p

Signed-off-by: Kevin H. Luu <khluu000@gmail.com>
p
2025-12-04 02:17:07 -08:00 · 2025-12-04 02:08:14 -08:00 · 2025-12-04 02:04:51 -08:00 · 2025-12-04 01:59:29 -08:00 · 2025-12-04 01:55:18 -08:00 · 2025-12-04 00:04:18 -08:00
59 changed files with 2583 additions and 363 deletions
@@ -1,6 +1,23 @@
 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:omni-latest"
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:omni-latest"
+    agents:
+      queue: "cpu_queue_premerge_us_east_1"
+
  - label: "Simple Unit Test"
+    depends_on: image-build
    commands:
      - ".buildkite/scripts/simple_test.sh"
    agents:
      queue: "cpu_queue_premerge"
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:omni-latest
+          always-pull: true
+          propagate-environment: true
+
@@ -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.
@@ -156,9 +156,6 @@ cython_debug/
 # cursor
 .cursor/

-# docker
-docker/
-
 # scripts
 /scripts/

@@ -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/)
@@ -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

@@ -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 []
@@ -7,11 +7,11 @@ 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
+      - 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
    - 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 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
  - General:
    - usage/*
@@ -33,7 +33,6 @@ nav:
    - design/architecture_overview.md
    - design/vllm_omni_design.md
    - design/mrs_design.md
-    - design/api_design_doc.md
  - Docs Guide: contributing/DOCS_GUIDE.md
 - API Reference:
  - api/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 +59,4 @@ 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 announcement blogpost](https://blog.vllm.ai/2025/11/30/vllm-omni.html)
@@ -102,6 +102,10 @@ Configuration classes.

 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][]
@@ -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
@@ -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!
@@ -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`.
@@ -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]"
 ```

@@ -7,7 +7,3 @@ This section contains design documents and architecture specifications for vLLM-
 - [Architecture Overview](architecture_overview.md)
 - [vLLM-Omni Software Design Document](vllm_omni_design.md)
 - [Multi-Request Streaming](mrs_design.md)
-
-## API Design Documentation
-
- [vLLM-Omni API Design Documentation](api_design_doc.md)
@@ -37,4 +37,7 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr
 ## Set up using Docker

 ### Pre-built images
-To be released... Stay tuned!
+
+=== "NVIDIA CUDA"
+
+    --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:pre-built-images"
@@ -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 +73,24 @@ uv pip install --no-build-isolation --editable .
 </details>

 # --8<-- [end:build-wheel-from-source]
+
+
+# --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]
@@ -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 \
@@ -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"

@@ -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;
 }

@@ -20,9 +20,10 @@ 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` |
+|`ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` |


-## List of Supported Models for Ascend NPU
+## List of Supported Models for NPU

 <style>
 th {
@@ -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 non 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!
@@ -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"
    ``````
@@ -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"
    ``````
@@ -1,6 +1,6 @@
 # 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/qwen_image>.


 This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Image` using vLLM-Omni:
@@ -49,9 +49,9 @@ 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/qwen_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/qwen_image/text_to_image.py"
    ``````
@@ -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"
    ``````
@@ -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 +108,13 @@ 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 "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"
    ``````
@@ -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.
@@ -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,
@@ -283,6 +463,39 @@ 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).",
+    )

    return parser.parse_args()

@@ -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.
@@ -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()

@@ -1,94 +0,0 @@
-# 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 2: Code2Wav (8-layer RVQ codes → audio waveform)
-
-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.scheduler.OmniScheduler
-      gpu_memory_utilization: 0.8
-      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
-    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
-
-  - 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.scheduler.OmniScheduler
-       gpu_memory_utilization: 0.5
-       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
-    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: 4096
-      seed: 42
-      detokenize: False
-      repetition_penalty: 1.05
-      stop_token_ids: [2150]
-
-  - stage_id: 2
-    runtime:
-      devices: "1"
-      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
-      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
-    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: 65536
-      seed: 42
-      detokenize: True
-      repetition_penalty: 1.1
@@ -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.
@@ -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:
@@ -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,
@@ -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.
@@ -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()

@@ -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)
@@ -264,7 +264,7 @@ 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 +282,9 @@ 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
@@ -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()

@@ -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
@@ -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/

@@ -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",
@@ -21,6 +21,8 @@ class OmniDiffusionConfig:

    model_class_name: str | None = None

+    dtype: torch.dtype = torch.bfloat16
+
    # Attention
    # attention_backend: str = None

@@ -80,6 +82,10 @@ class OmniDiffusionConfig:
    vae_cpu_offload: bool = True
    pin_cpu_memory: bool = True

+    # VAE memory optimization parameters
+    vae_use_slicing: bool = False
+    vae_use_tiling: bool = False
+
    # STA (Sliding Tile Attention) parameters
    mask_strategy_file_path: str | None = None
    # STA_mode: STA_Mode = STA_Mode.STA_INFERENCE
@@ -20,7 +20,6 @@ from diffusers.schedulers.scheduling_flow_match_euler_discrete import (
 from diffusers.utils.torch_utils import randn_tensor
 from torch import nn
 from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer
-from vllm.model_executor.model_loader.utils import set_default_torch_dtype

 from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
 from vllm_omni.diffusion.distributed.utils import get_local_device
@@ -236,7 +235,7 @@ class QwenImagePipeline(
    def __init__(
        self,
        *,
-        od_config: OmniDiffusionConfig = None,
+        od_config: OmniDiffusionConfig,
        prefix: str = "",
    ):
        super().__init__()
@@ -250,23 +249,14 @@ class QwenImagePipeline(
        self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
            model, subfolder="scheduler", local_files_only=local_files_only
        )
-        logger.info("Loaded Qwen-Image scheduler successfully")
-
-        with set_default_torch_dtype(torch.bfloat16):
-            self.text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-                model, subfolder="text_encoder", local_files_only=local_files_only
-            )
-            logger.info("Loaded Qwen-Image text encoder successfully")
-            self.vae = AutoencoderKLQwenImage.from_pretrained(
-                model, subfolder="vae", local_files_only=local_files_only
-            ).to(self.device)
-            logger.info("Loaded Qwen-Image VAE successfully")
-            self.transformer = QwenImageTransformer2DModel()
-            logger.info("Initialized Qwen-Image transformer successfully.")
-            self.tokenizer = Qwen2Tokenizer.from_pretrained(
-                model, subfolder="tokenizer", local_files_only=local_files_only
-            )
-            logger.info("Loaded Qwen-Image tokenizer successfully.")
+        self.text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            model, subfolder="text_encoder", local_files_only=local_files_only
+        )
+        self.vae = AutoencoderKLQwenImage.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to(
+            self.device
+        )
+        self.transformer = QwenImageTransformer2DModel()
+        self.tokenizer = Qwen2Tokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)

        self.stage = None

@@ -0,0 +1,648 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import json
+import os
+from typing import Any, Callable
+
+import torch
+import torch.nn as nn
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.autoencoders import AutoencoderKL
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import logging
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import AutoModel, AutoTokenizer
+
+from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.distributed.utils import get_local_device
+from vllm_omni.diffusion.models.z_image.z_image_transformer import (
+    ZImageTransformer2DModel,
+)
+from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.model_executor.model_loader.weight_utils import (
+    download_weights_from_hf_specific,
+)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def get_post_process_func(
+    od_config: OmniDiffusionConfig,
+):
+    model_name = od_config.model
+    if os.path.exists(model_name):
+        model_path = model_name
+    else:
+        model_path = download_weights_from_hf_specific(model_name, None, ["*"])
+    vae_config_path = os.path.join(model_path, "vae/config.json")
+    with open(vae_config_path) as f:
+        vae_config = json.load(f)
+        vae_scale_factor = 2 ** (len(vae_config["block_out_channels"]) - 1) if "block_out_channels" in vae_config else 8
+
+    image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor * 2)
+
+    def post_process_func(
+        images: torch.Tensor,
+    ):
+        return image_processor.postprocess(images)
+
+    return post_process_func
+
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: int | None = None,
+    device: str | torch.device | None = None,
+    timesteps: list[int] | None = None,
+    sigmas: list[float] | None = None,
+    **kwargs,
+) -> tuple[torch.Tensor, int]:
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`list[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`list[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class ZImagePipeline(nn.Module):
+    def __init__(
+        self,
+        *,
+        od_config: OmniDiffusionConfig = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.od_config = od_config
+        self._execution_device = get_local_device()
+        model = od_config.model
+        local_files_only = os.path.exists(model)
+        self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+            model, subfolder="scheduler", local_files_only=local_files_only
+        )
+
+        self.text_encoder = AutoModel.from_pretrained(
+            model, subfolder="text_encoder", local_files_only=local_files_only
+        )
+        self.vae = AutoencoderKL.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to(
+            self._execution_device
+        )
+        self.transformer = ZImageTransformer2DModel()
+        self.tokenizer = AutoTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
+
+        self.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+
+    def encode_prompt(
+        self,
+        prompt: str | list[str],
+        device: torch.device | None = None,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: str | list[str] | None = None,
+        prompt_embeds: list[torch.FloatTensor] | None = None,
+        negative_prompt_embeds: torch.FloatTensor | None = None,
+        max_sequence_length: int = 512,
+    ):
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt_embeds = self._encode_prompt(
+            prompt=prompt,
+            device=device,
+            prompt_embeds=prompt_embeds,
+            max_sequence_length=max_sequence_length,
+        )
+
+        if do_classifier_free_guidance:
+            if negative_prompt is None:
+                negative_prompt = ["" for _ in prompt]
+            else:
+                negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            assert len(prompt) == len(negative_prompt)
+            negative_prompt_embeds = self._encode_prompt(
+                prompt=negative_prompt,
+                device=device,
+                prompt_embeds=negative_prompt_embeds,
+                max_sequence_length=max_sequence_length,
+            )
+        else:
+            negative_prompt_embeds = []
+        return prompt_embeds, negative_prompt_embeds
+
+    def _encode_prompt(
+        self,
+        prompt: str | list[str],
+        device: torch.device | None = None,
+        prompt_embeds: list[torch.FloatTensor] | None = None,
+        max_sequence_length: int = 512,
+    ) -> list[torch.FloatTensor]:
+        device = device or self._execution_device
+
+        if prompt_embeds is not None:
+            return prompt_embeds
+
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        for i, prompt_item in enumerate(prompt):
+            messages = [
+                {"role": "user", "content": prompt_item},
+            ]
+            prompt_item = self.tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+                enable_thinking=True,
+            )
+            prompt[i] = prompt_item
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids.to(device)
+        prompt_masks = text_inputs.attention_mask.to(device).bool()
+
+        prompt_embeds = self.text_encoder(
+            input_ids=text_input_ids,
+            attention_mask=prompt_masks,
+            output_hidden_states=True,
+        ).hidden_states[-2]
+
+        embeddings_list = []
+
+        for i in range(len(prompt_embeds)):
+            embeddings_list.append(prompt_embeds[i][prompt_masks[i]])
+
+        return embeddings_list
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        width = 2 * (int(width) // (self.vae_scale_factor * 2))
+
+        shape = (batch_size, num_channels_latents, height, width)
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def joint_attention_kwargs(self):
+        return self._joint_attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    def forward(
+        self,
+        req: OmniDiffusionRequest,
+        prompt: str | list[str] | None = None,
+        height: int | None = None,
+        width: int | None = None,
+        num_inference_steps: int = 50,
+        sigmas: list[float] | None = None,
+        guidance_scale: float = 5.0,
+        cfg_normalization: bool = False,
+        cfg_truncation: float = 1.0,
+        negative_prompt: str | list[str] | None = None,
+        num_images_per_prompt: int | None = 1,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.FloatTensor | None = None,
+        prompt_embeds: list[torch.FloatTensor] | None = None,
+        negative_prompt_embeds: list[torch.FloatTensor] | None = None,
+        output_type: str | None = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: dict[str, Any] | None = None,
+        callback_on_step_end: Callable[[int, int, dict], None] | None = None,
+        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ) -> DiffusionOutput:
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `list[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to 1024):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 1024):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            sigmas (`list[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            cfg_normalization (`bool`, *optional*, defaults to False):
+                Whether to apply configuration normalization.
+            cfg_truncation (`float`, *optional*, defaults to 1.0):
+                The truncation value for configuration.
+            negative_prompt (`str` or `list[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `list[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will be generated by sampling using the supplied random `generator`.
+            prompt_embeds (`list[torch.FloatTensor]`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`list[torch.FloatTensor]`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.ZImagePipelineOutput`] instead of a plain
+                tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`list`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, *optional*, defaults to 512):
+                Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.z_image.ZImagePipelineOutput`] or `tuple`: [`~pipelines.z_image.ZImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the
+            generated images.
+        """
+        prompt = req.prompt
+        negative_prompt = req.negative_prompt
+        height: int = req.height or 1024
+        width: int = req.width or 1024
+        num_inference_steps = req.num_inference_steps or 50
+        generator = req.generator
+        guidance_scale = req.guidance_scale if req.guidance_rescale is not None else guidance_scale
+        req_num_outputs = getattr(req, "num_outputs_per_prompt", None)
+        if req_num_outputs and req_num_outputs > 0:
+            num_images_per_prompt = req_num_outputs
+
+        vae_scale = self.vae_scale_factor * 2
+        if height % vae_scale != 0:
+            raise ValueError(
+                f"Height must be divisible by {vae_scale} (got {height}). "
+                f"Please adjust the height to a multiple of {vae_scale}."
+            )
+        if width % vae_scale != 0:
+            raise ValueError(
+                f"Width must be divisible by {vae_scale} (got {width}). "
+                f"Please adjust the width to a multiple of {vae_scale}."
+            )
+
+        device = self._execution_device
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+        self._cfg_normalization = cfg_normalization
+        self._cfg_truncation = cfg_truncation
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = len(prompt_embeds)
+
+        # If prompt_embeds is provided and prompt is None, skip encoding
+        if prompt_embeds is not None and prompt is None:
+            if self.do_classifier_free_guidance and negative_prompt_embeds is None:
+                raise ValueError(
+                    "When `prompt_embeds` is provided without `prompt`, "
+                    "`negative_prompt_embeds` must also be provided for classifier-free guidance."
+                )
+        else:
+            (
+                prompt_embeds,
+                negative_prompt_embeds,
+            ) = self.encode_prompt(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                device=device,
+                max_sequence_length=max_sequence_length,
+            )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.in_channels
+
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            torch.float32,
+            device,
+            generator,
+            latents,
+        )
+
+        # Repeat prompt_embeds for num_images_per_prompt
+        if num_images_per_prompt > 1:
+            prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)]
+            if self.do_classifier_free_guidance and negative_prompt_embeds:
+                negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)]
+
+        actual_batch_size = batch_size * num_images_per_prompt
+        image_seq_len = (latents.shape[2] // 2) * (latents.shape[3] // 2)
+
+        # 5. Prepare timesteps
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.15),
+        )
+        self.scheduler.sigma_min = 0.0
+        scheduler_kwargs = {"mu": mu}
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+            **scheduler_kwargs,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 6. Denoising loop
+        for i, t in enumerate(timesteps):
+            if self.interrupt:
+                continue
+
+            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+            timestep = t.expand(latents.shape[0])
+            timestep = (1000 - timestep) / 1000
+            # Normalized time for time-aware config (0 at start, 1 at end)
+            t_norm = timestep[0].item()
+
+            # Handle cfg truncation
+            current_guidance_scale = self.guidance_scale
+            if (
+                self.do_classifier_free_guidance
+                and self._cfg_truncation is not None
+                and float(self._cfg_truncation) <= 1
+            ):
+                if t_norm > self._cfg_truncation:
+                    current_guidance_scale = 0.0
+
+            # Run CFG only if configured AND scale is non-zero
+            apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0
+
+            if apply_cfg:
+                latents_typed = latents.to(self.transformer.dtype)
+                latent_model_input = latents_typed.repeat(2, 1, 1, 1)
+                prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds
+                timestep_model_input = timestep.repeat(2)
+            else:
+                latent_model_input = latents.to(self.transformer.dtype)
+                prompt_embeds_model_input = prompt_embeds
+                timestep_model_input = timestep
+
+            latent_model_input = latent_model_input.unsqueeze(2)
+            latent_model_input_list = list(latent_model_input.unbind(dim=0))
+
+            model_out_list = self.transformer(
+                latent_model_input_list,
+                timestep_model_input,
+                prompt_embeds_model_input,
+            )[0]
+
+            if apply_cfg:
+                # Perform CFG
+                pos_out = model_out_list[:actual_batch_size]
+                neg_out = model_out_list[actual_batch_size:]
+
+                noise_pred = []
+                for j in range(actual_batch_size):
+                    pos = pos_out[j].float()
+                    neg = neg_out[j].float()
+
+                    pred = pos + current_guidance_scale * (pos - neg)
+
+                    # Renormalization
+                    if self._cfg_normalization and float(self._cfg_normalization) > 0.0:
+                        ori_pos_norm = torch.linalg.vector_norm(pos)
+                        new_pos_norm = torch.linalg.vector_norm(pred)
+                        max_new_norm = ori_pos_norm * float(self._cfg_normalization)
+                        if new_pos_norm > max_new_norm:
+                            pred = pred * (max_new_norm / new_pos_norm)
+
+                    noise_pred.append(pred)
+
+                noise_pred = torch.stack(noise_pred, dim=0)
+            else:
+                noise_pred = torch.stack([t.float() for t in model_out_list], dim=0)
+
+            noise_pred = noise_pred.squeeze(2)
+            noise_pred = -noise_pred
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred.to(torch.float32), t, latents, return_dict=False)[0]
+            assert latents.dtype == torch.float32
+
+            if callback_on_step_end is not None:
+                callback_kwargs = {}
+                for k in callback_on_step_end_tensor_inputs:
+                    callback_kwargs[k] = locals()[k]
+                callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                latents = callback_outputs.pop("latents", latents)
+                prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = latents.to(self.vae.dtype)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+            # image = self.image_processor.postprocess(image, output_type=output_type)
+
+        return DiffusionOutput(output=image)
+
+    def load_weights(self):
+        self.load_transformer()
+
+    def load_transformer(self):
+        import glob
+        import os
+
+        # Define the weight iterator
+        def weight_iterator(transformer_path):
+            if not os.path.exists(transformer_path):
+                logger.warning(f"Path {transformer_path} does not exist.")
+                return
+
+            # Look for safetensors first
+            safetensors_files = glob.glob(os.path.join(transformer_path, "*.safetensors"))
+            if safetensors_files:
+                try:
+                    from safetensors.torch import load_file
+                except ImportError:
+                    logger.warning("safetensors not installed, cannot load .safetensors files.")
+                    return
+
+                for file_path in safetensors_files:
+                    state_dict = load_file(file_path)
+                    for name, tensor in state_dict.items():
+                        yield name, tensor
+            else:
+                # Fallback to bin
+                bin_files = glob.glob(os.path.join(transformer_path, "*.bin"))
+                for file_path in bin_files:
+                    state_dict = torch.load(file_path)
+                    for name, tensor in state_dict.items():
+                        yield name, tensor
+
+        try:
+            # Get model path from config or download from HF
+            model_name = self.od_config.model
+            if os.path.exists(model_name):
+                model_path = model_name
+            else:
+                model_path = download_weights_from_hf_specific(model_name, None, ["*"])
+
+            transformer_path = os.path.join(model_path, "transformer")
+            self.transformer.load_weights(weight_iterator(transformer_path))
+
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            raise e
@@ -0,0 +1,668 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers.models.attention_dispatch import dispatch_attention_fn
+from torch.nn.utils.rnn import pad_sequence
+from vllm.model_executor.layers.layernorm import RMSNorm
+
+ADALN_EMBED_DIM = 256
+SEQ_MULTI_OF = 32
+
+
+class TimestepEmbedder(nn.Module):
+    def __init__(self, out_size, mid_size=None, frequency_embedding_size=256):
+        super().__init__()
+        if mid_size is None:
+            mid_size = out_size
+        self.mlp = nn.Sequential(
+            nn.Linear(
+                frequency_embedding_size,
+                mid_size,
+                bias=True,
+            ),
+            nn.SiLU(),
+            nn.Linear(
+                mid_size,
+                out_size,
+                bias=True,
+            ),
+        )
+
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half
+        )
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        weight_dtype = self.mlp[0].weight.dtype
+        if weight_dtype.is_floating_point:
+            t_freq = t_freq.to(weight_dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class ZImageAttention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        qk_norm: bool = True,
+        eps: float = 1e-6,
+    ) -> None:
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = dim // num_heads
+        self.qk_norm = qk_norm
+
+        self.to_q = nn.Linear(dim, dim, bias=False)
+        self.to_k = nn.Linear(dim, self.head_dim * num_kv_heads, bias=False)
+        self.to_v = nn.Linear(dim, self.head_dim * num_kv_heads, bias=False)
+        # TODO enable this when we refactor weight loader
+        # self.to_qkv = QKVParallelLinear(
+        #     hidden_size=dim,
+        #     head_size=self.head_dim,
+        #     total_num_heads=num_heads,
+        #     disable_tp=True,
+        # )
+
+        assert qk_norm is True
+        self.norm_q = RMSNorm(self.head_dim, eps=eps)
+        self.norm_k = RMSNorm(self.head_dim, eps=eps)
+
+        self.to_out = nn.ModuleList([nn.Linear(dim, dim, bias=False)])
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        freqs_cis: tuple[torch.Tensor, torch.Tensor] | None = None,
+    ):
+        query = self.to_q(hidden_states)
+        key = self.to_k(hidden_states)
+        value = self.to_v(hidden_states)
+        # qkv, _ = self.to_qkv(hidden_states)
+        # query, key, value = qkv.chunk(3, dim=-1)
+
+        query = query.unflatten(-1, (self.num_heads, -1))
+        key = key.unflatten(-1, (self.num_heads, -1))
+        value = value.unflatten(-1, (self.num_heads, -1))
+
+        query = self.norm_q(query)
+        key = self.norm_k(key)
+
+        def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
+            x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
+            freqs_cis = freqs_cis.unsqueeze(2)
+            x_out = torch.view_as_real(x * freqs_cis).flatten(3)
+            return x_out.type_as(x_in)
+
+        if freqs_cis is not None:
+            query = apply_rotary_emb(query, freqs_cis)
+            key = apply_rotary_emb(key, freqs_cis)
+
+        # Cast to correct dtype
+        dtype = query.dtype
+        query, key = query.to(dtype), key.to(dtype)
+
+        # From [batch, seq_len] to [batch, 1, 1, seq_len] -> broadcast to [batch, heads, seq_len, seq_len]
+        if attention_mask is not None and attention_mask.ndim == 2:
+            attention_mask = attention_mask[:, None, None, :]
+
+        # Compute joint attention
+        hidden_states = dispatch_attention_fn(
+            query,
+            key,
+            value,
+            attn_mask=attention_mask,
+            dropout_p=0.0,
+            is_causal=False,
+        )
+
+        # Reshape back
+        hidden_states = hidden_states.flatten(2, 3)
+        hidden_states = hidden_states.to(dtype)
+
+        hidden_states = self.to_out[0](hidden_states)
+
+        return hidden_states
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim: int, hidden_dim: int):
+        super().__init__()
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def _forward_silu_gating(self, x1, x3):
+        return F.silu(x1) * x3
+
+    def forward(self, x):
+        return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
+
+
+class ZImageTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        layer_id: int,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        norm_eps: float,
+        qk_norm: bool,
+        modulation=True,
+    ):
+        super().__init__()
+        self.dim = dim
+
+        self.attention = ZImageAttention(
+            dim=dim,
+            num_heads=n_heads,
+            num_kv_heads=n_kv_heads,
+            qk_norm=qk_norm,
+            eps=1e-5,
+        )
+
+        self.feed_forward = FeedForward(dim=dim, hidden_dim=int(dim / 3 * 8))
+        self.layer_id = layer_id
+
+        self.attention_norm1 = RMSNorm(dim, eps=norm_eps)
+        self.ffn_norm1 = RMSNorm(dim, eps=norm_eps)
+
+        self.attention_norm2 = RMSNorm(dim, eps=norm_eps)
+        self.ffn_norm2 = RMSNorm(dim, eps=norm_eps)
+
+        self.modulation = modulation
+        if modulation:
+            self.adaLN_modulation = nn.Sequential(
+                nn.Linear(min(dim, ADALN_EMBED_DIM), 4 * dim, bias=True),
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attn_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        adaln_input: Optional[torch.Tensor] = None,
+    ):
+        if self.modulation:
+            assert adaln_input is not None
+            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).unsqueeze(1).chunk(4, dim=2)
+            gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
+            scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
+
+            # Attention block
+            attn_out = self.attention(
+                self.attention_norm1(x) * scale_msa,
+                attention_mask=attn_mask,
+                freqs_cis=freqs_cis,
+            )
+            x = x + gate_msa * self.attention_norm2(attn_out)
+
+            # FFN block
+            x = x + gate_mlp * self.ffn_norm2(
+                self.feed_forward(
+                    self.ffn_norm1(x) * scale_mlp,
+                )
+            )
+        else:
+            # Attention block
+            attn_out = self.attention(
+                self.attention_norm1(x),
+                attention_mask=attn_mask,
+                freqs_cis=freqs_cis,
+            )
+            x = x + self.attention_norm2(attn_out)
+
+            # FFN block
+            x = x + self.ffn_norm2(
+                self.feed_forward(
+                    self.ffn_norm1(x),
+                )
+            )
+
+        return x
+
+
+class FinalLayer(nn.Module):
+    def __init__(self, hidden_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, out_channels, bias=True)
+
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(min(hidden_size, ADALN_EMBED_DIM), hidden_size, bias=True),
+        )
+
+    def forward(self, x, c):
+        scale = 1.0 + self.adaLN_modulation(c)
+        x = self.norm_final(x) * scale.unsqueeze(1)
+        x = self.linear(x)
+        return x
+
+
+class RopeEmbedder:
+    def __init__(
+        self,
+        theta: float = 256.0,
+        axes_dims: list[int] = (16, 56, 56),
+        axes_lens: list[int] = (64, 128, 128),
+    ):
+        self.theta = theta
+        self.axes_dims = axes_dims
+        self.axes_lens = axes_lens
+        assert len(axes_dims) == len(axes_lens), "axes_dims and axes_lens must have the same length"
+        self.freqs_cis = None
+
+    @staticmethod
+    def precompute_freqs_cis(dim: list[int], end: list[int], theta: float = 256.0):
+        with torch.device("cpu"):
+            freqs_cis = []
+            for i, (d, e) in enumerate(zip(dim, end)):
+                freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=torch.float64, device="cpu") / d))
+                timestep = torch.arange(e, device=freqs.device, dtype=torch.float64)
+                freqs = torch.outer(timestep, freqs).float()
+                freqs_cis_i = torch.polar(torch.ones_like(freqs), freqs).to(torch.complex64)  # complex64
+                freqs_cis.append(freqs_cis_i)
+
+            return freqs_cis
+
+    def __call__(self, ids: torch.Tensor):
+        assert ids.ndim == 2
+        assert ids.shape[-1] == len(self.axes_dims)
+        device = ids.device
+
+        if self.freqs_cis is None:
+            self.freqs_cis = self.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta)
+            self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
+        else:
+            # Ensure freqs_cis are on the same device as ids
+            if self.freqs_cis[0].device != device:
+                self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
+
+        result = []
+        for i in range(len(self.axes_dims)):
+            index = ids[:, i]
+            result.append(self.freqs_cis[i][index])
+        return torch.cat(result, dim=-1)
+
+
+class ZImageTransformer2DModel(nn.Module):
+    def __init__(
+        self,
+        all_patch_size=(2,),
+        all_f_patch_size=(1,),
+        in_channels=16,
+        dim=3840,
+        n_layers=30,
+        n_refiner_layers=2,
+        n_heads=30,
+        n_kv_heads=30,
+        norm_eps=1e-5,
+        qk_norm=True,
+        cap_feat_dim=2560,
+        rope_theta=256.0,
+        t_scale=1000.0,
+        axes_dims=[32, 48, 48],
+        axes_lens=[1024, 512, 512],
+    ) -> None:
+        super().__init__()
+        self.dtype = torch.bfloat16
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.all_patch_size = all_patch_size
+        self.all_f_patch_size = all_f_patch_size
+        self.dim = dim
+        self.n_heads = n_heads
+
+        self.rope_theta = rope_theta
+        self.t_scale = t_scale
+        self.gradient_checkpointing = False
+
+        assert len(all_patch_size) == len(all_f_patch_size)
+
+        all_x_embedder = {}
+        all_final_layer = {}
+        for patch_idx, (patch_size, f_patch_size) in enumerate(zip(all_patch_size, all_f_patch_size)):
+            x_embedder = nn.Linear(f_patch_size * patch_size * patch_size * in_channels, dim, bias=True)
+            all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
+
+            final_layer = FinalLayer(dim, patch_size * patch_size * f_patch_size * self.out_channels)
+            all_final_layer[f"{patch_size}-{f_patch_size}"] = final_layer
+
+        self.all_x_embedder = nn.ModuleDict(all_x_embedder)
+        self.all_final_layer = nn.ModuleDict(all_final_layer)
+        self.noise_refiner = nn.ModuleList(
+            [
+                ZImageTransformerBlock(
+                    1000 + layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    norm_eps,
+                    qk_norm,
+                    modulation=True,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+        self.context_refiner = nn.ModuleList(
+            [
+                ZImageTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    norm_eps,
+                    qk_norm,
+                    modulation=False,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+        self.t_embedder = TimestepEmbedder(min(dim, ADALN_EMBED_DIM), mid_size=1024)
+        self.cap_embedder = nn.Sequential(
+            RMSNorm(cap_feat_dim, eps=norm_eps),
+            nn.Linear(cap_feat_dim, dim, bias=True),
+        )
+
+        self.x_pad_token = nn.Parameter(torch.empty((1, dim)))
+        self.cap_pad_token = nn.Parameter(torch.empty((1, dim)))
+
+        self.layers = nn.ModuleList(
+            [
+                ZImageTransformerBlock(layer_id, dim, n_heads, n_kv_heads, norm_eps, qk_norm)
+                for layer_id in range(n_layers)
+            ]
+        )
+        self.axes_dims = axes_dims
+        self.axes_lens = axes_lens
+
+        self.rope_embedder = RopeEmbedder(theta=rope_theta, axes_dims=axes_dims, axes_lens=axes_lens)
+
+    def unpatchify(self, x: list[torch.Tensor], size: list[tuple], patch_size, f_patch_size) -> list[torch.Tensor]:
+        pH = pW = patch_size
+        pF = f_patch_size
+        bsz = len(x)
+        assert len(size) == bsz
+        for i in range(bsz):
+            F, H, W = size[i]
+            ori_len = (F // pF) * (H // pH) * (W // pW)
+            # "f h w pf ph pw c -> c (f pf) (h ph) (w pw)"
+            x[i] = (
+                x[i][:ori_len]
+                .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
+                .permute(6, 0, 3, 1, 4, 2, 5)
+                .reshape(self.out_channels, F, H, W)
+            )
+        return x
+
+    @staticmethod
+    def create_coordinate_grid(size, start=None, device=None):
+        if start is None:
+            start = (0 for _ in size)
+
+        axes = [torch.arange(x0, x0 + span, dtype=torch.int32, device=device) for x0, span in zip(start, size)]
+        grids = torch.meshgrid(axes, indexing="ij")
+        return torch.stack(grids, dim=-1)
+
+    def patchify_and_embed(
+        self,
+        all_image: list[torch.Tensor],
+        all_cap_feats: list[torch.Tensor],
+        patch_size: int,
+        f_patch_size: int,
+    ):
+        pH = pW = patch_size
+        pF = f_patch_size
+        device = all_image[0].device
+
+        all_image_out = []
+        all_image_size = []
+        all_image_pos_ids = []
+        all_image_pad_mask = []
+        all_cap_pos_ids = []
+        all_cap_pad_mask = []
+        all_cap_feats_out = []
+
+        for i, (image, cap_feat) in enumerate(zip(all_image, all_cap_feats)):
+            ### Process Caption
+            cap_ori_len = len(cap_feat)
+            cap_padding_len = (-cap_ori_len) % SEQ_MULTI_OF
+            # padded position ids
+            cap_padded_pos_ids = self.create_coordinate_grid(
+                size=(cap_ori_len + cap_padding_len, 1, 1),
+                start=(1, 0, 0),
+                device=device,
+            ).flatten(0, 2)
+            all_cap_pos_ids.append(cap_padded_pos_ids)
+            # pad mask
+            all_cap_pad_mask.append(
+                torch.cat(
+                    [
+                        torch.zeros((cap_ori_len,), dtype=torch.bool, device=device),
+                        torch.ones((cap_padding_len,), dtype=torch.bool, device=device),
+                    ],
+                    dim=0,
+                )
+            )
+            # padded feature
+            cap_padded_feat = torch.cat(
+                [cap_feat, cap_feat[-1:].repeat(cap_padding_len, 1)],
+                dim=0,
+            )
+            all_cap_feats_out.append(cap_padded_feat)
+
+            ### Process Image
+            C, F, H, W = image.size()
+            all_image_size.append((F, H, W))
+            F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
+
+            image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
+            # "c f pf h ph w pw -> (f h w) (pf ph pw c)"
+            image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
+
+            image_ori_len = len(image)
+            image_padding_len = (-image_ori_len) % SEQ_MULTI_OF
+
+            image_ori_pos_ids = self.create_coordinate_grid(
+                size=(F_tokens, H_tokens, W_tokens),
+                start=(cap_ori_len + cap_padding_len + 1, 0, 0),
+                device=device,
+            ).flatten(0, 2)
+            image_padding_pos_ids = (
+                self.create_coordinate_grid(
+                    size=(1, 1, 1),
+                    start=(0, 0, 0),
+                    device=device,
+                )
+                .flatten(0, 2)
+                .repeat(image_padding_len, 1)
+            )
+            image_padded_pos_ids = torch.cat([image_ori_pos_ids, image_padding_pos_ids], dim=0)
+            all_image_pos_ids.append(image_padded_pos_ids)
+            # pad mask
+            all_image_pad_mask.append(
+                torch.cat(
+                    [
+                        torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
+                        torch.ones((image_padding_len,), dtype=torch.bool, device=device),
+                    ],
+                    dim=0,
+                )
+            )
+            # padded feature
+            image_padded_feat = torch.cat([image, image[-1:].repeat(image_padding_len, 1)], dim=0)
+            all_image_out.append(image_padded_feat)
+
+        return (
+            all_image_out,
+            all_cap_feats_out,
+            all_image_size,
+            all_image_pos_ids,
+            all_cap_pos_ids,
+            all_image_pad_mask,
+            all_cap_pad_mask,
+        )
+
+    def forward(
+        self,
+        x: list[torch.Tensor],
+        t,
+        cap_feats: list[torch.Tensor],
+        patch_size=2,
+        f_patch_size=1,
+    ):
+        assert patch_size in self.all_patch_size
+        assert f_patch_size in self.all_f_patch_size
+
+        bsz = len(x)
+        device = x[0].device
+        t = t * self.t_scale
+        t = self.t_embedder(t)
+
+        (
+            x,
+            cap_feats,
+            x_size,
+            x_pos_ids,
+            cap_pos_ids,
+            x_inner_pad_mask,
+            cap_inner_pad_mask,
+        ) = self.patchify_and_embed(x, cap_feats, patch_size, f_patch_size)
+
+        # x embed & refine
+        x_item_seqlens = [len(_) for _ in x]
+        assert all(_ % SEQ_MULTI_OF == 0 for _ in x_item_seqlens)
+        x_max_item_seqlen = max(x_item_seqlens)
+
+        x = torch.cat(x, dim=0)
+        x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](x)
+
+        # Match t_embedder output dtype to x for layerwise casting compatibility
+        adaln_input = t.type_as(x)
+        x[torch.cat(x_inner_pad_mask)] = self.x_pad_token
+        x = list(x.split(x_item_seqlens, dim=0))
+        x_freqs_cis = list(self.rope_embedder(torch.cat(x_pos_ids, dim=0)).split(x_item_seqlens, dim=0))
+
+        x = pad_sequence(x, batch_first=True, padding_value=0.0)
+        x_freqs_cis = pad_sequence(x_freqs_cis, batch_first=True, padding_value=0.0)
+        x_attn_mask = torch.zeros((bsz, x_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(x_item_seqlens):
+            x_attn_mask[i, :seq_len] = 1
+
+        for layer in self.noise_refiner:
+            x = layer(x, x_attn_mask, x_freqs_cis, adaln_input)
+
+        # cap embed & refine
+        cap_item_seqlens = [len(_) for _ in cap_feats]
+        assert all(_ % SEQ_MULTI_OF == 0 for _ in cap_item_seqlens)
+        cap_max_item_seqlen = max(cap_item_seqlens)
+
+        cap_feats = torch.cat(cap_feats, dim=0)
+        cap_feats = self.cap_embedder(cap_feats)
+        cap_feats[torch.cat(cap_inner_pad_mask)] = self.cap_pad_token
+        cap_feats = list(cap_feats.split(cap_item_seqlens, dim=0))
+        cap_freqs_cis = list(self.rope_embedder(torch.cat(cap_pos_ids, dim=0)).split(cap_item_seqlens, dim=0))
+
+        cap_feats = pad_sequence(cap_feats, batch_first=True, padding_value=0.0)
+        cap_freqs_cis = pad_sequence(cap_freqs_cis, batch_first=True, padding_value=0.0)
+        cap_attn_mask = torch.zeros((bsz, cap_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(cap_item_seqlens):
+            cap_attn_mask[i, :seq_len] = 1
+
+        for layer in self.context_refiner:
+            cap_feats = layer(cap_feats, cap_attn_mask, cap_freqs_cis)
+
+        # unified
+        unified = []
+        unified_freqs_cis = []
+        for i in range(bsz):
+            x_len = x_item_seqlens[i]
+            cap_len = cap_item_seqlens[i]
+            unified.append(torch.cat([x[i][:x_len], cap_feats[i][:cap_len]]))
+            unified_freqs_cis.append(torch.cat([x_freqs_cis[i][:x_len], cap_freqs_cis[i][:cap_len]]))
+        unified_item_seqlens = [a + b for a, b in zip(cap_item_seqlens, x_item_seqlens)]
+        assert unified_item_seqlens == [len(_) for _ in unified]
+        unified_max_item_seqlen = max(unified_item_seqlens)
+
+        unified = pad_sequence(unified, batch_first=True, padding_value=0.0)
+        unified_freqs_cis = pad_sequence(unified_freqs_cis, batch_first=True, padding_value=0.0)
+        unified_attn_mask = torch.zeros((bsz, unified_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(unified_item_seqlens):
+            unified_attn_mask[i, :seq_len] = 1
+
+        for layer in self.layers:
+            unified = layer(unified, unified_attn_mask, unified_freqs_cis, adaln_input)
+
+        unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)
+        unified = list(unified.unbind(dim=0))
+        x = self.unpatchify(unified, x_size, patch_size, f_patch_size)
+
+        return x, {}
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        weights_map: dict[str, torch.Tensor] = {name: tensor for name, tensor in weights}
+        params_dict = dict(self.named_parameters())
+
+        for name, loaded_weight in weights_map.items():
+            # In ZImageAttention, to_q, to_k, to_v are separate.
+            # The weight names are like '...attention.to_q.weight'.
+            # The logic from qwen_image_transformer for stacking is not needed here.
+            # We can do a direct mapping.
+
+            # The names in the state dict seem to match the model structure.
+            # Let's try a direct load.
+            if name in params_dict:
+                param = params_dict[name]
+                with torch.no_grad():
+                    param.copy_(loaded_weight)
+            else:
+                # The provided weight list might use a different naming convention.
+                # For example, `layers.0.attention.to_out.0.weight` vs `...`
+                # Let's try to handle simple cases.
+
+                # Replace dots with hyphens for ModuleDict keys
+                # e.g. all_x_embedder.2-1.weight -> all_x_embedder.2-1.weight
+                # The names seem to match already.
+
+                # If there are any specific mismatches, they can be handled here.
+                # For now, we assume a direct mapping and warn if a weight is not found.
+                import logging
+
+                logging.warning(f"Weight {name} not found in model parameters.")
@@ -12,6 +12,11 @@ _DIFFUSION_MODELS = {
        "pipeline_qwen_image",
        "QwenImagePipeline",
    ),
+    "ZImagePipeline": (
+        "z_image",
+        "pipeline_z_image",
+        "ZImagePipeline",
+    ),
 }


@@ -24,6 +29,13 @@ def initialize_model(
        module = importlib.import_module(module_name)
        model_class = getattr(module, cls_name)
        model = model_class(od_config=od_config, prefix=mod_relname)
+
+        # Configure VAE memory optimization settings from config
+        if hasattr(model.vae, "use_slicing"):
+            model.vae.use_slicing = od_config.vae_use_slicing
+        if hasattr(model.vae, "use_tiling"):
+            model.vae.use_tiling = od_config.vae_use_tiling
+
        return model
    else:
        raise ValueError(f"Model class {od_config.model_class_name} not found in diffusion model registry.")
@@ -34,6 +46,7 @@ _DIFFUSION_POST_PROCESS_FUNCS = {
    # `post_process_func` function must be placed in {mod_folder}/{mod_relname}.py,
    # where mod_folder and mod_relname are  defined and mapped using `_DIFFUSION_MODELS` via the `arch` key
    "QwenImagePipeline": "get_qwen_image_post_process_func",
+    "ZImagePipeline": "get_post_process_func",
 }


@@ -1,12 +1,13 @@
 from functools import lru_cache

-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from vllm.logger import init_logger

 logger = init_logger(__name__)


 def load_diffusers_config(model_name) -> dict:
+    from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+
    config = DiffusionPipeline.load_config(model_name)
    return config

@@ -12,6 +12,7 @@ from vllm.distributed.parallel_state import (
    initialize_model_parallel,
 )
 from vllm.logger import init_logger
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype

 from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
 from vllm_omni.diffusion.registry import initialize_model
@@ -61,9 +62,10 @@ class GPUWorker:
        initialize_model_parallel(tensor_model_parallel_size=world_size)

        with device:
-            self.pipeline = initialize_model(self.od_config)
-            self.pipeline.load_weights()
-            self.pipeline.eval()
+            with set_default_torch_dtype(self.od_config.dtype):
+                self.pipeline = initialize_model(self.od_config)
+                self.pipeline.load_weights()
+                self.pipeline.eval()
        logger.info(f"Worker {self.rank}: Initialized device, model, and distributed environment.")
        logger.info(f"Worker {self.rank}: Model loaded successfully.")

@@ -129,7 +131,7 @@ class WorkerProc:
        """
        Receive requests from broadcast queue
        """
-        return self.mq.dequeue()
+        return self.mq.dequeue(indefinite=True)

    # TODO: queueing, cancellation
    def worker_busy_loop(self) -> None:
@@ -5,13 +5,15 @@ import os

 import torch
 import zmq
-from vllm.config import VllmConfig, set_current_vllm_config
+from transformers import PretrainedConfig
+from vllm.config import ModelConfig, VllmConfig, set_current_vllm_config
 from vllm.distributed.device_communicators.shm_broadcast import MessageQueue
 from vllm.distributed.parallel_state import (
    init_distributed_environment,
    initialize_model_parallel,
 )
 from vllm.logger import init_logger
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype

 from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
 from vllm_omni.diffusion.registry import initialize_model
@@ -53,7 +55,12 @@ class NPUWorker:
        torch.npu.set_device(device)

        # hack
-        vllm_config = VllmConfig()
+        # set hf_config to a fake one to avolid get attr error
+        class _FakePretrainedConfig(PretrainedConfig):
+            def __getattr__(self, name):
+                return "fake"
+
+        vllm_config = VllmConfig(model_config=ModelConfig(hf_config=_FakePretrainedConfig()))
        vllm_config.parallel_config.tensor_parallel_size = self.od_config.num_gpus
        set_current_vllm_config(vllm_config)

@@ -61,9 +68,10 @@ class NPUWorker:
        initialize_model_parallel(tensor_model_parallel_size=world_size)

        with device:
-            self.pipeline = initialize_model(self.od_config)
-            self.pipeline.load_weights()
-            self.pipeline.eval()
+            with set_default_torch_dtype(self.od_config.dtype):
+                self.pipeline = initialize_model(self.od_config)
+                self.pipeline.load_weights()
+                self.pipeline.eval()
        logger.info(f"Worker {self.rank}: Initialized device, model, and distributed environment.")
        logger.info(f"Worker {self.rank}: Model loaded successfully.")

@@ -129,7 +137,7 @@ class NPUWorkerProc:
        """
        Receive requests from broadcast queue
        """
-        return self.mq.dequeue()
+        return self.mq.dequeue(indefinite=True)

    # TODO: queueing, cancellation
    def worker_busy_loop(self) -> None:
@@ -90,7 +90,7 @@ class AsyncOmni(EngineClient):
        stage_configs_path: Optional[str] = None,
        log_stats: bool = False,
        log_file: Optional[str] = None,
-        init_sleep_seconds: int = 20,
+        init_sleep_seconds: int = 30,
        shm_threshold_bytes: int = 65536,
        batch_timeout: int = 10,
        init_timeout: int = 60000,
@@ -1,16 +1,38 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import os

 from vllm_omni.diffusion.omni_diffusion import OmniDiffusion
 from vllm_omni.diffusion.utils.hf_utils import is_diffusion_model
 from vllm_omni.entrypoints.omni_llm import OmniLLM


+def _dummy_snapshot_download(model_id):
+    return model_id
+
+
+def omni_snapshot_download(model_id) -> str:
+    # TODO: this is just a workaround for quickly use modelscope, we should support
+    # modelscope in weight loading feature instead of using `snapshot_download`
+    if os.environ.get("VLLM_USE_MODELSCOPE", False):
+        from modelscope.hub.snapshot_download import snapshot_download
+
+        return snapshot_download(model_id)
+    else:
+        return _dummy_snapshot_download(model_id)
+
+
 class Omni:
    """Unified entrypoint for both LLM and Diffusion models for better usability."""

    def __init__(self, *args, **kwargs):
        model = args[0] if args else kwargs.get("model", "")
+        assert model != "", "Null model id detected, please specify a model id."
+        model = omni_snapshot_download(model)
+        if args:
+            args[0] = model
+        elif kwargs.get("model", "") != "":
+            kwargs["model"] = model
        if is_diffusion_model(model):
            self.instance: OmniLLM | OmniDiffusion = OmniDiffusion(*args, **kwargs)
        else:
@@ -1,4 +1,6 @@
 # stage config for running qwen2.5-omni with architecture of OmniLLM.
+
+# The following config has been verified on 1x H100-80G GPU.
 stage_args:
  - stage_id: 0
    runtime:
@@ -3,24 +3,25 @@
 # Stage 1: Talker (text embeddings → 8-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"
+      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.scheduler.OmniScheduler
-      gpu_memory_utilization: 0.8
+      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  # Commented out - using single GPU for thinker
+      tensor_parallel_size: 2
    final_output: true
    final_output_type: text
    is_comprehension: true
@@ -42,7 +43,7 @@ stage_args:
       model_arch: Qwen3OmniMoeForConditionalGeneration
       worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
       scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
-       gpu_memory_utilization: 0.5
+       gpu_memory_utilization: 0.3
       enforce_eager: true
       trust_remote_code: true
       engine_output_type: latent  # Output codec codes for code2wav
@@ -65,7 +66,7 @@ stage_args:

  - stage_id: 2
    runtime:
-      devices: "1"
+      devices: "0"
      max_batch_size: 1
    engine_args:
      model_stage: code2wav
@@ -15,6 +15,9 @@ from vllm_omni.model_executor.layers.mrope import MRotaryEmbedding
 from vllm_omni.request import OmniRequest

 for module_name, module in sys.modules.items():
+    # only do patch on module of vllm, pass others
+    if "vllm" not in module_name:
+        continue
    if hasattr(module, "EngineCoreOutput") and module.EngineCoreOutput == _OriginalEngineCoreOutput:
        module.EngineCoreOutput = OmniEngineCoreOutput
    if hasattr(module, "EngineCoreOutputs") and module.EngineCoreOutputs == _OriginalEngineCoreOutputs:
Author	SHA1	Message	Date
Kevin H. Luu	edda01380d	p Signed-off-by: Kevin H. Luu <khluu000@gmail.com>	2025-12-04 02:17:07 -08:00
Kevin H. Luu	205238f7e3	p Signed-off-by: Kevin H. Luu <khluu000@gmail.com>	2025-12-04 02:08:14 -08:00
Kevin H. Luu	9dc7a70ca6	p Signed-off-by: Kevin H. Luu <khluu000@gmail.com>	2025-12-04 02:04:51 -08:00
Kevin H. Luu	ebd17001c4	p Signed-off-by: Kevin H. Luu <khluu000@gmail.com>	2025-12-04 01:59:29 -08:00
Kevin H. Luu	e63c151f86	p Signed-off-by: Kevin H. Luu <khluu000@gmail.com>	2025-12-04 01:55:18 -08:00
AliciaandRoger Wang	c0b88a87ba	[Doc] Add installation part for pre built docker. (#141 ) Signed-off-by: WANG Cong <115451386+congw729@users.noreply.github.com> Signed-off-by: Alicia Wang <115451386+congw729@users.noreply.github.com> Signed-off-by: Alicia <115451386+congw729@users.noreply.github.com> Signed-off-by: Roger Wang <hey@rogerw.io> Co-authored-by: Roger Wang <hey@rogerw.io>	2025-12-04 00:04:18 -08:00
Canlin Guo	863cdb82b6	[Docs] remove Ascend word to make docs general (#190 ) Signed-off-by: gcanlin <canlinguosdu@gmail.com>	2025-12-03 23:21:11 -08:00
knlnguyen1802andn00909098	f73791d246	[Bugfix] Init early toch cuda (#185 ) Signed-off-by: n00909098 <nguyen.kha.long@huawei.com> Co-authored-by: n00909098 <nguyen.kha.long@huawei.com>	2025-12-04 11:38:52 +08:00
Gao Han	0e07a0ae9e	[Docs] Supplement volunteers and faq docs (#182 ) Signed-off-by: Gaohan123 <hgaoaf@connect.ust.hk>	2025-12-04 10:37:00 +08:00
汪志鹏andZhipeng Wang	598d26580e	[Doc] change `vllm_omni` to `vllm-omni` (#177 ) Signed-off-by: Zhipeng Wang <zhipeng.wang@rakuten.com> Co-authored-by: Zhipeng Wang <zhipeng.wang@rakuten.com>	2025-12-03 17:43:59 +08:00
Cyrus Leung	574e1fb68d	[Doc] Update Security Advisory link (#173 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2025-12-03 12:39:08 +08:00
wjcwjc77 wangjiangcheng Gao Han	8ed9716d5b	[Doc] Fix wrong examples URLs (#166 ) Signed-off-by: wangjiangcheng <wangjiangcheng@kuaishou.com> Co-authored-by: wangjiangcheng <wangjiangcheng@kuaishou.com> Co-authored-by: Gao Han <gaohan19@huawei.com>	2025-12-03 03:25:58 +00:00
Jiangyun ZhuandSamit	7a6d2fe3f6	[diffusion] z-image support (#149 ) Signed-off-by: zjy0516 <riverclouds.zhu@qq.com> Co-authored-by: Samit <285365963@qq.com>	2025-12-03 02:16:36 +00:00
Samit	5a0d7882c5	[Misc] Qwen-Omni support offline inference with local files (#167 ) Signed-off-by: samithuang <285365963@qq.com>	2025-12-02 19:05:45 +08:00
Roger Wang	55ea6ef7e9	[Misc] Update link in issue template (#155 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2025-12-02 08:20:37 +08:00
Roger Wang	9fe730a44b	[Misc] update image reference for PyPI (#150 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2025-12-01 09:37:29 -08:00
Mengqing Cao	00b1a9a1a0	[Misc] Temporarily support downloading models from ModelScope by snapshot download (#132 ) Signed-off-by: MengqingCao <cmq0113@163.com>	2025-12-01 17:13:23 +00:00
Canlin Guo	7e60c49ce4	[Bugfix] Add Fake VllmConfig on NPU and add slicing/tiling args in Qwen-Image (#145 ) Signed-off-by: gcanlin <canlinguosdu@gmail.com>	2025-12-01 16:40:01 +00:00
Samit	98d94e0e3d	[Bugfix] Fix Qwen3-Omni gradio audio input bug (#147 ) Signed-off-by: samithuang <285365963@qq.com>	2025-12-01 13:52:31 +00:00
Roger Wang	510aeb7da4	[Misc] Misc description updates (#146 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2025-12-01 12:09:24 +00:00
Roger Wang	5939089c98	[Doc] Logo (#143 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2025-12-01 02:26:39 -08:00
Samit	1a23d70a97	[Misc][WIP] Support qwen-omni online inference with local video/audio/image path (#131 ) Signed-off-by: samithuang <285365963@qq.com>	2025-12-01 00:30:22 -08:00
Roger Wang	a6c5a6aca4	[Doc] Update developer & user channel (#138 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2025-12-01 00:25:44 -08:00
Gao Han	d53155f45c	[Bugfix] Fix the curl bug of qwen3-omni and doc errors (#135 ) Signed-off-by: Gaohan123 <gaohan19@huawei.com>	2025-12-01 08:01:54 +00:00
youkaichao	e8fb85d38c	Update README with vllm-omni blogpost link (#137 ) Signed-off-by: youkaichao <youkaichao@gmail.com>	2025-11-30 23:31:32 -08:00
Samit	f378f80430	[Bugfix] Fix redundant shm broadcast warnings in diffusion workers (#133 ) Signed-off-by: samithuang <285365963@qq.com>	2025-11-30 23:17:56 -08:00
Roger Wang	a1914e1176	[Doc] Fix arch pic reference (#136 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2025-11-30 23:17:21 -08:00
Roger Wang	3b12ed9cf4	[Doc] Cleanup reference to deleted files (#134 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2025-11-30 22:50:21 -08:00
Roger Wang	c852267a1a	[Misc] Update default stage config for qwen3-omni (#124 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2025-11-30 20:01:20 -08:00
Cyrus Leung	2c5d0481b3	[Doc] Fix outdated CONTRIBUTING link (#127 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2025-12-01 11:11:33 +08:00