update

Signed-off-by: Roger Wang <hey@rogerw.io>
2025-12-01 17:33:34 +00:00
57 changed files with 1191 additions and 2484 deletions
@@ -1,63 +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:$BUILDKITE_COMMIT"
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
-    agents:
-      queue: "cpu_queue_premerge_us_east_1"
-
  - label: "Simple Unit Test"
    commands:
      - ".buildkite/scripts/simple_test.sh"
    agents:
      queue: "cpu_queue_premerge"
-  - label: "Diffusion Model Test"
-    depends_on: image-build
-    commands:
-      - pytest -s -v tests/single_stage/test_diffusion_model.py
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-  - label: "Large Model Test (H100)"
-    depends_on: image-build
-    agents:
-      queue: mithril-h100-pool
-    commands:
-      - pytest -s -v tests/single_stage/test_diffusion_model.py # update this to your actual test file
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
@@ -1,5 +1,5 @@
 blank_issues_enabled: false
 contact_links:
  - name: Questions
-    url: https://slack.vllm.ai
-    about: Ask questions and discuss with other vLLM-Omni community members in our #sig-omni slack channel.
+    url: https://github.com/vllm-project/vllm-omni/discussions
+    about: Ask questions and discuss with other vLLM-Omni community members
@@ -156,6 +156,9 @@ cython_debug/
 # cursor
 .cursor/

+# docker
+docker/
+
 # scripts
 /scripts/

@@ -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 []
@@ -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-omni/security/advisories) feature
+- For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature
@@ -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!
@@ -23,7 +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]"
 ```

@@ -16,7 +16,7 @@ vLLM-Omni is a multi-modality extension for vLLM that supports non-autoregressiv

 API Server --> OmniLLM/AsyncOmni (New, including multi engines) --> LLMEngine/AsyncLLM --> Engine Core
 --> Scheduler (New one for DiT) --> Executor (New one for diffusers) --> Worker (New one for DiT)
- --> ModelRunner (New one for AR hiddenstate, New one for DiT) --> RequestState --> OutputProcessor (New one for final multimodal output)
+ --> ModelRunner (New one for AR hiddenstate, New one for DiT) --> RequestState --> OutputProcessoer (New one for final multimodal output)

 ## Core Components (Ordered by Data Flow)

@@ -137,7 +137,7 @@ class OmniLLM(LLM):
            prompt_str, engine_request, tokenization_kwargs = self._process_stage_inputs(stage_config, **stage_args)

            # Add inputs to Engine
-            stage_engine.add_request(request_id, prompt_str, tokenization_kwargs)
+            stage_engine.add_request(requesy_id, prompt_str, tokenization_kwargs)
            # Run Engine
            stage_output = stage_engine.step()

@@ -157,7 +157,7 @@ class OmniLLM(LLM):
            return self._process_ar_inputs(**stage_args)
        elif stage_config.engine_type == "DiT":
            return self._process_dit_inputs(**stage_args)
-        else:
+        else
            raise NotImplementedError

    def _process_dit_inputs(**stage_args)-> Any:
@@ -1,6 +1,6 @@
 # Installation

-vLLM-Omni supports the following hardware platforms:
+vLLM supports the following hardware platforms:

 - [GPU](gpu.md)
    - [NVIDIA CUDA](gpu.md)
@@ -37,7 +37,4 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr
 ## Set up using Docker

 ### Pre-built images
-
-=== "NVIDIA CUDA"
-
-    --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:pre-built-images"
+To be released... Stay tuned!
@@ -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 .
 ```

@@ -73,24 +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]
@@ -173,7 +173,7 @@ class Example:
            gh_file = gh_file.relative_to(ROOT_DIR)

            # Make GitHub URL
-            url = "https://github.com/vllm-project/vllm-omni/"
+            url = "https://github.com/vllm-project/vllm/"
            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-omni/"
+        url = "https://github.com/vllm-project/vllm/"
        url += "tree/main" if self.path.is_dir() else "blob/main"
        content += f"Source <{url}/{self.path.relative_to(ROOT_DIR)}>.\n\n"

@@ -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;
 }

@@ -20,10 +20,9 @@ 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 NPU
+## List of Supported Models for Ascend NPU

 <style>
 th {
@@ -2,7 +2,7 @@

 > Q: How many chips do I need to infer a model in vLLM-Omni?

-A: Now, we support natively disaggregated deployment for different model stages within a model. There is a restriction that one chip can only have one AutoRegressive model stage. This is because the unified KV cache management of vLLM. Stages of other types can coexist within a chip. The restriction will be resolved in later version.
+A: Now, we support natively disaggregated deployment for different model stages within a model. There is a restriction that one chip can only has one AutoRegressive model stage. This is because the unified KV cache management of vLLM. Stages of other types can coexist with in a chip. The restriction will be resolved in later version.

 > Q: When trying to run examples, I encounter error about backend of librosa or soundfile. How to solve it?

@@ -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 none of these satisfy your demand and it is urgent, please find these [volunteers](https://docs.vllm.ai/projects/vllm-omni/en/latest/community/volunteers/) for help.
-
-> Q: Does vLLM-Omni support AWQ or any other quantization?
-
-A: vLLM-Omni partitions model into several stages. For AR stages, it will reuse main logic of LLMEngine in vLLM. So current quantization supported in vLLM should be also supported in vLLM-Omni for them. But systematic verification is ongoing. For quantization for DiffusionEngine, we are working on it. Please stay tuned and welcome contribution!
-
-> Q: Does vLLM-Omni support multimodal streaming input and output?
-
-A: Not yet. We already put it on the [Roadmap](https://github.com/vllm-project/vllm-omni/issues/165). Please stay tuned!
@@ -1,14 +1,13 @@
 # Offline Example of vLLM-Omni for Qwen2.5-Omni

-Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen2_5_omni>.
+Source <https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen2_5_omni>.


 ## 🛠️ Installation

-Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
+Please refer to [README.md](https://github.com/vllm-project/vllm/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.
@@ -1,16 +1,15 @@
 # Offline Example of vLLM-Omni for Qwen3-Omni

-Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen3_omni>.
+Source <https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen3_omni>.


 ## 🛠️ Installation

-Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
+Please refer to [README.md](https://github.com/vllm-project/vllm/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)
+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)
 Get into the example folder
 ```bash
 cd examples/offline_inference/qwen3_omni
@@ -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.
@@ -1,6 +1,6 @@
 # Qwen-Image Offline Inference

-Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen_image>.
+Source <https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen_image>.


 This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Image` using vLLM-Omni:
@@ -1,16 +1,15 @@
 # Online serving Example of vLLM-Omni for Qwen2.5-Omni

-Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen2_5_omni>.
+Source <https://github.com/vllm-project/vllm/tree/main/examples/online_serving/qwen2_5_omni>.


 ## 🛠️ Installation

-Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
+Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/README.md)

 ## Run examples (Qwen2.5-Omni)

-### Launch the Server
-
+Launch the server
 ```bash
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```
@@ -20,59 +19,29 @@ 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
 ```

-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
-
+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.
@@ -87,22 +56,25 @@ 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:

- `--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)
+### Options

-### FAQ
+You can customize its basic launch parameters:

-If you encounter error about backend of librosa, try to install ffmpeg with command below.
-```
-sudo apt update
-sudo apt install ffmpeg
+```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
 ```

+- `--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"
@@ -1,16 +1,15 @@
 # Online serving Example of vLLM-Omni for Qwen3-omni

-Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen3_omni>.
+Source <https://github.com/vllm-project/vllm/tree/main/examples/online_serving/qwen3_omni>.


 ## 🛠️ Installation

-Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
+Please refer to [README.md](https://github.com/vllm-project/vllm/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
 ```
@@ -20,59 +19,21 @@ 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
 ```

-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
-
+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.
@@ -96,9 +57,23 @@ 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.


-The gradio script supports the following arguments:
+### 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
+```

 - `--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)
@@ -5,7 +5,6 @@
 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
@@ -24,7 +23,6 @@ Then run the command below.
 ```bash
 bash run_multiple_prompts.sh
 ```
-
 ### Single Prompt
 Get into the example folder
 ```bash
@@ -35,34 +33,6 @@ 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.
@@ -6,15 +6,12 @@ with the correct prompt format on Qwen2.5-Omni
 """

 import os
-from typing import NamedTuple, Optional
+from typing import NamedTuple

-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, video_to_ndarrays
+from vllm.assets.video import VideoAsset
 from vllm.multimodal.image import convert_image_mode
 from vllm.sampling_params import SamplingParams
 from vllm.utils import FlexibleArgumentParser
@@ -57,13 +54,7 @@ def get_text_query(question: str = None) -> 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:
+def get_mixed_modalities_query() -> 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"
@@ -73,49 +64,20 @@ def get_mixed_modalities_query(
        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": audio_data,
-                "image": image_data,
-                "video": video_frames,
+                "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,
            },
        },
        limit_mm_per_prompt={"audio": 1, "image": 1, "video": 1},
    )


-def get_use_audio_in_video_query(
-    video_path: Optional[str] = None, num_frames: int = 16, sampling_rate: int = 16000
-) -> QueryResult:
+def get_use_audio_in_video_query() -> 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"
@@ -123,24 +85,14 @@ def get_use_audio_in_video_query(
        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)
-        # 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)
+    asset = VideoAsset(name="baby_reading", num_frames=16)
+    audio = asset.get_audio(sampling_rate=16000)

    return QueryResult(
        inputs={
            "prompt": prompt,
            "multi_modal_data": {
-                "video": video_frames,
+                "video": asset.np_ndarrays,
                "audio": audio,
            },
            "mm_processor_kwargs": {
@@ -151,7 +103,7 @@ def get_use_audio_in_video_query(
    )


-def get_multi_audios_query(audio_path: Optional[str] = None, sampling_rate: int = 16000) -> QueryResult:
+def get_multi_audios_query() -> QueryResult:
    question = "Are these two audio clips the same?"
    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
@@ -160,28 +112,14 @@ def get_multi_audios_query(audio_path: Optional[str] = None, sampling_rate: int
        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": audio_list,
+                "audio": [
+                    AudioAsset("winning_call").audio_and_sample_rate,
+                    AudioAsset("mary_had_lamb").audio_and_sample_rate,
+                ],
            },
        },
        limit_mm_per_prompt={
@@ -190,135 +128,17 @@ def get_multi_audios_query(audio_path: Optional[str] = None, sampling_rate: int
    )


-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"
-
-    # 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()
+    query_result = query_map[args.query_type]()

    omni_llm = Omni(
        model=model_name,
@@ -463,39 +283,6 @@ 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()

@@ -5,7 +5,6 @@
 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
@@ -30,26 +29,6 @@ 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.
@@ -6,16 +6,13 @@ with the correct prompt format on Qwen3-Omni (thinker only).
 """

 import os
-from typing import NamedTuple, Optional
+from typing import NamedTuple

-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, video_to_ndarrays
+from vllm.assets.video import VideoAsset
 from vllm.multimodal.image import convert_image_mode
 from vllm.utils import FlexibleArgumentParser

@@ -57,7 +54,7 @@ def get_text_query(question: str = None) -> QueryResult:
    )


-def get_video_query(question: str = None, video_path: Optional[str] = None, num_frames: int = 16) -> QueryResult:
+def get_video_query(question: str = None) -> QueryResult:
    if question is None:
        question = "Why is this video funny?"
    prompt = (
@@ -67,25 +64,18 @@ def get_video_query(question: str = None, video_path: Optional[str] = None, num_
        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,
+                "video": VideoAsset(name="baby_reading", num_frames=16).np_ndarrays,
            },
        },
        limit_mm_per_prompt={"video": 1},
    )


-def get_image_query(question: str = None, image_path: Optional[str] = None) -> QueryResult:
+def get_image_query(question: str = None) -> QueryResult:
    if question is None:
        question = "What is the content of this image?"
    prompt = (
@@ -94,27 +84,18 @@ def get_image_query(question: str = None, image_path: Optional[str] = None) -> Q
        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,
+                "image": convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB"),
            },
        },
        limit_mm_per_prompt={"image": 1},
    )


-def get_audio_query(question: str = None, audio_path: Optional[str] = None, sampling_rate: int = 16000) -> QueryResult:
+def get_audio_query(question: str = None) -> QueryResult:
    if question is None:
        question = "What is the content of this audio?"
    prompt = (
@@ -123,20 +104,11 @@ def get_audio_query(question: str = None, audio_path: Optional[str] = None, samp
        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,
+                "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
            },
        },
        limit_mm_per_prompt={"audio": 1},
@@ -153,22 +125,7 @@ query_map = {

 def main(args):
    model_name = "Qwen/Qwen3-Omni-30B-A3B-Instruct"
-
-    # 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()
+    query_result = query_map[args.query_type]()

    omni_llm = Omni(
        model=model_name,
@@ -326,39 +283,6 @@ 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()

@@ -1,36 +0,0 @@
-# Stage config for running Qwen3-Omni-MoE-Thinking (text-only output)
-# This config is for models like Qwen3-Omni-30B-A3B-Thinking that only have the
-# thinker component and do not support audio output.
-#
-# Single stage: Thinker (multimodal understanding + text generation)
-
-# The following config has been verified on 2x H100-80G GPUs.
-stage_args:
-  - stage_id: 0
-    runtime:
-      devices: "0,1"
-      max_batch_size: 1
-    engine_args:
-      model_stage: thinker
-      model_arch: Qwen3OmniMoeForConditionalGeneration
-      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
-      gpu_memory_utilization: 0.9
-      enforce_eager: true
-      trust_remote_code: true
-      engine_output_type: text
-      distributed_executor_backend: "mp"
-      enable_prefix_caching: false
-      hf_config_name: thinker_config
-      tensor_parallel_size: 2
-    final_output: true
-    final_output_type: text
-    is_comprehension: true
-    default_sampling_params:
-      temperature: 0.4
-      top_p: 0.9
-      top_k: 1
-      max_tokens: 2048
-      seed: 42
-      detokenize: True
-      repetition_penalty: 1.05
@@ -1,28 +0,0 @@
-import pytest
-import torch
-
-from vllm_omni import Omni
-
-models = ["Tongyi-MAI/Z-Image-Turbo"]
-
-
-@pytest.mark.parametrize("model_name", models)
-def test_diffusion_model(model_name: str):
-    m = Omni(model=model_name)
-    # high resolution may cause OOM on L4
-    height = 256
-    width = 256
-    images = m.generate(
-        "a photo of a cat sitting on a laptop keyboard",
-        height=height,
-        width=width,
-        num_inference_steps=2,
-        guidance_scale=0.0,
-        generator=torch.Generator("cuda").manual_seed(42),
-        num_outputs_per_prompt=2,
-    )
-    assert len(images) == 2
-    # check image size
-    assert images[0].width == width
-    assert images[0].height == height
-    images[0].save("z_image_output.png")
@@ -2,12 +2,12 @@
 Scheduling components for vLLM-Omni.
 """

-from .omni_ar_scheduler import OmniARScheduler
-from .omni_generation_scheduler import OmniGenerationScheduler
+from .diffusion_scheduler import DiffusionScheduler
 from .output import OmniNewRequestData
+from .scheduler import OmniScheduler

 __all__ = [
-    "OmniARScheduler",
-    "OmniGenerationScheduler",
+    "OmniScheduler",
+    "DiffusionScheduler",
    "OmniNewRequestData",
 ]
@@ -0,0 +1,315 @@
+import time
+from collections import defaultdict
+from typing import Optional
+
+from vllm.v1.core.kv_cache_manager import KVCacheBlocks
+from vllm.v1.core.sched.request_queue import create_request_queue
+from vllm.v1.core.sched.scheduler import Request, RequestStatus, SchedulerOutput, SpecDecodingStats
+from vllm.v1.core.sched.utils import remove_all
+from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs
+
+from vllm_omni.core.sched.output import OmniNewRequestData
+from vllm_omni.core.sched.scheduler import OmniScheduler
+from vllm_omni.outputs import OmniModelRunnerOutput
+
+
+class DiffusionScheduler(OmniScheduler):
+    """Scheduler for diffusion-based non-autoregressive stages.
+
+    Implements a fast-path scheduling strategy for diffusion models where
+    all input tokens are fed at once. Falls back to standard vLLM scheduling
+    if the token budget cannot accommodate all tokens in a single iteration.
+    """
+
+    def schedule(self) -> SchedulerOutput:
+        """Schedule requests for diffusion model execution.
+
+        Uses a fast-path approach that feeds all input tokens at once
+        (or 1 placeholder token if input is empty). Falls back to
+        standard scheduling if token budget is insufficient.
+
+        Returns:
+            SchedulerOutput containing scheduled requests and token allocations
+        """
+
+        # Select requests with zero prompt and using pooling
+        # (diffusion results returned via pooler_output)
+        token_budget = self.max_num_scheduled_tokens
+        capacity = self.max_num_running_reqs - len(self.running)
+        scheduled_timestamp = time.monotonic()
+
+        scheduled_new_reqs: list[Request] = []
+        scheduled_resumed_reqs: list[Request] = []
+        scheduled_running_reqs: list[Request] = []
+
+        req_to_new_blocks: dict[str, KVCacheBlocks] = {}
+        num_scheduled_tokens: dict[str, int] = {}
+        scheduled_spec_decode_tokens: dict[str, list[int]] = {}
+        scheduled_encoder_inputs: dict[str, list[int]] = {}
+
+        # Temporary queue: preserve waiting order, do not disturb non-diffusion requests
+        skipped_waiting_requests = create_request_queue(self.policy)
+
+        # Fast path selection and scheduling (treat all as diffusion requests,
+        # independent of pooling_params)
+        while self.waiting and token_budget > 0 and capacity > 0:
+            request = self.waiting.peek_request()
+            # Uniformly treat as diffusion. A feature flag can be added later
+            # via config or request tag.
+
+            # Allocate all input tokens for the request in one shot
+            # (allocate 1 placeholder if zero)
+            required_tokens = max(getattr(request, "num_prompt_tokens", 0), 1)
+            if required_tokens > token_budget:
+                # Insufficient budget to process all inputs at once;
+                # stop fast path attempt
+                break
+            num_new_tokens = required_tokens
+            new_blocks = self.kv_cache_manager.allocate_slots(
+                request,
+                num_new_tokens,
+                num_lookahead_tokens=self.num_lookahead_tokens,
+            )
+            if new_blocks is None:
+                # Allocation failed (e.g., VRAM pressure); stop fast path and
+                # fall back to default scheduling
+                # Put the current request back to the head of the waiting queue
+                # Note: the original queue order is preserved
+                break
+
+            # Officially schedule this request
+            request = self.waiting.pop_request()
+            self.running.append(request)
+            request.status = RequestStatus.RUNNING
+            if self.log_stats:
+                request.record_event(EngineCoreEventType.SCHEDULED, scheduled_timestamp)
+
+            req_to_new_blocks[request.request_id] = new_blocks
+            num_scheduled_tokens[request.request_id] = num_new_tokens
+            token_budget -= num_new_tokens
+            capacity -= 1
+            scheduled_new_reqs.append(request)
+
+        # Return skipped waiting requests
+        if skipped_waiting_requests:
+            self.waiting.prepend_requests(skipped_waiting_requests)
+
+        # If fast path scheduled none, fall back to the original scheduling
+        if not num_scheduled_tokens:
+            return super().schedule()
+
+        # Compute common prefix blocks (aligned with v1)
+        num_common_prefix_blocks = [0] * len(self.kv_cache_config.kv_cache_groups)
+        if self.running:
+            any_request = self.running[0]
+            num_common_prefix_blocks = self.kv_cache_manager.get_num_common_prefix_blocks(
+                any_request, len(self.running)
+            )
+
+        # Assemble SchedulerOutput
+        new_reqs_data = [
+            OmniNewRequestData.from_request(req, req_to_new_blocks[req.request_id].get_block_ids())
+            for req in scheduled_new_reqs
+        ]
+        cached_reqs_data = self._make_cached_request_data(
+            scheduled_running_reqs,
+            scheduled_resumed_reqs,
+            num_scheduled_tokens,
+            scheduled_spec_decode_tokens,
+            req_to_new_blocks,
+        )
+        scheduled_requests = scheduled_new_reqs + scheduled_running_reqs + scheduled_resumed_reqs
+        structured_output_request_ids, grammar_bitmask = self.get_grammar_bitmask(
+            scheduled_requests, scheduled_spec_decode_tokens
+        )
+
+        total_num_scheduled_tokens = sum(num_scheduled_tokens.values())
+        scheduler_output = SchedulerOutput(
+            scheduled_new_reqs=new_reqs_data,
+            scheduled_cached_reqs=cached_reqs_data,
+            num_scheduled_tokens=num_scheduled_tokens,
+            total_num_scheduled_tokens=total_num_scheduled_tokens,
+            scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
+            scheduled_encoder_inputs=scheduled_encoder_inputs,
+            num_common_prefix_blocks=num_common_prefix_blocks,
+            finished_req_ids=self.finished_req_ids,
+            free_encoder_mm_hashes=self.encoder_cache_manager.get_freed_mm_hashes(),
+            structured_output_request_ids=structured_output_request_ids,
+            grammar_bitmask=grammar_bitmask,
+        )
+
+        # KVTransfer: package metadata
+        if self.connector is not None:
+            meta = self.connector.build_connector_meta(scheduler_output)
+            scheduler_output.kv_connector_metadata = meta
+
+        # Update internal state (advance num_computed_tokens, free encoder inputs,
+        # etc.)
+        self._update_after_schedule(scheduler_output)
+        return scheduler_output
+
+    """
+    Scheduler for the diffusion model.
+    This scheduler is modified to stop the request immediately for the diffusion model.
+    This is because the diffusion model can generate the final image/audio in one step.
+    Note: This is just a minimal modification to the original scheduler,
+    and there should be some further efforts to optimize the scheduler.
+    The original scheduler is still used for the AR model.
+    """
+
+    def update_from_output(
+        self,
+        scheduler_output: SchedulerOutput,
+        model_runner_output: OmniModelRunnerOutput,
+    ) -> dict[int, EngineCoreOutputs]:
+        """Update the scheduler state based on the model runner output.
+
+        This method is modified to stop the request immediately for the diffusion model.
+        """
+        sampled_token_ids = model_runner_output.sampled_token_ids
+        logprobs = model_runner_output.logprobs
+        prompt_logprobs_dict = model_runner_output.prompt_logprobs_dict
+        num_scheduled_tokens = scheduler_output.num_scheduled_tokens
+        pooler_outputs = model_runner_output.pooler_output
+        num_nans_in_logits = model_runner_output.num_nans_in_logits
+        kv_connector_output = model_runner_output.kv_connector_output
+
+        outputs: dict[int, list[EngineCoreOutput]] = defaultdict(list)
+        spec_decoding_stats: Optional[SpecDecodingStats] = None
+        kv_connector_stats = kv_connector_output.kv_connector_stats if kv_connector_output else None
+
+        # NOTE(woosuk): As len(num_scheduled_tokens) can be up to 1K or more,
+        # the below loop can be a performance bottleneck. We should do our best
+        # to avoid expensive operations inside the loop.
+        stopped_running_reqs: set[Request] = set()
+        stopped_preempted_reqs: set[Request] = set()
+        for req_id, num_tokens_scheduled in num_scheduled_tokens.items():
+            assert num_tokens_scheduled > 0
+            request = self.requests.get(req_id)
+            if request is None:
+                # The request is already finished. This can happen if the
+                # request is aborted while the model is executing it (e.g.,
+                # in pipeline parallelism).
+                continue
+
+            req_index = model_runner_output.req_id_to_index[req_id]
+            generated_token_ids = sampled_token_ids[req_index] if sampled_token_ids else []
+
+            scheduled_spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(req_id)
+            if scheduled_spec_token_ids:
+                num_draft_tokens = len(scheduled_spec_token_ids)
+                num_accepted = len(generated_token_ids) - 1
+                num_rejected = num_draft_tokens - num_accepted
+                # num_computed_tokens represents the number of tokens
+                # processed in the current step, considering scheduled
+                # tokens and rejections. If some tokens are rejected,
+                # num_computed_tokens is decreased by the number of rejected
+                # tokens.
+                request.num_computed_tokens -= num_rejected
+                spec_decoding_stats = self.make_spec_decoding_stats(
+                    spec_decoding_stats,
+                    num_draft_tokens=num_draft_tokens,
+                    num_accepted_tokens=num_accepted,
+                )
+
+            new_logprobs = None
+            new_token_ids = generated_token_ids
+            kv_transfer_params = None
+            status_before_stop = request.status
+            pooler_output = None
+            if pooler_outputs:
+                pooler_output = pooler_outputs[req_index]
+
+            # Diffusion request: completes in one step; mark finished and free resources
+            request.status = RequestStatus.FINISHED_STOPPED
+            # Optional: set a stop_reason for front-end clarity
+            # (does not affect protocol)
+            request.stop_reason = request.stop_reason or "diffusion_done"
+            kv_transfer_params = self._free_request(request)
+            if status_before_stop == RequestStatus.RUNNING:
+                stopped_running_reqs.add(request)
+            else:
+                stopped_preempted_reqs.add(request)
+
+            # Extract sample logprobs if needed.
+            if request.sampling_params is not None and request.sampling_params.logprobs is not None and logprobs:
+                # NOTE: once we support N tokens per step (spec decode),
+                # the outer lists can be of length > 1.
+                new_logprobs = logprobs.slice(req_index, req_index + 1)
+
+            if new_token_ids and self.structured_output_manager.should_advance(request):
+                # NOTE: structured_output_request should not be None if
+                # use_structured_output, we have check above, so safe to ignore
+                # type warning
+                request.structured_output_request.grammar.accept_tokens(req_id, new_token_ids)  # type: ignore[union-attr]  # noqa: E501
+
+            # spec_token_ids comes from the model runner output
+            if num_nans_in_logits is not None and req_id in num_nans_in_logits:
+                request.num_nans_in_logits = num_nans_in_logits[req_id]
+
+            # Get prompt logprobs for this request.
+            prompt_logprobs_tensors = prompt_logprobs_dict.get(req_id)
+            # Convert pooler_output tensor to dict format expected by OmniEngineCoreOutput
+            pooling_output_dict = None
+            if pooler_output is not None:
+                # Wrap tensor in dict to match OmniEngineCoreOutput.pooling_output type
+                # which expects Optional[dict[str, torch.Tensor]]
+                pooling_output_dict = {"model_outputs": pooler_output}
+            if new_token_ids or pooler_output is not None or kv_transfer_params:
+                # Add EngineCoreOutput for this Request.
+                outputs[request.client_index].append(
+                    EngineCoreOutput(
+                        request_id=req_id,
+                        new_token_ids=new_token_ids,
+                        finish_reason=request.get_finished_reason(),
+                        new_logprobs=new_logprobs,
+                        new_prompt_logprobs_tensors=prompt_logprobs_tensors,
+                        pooling_output=pooling_output_dict,
+                        stop_reason=request.stop_reason,
+                        events=request.take_events(),
+                        kv_transfer_params=kv_transfer_params,
+                        trace_headers=request.trace_headers,
+                        num_cached_tokens=request.num_cached_tokens,
+                    )
+                )
+            else:
+                # Invariant: EngineCore returns no partial prefill outputs.
+                assert not prompt_logprobs_tensors
+
+        # Remove the stopped requests from the running and waiting queues.
+        if stopped_running_reqs:
+            if stopped_running_reqs:
+                self.running = remove_all(self.running, stopped_running_reqs)
+        if stopped_preempted_reqs:
+            # This is a rare case and unlikely to impact performance.
+            self.waiting.remove_requests(stopped_preempted_reqs)
+
+        # KV Connector: update state for finished KV Transfers.
+        if model_runner_output.kv_connector_output:
+            self._update_from_kv_xfer_finished(model_runner_output.kv_connector_output)
+
+        # Create EngineCoreOutputs for all clients that have requests with
+        # outputs in this step.
+        engine_core_outputs = {client_index: EngineCoreOutputs(outputs=outs) for client_index, outs in outputs.items()}
+
+        finished_req_ids = self.finished_req_ids_dict
+        if finished_req_ids:
+            # Include ids of requests that finished since last outputs
+            # were sent.
+            for client_index, finished_set in finished_req_ids.items():
+                # Set finished request set in EngineCoreOutputs for this client.
+                if (eco := engine_core_outputs.get(client_index)) is not None:
+                    eco.finished_requests = finished_set
+                else:
+                    engine_core_outputs[client_index] = EngineCoreOutputs(finished_requests=finished_set)
+            finished_req_ids.clear()
+
+        if (stats := self.make_stats(spec_decoding_stats, kv_connector_stats)) is not None:
+            # Return stats to only one of the front-ends.
+            if (eco := next(iter(engine_core_outputs.values()), None)) is None:
+                # We must return the stats even if there are no request
+                # outputs this step.
+                engine_core_outputs[0] = eco = EngineCoreOutputs()
+            eco.scheduler_stats = stats
+
+        return engine_core_outputs
@@ -10,15 +10,15 @@ from vllm.v1.core.sched.scheduler import (
    SchedulerOutput,
    SpecDecodingStats,
 )
-from vllm.v1.core.sched.scheduler import Scheduler as VLLMScheduler
 from vllm.v1.core.sched.utils import remove_all
 from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs

 from vllm_omni.core.sched.output import OmniNewRequestData
+from vllm_omni.core.sched.scheduler import OmniScheduler
 from vllm_omni.outputs import OmniModelRunnerOutput


-class OmniGenerationScheduler(VLLMScheduler):
+class GenerationScheduler(OmniScheduler):
    def schedule(self) -> SchedulerOutput:
        """Diffusion fast path:
        - Feed all input tokens of the request at once
@@ -12,7 +12,7 @@ from vllm.v1.request import Request, RequestStatus
 from vllm.v1.spec_decode.metrics import SpecDecodingStats


-class OmniARScheduler(VLLMScheduler):
+class OmniScheduler(VLLMScheduler):
    """
    OmniScheduler: Scheduler for vLLM-Omni multimodal processing.

@@ -1,2 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
@@ -1,2 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
@@ -1,78 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-from typing import Generic, TypeVar
-
-import torch
-
-
-class AttentionBackend(ABC):
-    """Abstract class for diffusion attention backends."""
-
-    accept_output_buffer: bool = False
-
-    @staticmethod
-    @abstractmethod
-    def get_name() -> str:
-        raise NotImplementedError
-
-    @staticmethod
-    @abstractmethod
-    def get_impl_cls() -> type["AttentionImpl"]:
-        raise NotImplementedError
-
-    @staticmethod
-    @abstractmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        raise NotImplementedError
-
-    @staticmethod
-    @abstractmethod
-    def get_builder_cls():  # -> Type["AttentionMetadataBuilder"]:
-        raise NotImplementedError
-
-    @staticmethod
-    @abstractmethod
-    def get_supported_head_sizes() -> list[int]:
-        """Get the list of supported head sizes for this backend."""
-        raise NotImplementedError
-
-    @classmethod
-    def supports_head_size(cls, head_size: int) -> bool:
-        supported_head_sizes = cls.get_supported_head_sizes()
-        return (not supported_head_sizes) or head_size in supported_head_sizes
-
-
-@dataclass
-class AttentionMetadata:
-    attn_mask: torch.Tensor | None = None
-
-
-T = TypeVar("T", bound=AttentionMetadata)
-
-
-class AttentionImpl(ABC, Generic[T]):
-    @abstractmethod
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        softmax_scale: float,
-        causal: bool = False,
-        num_kv_heads: int | None = None,
-        prefix: str = "",
-        **extra_impl_args,
-    ) -> None:
-        raise NotImplementedError
-
-    @abstractmethod
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attn_metadata: T,
-    ) -> torch.Tensor:
-        raise NotImplementedError
@@ -1,74 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import torch
-from vllm.logger import init_logger
-
-from vllm_omni.diffusion.attention.backends.abstract import (
-    AttentionBackend,
-    AttentionImpl,
-    AttentionMetadata,
-)
-
-logger = init_logger(__name__)
-
-try:
-    # only tested with flash_attn v3
-    # from flash_attn_interface import flash_attn_func as flash_attn_3_func  # not available in flash-attn 2.8.1
-    from flash_attn import flash_attn_func  # can be FA2 or FA3
-except ImportError:
-    logger.warning(
-        "FlashAttentionBackend is not available. You may install flash-attn "
-        "by running `uv pip install flash-attn==2.8.1 --no-build-isolation`"
-        " or install pre-built flash-attn from https://github.com/Dao-AILab/flash-attention/releases"
-    )
-    flash_attn_3_func = None
-
-
-class FlashAttentionBackend(AttentionBackend):
-    accept_output_buffer: bool = True
-
-    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
-        return [64, 96, 128, 192, 256]
-
-    @staticmethod
-    def get_name() -> str:
-        return "FLASH_ATTN"
-
-    @staticmethod
-    def get_impl_cls() -> type["FlashAttentionImpl"]:
-        return FlashAttentionImpl
-
-
-class FlashAttentionImpl(AttentionImpl):
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        softmax_scale: float,
-        causal: bool = False,
-        num_kv_heads: int | None = None,
-        prefix: str = "",
-        **extra_impl_args,
-    ) -> None:
-        self.num_heads = num_heads
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attn_metadata: AttentionMetadata = None,
-    ) -> torch.Tensor:
-        # TODO: flash_attn_func does not support attn_mask.
-        out: torch.Tensor = flash_attn_func(
-            query,
-            key,
-            value,
-            causal=self.causal,
-            softmax_scale=self.softmax_scale,
-        )
-        return out
@@ -1,66 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import torch
-from vllm.logger import init_logger
-
-from vllm_omni.diffusion.attention.backends.abstract import (
-    AttentionBackend,
-    AttentionImpl,
-    AttentionMetadata,
-)
-
-logger = init_logger(__name__)
-
-
-class SDPABackend(AttentionBackend):
-    accept_output_buffer: bool = True
-
-    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
-        return [x for x in range(1024)]  # todo
-
-    @staticmethod
-    def get_name() -> str:
-        return "SDPA"
-
-    @staticmethod
-    def get_impl_cls() -> type["SDPAImpl"]:
-        return SDPAImpl
-
-
-class SDPAImpl(AttentionImpl):
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        softmax_scale: float,
-        causal: bool = False,
-        num_kv_heads: int | None = None,
-        prefix: str = "",
-        **extra_impl_args,
-    ) -> None:
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attn_metadata: AttentionMetadata = None,
-    ) -> torch.Tensor:
-        query, key, value = (x.permute(0, 2, 1, 3) for x in (query, key, value))
-        attention_mask = attn_metadata.attn_mask if attn_metadata else None
-
-        output = torch.nn.functional.scaled_dot_product_attention(
-            query,
-            key,
-            value,
-            attn_mask=attention_mask,
-            dropout_p=0.0,
-            is_causal=self.causal,
-            scale=self.softmax_scale,
-        )
-        out = output.permute(0, 2, 1, 3)
-        return out
@@ -1,43 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import torch
-import torch.nn as nn
-
-from vllm_omni.diffusion.attention.backends.abstract import (
-    AttentionMetadata,
-)
-from vllm_omni.diffusion.attention.selector import get_attn_backend
-
-
-class Attention(nn.Module):
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        causal: bool,
-        softmax_scale: float,
-        num_kv_heads: int | None = None,
-        prefix: str = "",
-    ):
-        super().__init__()
-        self.attn_backend = get_attn_backend(-1)
-        self.attn_impl_cls = self.attn_backend.get_impl_cls()
-        self.attention = self.attn_impl_cls(
-            num_heads=num_heads,
-            head_size=head_size,
-            softmax_scale=softmax_scale,
-            causal=causal,
-            num_kv_heads=num_kv_heads,
-        )
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attn_metadata: AttentionMetadata = None,
-    ) -> torch.Tensor:
-        # shape: (batch_size, seq_len, num_heads, head_size)
-        attn_output = self.attention.forward(query, key, value, attn_metadata)
-        return attn_output
@@ -1,53 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import os
-from functools import cache
-
-from vllm.logger import init_logger
-
-from vllm_omni.diffusion.attention.backends.abstract import (
-    AttentionBackend,
-)
-from vllm_omni.diffusion.attention.backends.flash_attn import FlashAttentionBackend
-from vllm_omni.diffusion.attention.backends.sdpa import SDPABackend
-
-logger = init_logger(__name__)
-
-# environment variable value -> backend class
-SUPPORTED_BACKENDS = {
-    "FLASH_ATTN": FlashAttentionBackend,
-    "TORCH_SDPA": SDPABackend,
-}
-
-
-@cache
-def get_attn_backend(head_size: int) -> type[AttentionBackend]:
-    """
-    Get attention backend for diffusion models.
-
-    The backend is selected based on the following priority:
-    1. DIFFUSION_ATTENTION_BACKEND environment variable (if set, e.g. export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN)
-    2. Default backend (SDPA)
-
-    Args:
-        head_size: Head size (currently not used for selection, but kept for API compatibility)
-
-    Returns:
-        The selected attention backend class
-    """
-    # Check environment variable
-    backend_name: str | None = os.environ.get("DIFFUSION_ATTENTION_BACKEND")
-
-    if backend_name is not None:
-        backend_name_upper = backend_name.upper()
-        if backend_name_upper not in SUPPORTED_BACKENDS:
-            valid_backends = list(SUPPORTED_BACKENDS.keys())
-            raise ValueError(
-                f"Invalid attention backend for diffusion: '{backend_name}'. Valid backends are: {valid_backends}"
-            )
-        logger.info(f"Using attention backend '{backend_name_upper}' for diffusion")
-        return SUPPORTED_BACKENDS[backend_name_upper]
-
-    # Default to SDPA
-    return SDPABackend
@@ -21,8 +21,6 @@ class OmniDiffusionConfig:

    model_class_name: str | None = None

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

@@ -20,6 +20,7 @@ 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
@@ -235,7 +236,7 @@ class QwenImagePipeline(
    def __init__(
        self,
        *,
-        od_config: OmniDiffusionConfig,
+        od_config: OmniDiffusionConfig = None,
        prefix: str = "",
    ):
        super().__init__()
@@ -249,14 +250,23 @@ class QwenImagePipeline(
        self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
            model, subfolder="scheduler", local_files_only=local_files_only
        )
-        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)
+        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.stage = None

@@ -8,6 +8,7 @@ from typing import Any, Optional, Union
 import torch
 import torch.nn as nn
 from diffusers.models.attention import FeedForward
+from diffusers.models.attention_dispatch import dispatch_attention_fn

 # TODO replace this with vLLM implementation
 from diffusers.models.embeddings import TimestepEmbedding, Timesteps
@@ -17,8 +18,6 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import QKVParallelLinear, ReplicatedLinear

-from vllm_omni.diffusion.attention.layer import Attention
-
 logger = init_logger(__name__)


@@ -263,12 +262,12 @@ class QwenImageCrossAttention(nn.Module):
        self.norm_added_q = RMSNorm(head_dim, eps=eps)
        self.norm_added_k = RMSNorm(head_dim, eps=eps)

-        self.attn = Attention(
-            num_heads=num_heads,
-            head_size=self.head_dim,
-            softmax_scale=1.0 / (self.head_dim**0.5),
-            causal=False,
-        )
+        # self.attn = Attention(
+        #     num_heads=num_heads,
+        #     head_size=self.head_dim,
+        #     scale=1.0,  # softmax_scale
+        #     attn_type=AttentionType.ENCODER,
+        # )

    def forward(
        self,
@@ -317,11 +316,20 @@ class QwenImageCrossAttention(nn.Module):
        joint_value = torch.cat([txt_value, img_value], dim=1)

        # Compute joint attention
-        joint_hidden_states = self.attn(
+        # joint_hidden_states = self.attn(
+        #     joint_query,
+        #     joint_key,
+        #     joint_value,
+        # )
+        joint_hidden_states = dispatch_attention_fn(
            joint_query,
            joint_key,
            joint_value,
+            dropout_p=0.0,
+            is_causal=False,
        )
+
+        # Reshape back
        joint_hidden_states = joint_hidden_states.flatten(2, 3)
        joint_hidden_states = joint_hidden_states.to(joint_query.dtype)

@@ -360,6 +368,19 @@ class QwenImageTransformerBlock(nn.Module):
            nn.Linear(dim, 6 * dim, bias=True),  # For scale, shift, gate for norm1 and norm2
        )
        self.img_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
+        # self.attn = Attention(
+        #     query_dim=dim,
+        #     cross_attention_dim=None,  # Enable cross attention for joint computation
+        #     added_kv_proj_dim=dim,  # Enable added KV projections for text stream
+        #     dim_head=attention_head_dim,
+        #     heads=num_attention_heads,
+        #     out_dim=dim,
+        #     context_pre_only=False,
+        #     bias=True,
+        #     processor=QwenDoubleStreamAttnProcessor2_0(),
+        #     qk_norm=qk_norm,
+        #     eps=eps,
+        # )
        self.attn = QwenImageCrossAttention(
            dim=dim,
            num_heads=num_attention_heads,
@@ -1,648 +0,0 @@
-# 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
@@ -1,674 +0,0 @@
-# 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 torch.nn.utils.rnn import pad_sequence
-from vllm.model_executor.layers.layernorm import RMSNorm
-
-from vllm_omni.diffusion.attention.layer import Attention
-
-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)])
-
-        self.attn = Attention(
-            num_heads=num_heads,
-            head_size=self.head_dim,
-            softmax_scale=1.0 / (self.head_dim**0.5),
-            causal=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 = self.attn(
-            query,
-            key,
-            value,
-            # attn_mask=attention_mask, # we don't support multi prompts now.
-        )
-
-        # 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.")
@@ -3,8 +3,6 @@

 import importlib

-from vllm.model_executor.models.registry import _LazyRegisteredModel, _ModelRegistry
-
 from vllm_omni.diffusion.data import OmniDiffusionConfig

 _DIFFUSION_MODELS = {
@@ -14,36 +12,25 @@ _DIFFUSION_MODELS = {
        "pipeline_qwen_image",
        "QwenImagePipeline",
    ),
-    "ZImagePipeline": (
-        "z_image",
-        "pipeline_z_image",
-        "ZImagePipeline",
-    ),
 }


-DiffusionModelRegistry = _ModelRegistry(
-    {
-        model_arch: _LazyRegisteredModel(
-            module_name=f"vllm_omni.diffusion.models.{mod_folder}.{mod_relname}",
-            class_name=cls_name,
-        )
-        for model_arch, (mod_folder, mod_relname, cls_name) in _DIFFUSION_MODELS.items()
-    }
-)
-
-
 def initialize_model(
    od_config: OmniDiffusionConfig,
 ):
-    model_class = DiffusionModelRegistry._try_load_model_cls(od_config.model_class_name)
-    if model_class is not None:
-        model = model_class(od_config=od_config)
+    if od_config.model_class_name in _DIFFUSION_MODELS:
+        mod_folder, mod_relname, cls_name = _DIFFUSION_MODELS[od_config.model_class_name]
+        module_name = f"vllm_omni.diffusion.models.{mod_folder}.{mod_relname}"
+        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.")
@@ -54,7 +41,6 @@ _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",
 }


@@ -1,13 +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

@@ -12,7 +12,6 @@ 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
@@ -62,10 +61,9 @@ class GPUWorker:
        initialize_model_parallel(tensor_model_parallel_size=world_size)

        with device:
-            with set_default_torch_dtype(self.od_config.dtype):
-                self.pipeline = initialize_model(self.od_config)
-                self.pipeline.load_weights()
-                self.pipeline.eval()
+            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.")

@@ -13,7 +13,6 @@ 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
@@ -68,10 +67,9 @@ class NPUWorker:
        initialize_model_parallel(tensor_model_parallel_size=world_size)

        with device:
-            with set_default_torch_dtype(self.od_config.dtype):
-                self.pipeline = initialize_model(self.od_config)
-                self.pipeline.load_weights()
-                self.pipeline.eval()
+            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.")

@@ -1415,7 +1415,7 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
            inputs_embeds = None

        if self.use_deepstack and inputs_embeds is not None and get_pp_group().is_first_rank:
-            deepstack_input_embeds = self._get_deepstack_input_embeds(input_ids.size(1))
+            deepstack_input_embeds = self._get_deepstack_input_embeds(inputs_embeds.size(0))
        else:
            deepstack_input_embeds = None

@@ -1431,7 +1431,7 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
        )

        if inputs_embeds is not None and get_pp_group().is_first_rank:
-            self._clear_deepstack_input_embeds(input_ids.size(1))
+            self._clear_deepstack_input_embeds(inputs_embeds.size(0))

        return hidden_states.unsqueeze(0), captured_hidden_states

@@ -9,7 +9,7 @@ stage_args:
      model_stage: thinker
      model_arch: Qwen2_5OmniForConditionalGeneration
      worker_cls: vllm_omni.worker.npu.npu_ar_worker.NPUARWorker
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
      gpu_memory_utilization: 0.8
      enforce_eager: true  # Now we only support eager mode
      trust_remote_code: true
@@ -35,7 +35,7 @@ stage_args:
      model_stage: talker
      model_arch: Qwen2_5OmniForConditionalGeneration
      worker_cls: vllm_omni.worker.npu.npu_ar_worker.NPUARWorker
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
      gpu_memory_utilization: 0.8
      enforce_eager: true
      trust_remote_code: true
@@ -60,8 +60,8 @@ stage_args:
    engine_args:
      model_stage: code2wav
      model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_cls: vllm_omni.worker.npu.npu_generation_worker.NPUGenerationWorker
-      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
+      worker_cls: vllm_omni.worker.npu.npu_diffusion_worker.NPUDiffusionWorker
+      scheduler_cls: vllm_omni.core.sched.diffusion_scheduler.DiffusionScheduler
      gpu_memory_utilization: 0.15
      enforce_eager: true
      trust_remote_code: true
@@ -11,13 +11,12 @@ stage_args:
      model_stage: thinker
      model_arch: Qwen2_5OmniForConditionalGeneration
      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
      gpu_memory_utilization: 0.8
      enforce_eager: true  # Now we only support eager mode
      trust_remote_code: true
      engine_output_type: latent
      enable_prefix_caching: false
-      max_num_batched_tokens: 32768
    is_comprehension: true
    final_output: true
    final_output_type: text
@@ -38,12 +37,11 @@ stage_args:
      model_stage: talker
      model_arch: Qwen2_5OmniForConditionalGeneration
      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
      gpu_memory_utilization: 0.8
      enforce_eager: true
      trust_remote_code: true
      enable_prefix_caching: false
-      max_num_batched_tokens: 32768
      engine_output_type: latent
    engine_input_source: [0]
    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen2_5_omni.thinker2talker
@@ -64,13 +62,12 @@ stage_args:
    engine_args:
      model_stage: code2wav
      model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
-      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
+      worker_cls: vllm_omni.worker.gpu_diffusion_worker.GPUDiffusionWorker
+      scheduler_cls: vllm_omni.core.sched.diffusion_scheduler.DiffusionScheduler
      gpu_memory_utilization: 0.15
      enforce_eager: true
      trust_remote_code: true
      enable_prefix_caching: false
-      max_num_batched_tokens: 32768
      engine_output_type: audio
    engine_input_source: [1]
    final_output: true
@@ -13,7 +13,7 @@ stage_args:
      model_stage: thinker
      model_arch: Qwen3OmniMoeForConditionalGeneration
      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
      gpu_memory_utilization: 0.6
      enforce_eager: true
      trust_remote_code: true
@@ -42,8 +42,8 @@ stage_args:
       model_stage: talker
       model_arch: Qwen3OmniMoeForConditionalGeneration
       worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-       scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-       gpu_memory_utilization: 0.5
+       scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
+       gpu_memory_utilization: 0.3
       enforce_eager: true
       trust_remote_code: true
       engine_output_type: latent  # Output codec codes for code2wav
@@ -72,7 +72,7 @@ stage_args:
      model_stage: code2wav
      model_arch: Qwen3OmniMoeForConditionalGeneration
      worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
-      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
+      scheduler_cls: vllm_omni.core.sched.generation_scheduler.GenerationScheduler
      enforce_eager: true
      trust_remote_code: true
      enable_prefix_caching: false
@@ -0,0 +1,576 @@
+from __future__ import annotations
+
+import gc
+import logging
+from typing import Any
+
+import numpy as np
+import torch
+from vllm.config import CUDAGraphMode
+from vllm.forward_context import BatchDescriptor
+from vllm.multimodal.inputs import MultiModalKwargs
+from vllm.v1.attention.backends.utils import CommonAttentionMetadata
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.spec_decode.eagle import EagleProposer
+from vllm.v1.utils import record_function_or_nullcontext
+from vllm.v1.worker.gpu_model_runner import (
+    EMPTY_MODEL_RUNNER_OUTPUT,
+    AsyncGPUModelRunnerOutput,
+    IntermediateTensors,
+    PerLayerAttnMetadata,
+    get_pp_group,
+    set_forward_context,
+)
+from vllm.v1.worker.ubatch_utils import UBatchSlices
+from vllm.v1.worker.utils import sanity_check_mm_encoder_outputs
+
+from vllm_omni.outputs import OmniModelRunnerOutput
+from vllm_omni.worker.gpu_model_runner import OmniGPUModelRunner
+
+logger = logging.getLogger(__name__)
+
+
+class GPUDiffusionModelRunner(OmniGPUModelRunner):
+    """Diffusion model runner for vLLM-Omni (non-autoregressive).
+
+    - Reuses GPUModelRunner preparation, multimodal handling, and TP/PP/DP glue.
+    - Does not compute logits or perform token sampling.
+    - Executes diffusion process and returns tensors via `pooler_output`.
+    """
+
+    def _preprocess(
+        self,
+        scheduler_output: SchedulerOutput,
+        intermediate_tensors: IntermediateTensors | None = None,
+        ubatch_slices: UBatchSlices | None = None,
+        num_tokens_after_padding: torch.Tensor | None = None,
+    ) -> tuple[
+        int,
+        int,
+        torch.Tensor | None,
+        torch.Tensor | None,
+        torch.Tensor | None,
+        torch.Tensor,
+        IntermediateTensors | None,
+        dict[str, Any],
+    ]:
+        # Input token count for this iteration (not used by diffusion, but
+        # retained to keep DP padding/ordering consistent)
+        num_input_tokens = scheduler_output.total_num_scheduled_tokens
+        num_pad, num_tokens_after_padding = self.get_dp_padding(num_input_tokens)
+        num_input_tokens += num_pad
+
+        # _prepare_inputs may reorder the batch, so we must gather multi
+        # modal outputs after that to ensure the correct order
+        if self.supports_mm_inputs and get_pp_group().is_first_rank and not self.model_config.is_encoder_decoder:
+            # Run the multimodal encoder if any.
+            self._execute_mm_encoder(scheduler_output)
+            mm_embeds = self._gather_mm_embeddings(scheduler_output)
+
+            # NOTE(woosuk): To unify token ids and soft tokens (vision
+            # embeddings), we always use embeddings (rather than token ids)
+            # as input to the multimodal model, even when the input is text.
+            inputs_embeds_scheduled = self.model.get_input_embeddings(
+                input_ids=self.input_ids.gpu[:num_input_tokens],
+                multimodal_embeddings=mm_embeds or None,
+            )
+
+            # TODO(woosuk): Avoid the copy. Optimize.
+            self.inputs_embeds.gpu[:num_input_tokens].copy_(inputs_embeds_scheduled)
+
+            input_ids = self.input_ids.gpu[:num_input_tokens]
+            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
+            model_kwargs = {
+                **self._init_model_kwargs(num_input_tokens),
+                **self._extract_mm_kwargs(scheduler_output),
+            }
+        elif self.enable_prompt_embeds and get_pp_group().is_first_rank:
+            # Get the input embeddings for the tokens that are not input embeds,
+            # then put them into the appropriate positions.
+            # TODO(qthequartermasterman): Since even when prompt embeds are
+            # enabled, (a) not all requests will use prompt embeds, and (b)
+            # after the initial prompt is processed, the rest of the generated
+            # tokens will be token ids, it is not desirable to have the
+            # embedding layer outside of the CUDA graph all the time. The v0
+            # engine avoids this by "double compiling" the CUDA graph, once
+            # with input_ids and again with inputs_embeds, for all num_tokens.
+            # If a batch only has token ids, then including the embedding layer
+            # in the CUDA graph will be more performant (like in the else case
+            # below).
+            token_ids_idx = self.is_token_ids.gpu[:num_input_tokens].nonzero(as_tuple=False).squeeze(1)
+            # Some tokens ids may need to become embeds
+            if token_ids_idx.numel() > 0:
+                token_ids = self.input_ids.gpu[token_ids_idx]
+                tokens_to_embeds = self.model.get_input_embeddings(input_ids=token_ids)
+                self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds
+
+            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
+            model_kwargs = self._init_model_kwargs(num_input_tokens)
+            input_ids = self.input_ids.gpu[:num_input_tokens]
+        else:
+            # For text-only models, we use token ids as input.
+            # While it is possible to use embeddings as input just like the
+            # multimodal models, it is not desirable for performance since
+            # then the embedding layer is not included in the CUDA graph.
+            input_ids = self.input_ids.gpu[:num_input_tokens]
+            inputs_embeds = None
+            model_kwargs = self._init_model_kwargs(num_input_tokens)
+        if self.uses_mrope:
+            positions = self.mrope_positions.gpu[:, :num_input_tokens]
+        else:
+            positions = self.positions.gpu[:num_input_tokens]
+
+        if get_pp_group().is_first_rank:
+            intermediate_tensors = None
+        else:
+            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
+                num_input_tokens, intermediate_tensors, True
+            )
+
+        if self.model_config.is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
+            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
+            model_kwargs.update(encoder_inputs)
+
+        return (
+            num_input_tokens,
+            num_input_tokens,
+            num_tokens_after_padding,
+            input_ids,
+            inputs_embeds,
+            positions,
+            intermediate_tensors,
+            model_kwargs,
+        )
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        scheduler_output: SchedulerOutput,
+        intermediate_tensors: IntermediateTensors | None = None,
+    ) -> OmniModelRunnerOutput | IntermediateTensors:
+        with record_function_or_nullcontext("Preprocess"):
+            with self.synchronize_input_prep():
+                # Update persistent batch states.
+                self._update_states(scheduler_output)
+                if not scheduler_output.total_num_scheduled_tokens:
+                    return EMPTY_MODEL_RUNNER_OUTPUT
+
+                # Prepare the decoder inputs.
+                (
+                    attn_metadata,
+                    logits_indices,
+                    spec_decode_metadata,
+                    num_scheduled_tokens_np,
+                    spec_decode_common_attn_metadata,
+                    max_query_len,
+                    ubatch_slices,
+                    num_tokens_after_padding,
+                ) = self._prepare_inputs(scheduler_output)
+
+            # Input token count for this iteration (not used by diffusion, but
+            # retained to keep DP padding/ordering consistent)
+            (
+                num_scheduled_tokens,
+                num_input_tokens,
+                num_tokens_across_dp,
+                input_ids,
+                inputs_embeds,
+                positions,
+                intermediate_tensors,
+                model_kwargs,
+            ) = self._preprocess(
+                scheduler_output,
+                intermediate_tensors,
+                ubatch_slices,
+                num_tokens_after_padding,
+            )
+
+        cudagraph_runtime_mode = CUDAGraphMode.NONE
+        # Run the model.
+        # Use persistent buffers for CUDA graphs.
+        with (
+            set_forward_context(
+                attn_metadata,
+                self.vllm_config,
+                num_tokens=num_input_tokens,
+                num_tokens_across_dp=num_tokens_across_dp,
+                cudagraph_runtime_mode=cudagraph_runtime_mode,
+                batch_descriptor=None,
+                ubatch_slices=ubatch_slices,
+            ),
+            record_function_or_nullcontext("Forward"),
+            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
+        ):
+            outputs = self._run_diffusion(
+                input_ids=input_ids,
+                positions=positions,
+                intermediate_tensors=intermediate_tensors,
+                inputs_embeds=inputs_embeds,
+                multimodal_kwargs=model_kwargs,
+                logits_indices=logits_indices,
+            )
+
+        _, multimodal_outputs = self.extract_multimodal_outputs(outputs)
+        # Ensure one tensor per request, map to CPU for output struct
+        pooler_output: list[torch.Tensor | None] = []
+        if isinstance(multimodal_outputs, torch.Tensor):
+            # If model returned a single stacked tensor, split by requests
+            assert multimodal_outputs.shape[0] == self.input_batch.num_reqs
+            for i in range(self.input_batch.num_reqs):
+                pooler_output.append(multimodal_outputs[i].detach().to("cpu").contiguous())
+        elif isinstance(multimodal_outputs, list):
+            for out in multimodal_outputs:
+                pooler_output.append(out.detach().to("cpu").contiguous() if out is not None else None)
+        elif isinstance(multimodal_outputs, dict):
+            for out in multimodal_outputs.values():
+                pooler_output.append(out.detach().to("cpu").contiguous() if out is not None else None)
+        else:
+            raise RuntimeError("Unsupported diffusion output type")
+
+        output = OmniModelRunnerOutput(
+            req_ids=self.input_batch.req_ids,
+            req_id_to_index=self.input_batch.req_id_to_index,
+            sampled_token_ids=[],
+            logprobs=None,
+            prompt_logprobs_dict={},
+            pooler_output=pooler_output,
+            kv_connector_output=kv_connector_output,
+            num_nans_in_logits={},
+        )
+
+        if not self.use_async_scheduling:
+            return output
+
+        return AsyncGPUModelRunnerOutput(
+            model_runner_output=output,
+            sampled_token_ids=[],
+            invalid_req_indices=[],
+            async_output_copy_stream=self.async_output_copy_stream,
+        )
+
+    def _run_diffusion(
+        self,
+        *,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None,
+        multimodal_kwargs: dict,
+        logits_indices: torch.Tensor,
+    ) -> torch.Tensor | list[torch.Tensor]:
+        """Runs the diffusion process and returns per-request tensors.
+
+        Tries model interfaces in the following order for maximal compatibility:
+        1) model.sample(condition=..., **kwargs)
+        2) model.forward(condition=..., **kwargs)
+        3) model.diffuse(condition=..., **kwargs)
+        """
+        # Keep inputs identical to AR runner
+        kwargs = dict(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+            **MultiModalKwargs.as_kwargs(multimodal_kwargs, device=self.device),
+            sampling_metadata=self.input_batch.sampling_metadata,
+            logits_index=logits_indices,
+            sampler=self.sampler,
+        )
+
+        # For Qwen 2.5 Omni's current implementation, we only support the forward method
+        if hasattr(self.model, "forward"):
+            return self.model.forward(**kwargs)
+        # TODO: add the diffuse method for other models
+
+        raise RuntimeError(
+            "The loaded model does not expose diffusion interfaces 'sample', 'forward', or 'diffuse'. "
+            "Please implement one of them or adapt the runner."
+        )
+
+    @torch.inference_mode()
+    def _dummy_run(
+        self,
+        num_tokens: int,
+        cudagraph_runtime_mode: CUDAGraphMode | None = None,
+        force_attention: bool = False,
+        uniform_decode: bool = False,
+        skip_eplb: bool = False,
+        is_profile: bool = False,
+        create_mixed_batch: bool = False,
+        remove_lora: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Run a dummy forward pass to warm up/profile run or capture the
+        CUDA graph for the model.
+
+        Args:
+            num_tokens: Number of tokens to run the dummy forward pass.
+            cudagraph_runtime_mode: used to control the behavior.
+                - if not set will determine the cudagraph mode based on using
+                    the self.cudagraph_dispatcher.
+                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
+                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
+                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
+                    needed.
+            force_attention: If True, always create attention metadata. Used to
+                warm up attention backend when mode is NONE.
+            uniform_decode: If True, the batch is a uniform decode batch.
+            skip_eplb: If True, skip EPLB state update.
+            is_profile: If True, this is a profile run.
+            create_mixed_batch: If True, create a mixed batch with both decode
+                (1 token) and prefill (multiple tokens) requests.
+            remove_lora: If False, dummy LoRAs are not destroyed after the run
+        """
+        assert cudagraph_runtime_mode is None or cudagraph_runtime_mode in {
+            CUDAGraphMode.NONE,
+            CUDAGraphMode.PIECEWISE,
+            CUDAGraphMode.FULL,
+        }
+
+        # If cudagraph_mode.decode_mode() == FULL and
+        # cudagraph_mode.separate_routine(). This means that we are using
+        # different graphs and/or modes for mixed prefill-decode batches vs.
+        # uniform decode batches. A uniform decode batch means that all
+        # requests have identical query length, except a potential virtual
+        # request (shorter) in the batch account for padding.
+        # Uniform decode batch could either be common pure decode, where
+        # max_query_len == 1, or speculative decode, where
+        # max_query_len == 1 + num_spec_decode_tokens.
+
+        # When setting max_query_len = 1, we switch to and capture the optimized
+        # routine of FA2 for pure decode, i.e., Flashdecode + an optimization
+        # for GQA/MQA.
+        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
+
+        # Set num_scheduled_tokens based on num_tokens and max_num_seqs
+        # for dummy run with LoRA so that the num_reqs collectively
+        # has num_tokens in total.
+        assert num_tokens <= self.scheduler_config.max_num_batched_tokens
+        max_num_reqs = self.scheduler_config.max_num_seqs
+
+        num_reqs = min(num_tokens, max_num_reqs)
+        min_tokens_per_req = num_tokens // num_reqs
+        num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
+        num_scheduled_tokens_list[-1] += num_tokens % num_reqs
+
+        assert sum(num_scheduled_tokens_list) == num_tokens
+        assert len(num_scheduled_tokens_list) == num_reqs
+        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
+
+        num_tokens_after_padding = None
+
+        # If we failed to microbatch, currently need to resynchronize
+        # TODO(lucas,sage): we should be able to avoid this second sync by
+        #  refactoring `get_dp_padding_ubatch` and `get_dp_padding` into
+        #  a single `coordinate_batch_across_dp` function.
+        if num_tokens_after_padding is None:
+            num_pad, num_tokens_across_dp = self.get_dp_padding(num_tokens)
+            num_tokens_after_padding = num_tokens + num_pad
+        else:
+            num_tokens_across_dp = num_tokens_after_padding
+            num_tokens_after_padding = int(num_tokens_after_padding[0].item())
+
+        attn_metadata: PerLayerAttnMetadata | None = None
+
+        # If force_attention is True, we always capture attention. Otherwise,
+        # it only happens for cudagraph_runtime_mode=FULL.
+        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
+            attn_metadata = {}
+
+            seq_lens = max_query_len
+            self.seq_lens.np[:num_reqs] = seq_lens
+            self.seq_lens.np[num_reqs:] = 0
+            self.seq_lens.copy_to_gpu()
+
+            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
+            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
+            self.query_start_loc.copy_to_gpu()
+
+            for kv_cache_group_id, kv_cache_group_spec in enumerate(self.kv_cache_config.kv_cache_groups):
+                common_attn_metadata = CommonAttentionMetadata(
+                    query_start_loc=self.query_start_loc.gpu[: num_reqs + 1],
+                    query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs + 1],
+                    seq_lens=self.seq_lens.gpu[:num_reqs],
+                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
+                    num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs],  # noqa: E501
+                    num_reqs=num_reqs,
+                    num_actual_tokens=num_tokens,
+                    max_query_len=max_query_len,
+                    max_seq_len=self.max_model_len,
+                    block_table_tensor=self.input_batch.block_table[kv_cache_group_id].get_device_tensor(num_reqs),
+                    slot_mapping=self.input_batch.block_table[kv_cache_group_id].slot_mapping.gpu[:num_tokens],
+                    causal=True,
+                )
+                for attn_group in self.attn_groups[kv_cache_group_id]:
+                    assert type(attn_metadata) is dict
+                    attn_metadata_i = attn_group.get_metadata_builder().build_for_cudagraph_capture(
+                        common_attn_metadata
+                    )  # noqa: E501
+                    for layer_name in attn_group.layer_names:
+                        attn_metadata[layer_name] = attn_metadata_i
+
+        with self.maybe_dummy_run_with_lora(self.lora_config, num_scheduled_tokens, remove_lora):
+            model_kwargs = self._init_model_kwargs(num_tokens)
+            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
+                input_ids = None
+                inputs_embeds = self.inputs_embeds.gpu[:num_tokens]
+                model_kwargs = {
+                    **model_kwargs,
+                    **self._dummy_mm_kwargs(num_reqs),
+                }
+            elif self.enable_prompt_embeds:
+                input_ids = None
+                inputs_embeds = self.inputs_embeds.gpu[:num_tokens]
+                model_kwargs = self._init_model_kwargs(num_tokens)
+            else:
+                input_ids = self.input_ids.gpu[:num_tokens]
+                inputs_embeds = None
+
+            if self.uses_mrope:
+                positions = self.mrope_positions.gpu[:, :num_tokens]
+            else:
+                positions = self.positions.gpu[:num_tokens]
+
+            if get_pp_group().is_first_rank:
+                intermediate_tensors = None
+            else:
+                if self.intermediate_tensors is None:
+                    self.intermediate_tensors = self.model.make_empty_intermediate_tensors(
+                        batch_size=self.max_num_tokens,
+                        dtype=self.model_config.dtype,
+                        device=self.device,
+                    )
+
+                intermediate_tensors = self.sync_and_slice_intermediate_tensors(num_tokens, None, False)
+
+            # filter out the valid batch descriptor
+            _cg_mode, batch_descriptor = (
+                self.cudagraph_dispatcher.dispatch(
+                    BatchDescriptor(
+                        num_tokens=num_tokens_after_padding,
+                        uniform_decode=uniform_decode,
+                    )
+                )
+                if not is_profile
+                else (CUDAGraphMode.NONE, None)
+            )
+            if cudagraph_runtime_mode is not None:
+                # we allow forcing NONE when the dispatcher disagrees to support
+                # warm ups for cudagraph capture
+                assert cudagraph_runtime_mode == CUDAGraphMode.NONE or cudagraph_runtime_mode == _cg_mode, (
+                    f"Cudagraph runtime mode mismatch at dummy_run. "
+                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
+                )
+            else:
+                cudagraph_runtime_mode = _cg_mode
+
+            with (
+                self.maybe_randomize_inputs(input_ids),
+                set_forward_context(
+                    attn_metadata,
+                    self.vllm_config,
+                    num_tokens=num_tokens_after_padding,
+                    num_tokens_across_dp=num_tokens_across_dp,
+                    cudagraph_runtime_mode=cudagraph_runtime_mode,
+                    batch_descriptor=batch_descriptor,
+                ),
+            ):
+                outputs = self.model(
+                    input_ids=input_ids,
+                    positions=positions,
+                    intermediate_tensors=intermediate_tensors,
+                    inputs_embeds=inputs_embeds,
+                    **model_kwargs,
+                )
+
+            if self.use_aux_hidden_state_outputs:
+                hidden_states, _ = outputs
+            else:
+                hidden_states = outputs
+
+            if self.speculative_config and self.speculative_config.use_eagle():
+                assert isinstance(self.drafter, EagleProposer)
+                self.drafter.dummy_run(num_tokens)
+
+        # This is necessary to avoid blocking DP.
+        # For dummy runs, we typically skip EPLB since we don't have any real
+        # requests to process.
+        # However, in DP settings, there may be cases when some DP ranks do
+        # not have any requests to process, so they're executing dummy batches.
+        # In such cases, we still have to trigger EPLB to make sure
+        # ranks execute the rearrangement in synchronization.
+
+        # logit_indices = np.cumsum(num_scheduled_tokens) - 1
+        hidden_states, _ = self.extract_multimodal_outputs(hidden_states)
+        return hidden_states, None
+
+    @torch.inference_mode()
+    def _dummy_sampler_run(self, hidden_states: torch.Tensor) -> None:
+        logger.warning("Dummy sampler run is not implemented for diffusion model")
+        return None
+
+    def profile_run(self) -> None:
+        # Profile with multimodal encoder & encoder cache.
+        if self.supports_mm_inputs:
+            if self.model_config.multimodal_config.skip_mm_profiling:
+                logger.info("Skipping memory profiling for multimodal encoder and encoder cache.")
+            else:
+                mm_budget = self.mm_budget
+                assert mm_budget is not None
+
+                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
+                    # NOTE: Currently model is profiled with a single non-text
+                    # modality with the max possible input tokens even when
+                    # it supports multiple.
+                    dummy_modality = mm_budget.get_modality_with_max_tokens()
+                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[dummy_modality]
+
+                    logger.info(
+                        "Encoder cache will be initialized with a budget "
+                        "of %s tokens, and profiled with %s %s items of the maximum feature size.",
+                        encoder_budget,
+                        max_mm_items_per_batch,
+                        dummy_modality,
+                    )
+
+                    # Create dummy batch of multimodal inputs.
+                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
+                        dummy_modality,
+                        max_mm_items_per_batch,
+                    )
+
+                    # Run multimodal encoder.
+                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(**batched_dummy_mm_inputs)
+
+                    sanity_check_mm_encoder_outputs(
+                        dummy_encoder_outputs,
+                        expected_num_items=max_mm_items_per_batch,
+                    )
+
+                    # NOTE: This happens when encoder cache needs to store
+                    # the embeddings that encoder outputs are scattered onto.
+                    # In this case we create dummy embeddings of size
+                    # (encode_budget, hidden_size) and scatter encoder
+                    # output into it.
+                    encoder_output_shape = dummy_encoder_outputs[0].shape
+                    if encoder_output_shape[0] < encoder_budget:
+                        expanded_outputs = []
+                        for output in dummy_encoder_outputs:
+                            expanded = output.new_zeros((encoder_budget, encoder_output_shape[-1]))
+                            num_tokens = output.shape[0]
+                            expanded[:num_tokens].copy_(output)
+                            expanded_outputs.append(expanded)
+
+                        dummy_encoder_outputs = expanded_outputs
+
+                    # Cache the dummy encoder outputs.
+                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
+
+        # Add `is_profile` here to pre-allocate communication buffers
+        hidden_states, _ = self._dummy_run(self.max_num_tokens, is_profile=True)
+        if get_pp_group().is_last_rank:
+            pass
+        self._sync_device()
+        del hidden_states
+        self.encoder_cache.clear()
+        gc.collect()
@@ -0,0 +1,86 @@
+import gc
+import os
+
+import torch
+from vllm.model_executor import set_random_seed
+from vllm.platforms import current_platform
+from vllm.utils import GiB_bytes, MemorySnapshot
+from vllm.v1.utils import report_usage_stats
+from vllm.v1.worker.gpu_worker import Worker as GPUWorker
+from vllm.v1.worker.gpu_worker import init_worker_distributed_environment
+
+from vllm_omni.worker.gpu_diffusion_model_runner import GPUDiffusionModelRunner
+
+
+class GPUDiffusionWorker(GPUWorker):
+    """GPU worker for diffusion-based omni model stages.
+
+    Extends the base GPUWorker to initialize and manage diffusion model
+    runners for non-autoregressive generation stages (e.g., image or
+    audio generation stages).
+    """
+
+    def init_device(self):
+        """Initialize the GPU device and diffusion model runner.
+
+        Sets up CUDA device, initializes distributed environment,
+        sets random seed, and creates a GPUDiffusionModelRunner instance.
+
+        Raises:
+            ValueError: If free GPU memory is insufficient for the
+                requested memory utilization
+            RuntimeError: If device type is not supported
+        """
+        if self.device_config.device.type == "cuda":
+            # This env var set by Ray causes exceptions with graph building.
+            os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
+            self.device = torch.device(f"cuda:{self.local_rank}")
+            current_platform.set_device(self.device)
+
+            current_platform.check_if_supports_dtype(self.model_config.dtype)
+
+            # Initialize the distributed environment BEFORE taking
+            # memory snapshot
+            # This ensures NCCL buffers are allocated before we measure
+            # available memory
+            init_worker_distributed_environment(
+                self.vllm_config,
+                self.rank,
+                self.distributed_init_method,
+                self.local_rank,
+                current_platform.dist_backend,
+            )
+
+            # Set random seed.
+            set_random_seed(self.model_config.seed)
+
+            # Now take memory snapshot after NCCL is initialized
+            gc.collect()
+            torch.cuda.empty_cache()
+
+            # take current memory snapshot
+            self.init_snapshot = MemorySnapshot()
+            self.requested_memory = self.init_snapshot.total_memory * self.cache_config.gpu_memory_utilization
+            if self.init_snapshot.free_memory < self.requested_memory:
+
+                def GiB(b):
+                    return round(b / GiB_bytes, 2)
+
+                raise ValueError(
+                    f"Free memory on device "
+                    f"({GiB(self.init_snapshot.free_memory)}/"
+                    f"{GiB(self.init_snapshot.total_memory)} GiB) on startup "
+                    f"is less than desired GPU memory utilization "
+                    f"({self.cache_config.gpu_memory_utilization}, "
+                    f"{GiB(self.requested_memory)} GiB). Decrease GPU memory "
+                    f"utilization or reduce GPU memory used by other processes."
+                )
+        else:
+            raise RuntimeError(f"Not support device type: {self.device_config.device}")
+
+        # Construct the model runner
+        self.model_runner: GPUDiffusionModelRunner = GPUDiffusionModelRunner(self.vllm_config, self.device)
+
+        if self.rank == 0:
+            # If usage stat is enabled, collect relevant info.
+            report_usage_stats(self.vllm_config)
@@ -53,8 +53,8 @@ if TYPE_CHECKING:
    from vllm.v1.core.sched.output import SchedulerOutput


-class NPUGenerationModelRunner(OmniNPUModelRunner):
-    """Generation model runner for vLLM-omni on NPU (non-autoregressive)."""
+class NPUDiffusionModelRunner(OmniNPUModelRunner):
+    """Diffusion model runner for vLLM-omni on NPU (non-autoregressive)."""

    def _prepare_inputs(
        self,
@@ -455,7 +455,7 @@ class NPUGenerationModelRunner(OmniNPUModelRunner):
            ):
                self.maybe_setup_kv_connector(scheduler_output)

-                outputs = self._run_generation(
+                outputs = self._run_diffusion(
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
@@ -525,7 +525,7 @@ class NPUGenerationModelRunner(OmniNPUModelRunner):
            async_output_copy_stream=self.async_output_copy_stream,
        )

-    def _run_generation(
+    def _run_diffusion(
        self,
        *,
        input_ids: torch.Tensor,
@@ -535,7 +535,7 @@ class NPUGenerationModelRunner(OmniNPUModelRunner):
        multimodal_kwargs: dict,
        logits_indices: torch.Tensor,
    ) -> torch.Tensor | list[torch.Tensor]:
-        """Runs the generation process and returns per-request tensors.
+        """Runs the diffusion process and returns per-request tensors.

        Tries model interfaces in the following order for maximal compatibility:
        1) model.sample(condition=..., **kwargs)
@@ -558,7 +558,7 @@ class NPUGenerationModelRunner(OmniNPUModelRunner):
        # TODO: add the diffuse method for other models

        raise RuntimeError(
-            "The loaded model does not expose generation interfaces 'sample', "
+            "The loaded model does not expose diffusion interfaces 'sample', "
            "'forward', or 'diffuse'. Please implement one of them or adapt the runner."
        )

@@ -0,0 +1,15 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from vllm_ascend.worker.worker_v1 import NPUWorker
+
+from vllm_omni.worker.npu.npu_diffusion_model_runner import NPUDiffusionModelRunner
+
+
+class NPUDiffusionWorker(NPUWorker):
+    """NPU diffusion worker for code2wav stage in Omni model."""
+
+    def init_device(self):
+        device = self._init_device()
+
+        self.model_runner: NPUDiffusionModelRunner = NPUDiffusionModelRunner(self.vllm_config, device)
@@ -1,15 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from vllm_ascend.worker.worker_v1 import NPUWorker
-
-from vllm_omni.worker.npu.npu_generation_model_runner import NPUGenerationModelRunner
-
-
-class NPUGenerationWorker(NPUWorker):
-    """NPU generation worker for code2wav stage in Omni model."""
-
-    def init_device(self):
-        device = self._init_device()
-
-        self.model_runner: NPUGenerationModelRunner = NPUGenerationModelRunner(self.vllm_config, device)
@@ -66,26 +66,17 @@ class OmniNPUModelRunner(NPUModelRunner):
            if use_audio_in_video_value is not None:
                use_audio_in_video = bool(use_audio_in_video_value.item())

-        if supports_mrope(self.model):
-            req_state.mrope_positions, req_state.mrope_position_delta = self.model.get_mrope_input_positions(
-                req_state.prompt_token_ids,
-                hf_config=self.model_config.hf_config,
-                image_grid_thw=image_grid_thw,
-                video_grid_thw=video_grid_thw,
-                second_per_grid_ts=second_per_grid_ts,
-                audio_feature_lengths=audio_feature_lengths,
-                use_audio_in_video=use_audio_in_video,
-            )
-        else:
-            req_state.mrope_positions, req_state.mrope_position_delta = MRotaryEmbedding.get_input_positions_tensor(
-                req_state.prompt_token_ids,
-                hf_config=self.model_config.hf_config,
-                image_grid_thw=image_grid_thw,
-                video_grid_thw=video_grid_thw,
-                second_per_grid_ts=second_per_grid_ts,
-                audio_feature_lengths=audio_feature_lengths,
-                use_audio_in_video=use_audio_in_video,
-            )
+        assert supports_mrope(self.get_model()), "M-RoPE support is not implemented."
+
+        req_state.mrope_positions, req_state.mrope_position_delta = MRotaryEmbedding.get_input_positions_tensor(
+            req_state.prompt_token_ids,
+            hf_config=self.model_config.hf_config,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            second_per_grid_ts=second_per_grid_ts,
+            audio_feature_lengths=audio_feature_lengths,
+            use_audio_in_video=use_audio_in_video,
+        )

    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
        # Remove finished requests from the cached states.