vllm.v1.engine.llm_engine import LLMEngine
-
-class OmniLLM(LLM):
-    """Extended LLM supporting multiple engines and stage-based processing"""
-
-    def __init__(self, stage_configs: List[StageConfig]):
-        super().__init__()
-        self.stage_configs = stage_configs
-        self.engine_list = []  # List of AsyncLLM instances for each stage
-        self.output_processor = MultimodalOutputProcessor()
-        self._initialize_stage_engines()
-
-    def _initialize_stage_engines(self) -> None:
-        """Initialize LLMEngine instances for each stage"""
-        for stage_config in self.stage_configs:
-            stage_llm = LLMEngine.from_vllm_config(
-                    vllm_config=stage_config.vllm_config,
-                    executor_class=self.executor_class,
-                    log_stats=self.log_stats
-                )
-            self.engine_list.append(stage_llm)
-
-    def generate(
-        self,
-        stage_args_list: List[stage_args],
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-        priority: Optional[list[int]] = None,
-    ) -> list[RequestOutput]:
-        """Main generation interface - orchestrates multi-stage processing"""
-
-        # Process through each stage sequentially
-        for i, stage_config in enumerate(self.stage_configs):
-            stage_engine = self.engine_list[i]
-
-            # Prepare input for this stage
-            stage_args = stage_args_list[i]
-            prompt_str, engine_request, tokenization_kwargs = self._process_stage_inputs(stage_config, **stage_args)
-
-            # Add inputs to Engine
-            stage_engine.add_request(requesy_id, prompt_str, tokenization_kwargs)
-            # Run Engine
-            stage_output = stage_engine.step()
-
-            # Update stage input and output of each stage for later usage
-            self._update_stage_io(stage_output, stage_config)
-
-
-        # Process final output
-        final_output = self.output_processor.process_output(
-            self.stage_configs[-1].stage_output
-        )
-        return final_output
-
-    def _process_stage_inputs(stage_config, **stage_args) -> Any:
-        """Prepare input for specific stage"""
-        if stage_config.engine_type == "AR":
-            return self._process_ar_inputs(**stage_args)
-        elif stage_config.engine_type == "DiT":
-            return self._process_dit_inputs(**stage_args)
-        else
-            raise NotImplementedError
-
-    def _process_dit_inputs(**stage_args)-> Any:
-        image_latent = self.vae.encode(**stage_args)
-
-        return image_latent
-```
-
-### 4. Stage config for setting of different model stages
-```python
-@dataclass
-class StageConfig:
-    """Configuration for a processing stage"""
-    stage_id: int
-    engine_type: str  # "AR" or "DiT"
-    model_path: str
-    input_modalities: List[str]
-    output_modalities: List[str]
-    vllm_config: Optional[VllmConfig] = None  # For engine config of corresponding stage
-    executor_class: type[Executor]  # For execute class config of corresponding stage
-    dit_config: Optional[DiTConfig] = None  # For diffusion stages
-    cache_config: Optional[DiTCacheConfig] = None
-```
-For AR stage, the setting is:
-```python
-from vllm.v1.core.sched.scheduler import Scheduler
-from vllm.v1.executor.multiproc_executor import MultiprocExecutor
-ar_stage_config = StageConfig()
-ar_stage_config.vllm_config.scheduler_config.scheduler_cls = Scheduler # original vllm scheduler for AR
-ar_stage_config.executor_class = MultiprocExecutor # original vllm executor for AR
-```
-For DiT stage, the setting is:
-```python
-from vllm.v1.executor.multiproc_executor import MultiprocExecutor
-dit_stage_config = StageConfig()
-dit_stage_config.vllm_config.scheduler_config.scheduler_cls = DiffusionScheduler # New scheduler for DiT
-
-# For diffusion models without using diffusers
-dit_stage_config.executor_class = MultiprocExecutor
-
-# For diffusion models using diffusers
-dit_stage_config.executor_class = DiffusersPipelineExecutor
-```
-
-### 5. Online inference main class
-
-Similar to OmniLLM in offline inference, add some asynchronous processing, referring to AsyncLLM
-```python
-from vllm.v1.engine.async_llm import AsyncLLM
-
-
-class AsyncOmni(AsyncLLM):
-    """Extended AsyncLLM supporting multiple engines and stage-based processing"""
-
-    def __init__(self, stage_configs: List[StageConfig]):
-        super().__init__()
-```
-
-### 6. Engine Core
-No need to change. The specific executor, scheduler and output type can be transferred into it with new configs.
-```python
-# class: EngineCore
-# EngineCore.step (simplified)
-class EngineCore:
-    def step(self):
-        scheduler_output = scheduler.schedule()
-        model_output = executor.execute_model(scheduler_output)
-        engine_outputs = scheduler.update_from_output(
-            scheduler_output, model_output
-        )
-        return engine_outputs
-```
-
-### 7. Scheduler
-Create a new Diffusion Scheduler for DiT, which is inherited from original vllm AR scheduler. At first, no complicated strategy.
-Just process the request first in first out. Then create a class of DiT Cache Manager for optimization in the future.
-```python
-from vllm.v1.core.sched.scheduler import Scheduler
-class DiffusionScheduler(Scheduler):
-
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        kv_cache_config: KVCacheConfig,
-        dit_cache_config: DiTCacheConfig,
-        structured_output_manager: StructuredOutputManager,
-        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
-        include_finished_set: bool = False,
-        log_stats: bool = False,
-    ) -> None:
-
-    self.dit_cache_manager = DiTCacheManager(dit_cache_config)
-```
-For DiT Cache Manager (Can refer to xDiT):
-```python
-class DiTCacheManager:
-    """Manages DiT-specific caching"""
-
-    def __init__(self, config: DiTCacheConfig):
-        self.cache_tensors: Dict[str, torch.Tensor] = {}
-        self.cache_groups: List[DiTCacheTensor] = config.dit_cache_tensors
-
-    def allocate_cache(self, request_id: str, size: int) -> torch.Tensor
-    def get_cache(self, request_id: str) -> Optional[torch.Tensor]
-    def release_cache(self, request_id: str) -> None
-    def clear_expired_cache(self) -> None
-```
-
-### 8. Executor for DiT without diffusers
-
-No need to change. Just import original AR executor.
-
-### 9. Diffusers Pipeline Executor (no worker)
-
-A single-process executor that directly runs the Diffusers pipeline without spawning workers or using RPC. Interfaces remain identical to `Executor` so the EngineCore loop is unchanged.
-
-#### Function map（Pipeline Executor）
-
-##### 1) Inherited and overridden
-```python
-from concurrent.futures import Future
-from typing import Optional, Union, Callable, Any
-import torch
-import torch.nn as nn
-from vllm.v1.executor.abstract import Executor
-from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
-from vllm.v1.outputs import ModelRunnerOutput
-from vllm.tasks import SupportedTask
-from diffusers import DiffusionPipeline
-
-class DiffusersPipelineExecutor(Executor):
-    supports_pp: bool = False  # Single-process, no TP/PP/DP
-
-    def _init_executor(self) -> None:
-        # Called by ExecutorBase.__init__
-        self._failure_callback: Optional[Callable[[], None]] = None
-        self._device = self._resolve_device()
-        self._dtype = self._resolve_dtype()
-        self._pipeline = self._build_pipeline(device=self._device, dtype=self._dtype)
-        self._profiler = None
-        self._is_failed = False
-        self.is_sleeping = False
-        self.sleeping_tags: set[str] = set()
-
-    # major functions to build/run diffusers pipeline
-    def _build_pipeline(self, device:torch.device, dtype:torch.dtype)->DiffusionPipeline:
-        model_name = "Qwen/Qwen-Image"
-
-        self.pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=dtype)
-        self.pipe = pipe.to(device)
-
-    def _run_pipeline(self, scheduler_output) -> ModelRunnerOutput: ...
-        positive_magic = {
-            "en": ", Ultra HD, 4K, cinematic composition.", # for english prompt
-            "zh": ", 超清，4K，电影级构图." # for chinese prompt
-        }
-
-        # Generate image
-        prompt_embeds = self._get_and_process_prompt_embeds(scheduler_output, positive_magic)
-        negative_prompt_embeds = self.pipe.embed_prompt(" ")
-
-        width, height = aspect_ratios["16:9"]
-
-        image = pipe(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            width=width,
-            height=height,
-            num_inference_steps=50,
-            true_cfg_scale=4.0,
-            generator=torch.Generator(device="cuda").manual_seed(42)
-        ).images[0]
-
-        output = self.wrap_image_as_ModelRunnerOutput(image)
-        return output
-
-    # ---- Internal helpers (implementation-specific, not public API) ----
-    def _resolve_device(self): ...
-    def _resolve_dtype(self): ...
-    def _get_model(self) -> nn.Module: ...
-    def _get_and_process_prompt_embeds(self, scheduler_output, positive_magic):
-        ...
-        #append the positive_magic to prompt and embed them to prompt embed tensors
-
-    # Functions related to workers should either raise NotImplementedError
-    # or return empty defaults in this no-worker executor.
-
-    def collective_rpc(self, method, timeout=None, args=(), kwargs=None) -> list[Any]:
-        # No workers in pipeline executor
-        raise NotImplementedError("No workers in DiffusersPipelineExecutor")
-
-    def initialize_from_config(self, kv_cache_configs: list[KVCacheConfig]) -> None:
-        return  # no-op (pipeline already built in _init_executor)
-
-    def register_failure_callback(self, callback):
-        self._failure_callback = callback
-
-    def determine_available_memory(self) -> list[int]:  # bytes
-        # Single device; return [available_bytes]. If CPU-only, return [0].
-        return [self._determine_available_bytes(self._device)]
-
-```
-
-### 10. Worker
-
-#### Inheritance strategy
-Prefer reusing the mature GPU Worker end-to-end. Worker class is selected by configuration (vllm_config). Do not add a new executor-specific worker binding. If customization is needed, override only `init_device` to construct the `DiffusionModelRunner`; all other behaviors (device init details, profiling, sleep/wake, PP/TP comms, execute path) remain from `vllm/v1/worker/gpu_worker.py::Worker`.
-
-
-Inherited and overridden
-```python
-# Optional: only if you need to plug a custom DiffusionModelRunner.
-from vllm.v1.worker.gpu_worker import Worker as GPUWorker
-from vllm.v1.worker.diffusion_model_runner import DiffusionModelRunner
-
-class Worker(GPUWorker):
-    def init_device(self) -> None:
-        #those related to device check and init
-        ...
-
-        self.model_runner = DiffusionModelRunner(self.vllm_config, ...)
-```
-
-### 11. Model Runner
-
-### Function map (Model Runner)
-#### 1) Inherited and overridden
-Those parts relied to the KV Cache will be omitted if we do not register the model to the vllm config. The engine core will view it as do not require KV Cache, and handle it properly
-
-Reuse `vllm/v1/outputs.py::ModelRunnerOutput`：
-- DiffusionModelRunner: Use the `pooler_output=[Tensor,...]` to return multi modal tensors
-- ARModelRunner: Use the `pooler_output=[Tensor,...]` to return hidden states.
-```python
-from typing import Optional, Union
-import torch
-from vllm.v1.worker.gpu_model_runner import GPUModelRunner
-from vllm.v1.outputs import ModelRunnerOutput
-
-
-class DiffusionModelRunner(GPUModelRunner):
-    @torch.inference_mode()
-    def execute_model(
-        self,
-        scheduler_output: "SchedulerOutput",
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-    ) -> Union[ModelRunnerOutput, IntermediateTensors]:
-        ...
-        return ModelRunnerOutput(
-            req_ids=[...],
-            req_id_to_index={...},
-            sampled_token_ids=[],
-            spec_token_ids=None,
-            logprobs=None,
-            prompt_logprobs_dict={},
-            pooler_output=[Tensor,...],         # Return Hidden states
-            kv_connector_output=None,
-            num_nans_in_logits=None,
-        )# return multi modal tensors via pooler_output=[Tensor,...]
-
-
-class ARModelRunner(GPUModelRunner):
-    @torch.inference_mode()
-    def execute_model(
-        self,
-        scheduler_output: "SchedulerOutput",
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-    ) -> Union[ModelRunnerOutput, IntermediateTensors]:
-        ...
-        return ModelRunnerOutput(
-            req_ids=[...],
-            req_id_to_index={...},
-            sampled_token_ids=[],
-            spec_token_ids=None,
-            logprobs=None,
-            prompt_logprobs_dict={},
-            pooler_output=[Tensor,...],         # Return Hidden states
-            kv_connector_output=None,
-            num_nans_in_logits=None,
-        )# return hidden states via pooler_output=[Tensor,...]
-```
-
-### 12. RequestState Role and Usage
-```bash
-# RequestState serves as the per-request state tracker in OutputProcessor:
-# - Maintains request-specific state (tokens, logprobs, detokenizer, etc.)
-# - Converts EngineCoreOutput → RequestOutput/PoolingRequestOutput
-# - Manages request lifecycle from registration to completion
-AsyncLLM.add_request()
-↓
-OutputProcessor.add_request()
-↓
-RequestState.from_new_request() → create request state
-
-AsyncLLM.__init__() / AsyncLLM.generate() / AsyncLLM.encode() →  create a background loop that continuously pulls output from EngineCore
-↓
-OutputProcessor.process_outputs() → update state and process outputs
-↓
-RequestState.make_request_output() → convert to the final output, formatted as RequestOutput or PoolingRequestOutput
-↓
-RequestOutputCollector.put() → push to the queue (AsyncLLM)
-```
-
-Need to add implementation for an existing method
-```python
-class RequestState:
-    def __init__(self, request_id: str, parent_req: Optional[ParentRequest],
-                 request_index: int, lora_name: Optional[str],
-                 output_kind: RequestOutputKind, prompt: Optional[str],
-                 prompt_token_ids: list[int], logprobs_processor: Optional[LogprobsProcessor],
-                 detokenizer: Optional[IncrementalDetokenizer],
-                 max_tokens_param: Optional[int], arrival_time: float,
-                 queue: Optional[RequestOutputCollector], log_stats: bool):
-
-    def _new_pooling_output(self, pooling_output: torch.Tensor) -> PoolingOutput:
-        """Create PoolingOutput for multimodal/pooling requests"""
-        return PoolingOutput(data=pooling_output)
-```
-
-
-### 13. OutputProcessor
-
-For hidden state output, original OutputProcessor already support.
-
-We only need to add new one for final multimodal output.
-```python
-class MultimodalOutputProcessor(OutputProcessor):
-    """Handles multimodal output processing"""
-
-    def __init__(self):
-        self.output_handlers: Dict[str, OutputProcessor] = {}
-
-    def process_outputs(self, engine_core_outputs: list[EngineCoreOutput], ...):
-        for engine_core_output in engine_core_outputs:
-            # Option 1: Use output_type field (if EngineCoreOutput is extended)
-            if engine_core_output.output_type == "image":
-                self._process_image_output(engine_core_output)
-            elif engine_core_output.output_type == "text+image":
-                self._process_text_image_output(engine_core_output)
-            elif engine_core_output.output_type == "latents":
-                self._process_latents_output(engine_core_output)
-            elif engine_core_output.output_type == "text":
-                self._process_text_output(engine_core_output)
-            else:
-                # Fallback: use existing pooling_output logic
-                if engine_core_output.pooling_output is not None:
-                    self._process_pooling_output(engine_core_output)
-                else:
-                    self._process_text_output(engine_core_output)
-```

docs/getting_started/installation/README.md

@@ -1,7 +1,8 @@
 # Installation
 
-vLLM supports the following hardware platforms:
+vLLM-Omni supports the following hardware platforms:
 
 - [GPU](gpu.md)
     - [NVIDIA CUDA](gpu.md)
+    - [AMD ROCm](gpu.md)
 - [NPU](npu.md)

docs/getting_started/installation/gpu.md

@@ -14,6 +14,10 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr
 
     --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:requirements"
 
+=== "AMD ROCm"
+
+    --8<-- "docs/getting_started/installation/gpu/rocm.inc.md:requirements"
+
 ## Set up using Python
 
 ### Create a new Python environment
@@ -26,6 +30,11 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr
 
     --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:pre-built-wheels"
 
+
+=== "AMD ROCm"
+
+    --8<-- "docs/getting_started/installation/gpu/rocm.inc.md:pre-built-wheels"
+
 [](){ #build-from-source }
 
 ### Build wheel from source
@@ -34,7 +43,24 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr
 
     --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:build-wheel-from-source"
 
+=== "AMD ROCm"
+
+    --8<-- "docs/getting_started/installation/gpu/rocm.inc.md:build-wheel-from-source"
+
 ## Set up using Docker
 
+### Build wheel from source
+
+=== "NVIDIA CUDA"
+
+    --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:build-wheel-from-source-in-docker"
+
+=== "AMD ROCm"
+
+    --8<-- "docs/getting_started/installation/gpu/rocm.inc.md:build-wheel-from-source-in-docker"
+
 ### Pre-built images
-To be released... Stay tuned!
+
+=== "NVIDIA CUDA"
+
+    --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:pre-built-images"

docs/getting_started/installation/gpu/cuda.inc.md

@@ -44,7 +44,7 @@ uv pip install vllm==0.11.0 --torch-backend=auto
 Install additional requirements for vLLM-Omni
 ```bash
 git clone https://github.com/vllm-project/vllm-omni.git
-cd vllm_omni
+cd vllm-omni
 uv pip install -e .
 ```
 
@@ -73,3 +73,27 @@ uv pip install --no-build-isolation --editable .
 </details>
 
 # --8<-- [end:build-wheel-from-source]
+
+# --8<-- [start:build-wheel-from-source-in-docker]
+
+# --8<-- [end:build-wheel-from-source-in-docker]
+
+# --8<-- [start:pre-built-images]
+
+vLLM-Omni offers an official docker image for deployment. These images are built on top of vLLM docker images and available on Docker Hub as [vllm/vllm-omni](https://hub.docker.com/r/vllm/vllm-omni/tags). The version of vLLM-Omni indicates which release of vLLM it is based on.
+
+Here's an example deployment command that has been verified on 2 x H100's:
+```bash
+docker run --runtime nvidia --gpus 2 \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HF_TOKEN=$HF_TOKEN" \
+    -p 8091:8091 \
+    --ipc=host \
+    vllm/vllm-omni:v0.11.0rc1 \
+    --model Qwen/Qwen3-Omni-30B-A3B-Instruct --port 8091
+```
+
+!!! tip
+    You can use this docker image to serve models the same way you would with in vLLM! To do so, make sure you overwrite the default entrypoint (`vllm serve --omni`) which works only for models supported in the vLLM-Omni project.
+
+# --8<-- [end:pre-built-images]

docs/getting_started/installation/gpu/rocm.inc.md

@@ -0,0 +1,115 @@
+# --8<-- [start:requirements]
+
+- GPU: Validated on gfx942 (It should be supported on the AMD GPUs that are supported by vLLM.)
+
+# --8<-- [end:requirements]
+# --8<-- [start:set-up-using-python]
+
+vLLM-Omni current recommends the steps in under setup through Docker Images.
+
+# --8<-- [start:pre-built-wheels]
+
+# --8<-- [end:pre-built-wheels]
+
+# --8<-- [start:build-wheel-from-source]
+
+# --8<-- [end:build-wheel-from-source]
+
+# --8<-- [start:build-wheel-from-source-in-docker]
+
+#### Get the correct docker environment
+
+The v0.11.0 releases is on Oct 3, 2025, so we will pull a AMD nightly docker image close to the release period. `rocm/vllm-dev:nightly_main_20251005` is picked as it has a correct `transformers` version that supports qwen3_omni.
+
+`docker pull rocm/vllm-dev:nightly_main_20251005`
+
+#### Launch the docker environment
+
+In this docker environment, we have all the dependencies for vLLM installed, like `flash-attn` and `aiter`
+
+```bash
+#!/bin/bash
+docker run -it \
+   --privileged \
+   --network=host \
+   --group-add=video \
+   --ipc=host \
+   --cap-add=SYS_PTRACE \
+   --security-opt seccomp=unconfined \
+   --device /dev/kfd \
+   --device /dev/dri \
+   --name vllmomni \
+   rocm/vllm-dev:nightly_main_20251005 \
+   bash
+```
+
+#### Update docker dependencies
+
+```bash
+sudo apt update
+sudo apt install ffmpeg -y
+```
+
+#### Installation of vLLM
+Install the v0.11.0 release of vLLM as vLLM-Omni is currently depend on v0.11.0.
+
+```bash
+# uninstall existing vllm which is based on the main branch, not on the releases branch
+python3 -m pip uninstall -y vllm
+git clone https://github.com/vllm-project/vllm.git vllm0110
+cd vllm0110/
+
+# checkout the version tag v0.11.0
+git checkout -b v0.11.0 v0.11.0
+
+# you should see 0.11.0
+python -c "import setuptools_scm; print(setuptools_scm.get_version())"
+
+# PYTORCH_ROCM_ARCH=<your-gpu-arch> python3 setup.py develop
+PYTORCH_ROCM_ARCH=gfx942 python3 setup.py develop
+```
+
+!!! note
+    vLLM release wheels based on the branch with prefix `releases/`, not from the tag as vLLM may cherry pick bugfixes after cutting a branch.
+
+
+#### Installation of vLLM-Omni
+
+There are two approaches to installing `vllm-omni`
+
+
+1. Install from PyPI
+
+```bash
+pip install vllm-omni
+
+# you might want to also download the source code of the same version to run the examples
+```
+
+2. Install from source
+
+```bash
+# clone and checkout to the release version.
+git clone https://github.com/vllm-project/vllm-omni.git
+cd vllm-omni
+git checkout v0.11.0rc1
+pip install -e .
+```
+
+#### Setup Environment Variables
+
+After installing `vllm` and `vllm-omni` please setup the environment variable as well before running any of the examples and command.
+
+```bash
+export MIOPEN_FIND_MODE=FAST
+export VLLM_ROCM_USE_AITER=1
+export VLLM_ROCM_USE_AITER_MHA=1
+export VLLM_ROCM_USE_AITER_LINEAR=0
+export VLLM_ROCM_USE_AITER_RMSNORM=0
+```
+
+# --8<-- [end:build-wheel-from-source-in-docker]
+
+# --8<-- [start:pre-built-images]
+
+# --8<-- [end:pre-built-images]

docs/getting_started/installation/npu/npu.inc.md

@@ -19,6 +19,7 @@ export DEVICE1=/dev/davinci1
 export IMAGE=quay.io/ascend/vllm-ascend:v0.11.0rc2
 docker run --rm \
     --name vllm-omni-npu \
+    --shm-size=1g \
     --device $DEVICE0 \
     --device $DEVICE1 \
     --device /dev/davinci_manager \

docs/getting_started/quickstart.md

@@ -16,7 +16,19 @@ Please refer to [installation](installation/README.md)
 
 ## Offline Inference
 
-Please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)
+Text-to-image generation quickstart with vLLM-Omni:
+
+```python
+from vllm_omni.entrypoints.omni import Omni
+
+if __name__ == "__main__":
+    omni = Omni(model="Tongyi-MAI/Z-Image-Turbo")
+    prompt = "a cup of coffee on the table"
+    images = omni.generate(prompt)
+    images[0].save("coffee.png")
+```
+
+For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)
 
 ## Online Serving with OpenAI-Completions API
 

docs/mkdocs/hooks/generate_examples.py

@@ -173,7 +173,7 @@ class Example:
             gh_file = gh_file.relative_to(ROOT_DIR)
 
             # Make GitHub URL
-            url = "https://github.com/vllm-project/vllm/"
+            url = "https://github.com/vllm-project/vllm-omni/"
             url += "tree/main" if self.path.is_dir() else "blob/main"
             gh_url = f"{url}/{gh_file}"
 
@@ -183,7 +183,7 @@ class Example:
 
     def generate(self) -> str:
         content = f"# {self.title}\n\n"
-        url = "https://github.com/vllm-project/vllm/"
+        url = "https://github.com/vllm-project/vllm-omni/"
         url += "tree/main" if self.path.is_dir() else "blob/main"
         content += f"Source <{url}/{self.path.relative_to(ROOT_DIR)}>.\n\n"
 
@@ -192,7 +192,8 @@ class Example:
         code_fence = "``````"
 
         if self.is_code:
-            content += f'{code_fence}{self.main_file.suffix[1:]}\n--8<-- "{self.main_file}"\n{code_fence}\n'
+            main_file_rel = self.main_file.relative_to(ROOT_DIR)
+            content += f'{code_fence}{self.main_file.suffix[1:]}\n--8<-- "{main_file_rel}"\n{code_fence}\n'
         else:
             with open(self.main_file) as f:
                 # Skip the title from md snippets as it's been included above
@@ -208,7 +209,7 @@ class Example:
             content += f'??? abstract "{file.relative_to(self.path)}"\n'
             if file.suffix != ".md":
                 content += f"    {code_fence}{file.suffix[1:]}\n"
-            content += f'    --8<-- "{file}"\n'
+            content += f'    --8<-- "{file.relative_to(ROOT_DIR)}"\n'
             if file.suffix != ".md":
                 content += f"    {code_fence}\n"
 

docs/mkdocs/stylesheets/extra.css

@@ -25,8 +25,11 @@ a:not(:has(svg)):not(.md-icon):not(.autorefs-external) {
 
 a[href*="localhost"]::after,
 a[href*="127.0.0.1"]::after,
-a[href*="org.readthedocs.build"]::after,
-a[href*="docs.vllm.ai"]::after {
+
+/* Hide external link icons for all links */
+a[href^="//"]::after,
+a[href^="http://"]::after,
+a[href^="https://"]::after {
     display: none !important;
 }
 

docs/models/supported_models.md

@@ -4,9 +4,9 @@ vLLM-Omni supports unified multimodal comprehension and generation models across
 
 ## Model Implementation
 
-If vLLM-Omni natively supports a model, its implementation can be found in <gh-file:vllm-omni/model_executor/models> and <gh-file:vllm_omni/diffusion/models>.
+If vLLM-Omni natively supports a model, its implementation can be found in <gh-file:vllm_omni/model_executor/models> and <gh-file:vllm_omni/diffusion/models>.
 
-## List of Supported Models for Nvidia GPU
+## List of Supported Models for Nvidia GPU / AMD GPU
 
 <style>
 th {
@@ -20,9 +20,12 @@ th {
 | `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` |
 | `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` |
 | `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` |
+| `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` |
+|`ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` |
+| `WanPipeline` | Wan2.2 | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` |
 
 
-## List of Supported Models for Ascend NPU
+## List of Supported Models for NPU
 
 <style>
 th {

docs/usage/faq.md

@@ -2,7 +2,7 @@
 
 > Q: How many chips do I need to infer a model in vLLM-Omni?
 
-A: Now, we support natively disaggregated deployment for different model stages within a model. There is a restriction that one chip can only has one AutoRegressive model stage. This is because the unified KV cache management of vLLM. Stages of other types can coexist with in a chip. The restriction will be resolved in later version.
+A: Now, we support natively disaggregated deployment for different model stages within a model. There is a restriction that one chip can only have one AutoRegressive model stage. This is because the unified KV cache management of vLLM. Stages of other types can coexist within a chip. The restriction will be resolved in later version.
 
 > Q: When trying to run examples, I encounter error about backend of librosa or soundfile. How to solve it?
 
@@ -11,3 +11,15 @@ A: If you encounter error about backend of librosa, try to install ffmpeg with c
 sudo apt update
 sudo apt install ffmpeg
 ```
+
+> Q: I encounter some bugs or CI problems, which is urgent. How can I solve it?
+
+A: At first, you can check current [issues](https://github.com/vllm-project/vllm-omni/issues) to find possible solutions. If none of these satisfy your demand and it is urgent, please find these [volunteers](https://docs.vllm.ai/projects/vllm-omni/en/latest/community/volunteers/) for help.
+
+> Q: Does vLLM-Omni support AWQ or any other quantization?
+
+A: vLLM-Omni partitions model into several stages. For AR stages, it will reuse main logic of LLMEngine in vLLM. So current quantization supported in vLLM should be also supported in vLLM-Omni for them. But systematic verification is ongoing. For quantization for DiffusionEngine, we are working on it. Please stay tuned and welcome contribution!
+
+> Q: Does vLLM-Omni support multimodal streaming input and output?
+
+A: Not yet. We already put it on the [Roadmap](https://github.com/vllm-project/vllm-omni/issues/165). Please stay tuned!

docs/user_guide/examples/offline_inference/image_to_image.md

@@ -0,0 +1,30 @@
+# Qwen-Image-Edit Offline Inference
+
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_image>. Added in PR [#196](https://github.com/vllm-project/vllm-omni/pull/196).
+
+This example edits an input image with `Qwen/Qwen-Image-Edit` using the `image_edit.py` CLI.
+
+## Local CLI Usage
+
+```bash
+python image_edit.py \
+  --image qwen_bear.png \
+  --prompt "Let this mascot dance under the moon, surrounded by floating stars and poetic bubbles such as 'Be Kind'" \
+  --output output_image_edit.png \
+  --num_inference_steps 50 \
+  --cfg_scale 4.0
+```
+
+Key arguments:
+- `--image`: path to the source image (PNG/JPG, converted to RGB).
+- `--prompt` / `--negative_prompt`: text description (string).
+- `--cfg_scale`: true CFG scale for Qwen-Image-Edit (quality vs. fidelity).
+- `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
+- `--output`: path to save the generated PNG.
+
+## Example materials
+
+??? abstract "image_edit.py"
+    ``````py
+    --8<-- "examples/offline_inference/image_to_image/image_edit.py"
+    ``````

docs/user_guide/examples/offline_inference/qwen2_5_omni.md

@@ -1,13 +1,14 @@
-# Offline Example of vLLM-Omni for Qwen2.5-omni
+# Offline Example of vLLM-Omni for Qwen2.5-Omni
 
-Source <https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen2_5_omni>.
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen2_5_omni>.
 
 
 ## 🛠️ Installation
 
-Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/README.md)
+Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
+
+## Run examples (Qwen2.5-Omni)
 
-## Run examples (Qwen2.5-omni)
 ### Multiple Prompts
 Download dataset from [seed_tts](https://drive.google.com/file/d/1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP/edit). To get the prompt, you can:
 ```bash
@@ -26,6 +27,7 @@ Then run the command below.
 ```bash
 bash run_multiple_prompts.sh
 ```
+
 ### Single Prompt
 Get into the example folder
 ```bash
@@ -36,6 +38,34 @@ Then run the command below.
 bash run_single_prompt.sh
 ```
 
+#### Using Local Media Files
+The `end2end.py` script supports local media files (audio, video, image) via CLI arguments:
+
+```bash
+# Use single local media files
+python end2end.py --query-type use_image --image-path /path/to/image.jpg
+python end2end.py --query-type use_video --video-path /path/to/video.mp4
+python end2end.py --query-type use_audio --audio-path /path/to/audio.wav
+
+# Combine multiple local media files
+python end2end.py --query-type mixed_modalities \
+    --video-path /path/to/video.mp4 \
+    --image-path /path/to/image.jpg \
+    --audio-path /path/to/audio.wav
+
+# Use audio from video file
+python end2end.py --query-type use_audio_in_video --video-path /path/to/video.mp4
+
+```
+
+If media file paths are not provided, the script will use default assets. Supported query types:
+- `use_image`: Image input only
+- `use_video`: Video input only
+- `use_audio`: Audio input only
+- `mixed_modalities`: Audio + image + video
+- `use_audio_in_video`: Extract audio from video
+- `text`: Text-only query
+
 ### FAQ
 
 If you encounter error about backend of librosa, try to install ffmpeg with command below.
@@ -48,17 +78,17 @@ sudo apt install ffmpeg
 
 ??? abstract "end2end.py"
     ``````py
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen2_5_omni/end2end.py"
+    --8<-- "examples/offline_inference/qwen2_5_omni/end2end.py"
     ``````
 ??? abstract "extract_prompts.py"
     ``````py
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen2_5_omni/extract_prompts.py"
+    --8<-- "examples/offline_inference/qwen2_5_omni/extract_prompts.py"
     ``````
 ??? abstract "run_multiple_prompts.sh"
     ``````sh
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen2_5_omni/run_multiple_prompts.sh"
+    --8<-- "examples/offline_inference/qwen2_5_omni/run_multiple_prompts.sh"
     ``````
 ??? abstract "run_single_prompt.sh"
     ``````sh
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen2_5_omni/run_single_prompt.sh"
+    --8<-- "examples/offline_inference/qwen2_5_omni/run_single_prompt.sh"
     ``````

docs/user_guide/examples/offline_inference/qwen3_omni.md

@@ -1,15 +1,16 @@
-# Offline Example of vLLM-Omni for Qwen3-omni
+# Offline Example of vLLM-Omni for Qwen3-Omni
 
-Source <https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen3_omni>.
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen3_omni>.
 
 
 ## 🛠️ Installation
 
-Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/README.md)
+Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
+
+## Run examples (Qwen3-Omni)
 
-## Run examples (Qwen3-omni)
 ### Multiple Prompts
-Download dataset from [seed_tts](https://drive.google.com/file/d/1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP/edit). For processing dataset please refer to [Qwen2.5-omni README.md](https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen2_5_omni/README.md)
+Download dataset from [seed_tts](https://drive.google.com/file/d/1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP/edit). For processing dataset please refer to [Qwen2.5-Omni README.md](https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen2_5_omni/README.md)
 Get into the example folder
 ```bash
 cd examples/offline_inference/qwen3_omni
@@ -32,6 +33,26 @@ If you have not enough memory, you can set thinker with tensor parallel. Just ru
 bash run_single_prompt_tp.sh
 ```
 
+#### Using Local Media Files
+The `end2end.py` script supports local media files (audio, video, image) via command-line arguments:
+
+```bash
+# Use local video file
+python end2end.py --query-type use_video --video-path /path/to/video.mp4
+
+# Use local image file
+python end2end.py --query-type use_image --image-path /path/to/image.jpg
+
+# Use local audio file
+python end2end.py --query-type use_audio --audio-path /path/to/audio.wav
+```
+
+If media file paths are not provided, the script will use default assets. Supported query types:
+- `use_video`: Video input
+- `use_image`: Image input
+- `use_audio`: Audio input
+- `text`: Text-only query
+
 ### FAQ
 
 If you encounter error about backend of librosa, try to install ffmpeg with command below.
@@ -44,21 +65,17 @@ sudo apt install ffmpeg
 
 ??? abstract "end2end.py"
     ``````py
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen3_omni/end2end.py"
-    ``````
-??? abstract "qwen3_omni_moe_tp.yaml"
-    ``````yaml
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen3_omni/qwen3_omni_moe_tp.yaml"
+    --8<-- "examples/offline_inference/qwen3_omni/end2end.py"
     ``````
 ??? abstract "run_multiple_prompts.sh"
     ``````sh
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen3_omni/run_multiple_prompts.sh"
+    --8<-- "examples/offline_inference/qwen3_omni/run_multiple_prompts.sh"
     ``````
 ??? abstract "run_single_prompt.sh"
     ``````sh
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen3_omni/run_single_prompt.sh"
+    --8<-- "examples/offline_inference/qwen3_omni/run_single_prompt.sh"
     ``````
 ??? abstract "run_single_prompt_tp.sh"
     ``````sh
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen3_omni/run_single_prompt_tp.sh"
+    --8<-- "examples/offline_inference/qwen3_omni/run_single_prompt_tp.sh"
     ``````

docs/user_guide/examples/offline_inference/text_to_image.md

@@ -1,9 +1,9 @@
 # Qwen-Image Offline Inference
 
-Source <https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen_image>.
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_image>.
 
 
-This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Image` using vLLM-Omni:
+This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Image` `Tongyi-MAI/Z-Image-Turbo` using vLLM-Omni:
 
 - `text_to_image.py`: command-line script for single image generation.
 - `web_demo.py`: lightweight Gradio UI for interactive prompt/seed/CFG exploration.
@@ -13,6 +13,7 @@ This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Im
 
 ```bash
 python text_to_image.py \
+  --model Tongyi-MAI/Z-Image-Turbo \
   --prompt "a cup of coffee on the table" \
   --seed 42 \
   --cfg_scale 4.0 \
@@ -33,8 +34,6 @@ Key arguments:
 - `--height/--width`: output resolution (defaults 1024x1024).
 - `--output`: path to save the generated PNG.
 
-> ℹ️ Qwen-Image currently publishes best-effort presets at `1328x1328`, `1664x928`, `928x1664`, `1472x1140`, `1140x1472`, `1584x1056`, and `1056x1584`. Adjust `--height/--width` accordingly for the most reliable outcomes.
-
 ## Web UI Demo
 
 Launch the gradio demo:
@@ -49,9 +48,9 @@ Then open `http://localhost:7862/` on your local browser to interact with the we
 
 ??? abstract "gradio_demo.py"
     ``````py
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen_image/gradio_demo.py"
+    --8<-- "examples/offline_inference/text_to_image/gradio_demo.py"
     ``````
 ??? abstract "text_to_image.py"
     ``````py
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen_image/text_to_image.py"
+    --8<-- "examples/offline_inference/text_to_image/text_to_image.py"
     ``````

docs/user_guide/examples/offline_inference/text_to_video.md

@@ -0,0 +1,38 @@
+# Wan2.2 Text-to-Video Offline Inference
+
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_video>. Added in PR [#202](https://github.com/vllm-project/vllm-omni/pull/202).
+
+The `Wan-AI/Wan2.2-T2V-A14B-Diffusers` pipeline generates short videos from text prompts.
+
+## Local CLI Usage
+
+```bash
+python text_to_video.py \
+  --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
+  --negative_prompt "<optional quality filter>" \
+  --height 480 \
+  --width 640 \
+  --num_frames 32 \
+  --guidance_scale 4.0 \
+  --guidance_scale_high 3.0 \
+  --num_inference_steps 40 \
+  --fps 16 \
+  --output t2v_out.mp4
+```
+
+Key arguments:
+- `--prompt`: text description (string).
+- `--height/--width`: output resolution (defaults 720x1280). Dimensions should align with Wan VAE downsampling (multiples of 8).
+- `--num_frames`: Number of frames (Wan default is 81).
+- `--guidance_scale` and `--guidance_scale_high`: CFG scale (applied to low/high)..
+- `--negative_prompt`: optional list of artifacts to suppress (the PR demo used a long Chinese string).
+- `--boundary_ratio`: Boundary split ratio for low/high DiT.
+- `--fps`: frames per second for the saved MP4 (requires `diffusers` export_to_video).
+- `--output`: path to save the generated video.
+
+## Example materials
+
+??? abstract "text_to_video.py"
+    ``````py
+    --8<-- "examples/offline_inference/wan22/text_to_video.py"
+    ``````

docs/user_guide/examples/online_serving/qwen2_5_omni.md

@@ -1,15 +1,16 @@
-# Online serving Example of vLLM-Omni for Qwen2.5-omni
+# Online serving Example of vLLM-Omni for Qwen2.5-Omni
 
-Source <https://github.com/vllm-project/vllm/tree/main/examples/online_serving/qwen2_5_omni>.
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen2_5_omni>.
 
 
 ## 🛠️ Installation
 
-Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/examples/README.md)
+Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
 
-## Run examples (Qwen2.5-omni)
+## Run examples (Qwen2.5-Omni)
+
+### Launch the Server
 
-Launch the server
 ```bash
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
@@ -19,29 +20,59 @@ If you have custom stage configs file, launch the server with command below
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
 ```
 
+### Send Multi-modal Request
+
 Get into the example folder
 ```bash
-cd examples/online_serving
+cd examples/online_serving/qwen2_5_omni
 ```
 
-Send request via python
+####  Send request via python
+
 ```bash
 python openai_chat_completion_client_for_multimodal_generation.py --query-type mixed_modalities
 ```
 
-Send request via curl
+The Python client supports the following command-line arguments:
+
+- `--query-type` (or `-q`): Query type (default: `mixed_modalities`)
+  - Options: `mixed_modalities`, `use_audio_in_video`, `multi_audios`, `text`
+- `--video-path` (or `-v`): Path to local video file or URL
+  - If not provided and query-type uses video, uses default video URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Example: `--video-path /path/to/video.mp4` or `--video-path https://example.com/video.mp4`
+- `--image-path` (or `-i`): Path to local image file or URL
+  - If not provided and query-type uses image, uses default image URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common image formats: JPEG, PNG, GIF, WebP
+  - Example: `--image-path /path/to/image.jpg` or `--image-path https://example.com/image.png`
+- `--audio-path` (or `-a`): Path to local audio file or URL
+  - If not provided and query-type uses audio, uses default audio URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common audio formats: MP3, WAV, OGG, FLAC, M4A
+  - Example: `--audio-path /path/to/audio.wav` or `--audio-path https://example.com/audio.mp3`
+- `--prompt` (or `-p`): Custom text prompt/question
+  - If not provided, uses default prompt for the selected query type
+  - Example: `--prompt "What are the main activities shown in this video?"`
+
+
+For example, to use mixed modalities with all local files:
+
+```bash
+python openai_chat_completion_client_for_multimodal_generation.py \
+    --query-type mixed_modalities \
+    --video-path /path/to/your/video.mp4 \
+    --image-path /path/to/your/image.jpg \
+    --audio-path /path/to/your/audio.wav \
+    --prompt "Analyze all the media content and provide a comprehensive summary."
+```
+
+####  Send request via curl
+
 ```bash
 bash run_curl_multimodal_generation.sh mixed_modalities
 ```
 
-### FAQ
-
-If you encounter error about backend of librosa, try to install ffmpeg with command below.
-```
-sudo apt update
-sudo apt install ffmpeg
-```
-
 ## Run Local Web UI Demo
 
 This Web UI demo allows users to interact with the model through a web browser.
@@ -56,36 +87,33 @@ python gradio_demo.py  --model Qwen/Qwen2.5-Omni-7B --port 7861
 
 Then open `http://localhost:7861/` on your local browser to interact with the web UI.
 
+The gradio script supports the following arguments:
 
-### Options
+- `--model`: Model name
+- `--ip`: Host/IP for Gradio server (default: 127.0.0.1)
+- `--port`: Port for Gradio server (default: 7861)
+- `--stage-configs-path`: Path to custom stage configs YAML file (optional)
+- `--share`: Share the Gradio demo publicly (creates a public link)
 
-You can customize its basic launch parameters:
+### FAQ
 
-```bash
-python gradio_demo.py \
-    --model Qwen/Qwen2.5-Omni-7B \
-    --ip 127.0.0.1 \
-    --port 7861 \
-    --stage-configs-path /path/to/stage_configs.yaml
+If you encounter error about backend of librosa, try to install ffmpeg with command below.
+```
+sudo apt update
+sudo apt install ffmpeg
 ```
-
-- `--model`: Local model checkpoint to load (default `Qwen/Qwen2.5-Omni-7B`).
-- `--ip`: Host/IP for the Gradio server (default `127.0.0.1`).
-- `--port`: Port for the Gradio server (default `7861`).
-- `--stage-configs-path`: Optional path to custom stage configs YAML.
-- `--share`: Set to expose a temporary public link via Gradio.
 
 ## Example materials
 
 ??? abstract "gradio_demo.py"
     ``````py
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen2_5_omni/gradio_demo.py"
+    --8<-- "examples/online_serving/qwen2_5_omni/gradio_demo.py"
     ``````
 ??? abstract "openai_chat_completion_client_for_multimodal_generation.py"
     ``````py
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen2_5_omni/openai_chat_completion_client_for_multimodal_generation.py"
+    --8<-- "examples/online_serving/qwen2_5_omni/openai_chat_completion_client_for_multimodal_generation.py"
     ``````
 ??? abstract "run_curl_multimodal_generation.sh"
     ``````sh
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen2_5_omni/run_curl_multimodal_generation.sh"
+    --8<-- "examples/online_serving/qwen2_5_omni/run_curl_multimodal_generation.sh"
     ``````

docs/user_guide/examples/online_serving/qwen3_omni.md

@@ -1,15 +1,16 @@
 # Online serving Example of vLLM-Omni for Qwen3-omni
 
-Source <https://github.com/vllm-project/vllm/tree/main/examples/online_serving/qwen3_omni>.
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen3_omni>.
 
 
 ## 🛠️ Installation
 
-Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/examples/README.md)
+Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
 
 ## Run examples (Qwen3-Omni)
 
-Launch the server
+### Launch the Server
+
 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
@@ -19,21 +20,59 @@ If you have custom stage configs file, launch the server with command below
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
 ```
 
+### Send Multi-modal Request
+
 Get into the example folder
 ```bash
-cd examples/online_serving
+cd examples/online_serving/qwen3_omni
 ```
 
-Send request via python
+####  Send request via python
+
 ```bash
 python openai_chat_completion_client_for_multimodal_generation.py --query-type use_image
 ```
 
-Send request via curl
+The Python client supports the following command-line arguments:
+
+- `--query-type` (or `-q`): Query type (default: `use_video`)
+  - Options: `text`, `use_audio`, `use_image`, `use_video`
+- `--model` (or `-m`): Model name/path (default: `Qwen/Qwen3-Omni-30B-A3B-Instruct`)
+- `--video-path` (or `-v`): Path to local video file or URL
+  - If not provided and query-type is `use_video`, uses default video URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Example: `--video-path /path/to/video.mp4` or `--video-path https://example.com/video.mp4`
+- `--image-path` (or `-i`): Path to local image file or URL
+  - If not provided and query-type is `use_image`, uses default image URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common image formats: JPEG, PNG, GIF, WebP
+  - Example: `--image-path /path/to/image.jpg` or `--image-path https://example.com/image.png`
+- `--audio-path` (or `-a`): Path to local audio file or URL
+  - If not provided and query-type is `use_audio`, uses default audio URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common audio formats: MP3, WAV, OGG, FLAC, M4A
+  - Example: `--audio-path /path/to/audio.wav` or `--audio-path https://example.com/audio.mp3`
+- `--prompt` (or `-p`): Custom text prompt/question
+  - If not provided, uses default prompt for the selected query type
+  - Example: `--prompt "What are the main activities shown in this video?"`
+
+
+For example, to use a local video file with custom prompt:
+
+```bash
+python openai_chat_completion_client_for_multimodal_generation.py \
+    --query-type use_video \
+    --video-path /path/to/your/video.mp4 \
+    --prompt "What are the main activities shown in this video?"
+```
+
+####  Send request via curl
+
 ```bash
 bash run_curl_multimodal_generation.sh use_image
 ```
 
+
 ### FAQ
 
 If you encounter error about backend of librosa, try to install ffmpeg with command below.
@@ -57,23 +96,9 @@ python gradio_demo.py  --model Qwen/Qwen3-Omni-30B-A3B-Instruct --port 7861
 Then open `http://localhost:7861/` on your local browser to interact with the web UI.
 
 
-### Options
-
-The gradio demo also supports running with an existing API server and can be customized with the following arguments.
-
-
-```bash
-python gradio_demo.py \
-    --model Qwen/Qwen3-Omni-30B-A3B-Instruct \
-    --use-api-server \
-    --api-base http://localhost:8091/v1 \
-    --ip 127.0.0.1 \
-    --port 7861
-```
+The gradio script supports the following arguments:
 
 - `--model`: Model name
-- `--use-api-server`: If set, connect to an existing vLLM HTTP API server instead of running AsyncOmni locally.
-- `--api-base`: Base URL for vllm serve (only used when `use-api-server` is set, default: http://localhost:8091/v1)
 - `--ip`: Host/IP for Gradio server (default: 127.0.0.1)
 - `--port`: Port for Gradio server (default: 7861)
 - `--stage-configs-path`: Path to custom stage configs YAML file (optional)
@@ -83,13 +108,17 @@ python gradio_demo.py \
 
 ??? abstract "gradio_demo.py"
     ``````py
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen3_omni/gradio_demo.py"
+    --8<-- "examples/online_serving/qwen3_omni/gradio_demo.py"
     ``````
 ??? abstract "openai_chat_completion_client_for_multimodal_generation.py"
     ``````py
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen3_omni/openai_chat_completion_client_for_multimodal_generation.py"
+    --8<-- "examples/online_serving/qwen3_omni/openai_chat_completion_client_for_multimodal_generation.py"
+    ``````
+??? abstract "qwen3_omni_moe_thinking.yaml"
+    ``````yaml
+    --8<-- "examples/online_serving/qwen3_omni/qwen3_omni_moe_thinking.yaml"
     ``````
 ??? abstract "run_curl_multimodal_generation.sh"
     ``````sh
-    --8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen3_omni/run_curl_multimodal_generation.sh"
+    --8<-- "examples/online_serving/qwen3_omni/run_curl_multimodal_generation.sh"
     ``````

docs/user_guide/teacache.md

@@ -0,0 +1,99 @@
+# TeaCache Configuration Guide
+
+TeaCache speeds up diffusion model inference by caching transformer computations when consecutive timesteps are similar. This typically provides **1.5x-2.0x speedup** with minimal quality loss.
+
+## Quick Start
+
+Enable TeaCache by setting `cache_adapter` to `"tea_cache"`:
+
+```python
+from vllm_omni import Omni
+
+# Simple configuration - model_class_name and model_type are auto-detected
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    cache_adapter="tea_cache",
+    cache_config={
+        "rel_l1_thresh": 0.2  # Optional, defaults to 0.2
+    }
+)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)
+```
+
+### Using Environment Variable
+
+You can also enable TeaCache via environment variable:
+
+```bash
+export DIFFUSION_CACHE_ADAPTER=tea_cache
+```
+
+Then initialize without explicitly setting `cache_adapter`:
+
+```python
+from vllm_omni import Omni
+
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    cache_config={"rel_l1_thresh": 0.2}  # Optional
+)
+```
+
+## Configuration Parameters
+
+### `rel_l1_thresh` (float, default: `0.2`)
+
+Controls the balance between speed and quality. Lower values prioritize quality, higher values prioritize speed.
+
+**Recommended values:**
+
+- `0.2` - **~1.5x speedup** with minimal quality loss (recommended)
+- `0.4` - **~1.8x speedup** with slight quality loss
+- `0.6` - **~2.0x speedup** with noticeable quality loss
+- `0.8` - **~2.25x speedup** with significant quality loss
+
+## Examples
+
+### Python API
+
+```python
+from vllm_omni import Omni
+
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    cache_adapter="tea_cache",
+    cache_config={"rel_l1_thresh": 0.2}
+)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)
+```
+
+## Performance Tuning
+
+Start with the default `rel_l1_thresh=0.2` and adjust based on your needs:
+
+- **Maximum quality**: Use `0.1-0.2`
+- **Balanced**: Use `0.2-0.4` (recommended)
+- **Maximum speed**: Use `0.6-0.8` (may reduce quality)
+
+## Troubleshooting
+
+### Quality Degradation
+
+If you notice quality issues, lower the threshold:
+
+```python
+cache_config={"rel_l1_thresh": 0.1}  # More conservative caching
+```
+
+## Supported Models
+
+Currently supported models:
+
+- **QwenImagePipeline**
+  - Model identifier: `"Qwen/Qwen-Image"`
+  - Example: `model="Qwen/Qwen-Image"`
+
+Future support (extractors in development):
+
+- **FluxPipeline** - Coming soon
+- **CogVideoXPipeline** - Coming soon

examples/offline_inference/image_to_image/image_edit.py

@@ -0,0 +1,143 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""
+Example script for image editing with Qwen-Image-Edit.
+
+Usage:
+    python image_edit.py \
+        --image input.png \
+        --prompt "Let this mascot dance under the moon, surrounded by floating stars and poetic bubbles such as 'Be Kind'" \
+        --output output_image_edit.png \
+        --num_inference_steps 50 \
+        --cfg_scale 4.0
+
+For more options, run:
+    python image_edit.py --help
+"""
+
+import argparse
+import os
+from pathlib import Path
+
+import torch
+from PIL import Image
+
+from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.utils.platform_utils import detect_device_type, is_npu
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Edit an image with Qwen-Image-Edit.")
+    parser.add_argument(
+        "--model",
+        default="Qwen/Qwen-Image-Edit",
+        help="Diffusion model name or local path.",
+    )
+    parser.add_argument(
+        "--image",
+        type=str,
+        required=True,
+        help="Path to input image file (PNG, JPG, etc.).",
+    )
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        required=True,
+        help="Text prompt describing the edit to make to the image.",
+    )
+    parser.add_argument(
+        "--negative_prompt",
+        type=str,
+        default=" ",
+        required=False,
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=0,
+        help="Random seed for deterministic results.",
+    )
+    parser.add_argument(
+        "--cfg_scale",
+        type=float,
+        default=4.0,
+        help="True classifier-free guidance scale specific to Qwen-Image-Edit.",
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="output_image_edit.png",
+        help="Path to save the edited image (PNG).",
+    )
+    parser.add_argument(
+        "--num_outputs_per_prompt",
+        type=int,
+        default=1,
+        help="Number of images to generate for the given prompt.",
+    )
+    parser.add_argument(
+        "--num_inference_steps",
+        type=int,
+        default=50,
+        help="Number of denoising steps for the diffusion sampler.",
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    # Validate input image exists
+    if not os.path.exists(args.image):
+        raise FileNotFoundError(f"Input image not found: {args.image}")
+
+    # Load input image
+    input_image = Image.open(args.image).convert("RGB")
+    print(f"Loaded input image from {args.image} (size: {input_image.size})")
+
+    device = detect_device_type()
+    generator = torch.Generator(device=device).manual_seed(args.seed)
+
+    # Enable VAE memory optimizations on NPU
+    vae_use_slicing = is_npu()
+    vae_use_tiling = is_npu()
+
+    # Initialize Omni with QwenImageEditPipeline
+    omni = Omni(
+        model=args.model,
+        model_class_name="QwenImageEditPipeline",
+        vae_use_slicing=vae_use_slicing,
+        vae_use_tiling=vae_use_tiling,
+    )
+    print("Pipeline loaded")
+
+    # Generate edited image
+    images = omni.generate(
+        prompt=args.prompt,
+        pil_image=input_image,
+        negative_prompt=args.negative_prompt,
+        generator=generator,
+        true_cfg_scale=args.cfg_scale,
+        num_inference_steps=args.num_inference_steps,
+        num_outputs_per_prompt=args.num_outputs_per_prompt,
+    )
+
+    # Save output image(s)
+    output_path = Path(args.output)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    suffix = output_path.suffix or ".png"
+    stem = output_path.stem or "output_image_edit"
+
+    if args.num_outputs_per_prompt <= 1:
+        images[0].save(output_path)
+        print(f"Saved edited image to {os.path.abspath(output_path)}")
+    else:
+        for idx, img in enumerate(images):
+            save_path = output_path.parent / f"{stem}_{idx}{suffix}"
+            img.save(save_path)
+            print(f"Saved edited image to {os.path.abspath(save_path)}")
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/image_to_image/image_to_image.md

@@ -0,0 +1,30 @@
+# Qwen-Image-Edit Offline Inference
+
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_image>. Added in PR [#196](https://github.com/vllm-project/vllm-omni/pull/196).
+
+This example edits an input image with `Qwen/Qwen-Image-Edit` using the `image_edit.py` CLI.
+
+## Local CLI Usage
+
+```bash
+python image_edit.py \
+  --image qwen_bear.png \
+  --prompt "Let this mascot dance under the moon, surrounded by floating stars and poetic bubbles such as 'Be Kind'" \
+  --output output_image_edit.png \
+  --num_inference_steps 50 \
+  --cfg_scale 4.0
+```
+
+Key arguments:
+- `--image`: path to the source image (PNG/JPG, converted to RGB).
+- `--prompt` / `--negative_prompt`: text description (string).
+- `--cfg_scale`: true CFG scale for Qwen-Image-Edit (quality vs. fidelity).
+- `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
+- `--output`: path to save the generated PNG.
+
+## Example materials
+
+??? abstract "image_edit.py"
+    ``````py
+    --8<-- "examples/offline_inference/image_to_image/image_edit.py"
+    ``````

examples/offline_inference/qwen2_5_omni/README.md

@@ -5,6 +5,7 @@
 Please refer to [README.md](../../../README.md)
 
 ## Run examples (Qwen2.5-Omni)
+
 ### Multiple Prompts
 Download dataset from [seed_tts](https://drive.google.com/file/d/1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP/edit). To get the prompt, you can:
 ```bash
@@ -23,6 +24,7 @@ Then run the command below.
 ```bash
 bash run_multiple_prompts.sh
 ```
+
 ### Single Prompt
 Get into the example folder
 ```bash
@@ -33,6 +35,34 @@ Then run the command below.
 bash run_single_prompt.sh
 ```
 
+#### Using Local Media Files
+The `end2end.py` script supports local media files (audio, video, image) via CLI arguments:
+
+```bash
+# Use single local media files
+python end2end.py --query-type use_image --image-path /path/to/image.jpg
+python end2end.py --query-type use_video --video-path /path/to/video.mp4
+python end2end.py --query-type use_audio --audio-path /path/to/audio.wav
+
+# Combine multiple local media files
+python end2end.py --query-type mixed_modalities \
+    --video-path /path/to/video.mp4 \
+    --image-path /path/to/image.jpg \
+    --audio-path /path/to/audio.wav
+
+# Use audio from video file
+python end2end.py --query-type use_audio_in_video --video-path /path/to/video.mp4
+
+```
+
+If media file paths are not provided, the script will use default assets. Supported query types:
+- `use_image`: Image input only
+- `use_video`: Video input only
+- `use_audio`: Audio input only
+- `mixed_modalities`: Audio + image + video
+- `use_audio_in_video`: Extract audio from video
+- `text`: Text-only query
+
 ### FAQ
 
 If you encounter error about backend of librosa, try to install ffmpeg with command below.

examples/offline_inference/qwen2_5_omni/end2end.py

@@ -6,12 +6,15 @@ with the correct prompt format on Qwen2.5-Omni
 """
 
 import os
-from typing import NamedTuple
+from typing import NamedTuple, Optional
 
+import librosa
+import numpy as np
 import soundfile as sf
+from PIL import Image
 from vllm.assets.audio import AudioAsset
 from vllm.assets.image import ImageAsset
-from vllm.assets.video import VideoAsset
+from vllm.assets.video import VideoAsset, video_to_ndarrays
 from vllm.multimodal.image import convert_image_mode
 from vllm.sampling_params import SamplingParams
 from vllm.utils import FlexibleArgumentParser
@@ -54,7 +57,13 @@ def get_text_query(question: str = None) -> QueryResult:
     )
 
 
-def get_mixed_modalities_query() -> QueryResult:
+def get_mixed_modalities_query(
+    video_path: Optional[str] = None,
+    image_path: Optional[str] = None,
+    audio_path: Optional[str] = None,
+    num_frames: int = 16,
+    sampling_rate: int = 16000,
+) -> QueryResult:
     question = "What is recited in the audio? What is the content of this image? Why is this video funny?"
     prompt = (
         f"<|im_start|>system\n{default_system}<|im_end|>\n"
@@ -64,20 +73,49 @@ def get_mixed_modalities_query() -> QueryResult:
         f"{question}<|im_end|>\n"
         f"<|im_start|>assistant\n"
     )
+
+    # Load video
+    if video_path:
+        if not os.path.exists(video_path):
+            raise FileNotFoundError(f"Video file not found: {video_path}")
+        video_frames = video_to_ndarrays(video_path, num_frames=num_frames)
+    else:
+        video_frames = VideoAsset(name="baby_reading", num_frames=num_frames).np_ndarrays
+
+    # Load image
+    if image_path:
+        if not os.path.exists(image_path):
+            raise FileNotFoundError(f"Image file not found: {image_path}")
+        pil_image = Image.open(image_path)
+        image_data = convert_image_mode(pil_image, "RGB")
+    else:
+        image_data = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
+
+    # Load audio
+    if audio_path:
+        if not os.path.exists(audio_path):
+            raise FileNotFoundError(f"Audio file not found: {audio_path}")
+        audio_signal, sr = librosa.load(audio_path, sr=sampling_rate)
+        audio_data = (audio_signal.astype(np.float32), sr)
+    else:
+        audio_data = AudioAsset("mary_had_lamb").audio_and_sample_rate
+
     return QueryResult(
         inputs={
             "prompt": prompt,
             "multi_modal_data": {
-                "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
-                "image": convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB"),
-                "video": VideoAsset(name="baby_reading", num_frames=16).np_ndarrays,
+                "audio": audio_data,
+                "image": image_data,
+                "video": video_frames,
             },
         },
         limit_mm_per_prompt={"audio": 1, "image": 1, "video": 1},
     )
 
 
-def get_use_audio_in_video_query() -> QueryResult:
+def get_use_audio_in_video_query(
+    video_path: Optional[str] = None, num_frames: int = 16, sampling_rate: int = 16000
+) -> QueryResult:
     question = "Describe the content of the video, then convert what the baby say into text."
     prompt = (
         f"<|im_start|>system\n{default_system}<|im_end|>\n"
@@ -85,14 +123,24 @@ def get_use_audio_in_video_query() -> QueryResult:
         f"{question}<|im_end|>\n"
         f"<|im_start|>assistant\n"
     )
-    asset = VideoAsset(name="baby_reading", num_frames=16)
-    audio = asset.get_audio(sampling_rate=16000)
+
+    if video_path:
+        if not os.path.exists(video_path):
+            raise FileNotFoundError(f"Video file not found: {video_path}")
+        video_frames = video_to_ndarrays(video_path, num_frames=num_frames)
+        # Extract audio from video file
+        audio_signal, sr = librosa.load(video_path, sr=sampling_rate)
+        audio = (audio_signal.astype(np.float32), sr)
+    else:
+        asset = VideoAsset(name="baby_reading", num_frames=num_frames)
+        video_frames = asset.np_ndarrays
+        audio = asset.get_audio(sampling_rate=sampling_rate)
 
     return QueryResult(
         inputs={
             "prompt": prompt,
             "multi_modal_data": {
-                "video": asset.np_ndarrays,
+                "video": video_frames,
                 "audio": audio,
             },
             "mm_processor_kwargs": {
@@ -103,7 +151,7 @@ def get_use_audio_in_video_query() -> QueryResult:
     )
 
 
-def get_multi_audios_query() -> QueryResult:
+def get_multi_audios_query(audio_path: Optional[str] = None, sampling_rate: int = 16000) -> QueryResult:
     question = "Are these two audio clips the same?"
     prompt = (
         f"<|im_start|>system\n{default_system}<|im_end|>\n"
@@ -112,14 +160,28 @@ def get_multi_audios_query() -> QueryResult:
         f"{question}<|im_end|>\n"
         f"<|im_start|>assistant\n"
     )
+
+    if audio_path:
+        if not os.path.exists(audio_path):
+            raise FileNotFoundError(f"Audio file not found: {audio_path}")
+        audio_signal, sr = librosa.load(audio_path, sr=sampling_rate)
+        audio_data = (audio_signal.astype(np.float32), sr)
+        # Use the provided audio as the first audio, default as second
+        audio_list = [
+            audio_data,
+            AudioAsset("mary_had_lamb").audio_and_sample_rate,
+        ]
+    else:
+        audio_list = [
+            AudioAsset("winning_call").audio_and_sample_rate,
+            AudioAsset("mary_had_lamb").audio_and_sample_rate,
+        ]
+
     return QueryResult(
         inputs={
             "prompt": prompt,
             "multi_modal_data": {
-                "audio": [
-                    AudioAsset("winning_call").audio_and_sample_rate,
-                    AudioAsset("mary_had_lamb").audio_and_sample_rate,
-                ],
+                "audio": audio_list,
             },
         },
         limit_mm_per_prompt={
@@ -128,17 +190,135 @@ def get_multi_audios_query() -> QueryResult:
     )
 
 
+def get_image_query(question: str = None, image_path: Optional[str] = None) -> QueryResult:
+    if question is None:
+        question = "What is the content of this image?"
+    prompt = (
+        f"<|im_start|>system\n{default_system}<|im_end|>\n"
+        "<|im_start|>user\n<|vision_bos|><|IMAGE|><|vision_eos|>"
+        f"{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+
+    if image_path:
+        if not os.path.exists(image_path):
+            raise FileNotFoundError(f"Image file not found: {image_path}")
+        pil_image = Image.open(image_path)
+        image_data = convert_image_mode(pil_image, "RGB")
+    else:
+        image_data = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
+
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+            "multi_modal_data": {
+                "image": image_data,
+            },
+        },
+        limit_mm_per_prompt={"image": 1},
+    )
+
+
+def get_video_query(question: str = None, video_path: Optional[str] = None, num_frames: int = 16) -> QueryResult:
+    if question is None:
+        question = "Why is this video funny?"
+    prompt = (
+        f"<|im_start|>system\n{default_system}<|im_end|>\n"
+        "<|im_start|>user\n<|vision_bos|><|VIDEO|><|vision_eos|>"
+        f"{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+
+    if video_path:
+        if not os.path.exists(video_path):
+            raise FileNotFoundError(f"Video file not found: {video_path}")
+        video_frames = video_to_ndarrays(video_path, num_frames=num_frames)
+    else:
+        video_frames = VideoAsset(name="baby_reading", num_frames=num_frames).np_ndarrays
+
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+            "multi_modal_data": {
+                "video": video_frames,
+            },
+        },
+        limit_mm_per_prompt={"video": 1},
+    )
+
+
+def get_audio_query(question: str = None, audio_path: Optional[str] = None, sampling_rate: int = 16000) -> QueryResult:
+    if question is None:
+        question = "What is the content of this audio?"
+    prompt = (
+        f"<|im_start|>system\n{default_system}<|im_end|>\n"
+        "<|im_start|>user\n<|audio_bos|><|AUDIO|><|audio_eos|>"
+        f"{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+
+    if audio_path:
+        if not os.path.exists(audio_path):
+            raise FileNotFoundError(f"Audio file not found: {audio_path}")
+        audio_signal, sr = librosa.load(audio_path, sr=sampling_rate)
+        audio_data = (audio_signal.astype(np.float32), sr)
+    else:
+        audio_data = AudioAsset("mary_had_lamb").audio_and_sample_rate
+
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+            "multi_modal_data": {
+                "audio": audio_data,
+            },
+        },
+        limit_mm_per_prompt={"audio": 1},
+    )
+
+
 query_map = {
     "mixed_modalities": get_mixed_modalities_query,
     "use_audio_in_video": get_use_audio_in_video_query,
     "multi_audios": get_multi_audios_query,
+    "use_image": get_image_query,
+    "use_video": get_video_query,
+    "use_audio": get_audio_query,
     "text": get_text_query,
 }
 
 
 def main(args):
     model_name = "Qwen/Qwen2.5-Omni-7B"
-    query_result = query_map[args.query_type]()
+
+    # Get paths from args
+    video_path = getattr(args, "video_path", None)
+    image_path = getattr(args, "image_path", None)
+    audio_path = getattr(args, "audio_path", None)
+    num_frames = getattr(args, "num_frames", 16)
+    sampling_rate = getattr(args, "sampling_rate", 16000)
+
+    # Get the query function and call it with appropriate parameters
+    query_func = query_map[args.query_type]
+    if args.query_type == "mixed_modalities":
+        query_result = query_func(
+            video_path=video_path,
+            image_path=image_path,
+            audio_path=audio_path,
+            num_frames=num_frames,
+            sampling_rate=sampling_rate,
+        )
+    elif args.query_type == "use_audio_in_video":
+        query_result = query_func(video_path=video_path, num_frames=num_frames, sampling_rate=sampling_rate)
+    elif args.query_type == "multi_audios":
+        query_result = query_func(audio_path=audio_path, sampling_rate=sampling_rate)
+    elif args.query_type == "use_image":
+        query_result = query_func(image_path=image_path)
+    elif args.query_type == "use_video":
+        query_result = query_func(video_path=video_path, num_frames=num_frames)
+    elif args.query_type == "use_audio":
+        query_result = query_func(audio_path=audio_path, sampling_rate=sampling_rate)
+    else:
+        query_result = query_func()
 
     omni_llm = Omni(
         model=model_name,
@@ -283,7 +463,48 @@ def parse_args():
         default=None,
         help="Path to a .txt file with one prompt per line (preferred).",
     )
-
+    parser.add_argument(
+        "--video-path",
+        "-v",
+        type=str,
+        default=None,
+        help="Path to local video file. If not provided, uses default video asset.",
+    )
+    parser.add_argument(
+        "--image-path",
+        "-i",
+        type=str,
+        default=None,
+        help="Path to local image file. If not provided, uses default image asset.",
+    )
+    parser.add_argument(
+        "--audio-path",
+        "-a",
+        type=str,
+        default=None,
+        help="Path to local audio file. If not provided, uses default audio asset.",
+    )
+    parser.add_argument(
+        "--num-frames",
+        type=int,
+        default=16,
+        help="Number of frames to extract from video (default: 16).",
+    )
+    parser.add_argument(
+        "--sampling-rate",
+        type=int,
+        default=16000,
+        help="Sampling rate for audio loading (default: 16000).",
+    )
+    parser.add_argument(
+        "--worker-backend", type=str, default="multi_process", choices=["multi_process", "ray"], help="backend"
+    )
+    parser.add_argument(
+        "--ray-address",
+        type=str,
+        default=None,
+        help="Address of the Ray cluster.",
+    )
     return parser.parse_args()
 
 

examples/offline_inference/qwen3_omni/README.md

@@ -5,6 +5,7 @@
 Please refer to [README.md](../../../README.md)
 
 ## Run examples (Qwen3-Omni)
+
 ### Multiple Prompts
 Download dataset from [seed_tts](https://drive.google.com/file/d/1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP/edit). For processing dataset please refer to [Qwen2.5-Omni README.md](../qwen2_5_omni/README.md)
 Get into the example folder
@@ -29,6 +30,26 @@ If you have not enough memory, you can set thinker with tensor parallel. Just ru
 bash run_single_prompt_tp.sh
 ```
 
+#### Using Local Media Files
+The `end2end.py` script supports local media files (audio, video, image) via command-line arguments:
+
+```bash
+# Use local video file
+python end2end.py --query-type use_video --video-path /path/to/video.mp4
+
+# Use local image file
+python end2end.py --query-type use_image --image-path /path/to/image.jpg
+
+# Use local audio file
+python end2end.py --query-type use_audio --audio-path /path/to/audio.wav
+```
+
+If media file paths are not provided, the script will use default assets. Supported query types:
+- `use_video`: Video input
+- `use_image`: Image input
+- `use_audio`: Audio input
+- `text`: Text-only query
+
 ### FAQ
 
 If you encounter error about backend of librosa, try to install ffmpeg with command below.

examples/offline_inference/qwen3_omni/end2end.py

@@ -6,13 +6,16 @@ with the correct prompt format on Qwen3-Omni (thinker only).
 """
 
 import os
-from typing import NamedTuple
+from typing import NamedTuple, Optional
 
+import librosa
+import numpy as np
 import soundfile as sf
+from PIL import Image
 from vllm import SamplingParams
 from vllm.assets.audio import AudioAsset
 from vllm.assets.image import ImageAsset
-from vllm.assets.video import VideoAsset
+from vllm.assets.video import VideoAsset, video_to_ndarrays
 from vllm.multimodal.image import convert_image_mode
 from vllm.utils import FlexibleArgumentParser
 
@@ -54,7 +57,7 @@ def get_text_query(question: str = None) -> QueryResult:
     )
 
 
-def get_video_query(question: str = None) -> QueryResult:
+def get_video_query(question: str = None, video_path: Optional[str] = None, num_frames: int = 16) -> QueryResult:
     if question is None:
         question = "Why is this video funny?"
     prompt = (
@@ -64,18 +67,25 @@ def get_video_query(question: str = None) -> QueryResult:
         f"<|im_start|>assistant\n"
     )
 
+    if video_path:
+        if not os.path.exists(video_path):
+            raise FileNotFoundError(f"Video file not found: {video_path}")
+        video_frames = video_to_ndarrays(video_path, num_frames=num_frames)
+    else:
+        video_frames = VideoAsset(name="baby_reading", num_frames=num_frames).np_ndarrays
+
     return QueryResult(
         inputs={
             "prompt": prompt,
             "multi_modal_data": {
-                "video": VideoAsset(name="baby_reading", num_frames=16).np_ndarrays,
+                "video": video_frames,
             },
         },
         limit_mm_per_prompt={"video": 1},
     )
 
 
-def get_image_query(question: str = None) -> QueryResult:
+def get_image_query(question: str = None, image_path: Optional[str] = None) -> QueryResult:
     if question is None:
         question = "What is the content of this image?"
     prompt = (
@@ -84,18 +94,27 @@ def get_image_query(question: str = None) -> QueryResult:
         f"{question}<|im_end|>\n"
         f"<|im_start|>assistant\n"
     )
+
+    if image_path:
+        if not os.path.exists(image_path):
+            raise FileNotFoundError(f"Image file not found: {image_path}")
+        pil_image = Image.open(image_path)
+        image_data = convert_image_mode(pil_image, "RGB")
+    else:
+        image_data = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
+
     return QueryResult(
         inputs={
             "prompt": prompt,
             "multi_modal_data": {
-                "image": convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB"),
+                "image": image_data,
             },
         },
         limit_mm_per_prompt={"image": 1},
     )
 
 
-def get_audio_query(question: str = None) -> QueryResult:
+def get_audio_query(question: str = None, audio_path: Optional[str] = None, sampling_rate: int = 16000) -> QueryResult:
     if question is None:
         question = "What is the content of this audio?"
     prompt = (
@@ -104,11 +123,20 @@ def get_audio_query(question: str = None) -> QueryResult:
         f"{question}<|im_end|>\n"
         f"<|im_start|>assistant\n"
     )
+
+    if audio_path:
+        if not os.path.exists(audio_path):
+            raise FileNotFoundError(f"Audio file not found: {audio_path}")
+        audio_signal, sr = librosa.load(audio_path, sr=sampling_rate)
+        audio_data = (audio_signal.astype(np.float32), sr)
+    else:
+        audio_data = AudioAsset("mary_had_lamb").audio_and_sample_rate
+
     return QueryResult(
         inputs={
             "prompt": prompt,
             "multi_modal_data": {
-                "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
+                "audio": audio_data,
             },
         },
         limit_mm_per_prompt={"audio": 1},
@@ -125,7 +153,22 @@ query_map = {
 
 def main(args):
     model_name = "Qwen/Qwen3-Omni-30B-A3B-Instruct"
-    query_result = query_map[args.query_type]()
+
+    # Get paths from args
+    video_path = getattr(args, "video_path", None)
+    image_path = getattr(args, "image_path", None)
+    audio_path = getattr(args, "audio_path", None)
+
+    # Get the query function and call it with appropriate parameters
+    query_func = query_map[args.query_type]
+    if args.query_type == "use_video":
+        query_result = query_func(video_path=video_path, num_frames=getattr(args, "num_frames", 16))
+    elif args.query_type == "use_image":
+        query_result = query_func(image_path=image_path)
+    elif args.query_type == "use_audio":
+        query_result = query_func(audio_path=audio_path, sampling_rate=getattr(args, "sampling_rate", 16000))
+    else:
+        query_result = query_func()
 
     omni_llm = Omni(
         model=model_name,
@@ -283,6 +326,39 @@ def parse_args():
         default=None,
         help="Path to a stage configs file.",
     )
+    parser.add_argument(
+        "--video-path",
+        "-v",
+        type=str,
+        default=None,
+        help="Path to local video file. If not provided, uses default video asset.",
+    )
+    parser.add_argument(
+        "--image-path",
+        "-i",
+        type=str,
+        default=None,
+        help="Path to local image file. If not provided, uses default image asset.",
+    )
+    parser.add_argument(
+        "--audio-path",
+        "-a",
+        type=str,
+        default=None,
+        help="Path to local audio file. If not provided, uses default audio asset.",
+    )
+    parser.add_argument(
+        "--num-frames",
+        type=int,
+        default=16,
+        help="Number of frames to extract from video (default: 16).",
+    )
+    parser.add_argument(
+        "--sampling-rate",
+        type=int,
+        default=16000,
+        help="Sampling rate for audio loading (default: 16000).",
+    )
 
     return parser.parse_args()
 

examples/offline_inference/qwen3_omni/run_single_prompt_tp.sh

@@ -1,6 +1,5 @@
 python end2end.py --output-wav output_audio \
                   --query-type use_audio \
-                  --init-sleep-seconds 90 \
-                  --stage-configs-path qwen3_omni_moe_tp.yaml
+                  --init-sleep-seconds 90
 
 # init-sleep-seconds works to avoid two vLLM stages initialized at the same time within a card.

examples/offline_inference/text_to_image/README.md

@@ -1,10 +1,21 @@
 # Qwen-Image Offline Inference
 
-This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Image` using vLLM-Omni:
+This folder provides several entrypoints for experimenting with `Qwen/Qwen-Image` using vLLM-Omni:
 
-- `text_to_image.py`: command-line script for single image generation.
+- `text_to_image.py`: command-line script for single image generation with advanced options.
 - `web_demo.py`: lightweight Gradio UI for interactive prompt/seed/CFG exploration.
 
+## Basic Usage
+
+```python
+from vllm_omni.entrypoints.omni import Omni
+
+if __name__ == "__main__":
+    omni = Omni(model="Qwen/Qwen-Image")
+    prompt = "a cup of coffee on the table"
+    images = omni.generate(prompt)
+    images[0].save("coffee.png")
+```
 
 ## Local CLI Usage
 

examples/offline_inference/text_to_image/gradio_demo.py

@@ -5,7 +5,7 @@ import gradio as gr
 import torch
 
 from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.utils.platform_utils import detect_device_type
+from vllm_omni.utils.platform_utils import detect_device_type, is_npu
 
 ASPECT_RATIOS: dict[str, tuple[int, int]] = {
     "1:1": (1328, 1328),
@@ -59,7 +59,14 @@ def parse_args() -> argparse.Namespace:
 
 @lru_cache(maxsize=1)
 def get_omni(model_name: str) -> Omni:
-    return Omni(model=model_name)
+    # Enable VAE memory optimizations on NPU
+    vae_use_slicing = is_npu()
+    vae_use_tiling = is_npu()
+    return Omni(
+        model=model_name,
+        vae_use_slicing=vae_use_slicing,
+        vae_use_tiling=vae_use_tiling,
+    )
 
 
 def build_demo(args: argparse.Namespace) -> gr.Blocks:
@@ -98,7 +105,6 @@ def build_demo(args: argparse.Namespace) -> gr.Blocks:
             generator=generator,
             true_cfg_scale=float(cfg_scale_value),
             num_inference_steps=num_steps,
-            num_images_per_prompt=num_images,
             num_outputs_per_prompt=num_images,
         )
         return [img for img in images[:num_images]]

examples/offline_inference/text_to_image/text_to_image.py

@@ -7,7 +7,7 @@ from pathlib import Path
 import torch
 
 from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.utils.platform_utils import detect_device_type
+from vllm_omni.utils.platform_utils import detect_device_type, is_npu
 
 
 def parse_args() -> argparse.Namespace:
@@ -49,7 +49,15 @@ def main():
     device = detect_device_type()
     generator = torch.Generator(device=device).manual_seed(args.seed)
 
-    omni = Omni(model=args.model)
+    # Enable VAE memory optimizations on NPU
+    vae_use_slicing = is_npu()
+    vae_use_tiling = is_npu()
+
+    omni = Omni(
+        model=args.model,
+        vae_use_slicing=vae_use_slicing,
+        vae_use_tiling=vae_use_tiling,
+    )
     images = omni.generate(
         args.prompt,
         height=args.height,
@@ -57,7 +65,6 @@ def main():
         generator=generator,
         true_cfg_scale=args.cfg_scale,
         num_inference_steps=args.num_inference_steps,
-        num_images_per_prompt=args.num_images_per_prompt,
         num_outputs_per_prompt=args.num_images_per_prompt,
     )
 

examples/offline_inference/text_to_video/text_to_video.md

@@ -0,0 +1,38 @@
+# Wan2.2 Text-to-Video Offline Inference
+
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_video>. Added in PR [#202](https://github.com/vllm-project/vllm-omni/pull/202).
+
+The `Wan-AI/Wan2.2-T2V-A14B-Diffusers` pipeline generates short videos from text prompts.
+
+## Local CLI Usage
+
+```bash
+python text_to_video.py \
+  --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
+  --negative_prompt "<optional quality filter>" \
+  --height 480 \
+  --width 640 \
+  --num_frames 32 \
+  --guidance_scale 4.0 \
+  --guidance_scale_high 3.0 \
+  --num_inference_steps 40 \
+  --fps 16 \
+  --output t2v_out.mp4
+```
+
+Key arguments:
+- `--prompt`: text description (string).
+- `--height/--width`: output resolution (defaults 720x1280). Dimensions should align with Wan VAE downsampling (multiples of 8).
+- `--num_frames`: Number of frames (Wan default is 81).
+- `--guidance_scale` and `--guidance_scale_high`: CFG scale (applied to low/high)..
+- `--negative_prompt`: optional list of artifacts to suppress (the PR demo used a long Chinese string).
+- `--boundary_ratio`: Boundary split ratio for low/high DiT.
+- `--fps`: frames per second for the saved MP4 (requires `diffusers` export_to_video).
+- `--output`: path to save the generated video.
+
+## Example materials
+
+??? abstract "text_to_video.py"
+    ``````py
+    --8<-- "examples/offline_inference/wan22/text_to_video.py"
+    ``````

examples/offline_inference/text_to_video/text_to_video.py

@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.utils.platform_utils import detect_device_type, is_npu
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Generate a video with Wan2.2 T2V.")
+    parser.add_argument(
+        "--model",
+        default="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
+        help="Diffusers Wan2.2 model ID or local path.",
+    )
+    parser.add_argument("--prompt", default="A serene lakeside sunrise with mist over the water.", help="Text prompt.")
+    parser.add_argument("--negative_prompt", default="", help="Negative prompt.")
+    parser.add_argument("--seed", type=int, default=42, help="Random seed.")
+    parser.add_argument("--guidance_scale", type=float, default=4.0, help="CFG scale (applied to low/high).")
+    parser.add_argument("--guidance_scale_high", type=float, default=None, help="Optional separate CFG for high-noise.")
+    parser.add_argument("--height", type=int, default=720, help="Video height.")
+    parser.add_argument("--width", type=int, default=1280, help="Video width.")
+    parser.add_argument("--num_frames", type=int, default=81, help="Number of frames (Wan default is 81).")
+    parser.add_argument("--num_inference_steps", type=int, default=40, help="Sampling steps.")
+    parser.add_argument("--boundary_ratio", type=float, default=0.875, help="Boundary split ratio for low/high DiT.")
+    parser.add_argument(
+        "--flow_shift", type=float, default=5.0, help="Scheduler flow_shift (5.0 for 720p, 12.0 for 480p)."
+    )
+    parser.add_argument("--output", type=str, default="wan22_output.mp4", help="Path to save the video (mp4).")
+    parser.add_argument("--fps", type=int, default=24, help="Frames per second for the output video.")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    device = detect_device_type()
+    generator = torch.Generator(device=device).manual_seed(args.seed)
+
+    # Enable VAE memory optimizations on NPU
+    vae_use_slicing = is_npu()
+    vae_use_tiling = is_npu()
+
+    omni = Omni(
+        model=args.model,
+        vae_use_slicing=vae_use_slicing,
+        vae_use_tiling=vae_use_tiling,
+        boundary_ratio=args.boundary_ratio,
+        flow_shift=args.flow_shift,
+    )
+
+    frames = omni.generate(
+        args.prompt,
+        negative_prompt=args.negative_prompt,
+        height=args.height,
+        width=args.width,
+        generator=generator,
+        guidance_scale=args.guidance_scale,
+        guidance_scale_2=args.guidance_scale_high,
+        num_inference_steps=args.num_inference_steps,
+        num_frames=args.num_frames,
+    )
+
+    output_path = Path(args.output)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    try:
+        from diffusers.utils import export_to_video
+    except ImportError:
+        raise ImportError("diffusers is required for export_to_video.")
+
+    # frames may be np.ndarray (preferred) or torch.Tensor
+    # export_to_video expects a list of frames with values in [0, 1]
+    if isinstance(frames, torch.Tensor):
+        video_tensor = frames.detach().cpu()
+        if video_tensor.dim() == 5:
+            # [B, C, F, H, W] or [B, F, H, W, C]
+            if video_tensor.shape[1] in (3, 4):
+                video_tensor = video_tensor[0].permute(1, 2, 3, 0)
+            else:
+                video_tensor = video_tensor[0]
+        elif video_tensor.dim() == 4 and video_tensor.shape[0] in (3, 4):
+            video_tensor = video_tensor.permute(1, 2, 3, 0)
+        # If float, assume [-1,1] and normalize to [0,1]
+        if video_tensor.is_floating_point():
+            video_tensor = video_tensor.clamp(-1, 1) * 0.5 + 0.5
+        video_array = video_tensor.float().numpy()
+    else:
+        video_array = frames
+        if hasattr(video_array, "shape") and video_array.ndim == 5:
+            video_array = video_array[0]
+
+    # Convert 4D array (frames, H, W, C) to list of frames for export_to_video
+    if isinstance(video_array, np.ndarray) and video_array.ndim == 4:
+        video_array = list(video_array)
+
+    export_to_video(video_array, str(output_path), fps=args.fps)
+    print(f"Saved generated video to {output_path}")
+
+
+if __name__ == "__main__":
+    main()

examples/online_serving/qwen2_5_omni/README.md

@@ -6,7 +6,8 @@ Please refer to [README.md](../../../README.md)
 
 ## Run examples (Qwen2.5-Omni)
 
-Launch the server
+### Launch the Server
+
 ```bash
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
@@ -16,29 +17,59 @@ If you have custom stage configs file, launch the server with command below
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
 ```
 
+### Send Multi-modal Request
+
 Get into the example folder
 ```bash
 cd examples/online_serving/qwen2_5_omni
 ```
 
-Send request via python
+####  Send request via python
+
 ```bash
 python openai_chat_completion_client_for_multimodal_generation.py --query-type mixed_modalities
 ```
 
-Send request via curl
+The Python client supports the following command-line arguments:
+
+- `--query-type` (or `-q`): Query type (default: `mixed_modalities`)
+  - Options: `mixed_modalities`, `use_audio_in_video`, `multi_audios`, `text`
+- `--video-path` (or `-v`): Path to local video file or URL
+  - If not provided and query-type uses video, uses default video URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Example: `--video-path /path/to/video.mp4` or `--video-path https://example.com/video.mp4`
+- `--image-path` (or `-i`): Path to local image file or URL
+  - If not provided and query-type uses image, uses default image URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common image formats: JPEG, PNG, GIF, WebP
+  - Example: `--image-path /path/to/image.jpg` or `--image-path https://example.com/image.png`
+- `--audio-path` (or `-a`): Path to local audio file or URL
+  - If not provided and query-type uses audio, uses default audio URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common audio formats: MP3, WAV, OGG, FLAC, M4A
+  - Example: `--audio-path /path/to/audio.wav` or `--audio-path https://example.com/audio.mp3`
+- `--prompt` (or `-p`): Custom text prompt/question
+  - If not provided, uses default prompt for the selected query type
+  - Example: `--prompt "What are the main activities shown in this video?"`
+
+
+For example, to use mixed modalities with all local files:
+
+```bash
+python openai_chat_completion_client_for_multimodal_generation.py \
+    --query-type mixed_modalities \
+    --video-path /path/to/your/video.mp4 \
+    --image-path /path/to/your/image.jpg \
+    --audio-path /path/to/your/audio.wav \
+    --prompt "Analyze all the media content and provide a comprehensive summary."
+```
+
+####  Send request via curl
+
 ```bash
 bash run_curl_multimodal_generation.sh mixed_modalities
 ```
 
-### FAQ
-
-If you encounter error about backend of librosa, try to install ffmpeg with command below.
-```
-sudo apt update
-sudo apt install ffmpeg
-```
-
 ## Run Local Web UI Demo
 
 This Web UI demo allows users to interact with the model through a web browser.
@@ -53,21 +84,18 @@ python gradio_demo.py  --model Qwen/Qwen2.5-Omni-7B --port 7861
 
 Then open `http://localhost:7861/` on your local browser to interact with the web UI.
 
+The gradio script supports the following arguments:
 
-### Options
+- `--model`: Model name
+- `--ip`: Host/IP for Gradio server (default: 127.0.0.1)
+- `--port`: Port for Gradio server (default: 7861)
+- `--stage-configs-path`: Path to custom stage configs YAML file (optional)
+- `--share`: Share the Gradio demo publicly (creates a public link)
 
-You can customize its basic launch parameters:
+### FAQ
 
-```bash
-python gradio_demo.py \
-    --model Qwen/Qwen2.5-Omni-7B \
-    --ip 127.0.0.1 \
-    --port 7861 \
-    --stage-configs-path /path/to/stage_configs.yaml
+If you encounter error about backend of librosa, try to install ffmpeg with command below.
+```
+sudo apt update
+sudo apt install ffmpeg
 ```
-
-- `--model`: Local model checkpoint to load (default `Qwen/Qwen2.5-Omni-7B`).
-- `--ip`: Host/IP for the Gradio server (default `127.0.0.1`).
-- `--port`: Port for the Gradio server (default `7861`).
-- `--stage-configs-path`: Optional path to custom stage configs YAML.
-- `--share`: Set to expose a temporary public link via Gradio.

examples/online_serving/qwen2_5_omni/gradio_demo.py

@@ -91,9 +91,58 @@ def parse_args():
         default=None,
         help="Path to custom stage configs YAML file (optional).",
     )
+    parser.add_argument(
+        "--log-stats",
+        action="store_true",
+        help="Enable statistics logging for AsyncOmni.",
+    )
+    parser.add_argument(
+        "--log-file",
+        type=str,
+        default=None,
+        help="Path prefix for AsyncOmni log files.",
+    )
+    parser.add_argument(
+        "--init-sleep-seconds",
+        type=int,
+        default=30,
+        help="Seconds to sleep between starting stage processes.",
+    )
+    parser.add_argument(
+        "--shm-threshold-bytes",
+        type=int,
+        default=65536,
+        help="Threshold in bytes for using shared memory IPC.",
+    )
+    parser.add_argument(
+        "--batch-timeout",
+        type=int,
+        default=10,
+        help="Batching timeout (seconds) inside each stage.",
+    )
+    parser.add_argument(
+        "--init-timeout",
+        type=int,
+        default=ASYNC_INIT_TIMEOUT,
+        help="Timeout (seconds) for initializing all stages.",
+    )
     return parser.parse_args()
 
 
+def build_async_omni_cli_args(base_args: argparse.Namespace) -> argparse.Namespace:
+    """Construct the minimal CLI args Namespace expected by AsyncOmni."""
+    return argparse.Namespace(
+        model=base_args.model,
+        stage_configs_path=getattr(base_args, "stage_configs_path", None),
+        log_stats=bool(getattr(base_args, "log_stats", False)),
+        log_file=getattr(base_args, "log_file", None),
+        init_sleep_seconds=int(getattr(base_args, "init_sleep_seconds", 30)),
+        shm_threshold_bytes=int(getattr(base_args, "shm_threshold_bytes", 65536)),
+        batch_timeout=int(getattr(base_args, "batch_timeout", 10)),
+        init_timeout=int(getattr(base_args, "init_timeout", ASYNC_INIT_TIMEOUT)),
+    )
+
+
 def build_sampling_params(seed: int, model_key: str) -> list[SamplingParams]:
     """Build SamplingParams objects by reusing the dict definitions."""
     return [SamplingParams(**params_dict) for params_dict in build_sampling_params_dict(seed, model_key)]
@@ -500,11 +549,8 @@ def main():
         print(f"Using custom stage configs: {args.stage_configs_path}")
 
     sampling_params = build_sampling_params(SEED, model_name)
-    omni = AsyncOmni(
-        model=args.model,
-        stage_configs_path=args.stage_configs_path,
-        init_timeout=ASYNC_INIT_TIMEOUT,
-    )
+    cli_args = build_async_omni_cli_args(args)
+    omni = AsyncOmni(model=args.model, cli_args=cli_args)
     print("✓ AsyncOmni initialized successfully")
     prompt_args_template = create_prompt_args(args)
 

examples/online_serving/qwen2_5_omni/openai_chat_completion_client_for_multimodal_generation.py

@@ -1,4 +1,6 @@
 import base64
+import os
+from typing import Optional
 
 import requests
 from openai import OpenAI
@@ -28,6 +30,129 @@ def encode_base64_content_from_url(content_url: str) -> str:
     return result
 
 
+def encode_base64_content_from_file(file_path: str) -> str:
+    """Encode a local file to base64 format."""
+    with open(file_path, "rb") as f:
+        content = f.read()
+        result = base64.b64encode(content).decode("utf-8")
+    return result
+
+
+def get_video_url_from_path(video_path: Optional[str]) -> str:
+    """Convert a video path (local file or URL) to a video URL format for the API.
+
+    If video_path is None or empty, returns the default URL.
+    If video_path is a local file path, encodes it to base64 data URL.
+    If video_path is a URL, returns it as-is.
+    """
+    if not video_path:
+        # Default video URL
+        return "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/sample_demo_1.mp4"
+
+    # Check if it's a URL (starts with http:// or https://)
+    if video_path.startswith(("http://", "https://")):
+        return video_path
+
+    # Otherwise, treat it as a local file path
+    if not os.path.exists(video_path):
+        raise FileNotFoundError(f"Video file not found: {video_path}")
+
+    # Detect video MIME type from file extension
+    video_path_lower = video_path.lower()
+    if video_path_lower.endswith(".mp4"):
+        mime_type = "video/mp4"
+    elif video_path_lower.endswith(".webm"):
+        mime_type = "video/webm"
+    elif video_path_lower.endswith(".mov"):
+        mime_type = "video/quicktime"
+    elif video_path_lower.endswith(".avi"):
+        mime_type = "video/x-msvideo"
+    elif video_path_lower.endswith(".mkv"):
+        mime_type = "video/x-matroska"
+    else:
+        # Default to mp4 if extension is unknown
+        mime_type = "video/mp4"
+
+    video_base64 = encode_base64_content_from_file(video_path)
+    return f"data:{mime_type};base64,{video_base64}"
+
+
+def get_image_url_from_path(image_path: Optional[str]) -> str:
+    """Convert an image path (local file or URL) to an image URL format for the API.
+
+    If image_path is None or empty, returns the default URL.
+    If image_path is a local file path, encodes it to base64 data URL.
+    If image_path is a URL, returns it as-is.
+    """
+    if not image_path:
+        # Default image URL
+        return "https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg"
+
+    # Check if it's a URL (starts with http:// or https://)
+    if image_path.startswith(("http://", "https://")):
+        return image_path
+
+    # Otherwise, treat it as a local file path
+    if not os.path.exists(image_path):
+        raise FileNotFoundError(f"Image file not found: {image_path}")
+
+    # Detect image MIME type from file extension
+    image_path_lower = image_path.lower()
+    if image_path_lower.endswith((".jpg", ".jpeg")):
+        mime_type = "image/jpeg"
+    elif image_path_lower.endswith(".png"):
+        mime_type = "image/png"
+    elif image_path_lower.endswith(".gif"):
+        mime_type = "image/gif"
+    elif image_path_lower.endswith(".webp"):
+        mime_type = "image/webp"
+    else:
+        # Default to jpeg if extension is unknown
+        mime_type = "image/jpeg"
+
+    image_base64 = encode_base64_content_from_file(image_path)
+    return f"data:{mime_type};base64,{image_base64}"
+
+
+def get_audio_url_from_path(audio_path: Optional[str]) -> str:
+    """Convert an audio path (local file or URL) to an audio URL format for the API.
+
+    If audio_path is None or empty, returns the default URL.
+    If audio_path is a local file path, encodes it to base64 data URL.
+    If audio_path is a URL, returns it as-is.
+    """
+    if not audio_path:
+        # Default audio URL
+        return AudioAsset("mary_had_lamb").url
+
+    # Check if it's a URL (starts with http:// or https://)
+    if audio_path.startswith(("http://", "https://")):
+        return audio_path
+
+    # Otherwise, treat it as a local file path
+    if not os.path.exists(audio_path):
+        raise FileNotFoundError(f"Audio file not found: {audio_path}")
+
+    # Detect audio MIME type from file extension
+    audio_path_lower = audio_path.lower()
+    if audio_path_lower.endswith((".mp3", ".mpeg")):
+        mime_type = "audio/mpeg"
+    elif audio_path_lower.endswith(".wav"):
+        mime_type = "audio/wav"
+    elif audio_path_lower.endswith(".ogg"):
+        mime_type = "audio/ogg"
+    elif audio_path_lower.endswith(".flac"):
+        mime_type = "audio/flac"
+    elif audio_path_lower.endswith(".m4a"):
+        mime_type = "audio/mp4"
+    else:
+        # Default to wav if extension is unknown
+        mime_type = "audio/wav"
+
+    audio_base64 = encode_base64_content_from_file(audio_path)
+    return f"data:{mime_type};base64,{audio_base64}"
+
+
 def get_system_prompt():
     return {
         "role": "system",
@@ -44,8 +169,10 @@ def get_system_prompt():
     }
 
 
-def get_text_query():
-    question = "Explain the system architecture for a scalable audio generation pipeline. Answer in 15 words."
+def get_text_query(custom_prompt: Optional[str] = None):
+    question = (
+        custom_prompt or "Explain the system architecture for a scalable audio generation pipeline. Answer in 15 words."
+    )
     prompt = {
         "role": "user",
         "content": [
@@ -58,26 +185,32 @@ def get_text_query():
     return prompt
 
 
-def get_mixed_modalities_query():
-    question = "What is recited in the audio? What is the content of this image? Why is this video funny?"
+def get_mixed_modalities_query(
+    video_path: Optional[str] = None,
+    image_path: Optional[str] = None,
+    audio_path: Optional[str] = None,
+    custom_prompt: Optional[str] = None,
+):
+    question = (
+        custom_prompt or "What is recited in the audio? What is the content of this image? Why is this video funny?"
+    )
+    video_url = get_video_url_from_path(video_path)
+    image_url = get_image_url_from_path(image_path)
+    audio_url = get_audio_url_from_path(audio_path)
     prompt = {
         "role": "user",
         "content": [
             {
                 "type": "audio_url",
-                "audio_url": {"url": AudioAsset("mary_had_lamb").url},
+                "audio_url": {"url": audio_url},
             },
             {
                 "type": "image_url",
-                "image_url": {
-                    "url": "https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg"
-                },
+                "image_url": {"url": image_url},
             },
             {
                 "type": "video_url",
-                "video_url": {
-                    "url": "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/sample_demo_1.mp4"
-                },
+                "video_url": {"url": video_url},
             },
             {
                 "type": "text",
@@ -89,8 +222,9 @@ def get_mixed_modalities_query():
     return prompt
 
 
-def get_use_audio_in_video_query():
-    question = "Describe the content of the video, then convert what the baby say into text."
+def get_use_audio_in_video_query(video_path: Optional[str] = None, custom_prompt: Optional[str] = None):
+    question = custom_prompt or "Describe the content of the video, then convert what the baby say into text."
+    video_url = get_video_url_from_path(video_path)
 
     prompt = {
         "role": "user",
@@ -98,7 +232,7 @@ def get_use_audio_in_video_query():
             {
                 "type": "video_url",
                 "video_url": {
-                    "url": "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/sample_demo_1.mp4",
+                    "url": video_url,
                     "num_frames": 16,
                 },
             },
@@ -112,14 +246,15 @@ def get_use_audio_in_video_query():
     return prompt
 
 
-def get_multi_audios_query():
-    question = "Are these two audio clips the same?"
+def get_multi_audios_query(audio_path: Optional[str] = None, custom_prompt: Optional[str] = None):
+    question = custom_prompt or "Are these two audio clips the same?"
+    audio_url = get_audio_url_from_path(audio_path)
     prompt = {
         "role": "user",
         "content": [
             {
                 "type": "audio_url",
-                "audio_url": {"url": AudioAsset("mary_had_lamb").url},
+                "audio_url": {"url": audio_url},
             },
             {
                 "type": "audio_url",
@@ -179,7 +314,27 @@ def run_multimodal_generation(args) -> None:
         code2wav_sampling_params,
     ]
 
-    prompt = query_map[args.query_type]()
+    # Get paths and custom prompt from args
+    video_path = getattr(args, "video_path", None)
+    image_path = getattr(args, "image_path", None)
+    audio_path = getattr(args, "audio_path", None)
+    custom_prompt = getattr(args, "prompt", None)
+
+    # Get the query function and call it with appropriate parameters
+    query_func = query_map[args.query_type]
+    if args.query_type == "mixed_modalities":
+        prompt = query_func(
+            video_path=video_path, image_path=image_path, audio_path=audio_path, custom_prompt=custom_prompt
+        )
+    elif args.query_type == "use_audio_in_video":
+        prompt = query_func(video_path=video_path, custom_prompt=custom_prompt)
+    elif args.query_type == "multi_audios":
+        prompt = query_func(audio_path=audio_path, custom_prompt=custom_prompt)
+    elif args.query_type == "text":
+        prompt = query_func(custom_prompt=custom_prompt)
+    else:
+        prompt = query_func()
+
     extra_body = {
         "sampling_params_list": sampling_params_list  # Optional, it has a default setting in stage_configs of the corresponding model.
     }
@@ -219,6 +374,34 @@ def parse_args():
         choices=query_map.keys(),
         help="Query type.",
     )
+    parser.add_argument(
+        "--video-path",
+        "-v",
+        type=str,
+        default=None,
+        help="Path to local video file or URL. If not provided and query-type uses video, uses default video URL.",
+    )
+    parser.add_argument(
+        "--image-path",
+        "-i",
+        type=str,
+        default=None,
+        help="Path to local image file or URL. If not provided and query-type uses image, uses default image URL.",
+    )
+    parser.add_argument(
+        "--audio-path",
+        "-a",
+        type=str,
+        default=None,
+        help="Path to local audio file or URL. If not provided and query-type uses audio, uses default audio URL.",
+    )
+    parser.add_argument(
+        "--prompt",
+        "-p",
+        type=str,
+        default=None,
+        help="Custom text prompt/question to use instead of the default prompt for the selected query type.",
+    )
 
     return parser.parse_args()
 

examples/online_serving/qwen3_omni/README.md

@@ -6,7 +6,8 @@ Please refer to [README.md](../../../README.md)
 
 ## Run examples (Qwen3-Omni)
 
-Launch the server
+### Launch the Server
+
 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
@@ -16,21 +17,59 @@ If you have custom stage configs file, launch the server with command below
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
 ```
 
+### Send Multi-modal Request
+
 Get into the example folder
 ```bash
 cd examples/online_serving/qwen3_omni
 ```
 
-Send request via python
+####  Send request via python
+
 ```bash
 python openai_chat_completion_client_for_multimodal_generation.py --query-type use_image
 ```
 
-Send request via curl
+The Python client supports the following command-line arguments:
+
+- `--query-type` (or `-q`): Query type (default: `use_video`)
+  - Options: `text`, `use_audio`, `use_image`, `use_video`
+- `--model` (or `-m`): Model name/path (default: `Qwen/Qwen3-Omni-30B-A3B-Instruct`)
+- `--video-path` (or `-v`): Path to local video file or URL
+  - If not provided and query-type is `use_video`, uses default video URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Example: `--video-path /path/to/video.mp4` or `--video-path https://example.com/video.mp4`
+- `--image-path` (or `-i`): Path to local image file or URL
+  - If not provided and query-type is `use_image`, uses default image URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common image formats: JPEG, PNG, GIF, WebP
+  - Example: `--image-path /path/to/image.jpg` or `--image-path https://example.com/image.png`
+- `--audio-path` (or `-a`): Path to local audio file or URL
+  - If not provided and query-type is `use_audio`, uses default audio URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common audio formats: MP3, WAV, OGG, FLAC, M4A
+  - Example: `--audio-path /path/to/audio.wav` or `--audio-path https://example.com/audio.mp3`
+- `--prompt` (or `-p`): Custom text prompt/question
+  - If not provided, uses default prompt for the selected query type
+  - Example: `--prompt "What are the main activities shown in this video?"`
+
+
+For example, to use a local video file with custom prompt:
+
+```bash
+python openai_chat_completion_client_for_multimodal_generation.py \
+    --query-type use_video \
+    --video-path /path/to/your/video.mp4 \
+    --prompt "What are the main activities shown in this video?"
+```
+
+####  Send request via curl
+
 ```bash
 bash run_curl_multimodal_generation.sh use_image
 ```
 
+
 ### FAQ
 
 If you encounter error about backend of librosa, try to install ffmpeg with command below.
@@ -54,23 +93,9 @@ python gradio_demo.py  --model Qwen/Qwen3-Omni-30B-A3B-Instruct --port 7861
 Then open `http://localhost:7861/` on your local browser to interact with the web UI.
 
 
-### Options
-
-The gradio demo also supports running with an existing API server and can be customized with the following arguments.
-
-
-```bash
-python gradio_demo.py \
-    --model Qwen/Qwen3-Omni-30B-A3B-Instruct \
-    --use-api-server \
-    --api-base http://localhost:8091/v1 \
-    --ip 127.0.0.1 \
-    --port 7861
-```
+The gradio script supports the following arguments:
 
 - `--model`: Model name
-- `--use-api-server`: If set, connect to an existing vLLM HTTP API server instead of running AsyncOmni locally.
-- `--api-base`: Base URL for vllm serve (only used when `use-api-server` is set, default: http://localhost:8091/v1)
 - `--ip`: Host/IP for Gradio server (default: 127.0.0.1)
 - `--port`: Port for Gradio server (default: 7861)
 - `--stage-configs-path`: Path to custom stage configs YAML file (optional)

examples/online_serving/qwen3_omni/gradio_demo.py

@@ -94,9 +94,58 @@ def parse_args():
         default=None,
         help="Path to custom stage configs YAML file (optional).",
     )
+    parser.add_argument(
+        "--log-stats",
+        action="store_true",
+        help="Enable statistics logging for AsyncOmni.",
+    )
+    parser.add_argument(
+        "--log-file",
+        type=str,
+        default=None,
+        help="Path prefix for AsyncOmni log files.",
+    )
+    parser.add_argument(
+        "--init-sleep-seconds",
+        type=int,
+        default=30,
+        help="Seconds to sleep between starting stage processes.",
+    )
+    parser.add_argument(
+        "--shm-threshold-bytes",
+        type=int,
+        default=65536,
+        help="Threshold in bytes for using shared memory IPC.",
+    )
+    parser.add_argument(
+        "--batch-timeout",
+        type=int,
+        default=10,
+        help="Batching timeout (seconds) inside each stage.",
+    )
+    parser.add_argument(
+        "--init-timeout",
+        type=int,
+        default=ASYNC_INIT_TIMEOUT,
+        help="Timeout (seconds) for initializing all stages.",
+    )
     return parser.parse_args()
 
 
+def build_async_omni_cli_args(base_args: argparse.Namespace) -> argparse.Namespace:
+    """Construct the minimal CLI args Namespace expected by AsyncOmni."""
+    return argparse.Namespace(
+        model=base_args.model,
+        stage_configs_path=getattr(base_args, "stage_configs_path", None),
+        log_stats=bool(getattr(base_args, "log_stats", False)),
+        log_file=getattr(base_args, "log_file", None),
+        init_sleep_seconds=int(getattr(base_args, "init_sleep_seconds", 30)),
+        shm_threshold_bytes=int(getattr(base_args, "shm_threshold_bytes", 65536)),
+        batch_timeout=int(getattr(base_args, "batch_timeout", 10)),
+        init_timeout=int(getattr(base_args, "init_timeout", ASYNC_INIT_TIMEOUT)),
+    )
+
+
 def build_sampling_params(seed: int, model_key: str) -> list[SamplingParams]:
     """Build SamplingParams objects by reusing the dict definitions."""
     return [SamplingParams(**params_dict) for params_dict in build_sampling_params_dict(seed, model_key)]
@@ -264,7 +313,7 @@ async def run_inference_async_omni(
         # Process audio
         audio_data = process_audio_file(audio_file)
         if audio_data is not None:
-            multi_modal_data["audio"] = audio_data
+            multi_modal_data["audio"] = [audio_data] if not isinstance(audio_data, list) else audio_data
 
         # Process image
         image_data = process_image_file(image_file)
@@ -282,7 +331,9 @@ async def run_inference_async_omni(
                 video_entry = video_frames
             multi_modal_data["video"] = video_entry
             if use_audio_in_video and extracted_audio is not None and "audio" not in multi_modal_data:
-                multi_modal_data["audio"] = extracted_audio
+                multi_modal_data["audio"] = (
+                    [extracted_audio] if not isinstance(extracted_audio, list) else extracted_audio
+                )
                 mm_processor_kwargs["use_audio_in_video"] = True
 
         # Build the prompt input
@@ -504,11 +555,8 @@ def main():
         print(f"Using custom stage configs: {args.stage_configs_path}")
 
     sampling_params = build_sampling_params(SEED, model_name)
-    omni = AsyncOmni(
-        model=args.model,
-        stage_configs_path=args.stage_configs_path,
-        init_timeout=ASYNC_INIT_TIMEOUT,
-    )
+    cli_args = build_async_omni_cli_args(args)
+    omni = AsyncOmni(model=args.model, cli_args=cli_args)
     print("✓ AsyncOmni initialized successfully")
     prompt_args_template = create_prompt_args(args)
 

examples/online_serving/qwen3_omni/openai_chat_completion_client_for_multimodal_generation.py

@@ -1,5 +1,6 @@
 import base64
-from typing import NamedTuple
+import os
+from typing import NamedTuple, Optional
 
 import requests
 from openai import OpenAI
@@ -34,6 +35,129 @@ def encode_base64_content_from_url(content_url: str) -> str:
     return result
 
 
+def encode_base64_content_from_file(file_path: str) -> str:
+    """Encode a local file to base64 format."""
+    with open(file_path, "rb") as f:
+        content = f.read()
+        result = base64.b64encode(content).decode("utf-8")
+    return result
+
+
+def get_video_url_from_path(video_path: Optional[str]) -> str:
+    """Convert a video path (local file or URL) to a video URL format for the API.
+
+    If video_path is None or empty, returns the default URL.
+    If video_path is a local file path, encodes it to base64 data URL.
+    If video_path is a URL, returns it as-is.
+    """
+    if not video_path:
+        # Default video URL
+        return "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/sample_demo_1.mp4"
+
+    # Check if it's a URL (starts with http:// or https://)
+    if video_path.startswith(("http://", "https://")):
+        return video_path
+
+    # Otherwise, treat it as a local file path
+    if not os.path.exists(video_path):
+        raise FileNotFoundError(f"Video file not found: {video_path}")
+
+    # Detect video MIME type from file extension
+    video_path_lower = video_path.lower()
+    if video_path_lower.endswith(".mp4"):
+        mime_type = "video/mp4"
+    elif video_path_lower.endswith(".webm"):
+        mime_type = "video/webm"
+    elif video_path_lower.endswith(".mov"):
+        mime_type = "video/quicktime"
+    elif video_path_lower.endswith(".avi"):
+        mime_type = "video/x-msvideo"
+    elif video_path_lower.endswith(".mkv"):
+        mime_type = "video/x-matroska"
+    else:
+        # Default to mp4 if extension is unknown
+        mime_type = "video/mp4"
+
+    video_base64 = encode_base64_content_from_file(video_path)
+    return f"data:{mime_type};base64,{video_base64}"
+
+
+def get_image_url_from_path(image_path: Optional[str]) -> str:
+    """Convert an image path (local file or URL) to an image URL format for the API.
+
+    If image_path is None or empty, returns the default URL.
+    If image_path is a local file path, encodes it to base64 data URL.
+    If image_path is a URL, returns it as-is.
+    """
+    if not image_path:
+        # Default image URL
+        return "https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg"
+
+    # Check if it's a URL (starts with http:// or https://)
+    if image_path.startswith(("http://", "https://")):
+        return image_path
+
+    # Otherwise, treat it as a local file path
+    if not os.path.exists(image_path):
+        raise FileNotFoundError(f"Image file not found: {image_path}")
+
+    # Detect image MIME type from file extension
+    image_path_lower = image_path.lower()
+    if image_path_lower.endswith((".jpg", ".jpeg")):
+        mime_type = "image/jpeg"
+    elif image_path_lower.endswith(".png"):
+        mime_type = "image/png"
+    elif image_path_lower.endswith(".gif"):
+        mime_type = "image/gif"
+    elif image_path_lower.endswith(".webp"):
+        mime_type = "image/webp"
+    else:
+        # Default to jpeg if extension is unknown
+        mime_type = "image/jpeg"
+
+    image_base64 = encode_base64_content_from_file(image_path)
+    return f"data:{mime_type};base64,{image_base64}"
+
+
+def get_audio_url_from_path(audio_path: Optional[str]) -> str:
+    """Convert an audio path (local file or URL) to an audio URL format for the API.
+
+    If audio_path is None or empty, returns the default URL.
+    If audio_path is a local file path, encodes it to base64 data URL.
+    If audio_path is a URL, returns it as-is.
+    """
+    if not audio_path:
+        # Default audio URL
+        return AudioAsset("mary_had_lamb").url
+
+    # Check if it's a URL (starts with http:// or https://)
+    if audio_path.startswith(("http://", "https://")):
+        return audio_path
+
+    # Otherwise, treat it as a local file path
+    if not os.path.exists(audio_path):
+        raise FileNotFoundError(f"Audio file not found: {audio_path}")
+
+    # Detect audio MIME type from file extension
+    audio_path_lower = audio_path.lower()
+    if audio_path_lower.endswith((".mp3", ".mpeg")):
+        mime_type = "audio/mpeg"
+    elif audio_path_lower.endswith(".wav"):
+        mime_type = "audio/wav"
+    elif audio_path_lower.endswith(".ogg"):
+        mime_type = "audio/ogg"
+    elif audio_path_lower.endswith(".flac"):
+        mime_type = "audio/flac"
+    elif audio_path_lower.endswith(".m4a"):
+        mime_type = "audio/mp4"
+    else:
+        # Default to wav if extension is unknown
+        mime_type = "audio/wav"
+
+    audio_base64 = encode_base64_content_from_file(audio_path)
+    return f"data:{mime_type};base64,{audio_base64}"
+
+
 def get_system_prompt():
     return {
         "role": "system",
@@ -50,8 +174,10 @@ def get_system_prompt():
     }
 
 
-def get_text_query():
-    question = "Explain the system architecture for a scalable audio generation pipeline. Answer in 15 words."
+def get_text_query(custom_prompt: Optional[str] = None):
+    question = (
+        custom_prompt or "Explain the system architecture for a scalable audio generation pipeline. Answer in 15 words."
+    )
     prompt = {
         "role": "user",
         "content": [
@@ -71,16 +197,15 @@ default_system = (
 )
 
 
-def get_video_query() -> QueryResult:
-    question = "Why is this video funny?"
+def get_video_query(video_path: Optional[str] = None, custom_prompt: Optional[str] = None):
+    question = custom_prompt or "Why is this video funny?"
+    video_url = get_video_url_from_path(video_path)
     prompt = {
         "role": "user",
         "content": [
             {
                 "type": "video_url",
-                "video_url": {
-                    "url": "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/sample_demo_1.mp4"
-                },
+                "video_url": {"url": video_url},
             },
             {
                 "type": "text",
@@ -91,16 +216,15 @@ def get_video_query() -> QueryResult:
     return prompt
 
 
-def get_image_query() -> QueryResult:
-    question = "What is the content of this image?"
+def get_image_query(image_path: Optional[str] = None, custom_prompt: Optional[str] = None):
+    question = custom_prompt or "What is the content of this image?"
+    image_url = get_image_url_from_path(image_path)
     prompt = {
         "role": "user",
         "content": [
             {
                 "type": "image_url",
-                "image_url": {
-                    "url": "https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg"
-                },
+                "image_url": {"url": image_url},
             },
             {
                 "type": "text",
@@ -111,14 +235,15 @@ def get_image_query() -> QueryResult:
     return prompt
 
 
-def get_audio_query() -> QueryResult:
-    question = "What is the content of this audio?"
+def get_audio_query(audio_path: Optional[str] = None, custom_prompt: Optional[str] = None):
+    question = custom_prompt or "What is the content of this audio?"
+    audio_url = get_audio_url_from_path(audio_path)
     prompt = {
         "role": "user",
         "content": [
             {
                 "type": "audio_url",
-                "audio_url": {"url": AudioAsset("mary_had_lamb").url},
+                "audio_url": {"url": audio_url},
             },
             {
                 "type": "text",
@@ -178,7 +303,25 @@ def run_multimodal_generation(args) -> None:
         code2wav_sampling_params,
     ]
 
-    prompt = query_map[args.query_type]()
+    # Get paths and custom prompt from args
+    video_path = getattr(args, "video_path", None)
+    image_path = getattr(args, "image_path", None)
+    audio_path = getattr(args, "audio_path", None)
+    custom_prompt = getattr(args, "prompt", None)
+
+    # Get the query function and call it with appropriate parameters
+    query_func = query_map[args.query_type]
+    if args.query_type == "use_video":
+        prompt = query_func(video_path=video_path, custom_prompt=custom_prompt)
+    elif args.query_type == "use_image":
+        prompt = query_func(image_path=image_path, custom_prompt=custom_prompt)
+    elif args.query_type == "use_audio":
+        prompt = query_func(audio_path=audio_path, custom_prompt=custom_prompt)
+    elif args.query_type == "text":
+        prompt = query_func(custom_prompt=custom_prompt)
+    else:
+        prompt = query_func()
+
     extra_body = {
         "sampling_params_list": sampling_params_list  # Optional, it has a default setting in stage_configs of the corresponding model.
     }
@@ -225,6 +368,34 @@ def parse_args():
         default="Qwen/Qwen3-Omni-30B-A3B-Instruct",
         help="Model Name / Path",
     )
+    parser.add_argument(
+        "--video-path",
+        "-v",
+        type=str,
+        default=None,
+        help="Path to local video file or URL. If not provided and query-type is 'use_video', uses default video URL.",
+    )
+    parser.add_argument(
+        "--image-path",
+        "-i",
+        type=str,
+        default=None,
+        help="Path to local image file or URL. If not provided and query-type is 'use_image', uses default image URL.",
+    )
+    parser.add_argument(
+        "--audio-path",
+        "-a",
+        type=str,
+        default=None,
+        help="Path to local audio file or URL. If not provided and query-type is 'use_audio', uses default audio URL.",
+    )
+    parser.add_argument(
+        "--prompt",
+        "-p",
+        type=str,
+        default=None,
+        help="Custom text prompt/question to use instead of the default prompt for the selected query type.",
+    )
 
     return parser.parse_args()
 

examples/online_serving/qwen3_omni/qwen3_omni_moe_thinking.yaml

@@ -0,0 +1,36 @@
+# Stage config for running Qwen3-Omni-MoE-Thinking (text-only output)
+# This config is for models like Qwen3-Omni-30B-A3B-Thinking that only have the
+# thinker component and do not support audio output.
+#
+# Single stage: Thinker (multimodal understanding + text generation)
+
+# The following config has been verified on 2x H100-80G GPUs.
+stage_args:
+  - stage_id: 0
+    runtime:
+      devices: "0,1"
+      max_batch_size: 1
+    engine_args:
+      model_stage: thinker
+      model_arch: Qwen3OmniMoeForConditionalGeneration
+      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      gpu_memory_utilization: 0.9
+      enforce_eager: true
+      trust_remote_code: true
+      engine_output_type: text
+      distributed_executor_backend: "mp"
+      enable_prefix_caching: false
+      hf_config_name: thinker_config
+      tensor_parallel_size: 2
+    final_output: true
+    final_output_type: text
+    is_comprehension: true
+    default_sampling_params:
+      temperature: 0.4
+      top_p: 0.9
+      top_k: 1
+      max_tokens: 2048
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.05

examples/online_serving/qwen3_omni/run_curl_multimodal_generation.sh

@@ -41,7 +41,7 @@ code2wav_sampling_params='{
   "temperature": 0.0,
   "top_p": 1.0,
   "top_k": -1,
-  "max_tokens": 4096 * 16,
+  "max_tokens": 65536,
   "seed": 42,
   "detokenize": true,
   "repetition_penalty": 1.1

mkdocs.yml

@@ -1,5 +1,5 @@
 site_name: vLLM-Omni
-site_description: Multi-modality models inference and serving with non-autoregressive structures
+site_description: Efficient omni-modality model serving for everyone
 site_author: vLLM-Omni Team
 site_url: https://vllm-project.github.io/vllm-omni/
 

pyproject.toml

@@ -5,12 +5,12 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "vllm-omni"
 version = "0.11.0rc1"
-description = "vLLM-Omni: Multi-modality models inference and serving with non-autoregressive structures"
+description = "A framework for efficient model inference with omni-modality models"
 readme = "README.md"
 requires-python = ">=3.9,<3.14"  # Align with vLLM v0.11 supported Python versions
 license = {text = "Apache-2.0"}
 authors = [
-    {name = "vLLM-Omni Team", email = "vllm-project@gmail.com"}
+    {name = "vLLM-Omni Team"}
 ]
 keywords = ["vllm", "multimodal", "diffusion", "transformer", "inference", "serving"]
 classifiers = [
@@ -27,6 +27,7 @@ classifiers = [
     "Topic :: Software Development :: Libraries :: Python Modules",
 ]
 
+
 dependencies = [
     # Core runtime dependencies (required for actual usage)
     "omegaconf>=2.3.0",
@@ -155,6 +156,7 @@ markers = [
     "integration: Integration tests",
     "benchmark: Benchmark tests",
     "slow: Slow tests",
+    "core_model: enable this model test in each PR instead of only nightly",
 ]
 
 [tool.typos.default]

tests/conftest.py

@@ -0,0 +1,36 @@
+import os
+
+import pytest
+import torch
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+@pytest.fixture(autouse=True)
+def clean_gpu_memory_between_tests():
+    if os.getenv("VLLM_TEST_CLEAN_GPU_MEMORY", "0") != "1":
+        yield
+        return
+
+    # Wait for GPU memory to be cleared before starting the test
+    import gc
+
+    from tests.utils import wait_for_gpu_memory_to_clear
+
+    num_gpus = torch.cuda.device_count()
+    if num_gpus > 0:
+        try:
+            wait_for_gpu_memory_to_clear(
+                devices=list(range(num_gpus)),
+                threshold_ratio=0.1,
+            )
+        except ValueError as e:
+            logger.info("Failed to clean GPU memory: %s", e)
+
+    yield
+
+    # Clean up GPU memory after the test
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        gc.collect()

tests/distributed/omni_connectors/test_adapter_and_flow.py

@@ -0,0 +1,236 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from vllm_omni.distributed.omni_connectors.adapter import try_recv_via_connector, try_send_via_connector
+from vllm_omni.distributed.omni_connectors.connectors.shm_connector import SharedMemoryConnector
+from vllm_omni.distributed.omni_connectors.utils.config import ConnectorSpec, OmniTransferConfig
+from vllm_omni.distributed.omni_connectors.utils.initialization import get_connectors_config_for_stage
+
+
+@pytest.fixture
+def mock_objects():
+    return {"connector": MagicMock(), "metrics": MagicMock(), "queue_fn": MagicMock()}
+
+
+def test_send_success(mock_objects):
+    """Test try_send_via_connector success path."""
+    # Setup
+    mock_connector = mock_objects["connector"]
+    mock_metrics = mock_objects["metrics"]
+    mock_queue_fn = mock_objects["queue_fn"]
+
+    stage_id = 0
+    next_stage_id = 1
+    req_id = "req_123"
+    inputs = {"input_ids": [1, 2, 3]}
+    sampling_params = {"temperature": 0.7}
+    prompt = "test prompt"
+
+    # Mock connector.put return
+    # Returns: (success, size, metadata)
+    mock_metadata = {"handle": "xyz"}
+    mock_connector.put.return_value = (True, 100, mock_metadata)
+
+    # Execute
+    result = try_send_via_connector(
+        connector=mock_connector,
+        stage_id=stage_id,
+        next_stage_id=next_stage_id,
+        req_id=req_id,
+        next_inputs=inputs,
+        sampling_params=sampling_params,
+        original_prompt=prompt,
+        next_stage_queue_submit_fn=mock_queue_fn,
+        metrics=mock_metrics,
+    )
+
+    # Verify
+    assert result is True
+
+    # 1. Verify connector.put called correctly
+    mock_connector.put.assert_called_once()
+    args, _ = mock_connector.put.call_args
+    assert args[0] == "0"  # from_stage
+    assert args[1] == "1"  # to_stage
+    assert args[2] == req_id
+    # Verify payload structure in put
+    payload = args[3]
+    assert payload["engine_inputs"] == inputs
+    assert payload["sampling_params"] == sampling_params
+
+    # 2. Verify queue notification submitted
+    mock_queue_fn.assert_called_once()
+    notify_payload = mock_queue_fn.call_args[0][0]
+    assert notify_payload["request_id"] == req_id
+    assert notify_payload["from_connector"] is True
+    assert notify_payload["connector_metadata"] == mock_metadata
+
+    # 3. Verify metrics recorded
+    mock_metrics.on_forward.assert_called_once()
+
+
+def test_send_fail(mock_objects):
+    """Test try_send_via_connector when connector fails."""
+    mock_connector = mock_objects["connector"]
+    mock_metrics = mock_objects["metrics"]
+    mock_queue_fn = mock_objects["queue_fn"]
+
+    mock_connector.put.return_value = (False, 0, None)
+
+    result = try_send_via_connector(
+        connector=mock_connector,
+        stage_id=0,
+        next_stage_id=1,
+        req_id="req_fail",
+        next_inputs={},
+        sampling_params={},
+        original_prompt="",
+        next_stage_queue_submit_fn=mock_queue_fn,
+        metrics=mock_metrics,
+    )
+
+    assert result is False
+    mock_queue_fn.assert_not_called()
+
+
+def test_recv_success(mock_objects):
+    """Test try_recv_via_connector success path."""
+    mock_connector = mock_objects["connector"]
+
+    # Setup task received from queue
+    task = {
+        "request_id": "req_recv",
+        "from_connector": True,
+        "from_stage": "0",
+        "connector_metadata": {"handle": "xyz"},
+    }
+
+    # Setup connectors dict
+    connectors = {("0", "1"): mock_connector}
+
+    # Mock connector.get return
+    expected_data = {"engine_inputs": {"ids": [1]}}
+    # get returns: (data_obj, size)
+    mock_connector.get.return_value = (expected_data, 50)
+    # serialize_obj needed for metrics calculation if size not returned directly
+    mock_connector.serialize_obj.return_value = b"bytes"
+
+    # Execute
+    # We are stage 1 receiving from stage 0
+    inputs, rx_metrics = try_recv_via_connector(task, connectors, stage_id=1)
+
+    # Verify
+    assert inputs == expected_data["engine_inputs"]
+    assert rx_metrics is not None
+    mock_connector.get.assert_called_once_with("0", "1", "req_recv", metadata={"handle": "xyz"})
+
+
+def test_recv_no_connector():
+    """Test recv fails when no connector exists for edge."""
+    task = {"request_id": "req_missing", "from_connector": True, "from_stage": "0"}
+    connectors = {}  # Empty connectors
+
+    inputs, _ = try_recv_via_connector(task, connectors, stage_id=1)
+    assert inputs is None
+
+
+def test_shm_connector_flow():
+    """
+    Verify the full flow: Send -> Adapter -> Connector -> Adapter -> Recv.
+    Using real SharedMemoryConnector (inline mode for simplicity).
+    """
+    # 1. Setup Connector
+    config = {"shm_threshold_bytes": 1024}  # Large threshold to use inline
+    connector = SharedMemoryConnector(config)
+    connectors_map = {("0", "1"): connector}
+
+    # 2. Setup Data
+    stage_id = 0
+    next_stage_id = 1
+    req_id = "flow_req"
+    inputs = {"tokens": [10, 20, 30]}
+    sampling_params = {"n": 1}
+
+    # Queue capture mechanism
+    queue_capture = []
+
+    def mock_submit(payload):
+        queue_capture.append(payload)
+
+    mock_metrics = MagicMock()
+
+    # 3. Send
+    success = try_send_via_connector(
+        connector=connector,
+        stage_id=stage_id,
+        next_stage_id=next_stage_id,
+        req_id=req_id,
+        next_inputs=inputs,
+        sampling_params=sampling_params,
+        original_prompt="prompt",
+        next_stage_queue_submit_fn=mock_submit,
+        metrics=mock_metrics,
+    )
+    assert success is True
+    assert len(queue_capture) == 1
+
+    # 4. Recv
+    # The 'task' is what would be popped from the queue
+    received_task = queue_capture[0]
+
+    # Verify queue payload contains what we expect
+    assert received_task["from_connector"] is True
+    assert received_task["from_stage"] == "0"
+
+    # Decode
+    decoded_inputs, _ = try_recv_via_connector(received_task, connectors_map, stage_id=1)
+
+    # 5. Verify Data Integrity
+    assert decoded_inputs == inputs
+
+
+def test_get_connectors_for_stage():
+    """Test filtering logic for stage config."""
+    # Config has edges: 0->1, 1->2
+    config = OmniTransferConfig(connectors={("0", "1"): ConnectorSpec(name="C1"), ("1", "2"): ConnectorSpec(name="C2")})
+
+    # Get config for Stage 1
+    # Stage 1 receives from 0 (input) and sends to 2 (output)
+    # get_connectors_config_for_stage ONLY returns INPUT connectors for the worker to initialize
+
+    stage_config = get_connectors_config_for_stage(config, stage_id=1)
+
+    # Should contain "from_stage_0"
+    assert "from_stage_0" in stage_config
+    assert stage_config["from_stage_0"]["spec"]["name"] == "C1"
+
+    # Should NOT contain "from_stage_1" or related to output
+    assert "from_stage_1" not in stage_config
+
+    # Verify Stage 2
+    stage_2_config = get_connectors_config_for_stage(config, stage_id=2)
+    assert "from_stage_1" in stage_2_config
+    assert stage_2_config["from_stage_1"]["spec"]["name"] == "C2"
+
+
+def test_recv_with_missing_metadata():
+    """Test recv when queue payload is malformed (missing metadata)."""
+    # Connector expects metadata but task doesn't have it
+    task = {
+        "request_id": "req_bad",
+        "from_connector": True,
+        "from_stage": "0",
+        # Missing "connector_metadata"
+    }
+    mock_conn = MagicMock()
+    # If get is called with None metadata, connector usually handles it or adapter handles exception
+    mock_conn.get.side_effect = Exception("Get failed")
+
+    connectors = {("0", "1"): mock_conn}
+
+    inputs, _ = try_recv_via_connector(task, connectors, stage_id=1)
+    assert inputs is None

tests/distributed/omni_connectors/test_basic_connectors.py

@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from unittest.mock import MagicMock
+
+import pytest
+
+from vllm_omni.distributed.omni_connectors.connectors.shm_connector import SharedMemoryConnector
+from vllm_omni.distributed.omni_connectors.factory import OmniConnectorFactory
+from vllm_omni.distributed.omni_connectors.utils.config import ConnectorSpec
+from vllm_omni.distributed.omni_connectors.utils.serialization import OmniSerializer
+
+
+def test_pickle_serialization():
+    """Test basic pickle serialization."""
+    data = {"key": "value", "list": [1, 2, 3]}
+    serialized = OmniSerializer.serialize(data, method="cloudpickle")
+    assert isinstance(serialized, bytes)
+
+    deserialized = OmniSerializer.deserialize(serialized, method="cloudpickle")
+    assert data == deserialized
+
+
+def test_create_shm_connector():
+    """Test creating SharedMemoryConnector via Factory."""
+    spec = ConnectorSpec(name="SharedMemoryConnector", extra={"shm_threshold_bytes": 1024})
+    connector = OmniConnectorFactory.create_connector(spec)
+    assert isinstance(connector, SharedMemoryConnector)
+    assert connector.threshold == 1024
+
+
+def test_create_unknown_connector():
+    """Test error when creating unknown connector."""
+    spec = ConnectorSpec(name="UnknownConnector")
+    with pytest.raises(ValueError):
+        OmniConnectorFactory.create_connector(spec)
+
+
+@pytest.fixture
+def shm_connector():
+    config = {"shm_threshold_bytes": 100}  # Small threshold for testing
+    return SharedMemoryConnector(config)
+
+
+def test_put_get_inline(shm_connector):
+    """Test inline transfer for small data."""
+    data = {"small": "data"}
+    # Ensure data is smaller than threshold (100 bytes)
+
+    success, size, metadata = shm_connector.put("stage_0", "stage_1", "req_1", data)
+    assert success is True
+    assert "inline_bytes" in metadata
+    assert "shm" not in metadata
+
+    # Retrieve
+    retrieved_data, ret_size = shm_connector.get("stage_0", "stage_1", "req_1", metadata)
+    assert data == retrieved_data
+    assert size == ret_size
+
+
+def test_put_get_shm(shm_connector, monkeypatch):
+    """Test SHM transfer logic for large data (Mocked)."""
+    # Create data larger than 100 bytes
+    data = {"large": "x" * 200}
+
+    # Mock SHM return values
+    mock_handle = {"name": "test_shm", "size": 200}
+    mock_write = MagicMock(return_value=mock_handle)
+    monkeypatch.setattr("vllm_omni.distributed.omni_connectors.connectors.shm_connector.shm_write_bytes", mock_write)
+
+    # When reading, return the serialized bytes of the data
+    serialized_data = shm_connector.serialize_obj(data)
+    mock_read = MagicMock(return_value=serialized_data)
+    monkeypatch.setattr("vllm_omni.distributed.omni_connectors.connectors.shm_connector.shm_read_bytes", mock_read)
+
+    # Put
+    success, size, metadata = shm_connector.put("stage_0", "stage_1", "req_2", data)
+
+    assert success is True
+    # Should use SHM because data > threshold
+    assert "shm" in metadata
+    assert metadata["shm"] == mock_handle
+    assert "inline_bytes" not in metadata
+
+    mock_write.assert_called_once()
+
+    # Get
+    retrieved_data, ret_size = shm_connector.get("stage_0", "stage_1", "req_2", metadata)
+
+    assert data == retrieved_data
+    mock_read.assert_called_once_with(mock_handle)
+
+
+def test_get_invalid_metadata(shm_connector):
+    """Test get with invalid metadata."""
+    result = shm_connector.get("stage_0", "stage_1", "req_3", {})
+    assert result is None
+
+    result = shm_connector.get("stage_0", "stage_1", "req_3", {"unknown": "format"})
+    assert result is None

tests/distributed/omni_connectors/test_omni_connector_configs.py

@@ -0,0 +1,64 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from pathlib import Path
+
+import pytest
+
+# Use the new import path for initialization utilities
+from vllm_omni.distributed.omni_connectors.utils.initialization import load_omni_transfer_config
+
+
+def get_config_files():
+    """Helper to find config files."""
+    # Go up two levels from 'tests/distributed/omni_connectors' (approx) to 'vllm-omni' root
+    # Adjust based on file location: vllm-omni/tests/distributed/omni_connectors/test_omni_connector_configs.py
+    # This file is 4 levels deep from root if we count from tests?
+    # vllm-omni/tests/distributed/omni_connectors -> parent -> distributed -> parent -> tests -> parent -> vllm-omni
+    # Let's use resolve to be safe.
+
+    # Path(__file__) = .../vllm-omni/tests/distributed/omni_connectors/test_omni_connector_configs.py
+    # .parent = omni_connectors
+    # .parent = distributed
+    # .parent = tests
+    # .parent = vllm-omni
+
+    base_dir = Path(__file__).resolve().parent.parent.parent.parent
+    config_dir = base_dir / "vllm_omni" / "model_executor" / "stage_configs"
+
+    if not config_dir.exists():
+        return []
+
+    return list(config_dir.glob("qwen*.yaml"))
+
+
+# Collect files at module level for parametrization
+config_files = get_config_files()
+
+
+@pytest.mark.skipif(len(config_files) == 0, reason="No config files found or directory missing")
+@pytest.mark.parametrize("yaml_file", config_files, ids=lambda p: p.name)
+def test_load_qwen_yaml_configs(yaml_file):
+    """
+    Scan and test loading of all qwen*.yaml config files.
+    This ensures that existing stage configs are compatible with the OmniConnector system.
+    """
+    print(f"Testing config load: {yaml_file.name}")
+    try:
+        # Attempt to load the config
+        # default_shm_threshold doesn't matter much for loading correctness, using default
+        config = load_omni_transfer_config(yaml_file)
+
+        assert config is not None, "Config should not be None"
+
+        # Basic validation
+        # Note: Some configs might not have 'runtime' or 'connectors' section if they rely on auto-shm
+        # but the load function should succeed regardless.
+
+        # If the config defines stages, we expect connectors to be populated (either explicit or auto SHM)
+        # We can't strictly assert len(config.connectors) > 0 because a single stage pipeline might have 0 edges.
+
+        print(f"  -> Successfully loaded. Connectors: {len(config.connectors)}")
+
+    except Exception as e:
+        pytest.fail(f"Failed to load config {yaml_file.name}: {e}")

tests/e2e/offline_inference/conftest.py

@@ -0,0 +1,341 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Pytest configuration and fixtures for vllm-omni tests.
+"""
+
+from typing import Any
+
+import pytest
+from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
+from vllm.sampling_params import SamplingParams
+
+from vllm_omni.entrypoints.omni import Omni
+
+PromptAudioInput = list[tuple[Any, int]] | tuple[Any, int] | None
+PromptImageInput = list[Any] | Any | None
+PromptVideoInput = list[Any] | Any | None
+
+
+class OmniRunner:
+    """
+    Test runner for Omni models.
+    """
+
+    def __init__(
+        self,
+        model_name: str,
+        seed: int = 42,
+        init_sleep_seconds: int = 20,
+        batch_timeout: int = 10,
+        init_timeout: int = 300,
+        shm_threshold_bytes: int = 65536,
+        log_stats: bool = False,
+        stage_configs_path: str | None = None,
+        **kwargs,
+    ) -> None:
+        """
+        Initialize an OmniRunner for testing.
+
+        Args:
+            model_name: The model name or path
+            seed: Random seed for reproducibility
+            init_sleep_seconds: Sleep time after starting each stage
+            batch_timeout: Timeout for batching in seconds
+            init_timeout: Timeout for initializing stages in seconds
+            shm_threshold_bytes: Threshold for using shared memory
+            log_stats: Enable detailed statistics logging
+            stage_configs_path: Optional path to YAML stage config file
+            **kwargs: Additional arguments passed to Omni
+        """
+        self.model_name = model_name
+        self.seed = seed
+
+        self.omni = Omni(
+            model=model_name,
+            log_stats=log_stats,
+            init_sleep_seconds=init_sleep_seconds,
+            batch_timeout=batch_timeout,
+            init_timeout=init_timeout,
+            shm_threshold_bytes=shm_threshold_bytes,
+            stage_configs_path=stage_configs_path,
+            **kwargs,
+        )
+
+    def get_default_sampling_params_list(self) -> list[SamplingParams]:
+        """
+        Get a list of default sampling parameters for all stages.
+
+        Returns:
+            List of SamplingParams with default decoding for each stage
+        """
+        return [st.default_sampling_params for st in self.omni.instance.stage_list]
+
+    def get_omni_inputs(
+        self,
+        prompts: list[str] | str,
+        system_prompt: str | None = None,
+        audios: PromptAudioInput = None,
+        images: PromptImageInput = None,
+        videos: PromptVideoInput = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
+    ) -> list[dict[str, Any]]:
+        """
+        Construct Omni input format from prompts and multimodal data.
+
+        Args:
+            prompts: Text prompt(s) - either a single string or list of strings
+            system_prompt: Optional system prompt (defaults to Qwen system prompt)
+            audios: Audio input(s) - tuple of (audio_array, sample_rate) or list of tuples
+            images: Image input(s) - PIL Image or list of PIL Images
+            videos: Video input(s) - numpy array or list of numpy arrays
+            mm_processor_kwargs: Optional processor kwargs (e.g., use_audio_in_video)
+
+        Returns:
+            List of prompt dictionaries suitable for Omni.generate()
+        """
+        if system_prompt is None:
+            system_prompt = (
+                "You are Qwen, a virtual human developed by the Qwen Team, Alibaba "
+                "Group, capable of perceiving auditory and visual inputs, as well as "
+                "generating text and speech."
+            )
+
+        video_padding_token = "<|VIDEO|>"
+        image_padding_token = "<|IMAGE|>"
+        audio_padding_token = "<|AUDIO|>"
+
+        if self.model_name == "Qwen/Qwen3-Omni-30B-A3B-Instruct":
+            video_padding_token = "<|video_pad|>"
+            image_padding_token = "<|image_pad|>"
+            audio_padding_token = "<|audio_pad|>"
+
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        def _normalize_mm_input(mm_input, num_prompts):
+            if mm_input is None:
+                return [None] * num_prompts
+            if isinstance(mm_input, list):
+                if len(mm_input) != num_prompts:
+                    raise ValueError(
+                        f"Multimodal input list length ({len(mm_input)}) must match prompts length ({num_prompts})"
+                    )
+                return mm_input
+            return [mm_input] * num_prompts
+
+        num_prompts = len(prompts)
+        audios_list = _normalize_mm_input(audios, num_prompts)
+        images_list = _normalize_mm_input(images, num_prompts)
+        videos_list = _normalize_mm_input(videos, num_prompts)
+
+        omni_inputs = []
+        for i, prompt_text in enumerate(prompts):
+            user_content = ""
+            multi_modal_data = {}
+
+            audio = audios_list[i]
+            if audio is not None:
+                if isinstance(audio, list):
+                    for _ in audio:
+                        user_content += f"<|audio_bos|>{audio_padding_token}<|audio_eos|>"
+                    multi_modal_data["audio"] = audio
+                else:
+                    user_content += f"<|audio_bos|>{audio_padding_token}<|audio_eos|>"
+                    multi_modal_data["audio"] = audio
+
+            image = images_list[i]
+            if image is not None:
+                if isinstance(image, list):
+                    for _ in image:
+                        user_content += f"<|vision_bos|>{image_padding_token}<|vision_eos|>"
+                    multi_modal_data["image"] = image
+                else:
+                    user_content += f"<|vision_bos|>{image_padding_token}<|vision_eos|>"
+                    multi_modal_data["image"] = image
+
+            video = videos_list[i]
+            if video is not None:
+                if isinstance(video, list):
+                    for _ in video:
+                        user_content += f"<|vision_bos|>{video_padding_token}<|vision_eos|>"
+                    multi_modal_data["video"] = video
+                else:
+                    user_content += f"<|vision_bos|>{video_padding_token}<|vision_eos|>"
+                    multi_modal_data["video"] = video
+
+            user_content += prompt_text
+
+            full_prompt = (
+                f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
+                f"<|im_start|>user\n{user_content}<|im_end|>\n"
+                f"<|im_start|>assistant\n"
+            )
+
+            input_dict: dict[str, Any] = {"prompt": full_prompt}
+            if multi_modal_data:
+                input_dict["multi_modal_data"] = multi_modal_data
+            if mm_processor_kwargs:
+                input_dict["mm_processor_kwargs"] = mm_processor_kwargs
+
+            omni_inputs.append(input_dict)
+
+        return omni_inputs
+
+    def generate(
+        self,
+        prompts: list[dict[str, Any]],
+        sampling_params_list: list[SamplingParams] | None = None,
+    ) -> list[Any]:
+        """
+        Generate outputs for the given prompts.
+
+        Args:
+            prompts: List of prompt dictionaries with 'prompt' and optionally
+                    'multi_modal_data' keys
+            sampling_params_list: List of sampling parameters for each stage.
+                                 If None, uses default parameters.
+
+        Returns:
+            List of OmniRequestOutput objects from stages with final_output=True
+        """
+        if sampling_params_list is None:
+            sampling_params_list = self.get_default_sampling_params_list()
+
+        return self.omni.generate(prompts, sampling_params_list)
+
+    def generate_multimodal(
+        self,
+        prompts: list[str] | str,
+        sampling_params_list: list[SamplingParams] | None = None,
+        system_prompt: str | None = None,
+        audios: PromptAudioInput = None,
+        images: PromptImageInput = None,
+        videos: PromptVideoInput = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
+    ) -> list[Any]:
+        """
+        Convenience method to generate with multimodal inputs.
+
+        Args:
+            prompts: Text prompt(s)
+            sampling_params_list: List of sampling parameters for each stage
+            system_prompt: Optional system prompt
+            audios: Audio input(s)
+            images: Image input(s)
+            videos: Video input(s)
+            mm_processor_kwargs: Optional processor kwargs
+
+        Returns:
+            List of OmniRequestOutput objects from stages with final_output=True
+        """
+        omni_inputs = self.get_omni_inputs(
+            prompts=prompts,
+            system_prompt=system_prompt,
+            audios=audios,
+            images=images,
+            videos=videos,
+            mm_processor_kwargs=mm_processor_kwargs,
+        )
+        return self.generate(omni_inputs, sampling_params_list)
+
+    def generate_audio(
+        self,
+        prompts: list[str] | str,
+        sampling_params_list: list[SamplingParams] | None = None,
+        system_prompt: str | None = None,
+        audios: PromptAudioInput = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
+    ) -> list[Any]:
+        """
+        Convenience method to generate with multimodal inputs.
+        Args:
+            prompts: Text prompt(s)
+            sampling_params_list: List of sampling parameters for each stage
+            system_prompt: Optional system prompt
+            audios: Audio input(s)
+            mm_processor_kwargs: Optional processor kwargs
+        Returns:
+            List of OmniRequestOutput objects from stages with final_output=True
+        """
+        omni_inputs = self.get_omni_inputs(
+            prompts=prompts,
+            system_prompt=system_prompt,
+            audios=audios,
+            mm_processor_kwargs=mm_processor_kwargs,
+        )
+        return self.generate(omni_inputs, sampling_params_list)
+
+    def generate_video(
+        self,
+        prompts: list[str] | str,
+        sampling_params_list: list[SamplingParams] | None = None,
+        system_prompt: str | None = None,
+        videos: PromptVideoInput = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
+    ) -> list[Any]:
+        """
+        Convenience method to generate with multimodal inputs.
+        Args:
+            prompts: Text prompt(s)
+            sampling_params_list: List of sampling parameters for each stage
+            system_prompt: Optional system prompt
+            videos: Video input(s)
+            mm_processor_kwargs: Optional processor kwargs
+        Returns:
+            List of OmniRequestOutput objects from stages with final_output=True
+        """
+        omni_inputs = self.get_omni_inputs(
+            prompts=prompts,
+            system_prompt=system_prompt,
+            videos=videos,
+            mm_processor_kwargs=mm_processor_kwargs,
+        )
+        return self.generate(omni_inputs, sampling_params_list)
+
+    def generate_image(
+        self,
+        prompts: list[str] | str,
+        sampling_params_list: list[SamplingParams] | None = None,
+        system_prompt: str | None = None,
+        images: PromptImageInput = None,
+        mm_processor_kwargs: dict[str, Any] | None = None,
+    ) -> list[Any]:
+        """
+        Convenience method to generate with multimodal inputs.
+        Args:
+            prompts: Text prompt(s)
+            sampling_params_list: List of sampling parameters for each stage
+            system_prompt: Optional system prompt
+            images: Image input(s)
+            mm_processor_kwargs: Optional processor kwargs
+        Returns:
+            List of OmniRequestOutput objects from stages with final_output=True
+        """
+        omni_inputs = self.get_omni_inputs(
+            prompts=prompts,
+            system_prompt=system_prompt,
+            images=images,
+            mm_processor_kwargs=mm_processor_kwargs,
+        )
+        return self.generate(omni_inputs, sampling_params_list)
+
+    def __enter__(self):
+        """Context manager entry."""
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Context manager exit - cleanup resources."""
+        self.close()
+        del self.omni
+        cleanup_dist_env_and_memory()
+
+    def close(self):
+        """Close and cleanup the Omni instance."""
+        if hasattr(self.omni.instance, "close"):
+            self.omni.instance.close()
+
+
+@pytest.fixture(scope="session")
+def omni_runner():
+    return OmniRunner

tests/e2e/offline_inference/stage_configs/qwen2_5_omni_ci.yaml

@@ -0,0 +1,105 @@
+# stage config for running qwen2.5-omni with architecture of OmniLLM.
+
+# The following config has been verified on 2x 24GB GPU (L4/RTX3090/RTX4090).
+# This config is optimized for CI e2e tests.
+stage_args:
+  - stage_id: 0
+    runtime:
+      process: true            # Run this stage in a separate process
+      devices: "0"            # Visible devices for this stage (CUDA_VISIBLE_DEVICES/torch.cuda.set_device)
+      max_batch_size: 1
+    engine_args:
+      model_stage: thinker
+      model_arch: Qwen2_5OmniForConditionalGeneration
+      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      max_model_len: 896
+      max_num_batched_tokens: 896
+      max_num_seqs: 1
+      gpu_memory_utilization: 0.8
+      skip_mm_profiling: true
+      enforce_eager: true  # Now we only support eager mode
+      trust_remote_code: true
+      engine_output_type: latent
+      enable_prefix_caching: false
+    is_comprehension: true
+    final_output: true
+    final_output_type: text
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 128
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.1
+  - stage_id: 1
+    runtime:
+      process: true
+      devices: "1"
+      max_batch_size: 1
+    engine_args:
+      model_stage: talker
+      model_arch: Qwen2_5OmniForConditionalGeneration
+      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      max_model_len: 896
+      max_num_batched_tokens: 896
+      max_num_seqs: 1
+      gpu_memory_utilization: 0.8
+      skip_mm_profiling: true
+      enforce_eager: true
+      trust_remote_code: true
+      enable_prefix_caching: false
+      engine_output_type: latent
+    engine_input_source: [0]
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen2_5_omni.thinker2talker
+    default_sampling_params:
+      temperature: 0.9
+      top_p: 0.8
+      top_k: 40
+      max_tokens: 128
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.05
+      stop_token_ids: [8294]
+  - stage_id: 2
+    runtime:
+      process: true
+      devices: "0"            # Example: use a different GPU than the previous stage; use "0" if single GPU
+      max_batch_size: 1
+    engine_args:
+      model_stage: code2wav
+      model_arch: Qwen2_5OmniForConditionalGeneration
+      worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
+      gpu_memory_utilization: 0.15
+      enforce_eager: true
+      trust_remote_code: true
+      enable_prefix_caching: false
+      engine_output_type: audio
+    engine_input_source: [1]
+    final_output: true
+    final_output_type: audio
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 128
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.1
+
+# Top-level runtime config (concise): default windows and stage edges
+runtime:
+  enabled: true
+  defaults:
+    window_size: -1             # Simplified: trigger downstream only after full upstream completion
+    max_inflight: 1             # Simplified: process serially within each stage
+  edges:
+    - from: 0                   # thinker → talker: trigger only after receiving full input (-1)
+      to: 1
+      window_size: -1
+    - from: 1                   # talker → code2wav: trigger only after receiving full input (-1)
+      to: 2
+      window_size: -1

tests/e2e/offline_inference/stage_configs/qwen3_omni_ci.yaml

@@ -1,8 +1,9 @@
 # Stage config for running Qwen3-Omni-MoE with 3-stage architecture
 # Stage 0: Thinker (multimodal understanding + text generation)
-# Stage 1: Talker (text embeddings → 8-layer RVQ codec codes)
+# Stage 1: Talker (text embeddings → 16-layer RVQ codec codes)
 # Stage 2: Code2Wav (8-layer RVQ codes → audio waveform)
 
+# The following config has been verified on 2x H100-80G GPUs.
 stage_args:
   - stage_id: 0
     runtime:
@@ -12,8 +13,8 @@ stage_args:
       model_stage: thinker
       model_arch: Qwen3OmniMoeForConditionalGeneration
       worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
-      gpu_memory_utilization: 0.8
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      gpu_memory_utilization: 0.6
       enforce_eager: true
       trust_remote_code: true
       engine_output_type: latent  # Output hidden states for talker
@@ -21,6 +22,7 @@ stage_args:
       enable_prefix_caching: false
       hf_config_name: thinker_config
       tensor_parallel_size: 2
+      load_format: dummy
     final_output: true
     final_output_type: text
     is_comprehension: true
@@ -28,7 +30,7 @@ stage_args:
       temperature: 0.4
       top_p: 0.9
       top_k: 1
-      max_tokens: 2048
+      max_tokens: 100
       seed: 42
       detokenize: True
       repetition_penalty: 1.05
@@ -41,7 +43,7 @@ stage_args:
        model_stage: talker
        model_arch: Qwen3OmniMoeForConditionalGeneration
        worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-       scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
+       scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
        gpu_memory_utilization: 0.5
        enforce_eager: true
        trust_remote_code: true
@@ -50,6 +52,7 @@ stage_args:
        enable_prefix_caching: false
        distributed_executor_backend: "mp"
        hf_config_name: talker_config
+       load_format: dummy
     engine_input_source: [0]
     custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker
     # final_output: true
@@ -57,7 +60,7 @@ stage_args:
     default_sampling_params:
       temperature: 0.9
       top_k: 50
-      max_tokens: 4096
+      max_tokens: 100
       seed: 42
       detokenize: False
       repetition_penalty: 1.05
@@ -65,13 +68,13 @@ stage_args:
 
   - stage_id: 2
     runtime:
-      devices: "1"
+      devices: "0"
       max_batch_size: 1
     engine_args:
       model_stage: code2wav
       model_arch: Qwen3OmniMoeForConditionalGeneration
       worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
-      scheduler_cls: vllm_omni.core.sched.generation_scheduler.GenerationScheduler
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
       enforce_eager: true
       trust_remote_code: true
       enable_prefix_caching: false
@@ -80,6 +83,7 @@ stage_args:
       distributed_executor_backend: "mp"
       max_num_batched_tokens: 1000000
       hf_config_name: thinker_config
+      load_format: dummy
     engine_input_source: [1]
     custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav
     final_output: true
@@ -88,7 +92,7 @@ stage_args:
       temperature: 0.0
       top_p: 1.0
       top_k: -1
-      max_tokens: 65536
+      max_tokens: 200
       seed: 42
       detokenize: True
       repetition_penalty: 1.1

tests/e2e/offline_inference/test_qwen2_5_omni.py

@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+E2E tests for Qwen2.5-Omni model with mixed modality inputs and audio output.
+"""
+
+from pathlib import Path
+
+import pytest
+from vllm.assets.audio import AudioAsset
+from vllm.assets.image import ImageAsset
+from vllm.assets.video import VideoAsset
+from vllm.multimodal.image import convert_image_mode
+
+from .conftest import OmniRunner
+from .utils import create_new_process_for_each_test
+
+models = ["Qwen/Qwen2.5-Omni-3B"]
+
+# CI stage config optimized for 24GB GPU (L4/RTX3090)
+stage_configs = [str(Path(__file__).parent / "stage_configs" / "qwen2_5_omni_ci.yaml")]
+
+# Create parameter combinations for model and stage config
+test_params = [(model, stage_config) for model in models for stage_config in stage_configs]
+
+
+@pytest.mark.core_model
+@pytest.mark.parametrize("test_config", test_params)
+@create_new_process_for_each_test()
+def test_mixed_modalities_to_audio(omni_runner: type[OmniRunner], test_config: tuple[str, str]) -> None:
+    """Test processing audio, image, and video together, generating audio output."""
+    model, stage_config_path = test_config
+    with omni_runner(model, seed=42, stage_configs_path=stage_config_path) as runner:
+        # Prepare multimodal inputs
+        question = "What is recited in the audio? What is in this image? Describe the video briefly."
+        audio = AudioAsset("mary_had_lamb").audio_and_sample_rate
+        audio = (audio[0][: 16000 * 5], audio[1])  # Trim to first 5 seconds
+        image = convert_image_mode(ImageAsset("cherry_blossom").pil_image.resize((128, 128)), "RGB")
+        video = VideoAsset(name="baby_reading", num_frames=4).np_ndarrays
+
+        outputs = runner.generate_multimodal(
+            prompts=question,
+            audios=audio,
+            images=image,
+            videos=video,
+        )
+
+        # Verify we got outputs from multiple stages
+        assert len(outputs) > 0
+
+        # Find and verify text output (thinker stage)
+        text_output = None
+        for stage_output in outputs:
+            if stage_output.final_output_type == "text":
+                text_output = stage_output
+                break
+
+        assert text_output is not None
+        assert len(text_output.request_output) > 0
+        text_content = text_output.request_output[0].outputs[0].text
+        assert text_content is not None
+        assert len(text_content.strip()) > 0
+
+        # Find and verify audio output (code2wav stage)
+        audio_output = None
+        for stage_output in outputs:
+            if stage_output.final_output_type == "audio":
+                audio_output = stage_output
+                break
+
+        assert audio_output is not None
+        assert len(audio_output.request_output) > 0
+
+        # Verify audio tensor exists and has content
+        audio_tensor = audio_output.request_output[0].multimodal_output["audio"]
+        assert audio_tensor is not None
+        assert audio_tensor.numel() > 0

tests/e2e/offline_inference/test_qwen3_omni.py

@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+E2E offline tests for Omni model with video input and audio output.
+"""
+
+import os
+from pathlib import Path
+
+import pytest
+from vllm.assets.video import VideoAsset
+
+from .conftest import OmniRunner
+
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+models = ["Qwen/Qwen3-Omni-30B-A3B-Instruct"]
+
+# CI stage config for 2xH100-80G GPUs
+stage_configs = [str(Path(__file__).parent / "stage_configs" / "qwen3_omni_ci.yaml")]
+
+# Create parameter combinations for model and stage config
+test_params = [(model, stage_config) for model in models for stage_config in stage_configs]
+
+
+@pytest.mark.parametrize("test_config", test_params)
+def test_video_to_audio(omni_runner: type[OmniRunner], test_config) -> None:
+    """Test processing video, generating audio output."""
+    model, stage_config_path = test_config
+    with omni_runner(model, seed=42, stage_configs_path=stage_config_path) as runner:
+        # Prepare inputs
+        question = "Describe the video briefly."
+        video = VideoAsset(name="baby_reading", num_frames=4).np_ndarrays
+
+        outputs = runner.generate_multimodal(
+            prompts=question,
+            videos=video,
+        )
+
+        # Verify we got outputs from multiple stages
+        assert len(outputs) > 0
+
+        # Find and verify text output (thinker stage)
+        text_output = None
+        for stage_output in outputs:
+            if stage_output.final_output_type == "text":
+                text_output = stage_output
+                break
+
+        assert text_output is not None
+        assert len(text_output.request_output) > 0
+        text_content = text_output.request_output[0].outputs[0].text
+        assert text_content is not None
+        assert len(text_content.strip()) > 0
+
+        # Find and verify audio output (code2wav stage)
+        audio_output = None
+        for stage_output in outputs:
+            if stage_output.final_output_type == "audio":
+                audio_output = stage_output
+                break
+
+        assert audio_output is not None
+        assert len(audio_output.request_output) > 0
+
+        # Verify audio tensor exists and has content
+        audio_tensor = audio_output.request_output[0].multimodal_output["audio"]
+        assert audio_tensor is not None
+        assert audio_tensor.numel() > 0

tests/e2e/offline_inference/test_t2i_model.py

@@ -0,0 +1,39 @@
+import os
+import sys
+from pathlib import Path
+
+import pytest
+import torch
+
+# ruff: noqa: E402
+REPO_ROOT = Path(__file__).resolve().parents[2]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+from vllm_omni import Omni
+
+os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "1"
+
+models = ["Tongyi-MAI/Z-Image-Turbo", "riverclouds/qwen_image_random"]
+
+
+@pytest.mark.parametrize("model_name", models)
+def test_diffusion_model(model_name: str):
+    m = Omni(model=model_name)
+    # high resolution may cause OOM on L4
+    height = 256
+    width = 256
+    images = m.generate(
+        "a photo of a cat sitting on a laptop keyboard",
+        height=height,
+        width=width,
+        num_inference_steps=2,
+        guidance_scale=0.0,
+        generator=torch.Generator("cuda").manual_seed(42),
+        num_outputs_per_prompt=2,
+    )
+    assert len(images) == 2
+    # check image size
+    assert images[0].width == width
+    assert images[0].height == height
+    images[0].save("image_output.png")

tests/e2e/offline_inference/test_t2v_model.py

@@ -0,0 +1,48 @@
+import os
+import sys
+from pathlib import Path
+
+import pytest
+import torch
+
+# ruff: noqa: E402
+REPO_ROOT = Path(__file__).resolve().parents[2]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+from vllm_omni import Omni
+
+os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "1"
+
+models = ["Wan-AI/Wan2.2-T2V-A14B-Diffusers"]
+
+
+@pytest.mark.parametrize("model_name", models)
+def test_video_diffusion_model(model_name: str):
+    m = Omni(
+        model=model_name,
+        boundary_ratio=0.875,
+        flow_shift=5.0,
+    )
+    # Use minimal settings for testing
+    # num_frames must satisfy: num_frames % vae_scale_factor_temporal == 1
+    # For Wan2.2, vae_scale_factor_temporal=4, so valid values are 5, 9, 13, 17, ...
+    height = 480
+    width = 640
+    num_frames = 5
+    frames = m.generate(
+        "A cat sitting on a table",
+        height=height,
+        width=width,
+        num_frames=num_frames,
+        num_inference_steps=2,
+        guidance_scale=1.0,
+        generator=torch.Generator("cuda").manual_seed(42),
+    )
+
+    assert frames is not None
+    assert hasattr(frames, "shape")
+    # frames shape: (batch, num_frames, height, width, channels)
+    assert frames.shape[1] == num_frames
+    assert frames.shape[2] == height
+    assert frames.shape[3] == width

tests/e2e/offline_inference/utils.py

@@ -0,0 +1,206 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import contextlib
+import functools
+import os
+import signal
+import subprocess
+import sys
+import tempfile
+from collections.abc import Callable
+from contextlib import ExitStack, suppress
+from pathlib import Path
+from typing import Any, Literal
+
+import cloudpickle
+from typing_extensions import ParamSpec
+from vllm.platforms import current_platform
+
+VLLM_PATH = Path(__file__).parent.parent.parent
+"""Path to root of the vLLM repository."""
+
+
+_P = ParamSpec("_P")
+
+
+def fork_new_process_for_each_test(func: Callable[_P, None]) -> Callable[_P, None]:
+    """Decorator to fork a new process for each test function.
+    See https://github.com/vllm-project/vllm/issues/7053 for more details.
+    """
+
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None:
+        # Make the process the leader of its own process group
+        # to avoid sending SIGTERM to the parent process
+        os.setpgrp()
+        from _pytest.outcomes import Skipped
+
+        # Create a unique temporary file to store exception info from child
+        # process. Use test function name and process ID to avoid collisions.
+        with (
+            tempfile.NamedTemporaryFile(
+                delete=False,
+                mode="w+b",
+                prefix=f"vllm_test_{func.__name__}_{os.getpid()}_",
+                suffix=".exc",
+            ) as exc_file,
+            ExitStack() as delete_after,
+        ):
+            exc_file_path = exc_file.name
+            delete_after.callback(os.remove, exc_file_path)
+
+            pid = os.fork()
+            print(f"Fork a new process to run a test {pid}")
+            if pid == 0:
+                # Parent process responsible for deleting, don't delete
+                # in child.
+                delete_after.pop_all()
+                try:
+                    func(*args, **kwargs)
+                except Skipped as e:
+                    # convert Skipped to exit code 0
+                    print(str(e))
+                    os._exit(0)
+                except Exception as e:
+                    import traceback
+
+                    tb_string = traceback.format_exc()
+
+                    # Try to serialize the exception object first
+                    exc_to_serialize: dict[str, Any]
+                    try:
+                        # First, try to pickle the actual exception with
+                        # its traceback.
+                        exc_to_serialize = {"pickled_exception": e}
+                        # Test if it can be pickled
+                        cloudpickle.dumps(exc_to_serialize)
+                    except (Exception, KeyboardInterrupt):
+                        # Fall back to string-based approach.
+                        exc_to_serialize = {
+                            "exception_type": type(e).__name__,
+                            "exception_msg": str(e),
+                            "traceback": tb_string,
+                        }
+                    try:
+                        with open(exc_file_path, "wb") as f:
+                            cloudpickle.dump(exc_to_serialize, f)
+                    except Exception:
+                        # Fallback: just print the traceback.
+                        print(tb_string)
+                    os._exit(1)
+                else:
+                    os._exit(0)
+            else:
+                pgid = os.getpgid(pid)
+                _pid, _exitcode = os.waitpid(pid, 0)
+                # ignore SIGTERM signal itself
+                old_signal_handler = signal.signal(signal.SIGTERM, signal.SIG_IGN)
+                # kill all child processes
+                os.killpg(pgid, signal.SIGTERM)
+                # restore the signal handler
+                signal.signal(signal.SIGTERM, old_signal_handler)
+                if _exitcode != 0:
+                    # Try to read the exception from the child process
+                    exc_info = {}
+                    if os.path.exists(exc_file_path):
+                        with (
+                            contextlib.suppress(Exception),
+                            open(exc_file_path, "rb") as f,
+                        ):
+                            exc_info = cloudpickle.load(f)
+
+                    if (original_exception := exc_info.get("pickled_exception")) is not None:
+                        # Re-raise the actual exception object if it was
+                        # successfully pickled.
+                        assert isinstance(original_exception, Exception)
+                        raise original_exception
+
+                    if (original_tb := exc_info.get("traceback")) is not None:
+                        # Use string-based traceback for fallback case
+                        raise AssertionError(
+                            f"Test {func.__name__} failed when called with"
+                            f" args {args} and kwargs {kwargs}"
+                            f" (exit code: {_exitcode}):\n{original_tb}"
+                        ) from None
+
+                    # Fallback to the original generic error
+                    raise AssertionError(
+                        f"function {func.__name__} failed when called with"
+                        f" args {args} and kwargs {kwargs}"
+                        f" (exit code: {_exitcode})"
+                    ) from None
+
+    return wrapper
+
+
+def spawn_new_process_for_each_test(f: Callable[_P, None]) -> Callable[_P, None]:
+    """Decorator to spawn a new process for each test function."""
+
+    @functools.wraps(f)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None:
+        # Check if we're already in a subprocess
+        if os.environ.get("RUNNING_IN_SUBPROCESS") == "1":
+            # If we are, just run the function directly
+            return f(*args, **kwargs)
+
+        import torch.multiprocessing as mp
+
+        with suppress(RuntimeError):
+            mp.set_start_method("spawn")
+
+        # Get the module
+        module_name = f.__module__
+
+        # Create a process with environment variable set
+        env = os.environ.copy()
+        env["RUNNING_IN_SUBPROCESS"] = "1"
+
+        with tempfile.TemporaryDirectory() as tempdir:
+            output_filepath = os.path.join(tempdir, "new_process.tmp")
+
+            # `cloudpickle` allows pickling complex functions directly
+            input_bytes = cloudpickle.dumps((f, output_filepath))
+
+            repo_root = str(VLLM_PATH.resolve())
+
+            env = dict(env or os.environ)
+            env["PYTHONPATH"] = repo_root + os.pathsep + env.get("PYTHONPATH", "")
+
+            cmd = [sys.executable, "-m", f"{module_name}"]
+
+            returned = subprocess.run(cmd, input=input_bytes, capture_output=True, env=env)
+
+            # check if the subprocess is successful
+            try:
+                returned.check_returncode()
+            except Exception as e:
+                # wrap raised exception to provide more information
+                raise RuntimeError(f"Error raised in subprocess:\n{returned.stderr.decode()}") from e
+
+    return wrapper
+
+
+def create_new_process_for_each_test(
+    method: Literal["spawn", "fork"] | None = None,
+) -> Callable[[Callable[_P, None]], Callable[_P, None]]:
+    """Creates a decorator that runs each test function in a new process.
+
+    Args:
+        method: The process creation method. Can be either "spawn" or "fork".
+               If not specified, it defaults to "spawn" on ROCm and XPU
+               platforms and "fork" otherwise.
+
+    Returns:
+        A decorator to run test functions in separate processes.
+    """
+    if method is None:
+        use_spawn = current_platform.is_rocm() or current_platform.is_xpu()
+        method = "spawn" if use_spawn else "fork"
+
+    assert method in ["spawn", "fork"], "Method must be either 'spawn' or 'fork'"
+
+    if method == "fork":
+        return fork_new_process_for_each_test
+
+    return spawn_new_process_for_each_test

tests/e2e/online_serving/stage_configs/qwen3_omni_ci.yaml

@@ -0,0 +1,98 @@
+# Stage config for running Qwen3-Omni-MoE with 3-stage architecture
+# Stage 0: Thinker (multimodal understanding + text generation)
+# Stage 1: Talker (text embeddings → 16-layer RVQ codec codes)
+# Stage 2: Code2Wav (8-layer RVQ codes → audio waveform)
+
+# The following config has been verified on 2x H100-80G GPUs.
+stage_args:
+  - stage_id: 0
+    runtime:
+      devices: "0,1"
+      max_batch_size: 1
+    engine_args:
+      model_stage: thinker
+      model_arch: Qwen3OmniMoeForConditionalGeneration
+      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      gpu_memory_utilization: 0.6
+      enforce_eager: true
+      trust_remote_code: true
+      engine_output_type: latent  # Output hidden states for talker
+      distributed_executor_backend: "mp"
+      enable_prefix_caching: false
+      hf_config_name: thinker_config
+      tensor_parallel_size: 2
+      load_format: dummy
+    final_output: true
+    final_output_type: text
+    is_comprehension: true
+    default_sampling_params:
+      temperature: 0.4
+      top_p: 0.9
+      top_k: 1
+      max_tokens: 100
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.05
+
+  - stage_id: 1
+    runtime:
+       devices: "1"
+       max_batch_size: 1
+    engine_args:
+       model_stage: talker
+       model_arch: Qwen3OmniMoeForConditionalGeneration
+       worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
+       scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+       gpu_memory_utilization: 0.3
+       enforce_eager: true
+       trust_remote_code: true
+       engine_output_type: latent  # Output codec codes for code2wav
+      #  tensor_parallel_size: 2
+       enable_prefix_caching: false
+       distributed_executor_backend: "mp"
+       hf_config_name: talker_config
+       load_format: dummy
+    engine_input_source: [0]
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker
+    # final_output: true
+    # final_output_type: text
+    default_sampling_params:
+      temperature: 0.9
+      top_k: 50
+      max_tokens: 100
+      seed: 42
+      detokenize: False
+      repetition_penalty: 1.05
+      stop_token_ids: [2150]
+
+  - stage_id: 2
+    runtime:
+      devices: "0"
+      max_batch_size: 1
+    engine_args:
+      model_stage: code2wav
+      model_arch: Qwen3OmniMoeForConditionalGeneration
+      worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
+      enforce_eager: true
+      trust_remote_code: true
+      enable_prefix_caching: false
+      engine_output_type: audio  # Final output: audio waveform
+      gpu_memory_utilization: 0.1
+      distributed_executor_backend: "mp"
+      max_num_batched_tokens: 1000000
+      hf_config_name: thinker_config
+      load_format: dummy
+    engine_input_source: [1]
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav
+    final_output: true
+    final_output_type: audio
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 200
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.1

tests/e2e/online_serving/test_qwen3_omni.py

@@ -0,0 +1,206 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+E2E Online tests for Qwen3-Omni model with video input and audio output.
+"""
+
+import os
+import socket
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+import openai
+import pytest
+from vllm.assets.video import VideoAsset
+from vllm.utils import get_open_port
+
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+models = ["Qwen/Qwen3-Omni-30B-A3B-Instruct"]
+
+# CI stage config for 2*H100-80G GPUs
+stage_configs = [str(Path(__file__).parent / "stage_configs" / "qwen3_omni_ci.yaml")]
+
+# Create parameter combinations for model and stage config
+test_params = [(model, stage_config) for model in models for stage_config in stage_configs]
+
+
+class OmniServer:
+    """Omniserver for vLLM-Omni tests."""
+
+    def __init__(
+        self,
+        model: str,
+        serve_args: list[str],
+        *,
+        env_dict: dict[str, str] | None = None,
+    ) -> None:
+        self.model = model
+        self.serve_args = serve_args
+        self.env_dict = env_dict
+        self.proc: subprocess.Popen | None = None
+        self.host = "127.0.0.1"
+        self.port = get_open_port()
+
+    def _start_server(self) -> None:
+        """Start the vLLM-Omni server subprocess."""
+        env = os.environ.copy()
+        env["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+        if self.env_dict is not None:
+            env.update(self.env_dict)
+
+        cmd = [
+            sys.executable,
+            "-m",
+            "vllm_omni.entrypoints.cli.main",
+            "serve",
+            self.model,
+            "--omni",
+            "--host",
+            self.host,
+            "--port",
+            str(self.port),
+        ] + self.serve_args
+
+        print(f"Launching OmniServer with: {' '.join(cmd)}")
+        self.proc = subprocess.Popen(
+            cmd,
+            env=env,
+            cwd=os.path.dirname(os.path.dirname(os.path.abspath(__file__))),  # Set working directory to vllm-omni root
+        )
+
+        # Wait for server to be ready
+        max_wait = 600  # 10 minutes
+        start_time = time.time()
+        while time.time() - start_time < max_wait:
+            try:
+                with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+                    sock.settimeout(1)
+                    result = sock.connect_ex((self.host, self.port))
+                    if result == 0:
+                        print(f"Server ready on {self.host}:{self.port}")
+                        return
+            except Exception:
+                pass
+            time.sleep(2)
+
+        raise RuntimeError(f"Server failed to start within {max_wait} seconds")
+
+    def __enter__(self):
+        self._start_server()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self.proc:
+            self.proc.terminate()
+            try:
+                self.proc.wait(timeout=30)
+            except subprocess.TimeoutExpired:
+                self.proc.kill()
+                self.proc.wait()
+
+
+@pytest.fixture
+def omni_server(request):
+    """Start vLLM-Omni server as a subprocess with actual model weights.
+    Uses module scope so the server starts only once for all tests.
+    Multi-stage initialization can take 10-20+ minutes.
+    """
+    model, stage_config_path = request.param
+    with OmniServer(model, ["--stage-configs-path", stage_config_path]) as server:
+        yield server
+
+
+@pytest.fixture
+def client(omni_server):
+    """OpenAI client for the running vLLM-Omni server."""
+    return openai.OpenAI(
+        base_url=f"http://{omni_server.host}:{omni_server.port}/v1",
+        api_key="EMPTY",
+    )
+
+
+@pytest.fixture(scope="session")
+def base64_encoded_video() -> str:
+    """Base64 encoded video for testing."""
+    import base64
+
+    video = VideoAsset(name="baby_reading", num_frames=4)
+    with open(video.video_path, "rb") as f:
+        content = f.read()
+        return base64.b64encode(content).decode("utf-8")
+
+
+def get_system_prompt():
+    return {
+        "role": "system",
+        "content": [
+            {
+                "type": "text",
+                "text": (
+                    "You are Qwen, a virtual human developed by the Qwen Team, "
+                    "Alibaba Group, capable of perceiving auditory and visual inputs, "
+                    "as well as generating text and speech."
+                ),
+            }
+        ],
+    }
+
+
+def dummy_messages_from_video_data(
+    video_data_url: str,
+    content_text: str = "Describe the video briefly.",
+):
+    """Create messages with video data URL for OpenAI API."""
+    return [
+        get_system_prompt(),
+        {
+            "role": "user",
+            "content": [
+                {"type": "video_url", "video_url": {"url": video_data_url}},
+                {"type": "text", "text": content_text},
+            ],
+        },
+    ]
+
+
+@pytest.mark.parametrize("omni_server", test_params, indirect=True)
+def test_video_to_audio(
+    client: openai.OpenAI,
+    omni_server,
+    base64_encoded_video: str,
+) -> None:
+    """Test processing video, generating audio output via OpenAI API."""
+    # Create data URL for the base64 encoded video
+    video_data_url = f"data:video/mp4;base64,{base64_encoded_video}"
+
+    messages = dummy_messages_from_video_data(video_data_url)
+
+    # Test single completion
+    chat_completion = client.chat.completions.create(
+        model=omni_server.model,
+        messages=messages,
+    )
+
+    assert len(chat_completion.choices) == 2  # 1 for text output, 1 for audio output
+
+    # Verify text output
+    text_choice = chat_completion.choices[0]
+    assert text_choice.finish_reason == "length"
+
+    # Verify we got a response
+    text_message = text_choice.message
+    assert text_message.content is not None and len(text_message.content) >= 10
+    assert text_message.role == "assistant"
+
+    # Verify audio output
+    audio_choice = chat_completion.choices[1]
+    assert audio_choice.finish_reason == "stop"
+    audio_message = audio_choice.message
+
+    # Check if audio was generated
+    if hasattr(audio_message, "audio") and audio_message.audio:
+        assert audio_message.audio.data is not None
+        assert len(audio_message.audio.data) > 0

tests/entrypoints/test_stage_utils.py

@@ -0,0 +1,58 @@
+import os
+import sys
+
+import pytest
+
+from vllm_omni.entrypoints.stage_utils import set_stage_devices
+
+
+def _make_dummy_torch(call_log):
+    class _Props:
+        def __init__(self, total):
+            self.total_memory = total
+
+    class _Cuda:
+        @staticmethod
+        def is_available():
+            return True
+
+        @staticmethod
+        def set_device(idx):
+            call_log.append(idx)
+
+        @staticmethod
+        def device_count():
+            return 2
+
+        @staticmethod
+        def get_device_properties(idx):
+            return _Props(total=16000)
+
+        @staticmethod
+        def mem_get_info(idx):
+            return (8000, 16000)
+
+        @staticmethod
+        def get_device_name(idx):
+            return f"gpu-{idx}"
+
+    class _Torch:
+        cuda = _Cuda
+
+    return _Torch
+
+
+@pytest.mark.usefixtures("clean_gpu_memory_between_tests")
+def test_set_stage_devices_respects_logical_ids(monkeypatch):
+    # Preserve an existing logical mapping and ensure devices "0,1" map through it.
+    monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "6,7")
+    call_log: list[int] = []
+    dummy_torch = _make_dummy_torch(call_log)
+    monkeypatch.setitem(sys.modules, "torch", dummy_torch)
+    monkeypatch.setattr("vllm_omni.utils.detect_device_type", lambda: "cuda")
+    monkeypatch.setattr("vllm_omni.utils.get_device_control_env_var", lambda: "CUDA_VISIBLE_DEVICES")
+
+    set_stage_devices(stage_id=0, devices="0,1")
+
+    assert os.environ["CUDA_VISIBLE_DEVICES"] == "6,7"
+    assert call_log and call_log[0] == 0  # current device set after remap

tests/test_omni_llm.py

@@ -104,6 +104,7 @@ class _FakeStage:
         shm_threshold_bytes: int = 65536,
         ctx=None,
         batch_timeout: int = 10,
+        **kwargs,
     ):
         """Mock init_stage_worker: don't start real process, just send stage_ready message."""
         # Create a mock process object

tests/utils.py

@@ -0,0 +1,107 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+import time
+from contextlib import contextmanager
+
+from vllm.platforms import current_platform
+
+if current_platform.is_rocm():
+    from amdsmi import (
+        amdsmi_get_gpu_vram_usage,
+        amdsmi_get_processor_handles,
+        amdsmi_init,
+        amdsmi_shut_down,
+    )
+
+    @contextmanager
+    def _nvml():
+        try:
+            amdsmi_init()
+            yield
+        finally:
+            amdsmi_shut_down()
+elif current_platform.is_cuda():
+    from vllm.third_party.pynvml import (
+        nvmlDeviceGetHandleByIndex,
+        nvmlDeviceGetMemoryInfo,
+        nvmlInit,
+        nvmlShutdown,
+    )
+
+    @contextmanager
+    def _nvml():
+        try:
+            nvmlInit()
+            yield
+        finally:
+            nvmlShutdown()
+else:
+
+    @contextmanager
+    def _nvml():
+        yield
+
+
+def get_physical_device_indices(devices):
+    visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES")
+    if visible_devices is None:
+        return devices
+
+    visible_indices = [int(x) for x in visible_devices.split(",")]
+    index_mapping = {i: physical for i, physical in enumerate(visible_indices)}
+    return [index_mapping[i] for i in devices if i in index_mapping]
+
+
+@_nvml()
+def wait_for_gpu_memory_to_clear(
+    *,
+    devices: list[int],
+    threshold_bytes: int | None = None,
+    threshold_ratio: float | None = None,
+    timeout_s: float = 120,
+) -> None:
+    assert threshold_bytes is not None or threshold_ratio is not None
+    # Use nvml instead of pytorch to reduce measurement error from torch cuda
+    # context.
+    devices = get_physical_device_indices(devices)
+    start_time = time.time()
+    while True:
+        output: dict[int, str] = {}
+        output_raw: dict[int, tuple[float, float]] = {}
+        for device in devices:
+            if current_platform.is_rocm():
+                dev_handle = amdsmi_get_processor_handles()[device]
+                mem_info = amdsmi_get_gpu_vram_usage(dev_handle)
+                gb_used = mem_info["vram_used"] / 2**10
+                gb_total = mem_info["vram_total"] / 2**10
+            else:
+                dev_handle = nvmlDeviceGetHandleByIndex(device)
+                mem_info = nvmlDeviceGetMemoryInfo(dev_handle)
+                gb_used = mem_info.used / 2**30
+                gb_total = mem_info.total / 2**30
+            output_raw[device] = (gb_used, gb_total)
+            output[device] = f"{gb_used:.02f}/{gb_total:.02f}"
+
+        print("gpu memory used/total (GiB): ", end="")
+        for k, v in output.items():
+            print(f"{k}={v}; ", end="")
+        print("")
+
+        if threshold_bytes is not None:
+            is_free = lambda used, total: used <= threshold_bytes / 2**30  # noqa E731
+            threshold = f"{threshold_bytes / 2**30} GiB"
+        else:
+            is_free = lambda used, total: used / total <= threshold_ratio  # noqa E731
+            threshold = f"{threshold_ratio:.2f}"
+
+        dur_s = time.time() - start_time
+        if all(is_free(used, total) for used, total in output_raw.values()):
+            print(f"Done waiting for free GPU memory on devices {devices=} ({threshold=}) {dur_s=:.02f}")
+            break
+
+        if dur_s >= timeout_s:
+            raise ValueError(f"Memory of devices {devices=} not free after {dur_s=:.02f} ({threshold=})")
+
+        time.sleep(5)

vllm_omni/core/sched/__init__.py

@@ -2,12 +2,12 @@
 Scheduling components for vLLM-Omni.
 """
 
-from .diffusion_scheduler import DiffusionScheduler
+from .omni_ar_scheduler import OmniARScheduler
+from .omni_generation_scheduler import OmniGenerationScheduler
 from .output import OmniNewRequestData
-from .scheduler import OmniScheduler
 
 __all__ = [
-    "OmniScheduler",
-    "DiffusionScheduler",
+    "OmniARScheduler",
+    "OmniGenerationScheduler",
     "OmniNewRequestData",
 ]

vllm_omni/core/sched/diffusion_scheduler.py

@@ -1,315 +0,0 @@
-import time
-from collections import defaultdict
-from typing import Optional
-
-from vllm.v1.core.kv_cache_manager import KVCacheBlocks
-from vllm.v1.core.sched.request_queue import create_request_queue
-from vllm.v1.core.sched.scheduler import Request, RequestStatus, SchedulerOutput, SpecDecodingStats
-from vllm.v1.core.sched.utils import remove_all
-from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs
-
-from vllm_omni.core.sched.output import OmniNewRequestData
-from vllm_omni.core.sched.scheduler import OmniScheduler
-from vllm_omni.outputs import OmniModelRunnerOutput
-
-
-class DiffusionScheduler(OmniScheduler):
-    """Scheduler for diffusion-based non-autoregressive stages.
-
-    Implements a fast-path scheduling strategy for diffusion models where
-    all input tokens are fed at once. Falls back to standard vLLM scheduling
-    if the token budget cannot accommodate all tokens in a single iteration.
-    """
-
-    def schedule(self) -> SchedulerOutput:
-        """Schedule requests for diffusion model execution.
-
-        Uses a fast-path approach that feeds all input tokens at once
-        (or 1 placeholder token if input is empty). Falls back to
-        standard scheduling if token budget is insufficient.
-
-        Returns:
-            SchedulerOutput containing scheduled requests and token allocations
-        """
-
-        # Select requests with zero prompt and using pooling
-        # (diffusion results returned via pooler_output)
-        token_budget = self.max_num_scheduled_tokens
-        capacity = self.max_num_running_reqs - len(self.running)
-        scheduled_timestamp = time.monotonic()
-
-        scheduled_new_reqs: list[Request] = []
-        scheduled_resumed_reqs: list[Request] = []
-        scheduled_running_reqs: list[Request] = []
-
-        req_to_new_blocks: dict[str, KVCacheBlocks] = {}
-        num_scheduled_tokens: dict[str, int] = {}
-        scheduled_spec_decode_tokens: dict[str, list[int]] = {}
-        scheduled_encoder_inputs: dict[str, list[int]] = {}
-
-        # Temporary queue: preserve waiting order, do not disturb non-diffusion requests
-        skipped_waiting_requests = create_request_queue(self.policy)
-
-        # Fast path selection and scheduling (treat all as diffusion requests,
-        # independent of pooling_params)
-        while self.waiting and token_budget > 0 and capacity > 0:
-            request = self.waiting.peek_request()
-            # Uniformly treat as diffusion. A feature flag can be added later
-            # via config or request tag.
-
-            # Allocate all input tokens for the request in one shot
-            # (allocate 1 placeholder if zero)
-            required_tokens = max(getattr(request, "num_prompt_tokens", 0), 1)
-            if required_tokens > token_budget:
-                # Insufficient budget to process all inputs at once;
-                # stop fast path attempt
-                break
-            num_new_tokens = required_tokens
-            new_blocks = self.kv_cache_manager.allocate_slots(
-                request,
-                num_new_tokens,
-                num_lookahead_tokens=self.num_lookahead_tokens,
-            )
-            if new_blocks is None:
-                # Allocation failed (e.g., VRAM pressure); stop fast path and
-                # fall back to default scheduling
-                # Put the current request back to the head of the waiting queue
-                # Note: the original queue order is preserved
-                break
-
-            # Officially schedule this request
-            request = self.waiting.pop_request()
-            self.running.append(request)
-            request.status = RequestStatus.RUNNING
-            if self.log_stats:
-                request.record_event(EngineCoreEventType.SCHEDULED, scheduled_timestamp)
-
-            req_to_new_blocks[request.request_id] = new_blocks
-            num_scheduled_tokens[request.request_id] = num_new_tokens
-            token_budget -= num_new_tokens
-            capacity -= 1
-            scheduled_new_reqs.append(request)
-
-        # Return skipped waiting requests
-        if skipped_waiting_requests:
-            self.waiting.prepend_requests(skipped_waiting_requests)
-
-        # If fast path scheduled none, fall back to the original scheduling
-        if not num_scheduled_tokens:
-            return super().schedule()
-
-        # Compute common prefix blocks (aligned with v1)
-        num_common_prefix_blocks = [0] * len(self.kv_cache_config.kv_cache_groups)
-        if self.running:
-            any_request = self.running[0]
-            num_common_prefix_blocks = self.kv_cache_manager.get_num_common_prefix_blocks(
-                any_request, len(self.running)
-            )
-
-        # Assemble SchedulerOutput
-        new_reqs_data = [
-            OmniNewRequestData.from_request(req, req_to_new_blocks[req.request_id].get_block_ids())
-            for req in scheduled_new_reqs
-        ]
-        cached_reqs_data = self._make_cached_request_data(
-            scheduled_running_reqs,
-            scheduled_resumed_reqs,
-            num_scheduled_tokens,
-            scheduled_spec_decode_tokens,
-            req_to_new_blocks,
-        )
-        scheduled_requests = scheduled_new_reqs + scheduled_running_reqs + scheduled_resumed_reqs
-        structured_output_request_ids, grammar_bitmask = self.get_grammar_bitmask(
-            scheduled_requests, scheduled_spec_decode_tokens
-        )
-
-        total_num_scheduled_tokens = sum(num_scheduled_tokens.values())
-        scheduler_output = SchedulerOutput(
-            scheduled_new_reqs=new_reqs_data,
-            scheduled_cached_reqs=cached_reqs_data,
-            num_scheduled_tokens=num_scheduled_tokens,
-            total_num_scheduled_tokens=total_num_scheduled_tokens,
-            scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
-            scheduled_encoder_inputs=scheduled_encoder_inputs,
-            num_common_prefix_blocks=num_common_prefix_blocks,
-            finished_req_ids=self.finished_req_ids,
-            free_encoder_mm_hashes=self.encoder_cache_manager.get_freed_mm_hashes(),
-            structured_output_request_ids=structured_output_request_ids,
-            grammar_bitmask=grammar_bitmask,
-        )
-
-        # KVTransfer: package metadata
-        if self.connector is not None:
-            meta = self.connector.build_connector_meta(scheduler_output)
-            scheduler_output.kv_connector_metadata = meta
-
-        # Update internal state (advance num_computed_tokens, free encoder inputs,
-        # etc.)
-        self._update_after_schedule(scheduler_output)
-        return scheduler_output
-
-    """
-    Scheduler for the diffusion model.
-    This scheduler is modified to stop the request immediately for the diffusion model.
-    This is because the diffusion model can generate the final image/audio in one step.
-    Note: This is just a minimal modification to the original scheduler,
-    and there should be some further efforts to optimize the scheduler.
-    The original scheduler is still used for the AR model.
-    """
-
-    def update_from_output(
-        self,
-        scheduler_output: SchedulerOutput,
-        model_runner_output: OmniModelRunnerOutput,
-    ) -> dict[int, EngineCoreOutputs]:
-        """Update the scheduler state based on the model runner output.
-
-        This method is modified to stop the request immediately for the diffusion model.
-        """
-        sampled_token_ids = model_runner_output.sampled_token_ids
-        logprobs = model_runner_output.logprobs
-        prompt_logprobs_dict = model_runner_output.prompt_logprobs_dict
-        num_scheduled_tokens = scheduler_output.num_scheduled_tokens
-        pooler_outputs = model_runner_output.pooler_output
-        num_nans_in_logits = model_runner_output.num_nans_in_logits
-        kv_connector_output = model_runner_output.kv_connector_output
-
-        outputs: dict[int, list[EngineCoreOutput]] = defaultdict(list)
-        spec_decoding_stats: Optional[SpecDecodingStats] = None
-        kv_connector_stats = kv_connector_output.kv_connector_stats if kv_connector_output else None
-
-        # NOTE(woosuk): As len(num_scheduled_tokens) can be up to 1K or more,
-        # the below loop can be a performance bottleneck. We should do our best
-        # to avoid expensive operations inside the loop.
-        stopped_running_reqs: set[Request] = set()
-        stopped_preempted_reqs: set[Request] = set()
-        for req_id, num_tokens_scheduled in num_scheduled_tokens.items():
-            assert num_tokens_scheduled > 0
-            request = self.requests.get(req_id)
-            if request is None:
-                # The request is already finished. This can happen if the
-                # request is aborted while the model is executing it (e.g.,
-                # in pipeline parallelism).
-                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 []
-
-            scheduled_spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(req_id)
-            if scheduled_spec_token_ids:
-                num_draft_tokens = len(scheduled_spec_token_ids)
-                num_accepted = len(generated_token_ids) - 1
-                num_rejected = num_draft_tokens - num_accepted
-                # num_computed_tokens represents the number of tokens
-                # processed in the current step, considering scheduled
-                # tokens and rejections. If some tokens are rejected,
-                # num_computed_tokens is decreased by the number of rejected
-                # tokens.
-                request.num_computed_tokens -= num_rejected
-                spec_decoding_stats = self.make_spec_decoding_stats(
-                    spec_decoding_stats,
-                    num_draft_tokens=num_draft_tokens,
-                    num_accepted_tokens=num_accepted,
-                )
-
-            new_logprobs = None
-            new_token_ids = generated_token_ids
-            kv_transfer_params = None
-            status_before_stop = request.status
-            pooler_output = None
-            if pooler_outputs:
-                pooler_output = pooler_outputs[req_index]
-
-            # Diffusion request: completes in one step; mark finished and free resources
-            request.status = RequestStatus.FINISHED_STOPPED
-            # Optional: set a stop_reason for front-end clarity
-            # (does not affect protocol)
-            request.stop_reason = request.stop_reason or "diffusion_done"
-            kv_transfer_params = self._free_request(request)
-            if status_before_stop == RequestStatus.RUNNING:
-                stopped_running_reqs.add(request)
-            else:
-                stopped_preempted_reqs.add(request)
-
-            # Extract sample logprobs if needed.
-            if request.sampling_params is not None and request.sampling_params.logprobs is not None and logprobs:
-                # NOTE: once we support N tokens per step (spec decode),
-                # the outer lists can be of length > 1.
-                new_logprobs = logprobs.slice(req_index, req_index + 1)
-
-            if new_token_ids and self.structured_output_manager.should_advance(request):
-                # NOTE: structured_output_request should not be None if
-                # use_structured_output, we have check above, so safe to ignore
-                # type warning
-                request.structured_output_request.grammar.accept_tokens(req_id, new_token_ids)  # type: ignore[union-attr]  # noqa: E501
-
-            # spec_token_ids comes from the model runner output
-            if num_nans_in_logits is not None and req_id in num_nans_in_logits:
-                request.num_nans_in_logits = num_nans_in_logits[req_id]
-
-            # Get prompt logprobs for this request.
-            prompt_logprobs_tensors = prompt_logprobs_dict.get(req_id)
-            # Convert pooler_output tensor to dict format expected by OmniEngineCoreOutput
-            pooling_output_dict = None
-            if pooler_output is not None:
-                # Wrap tensor in dict to match OmniEngineCoreOutput.pooling_output type
-                # which expects Optional[dict[str, torch.Tensor]]
-                pooling_output_dict = {"model_outputs": pooler_output}
-            if new_token_ids or pooler_output is not None or kv_transfer_params:
-                # Add EngineCoreOutput for this Request.
-                outputs[request.client_index].append(
-                    EngineCoreOutput(
-                        request_id=req_id,
-                        new_token_ids=new_token_ids,
-                        finish_reason=request.get_finished_reason(),
-                        new_logprobs=new_logprobs,
-                        new_prompt_logprobs_tensors=prompt_logprobs_tensors,
-                        pooling_output=pooling_output_dict,
-                        stop_reason=request.stop_reason,
-                        events=request.take_events(),
-                        kv_transfer_params=kv_transfer_params,
-                        trace_headers=request.trace_headers,
-                        num_cached_tokens=request.num_cached_tokens,
-                    )
-                )
-            else:
-                # Invariant: EngineCore returns no partial prefill outputs.
-                assert not prompt_logprobs_tensors
-
-        # Remove the stopped requests from the running and waiting queues.
-        if stopped_running_reqs:
-            if stopped_running_reqs:
-                self.running = remove_all(self.running, stopped_running_reqs)
-        if stopped_preempted_reqs:
-            # This is a rare case and unlikely to impact performance.
-            self.waiting.remove_requests(stopped_preempted_reqs)
-
-        # KV Connector: update state for finished KV Transfers.
-        if model_runner_output.kv_connector_output:
-            self._update_from_kv_xfer_finished(model_runner_output.kv_connector_output)
-
-        # Create EngineCoreOutputs for all clients that have requests with
-        # outputs in this step.
-        engine_core_outputs = {client_index: EngineCoreOutputs(outputs=outs) for client_index, outs in outputs.items()}
-
-        finished_req_ids = self.finished_req_ids_dict
-        if finished_req_ids:
-            # Include ids of requests that finished since last outputs
-            # were sent.
-            for client_index, finished_set in finished_req_ids.items():
-                # Set finished request set in EngineCoreOutputs for this client.
-                if (eco := engine_core_outputs.get(client_index)) is not None:
-                    eco.finished_requests = finished_set
-                else:
-                    engine_core_outputs[client_index] = EngineCoreOutputs(finished_requests=finished_set)
-            finished_req_ids.clear()
-
-        if (stats := self.make_stats(spec_decoding_stats, kv_connector_stats)) is not None:
-            # Return stats to only one of the front-ends.
-            if (eco := next(iter(engine_core_outputs.values()), None)) is None:
-                # We must return the stats even if there are no request
-                # outputs this step.
-                engine_core_outputs[0] = eco = EngineCoreOutputs()
-            eco.scheduler_stats = stats
-
-        return engine_core_outputs

vllm_omni/core/sched/omni_ar_scheduler.py

@@ -12,7 +12,7 @@ from vllm.v1.request import Request, RequestStatus
 from vllm.v1.spec_decode.metrics import SpecDecodingStats
 
 
-class OmniScheduler(VLLMScheduler):
+class OmniARScheduler(VLLMScheduler):
     """
     OmniScheduler: Scheduler for vLLM-Omni multimodal processing.
 

vllm_omni/core/sched/omni_generation_scheduler.py

@@ -10,15 +10,15 @@ from vllm.v1.core.sched.scheduler import (
     SchedulerOutput,
     SpecDecodingStats,
 )
+from vllm.v1.core.sched.scheduler import Scheduler as VLLMScheduler
 from vllm.v1.core.sched.utils import remove_all
 from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs
 
 from vllm_omni.core.sched.output import OmniNewRequestData
-from vllm_omni.core.sched.scheduler import OmniScheduler
 from vllm_omni.outputs import OmniModelRunnerOutput
 
 
-class GenerationScheduler(OmniScheduler):
+class OmniGenerationScheduler(VLLMScheduler):
     def schedule(self) -> SchedulerOutput:
         """Diffusion fast path:
         - Feed all input tokens of the request at once

vllm_omni/diffusion/attention/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

vllm_omni/diffusion/attention/backends/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

vllm_omni/diffusion/attention/backends/abstract.py

@@ -0,0 +1,78 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Generic, TypeVar
+
+import torch
+
+
+class AttentionBackend(ABC):
+    """Abstract class for diffusion attention backends."""
+
+    accept_output_buffer: bool = False
+
+    @staticmethod
+    @abstractmethod
+    def get_name() -> str:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def get_impl_cls() -> type["AttentionImpl"]:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def get_builder_cls():  # -> Type["AttentionMetadataBuilder"]:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def get_supported_head_sizes() -> list[int]:
+        """Get the list of supported head sizes for this backend."""
+        raise NotImplementedError
+
+    @classmethod
+    def supports_head_size(cls, head_size: int) -> bool:
+        supported_head_sizes = cls.get_supported_head_sizes()
+        return (not supported_head_sizes) or head_size in supported_head_sizes
+
+
+@dataclass
+class AttentionMetadata:
+    attn_mask: torch.Tensor | None = None
+
+
+T = TypeVar("T", bound=AttentionMetadata)
+
+
+class AttentionImpl(ABC, Generic[T]):
+    @abstractmethod
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        softmax_scale: float,
+        causal: bool = False,
+        num_kv_heads: int | None = None,
+        prefix: str = "",
+        **extra_impl_args,
+    ) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: T,
+    ) -> torch.Tensor:
+        raise NotImplementedError

vllm_omni/diffusion/attention/backends/flash_attn.py

@@ -0,0 +1,74 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+from vllm.logger import init_logger
+
+from vllm_omni.diffusion.attention.backends.abstract import (
+    AttentionBackend,
+    AttentionImpl,
+    AttentionMetadata,
+)
+
+logger = init_logger(__name__)
+
+try:
+    # only tested with flash_attn v3
+    # from flash_attn_interface import flash_attn_func as flash_attn_3_func  # not available in flash-attn 2.8.1
+    from flash_attn import flash_attn_func  # can be FA2 or FA3
+except ImportError:
+    logger.warning(
+        "FlashAttentionBackend is not available. You may install flash-attn "
+        "by running `uv pip install flash-attn==2.8.1 --no-build-isolation`"
+        " or install pre-built flash-attn from https://github.com/Dao-AILab/flash-attention/releases"
+    )
+    flash_attn_3_func = None
+
+
+class FlashAttentionBackend(AttentionBackend):
+    accept_output_buffer: bool = True
+
+    @staticmethod
+    def get_supported_head_sizes() -> list[int]:
+        return [64, 96, 128, 192, 256]
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASH_ATTN"
+
+    @staticmethod
+    def get_impl_cls() -> type["FlashAttentionImpl"]:
+        return FlashAttentionImpl
+
+
+class FlashAttentionImpl(AttentionImpl):
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        softmax_scale: float,
+        causal: bool = False,
+        num_kv_heads: int | None = None,
+        prefix: str = "",
+        **extra_impl_args,
+    ) -> None:
+        self.num_heads = num_heads
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: AttentionMetadata = None,
+    ) -> torch.Tensor:
+        # TODO: flash_attn_func does not support attn_mask.
+        out: torch.Tensor = flash_attn_func(
+            query,
+            key,
+            value,
+            causal=self.causal,
+            softmax_scale=self.softmax_scale,
+        )
+        return out

vllm_omni/diffusion/attention/backends/sdpa.py

@@ -0,0 +1,66 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+from vllm.logger import init_logger
+
+from vllm_omni.diffusion.attention.backends.abstract import (
+    AttentionBackend,
+    AttentionImpl,
+    AttentionMetadata,
+)
+
+logger = init_logger(__name__)
+
+
+class SDPABackend(AttentionBackend):
+    accept_output_buffer: bool = True
+
+    @staticmethod
+    def get_supported_head_sizes() -> list[int]:
+        return [x for x in range(1024)]  # todo
+
+    @staticmethod
+    def get_name() -> str:
+        return "SDPA"
+
+    @staticmethod
+    def get_impl_cls() -> type["SDPAImpl"]:
+        return SDPAImpl
+
+
+class SDPAImpl(AttentionImpl):
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        softmax_scale: float,
+        causal: bool = False,
+        num_kv_heads: int | None = None,
+        prefix: str = "",
+        **extra_impl_args,
+    ) -> None:
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: AttentionMetadata = None,
+    ) -> torch.Tensor:
+        query, key, value = (x.permute(0, 2, 1, 3) for x in (query, key, value))
+        attention_mask = attn_metadata.attn_mask if attn_metadata else None
+
+        output = torch.nn.functional.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            attn_mask=attention_mask,
+            dropout_p=0.0,
+            is_causal=self.causal,
+            scale=self.softmax_scale,
+        )
+        out = output.permute(0, 2, 1, 3)
+        return out