typo

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

.buildkite/pipeline.yml

@@ -1,23 +1,6 @@
 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
-

.gitignore

@@ -156,6 +156,9 @@ cython_debug/
 # cursor
 .cursor/
 
+# docker
+docker/
+
 # scripts
 /scripts/
 

docker/Dockerfile.ci

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

docs/community/volunteers.md

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

docs/getting_started/installation/gpu.md

@@ -37,7 +37,9 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr
 ## Set up using Docker
 
 ### 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.
 
-=== "NVIDIA CUDA"
-
-    --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:pre-built-images"
+!!! tip
+    You can use this docker image to serve models the same way you would do 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.

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

@@ -73,24 +73,3 @@ 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]

docs/mkdocs/stylesheets/extra.css

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

docs/models/supported_models.md

@@ -23,7 +23,7 @@ th {
 |`ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` |
 
 
-## List of Supported Models for NPU
+## List of Supported Models for Ascend NPU
 
 <style>
 th {

docs/usage/faq.md

@@ -11,15 +11,3 @@ 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!

docs/user_guide/examples/offline_inference/qwen2_5_omni.md

@@ -8,7 +8,6 @@ Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inf
 Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/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
@@ -27,7 +26,6 @@ Then run the command below.
 ```bash
 bash run_multiple_prompts.sh
 ```
-
 ### Single Prompt
 Get into the example folder
 ```bash
@@ -38,34 +36,6 @@ 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.

docs/user_guide/examples/offline_inference/qwen3_omni.md

@@ -8,7 +8,6 @@ Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inf
 Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/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](https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen2_5_omni/README.md)
 Get into the example folder
@@ -33,26 +32,6 @@ 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.

vllm_omni/diffusion/utils/hf_utils.py

@@ -1,13 +1,12 @@
 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