push

Signed-off-by: Kevin H. Luu <khluu000@gmail.com>

.buildkite/ci_config.yaml

@@ -0,0 +1,10 @@
+name: vllm_omni_ci
+github_repo_name: vllm-project/vllm-omni
+job_dirs:
+  - ".buildkite/jobs"
+run_all_patterns: []
+run_all_exclude_patterns: []
+registries: public.ecr.aws/q9t5s3a7
+repositories:
+  main: "vllm-ci-postmerge-repo"
+  premerge: "vllm-ci-test-repo"

.buildkite/jobs/build.yaml

@@ -0,0 +1,10 @@
+group: Build
+steps:
+  - label: ":docker: build vllm-omni-ci image"
+    key: image-build
+    depends_on: []
+    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"

.buildkite/jobs/tests.yaml

@@ -0,0 +1,30 @@
+group: Tests
+depends_on:
+  - image-build
+steps:
+- label: "Simple Unit Test"
+  commands:
+    - ".buildkite/scripts/simple_test.sh"
+  no_gpu: true
+  no_plugin: true
+
+- label: "Diffusion Model Test"
+  timeout_in_minutes: 15
+  commands:
+    - pytest -s -v tests/single_stage/test_diffusion_model.py
+
+- label: "Omni Model Test"
+  timeout_in_minutes: 15
+  num_gpus: 4
+  commands:
+    - export VLLM_LOGGING_LEVEL=DEBUG
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - pytest -s -v tests/multi_stages/
+
+- label: "Omni Model Test with H100"
+  timeout_in_minutes: 20
+  gpu: h100
+  num_gpus: 2
+  commands:
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - pytest -s -v tests/multi_stages_h100/

.buildkite/pipeline.yml

@@ -1,6 +0,0 @@
-steps:
-  - label: "Simple Unit Test"
-    commands:
-      - ".buildkite/scripts/simple_test.sh"
-    agents:
-      queue: "cpu_queue_premerge"

.github/ISSUE_TEMPLATE/config.yml

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

.gitignore

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

README.md

@@ -1,7 +1,7 @@
 <p align="center">
   <picture>
-    <source media="(prefers-color-scheme: dark)" src="./docs/source/logos/vllm-omni-logo.png">
-    <img alt="vllm-omni" src="./docs/source/logos/vllm-omni-logo.png" width=55%>
+    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/logos/vllm-omni-logo.png">
+    <img alt="vllm-omni" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/logos/vllm-omni-logo.png" width=55%>
   </picture>
 </p>
 <h3 align="center">
@@ -30,7 +30,7 @@ Easy, fast, and cheap omni-modality model serving for everyone
 
 <p align="center">
   <picture>
-    <img alt="vllm-omni" src="./docs/source/architecture/omni-modality-model-architecture.png" width=55%>
+    <img alt="vllm-omni" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/omni-modality-model-architecture.png" width=55%>
   </picture>
 </p>
 

docker/Dockerfile.ci

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

docs/.nav.yml

@@ -32,7 +32,13 @@ nav:
     - design/index.md
     - design/architecture_overview.md
     - design/vllm_omni_design.md
-    - design/mrs_design.md
+    - Feature Design:
+      - design/feature/multi_request_streaming_design.md
+      - design/feature/omni_connector_design.md
+    - Module Design:
+      - design/module/ar_module_design.md
+      - design/module/dit_module_design.md
+      - design/module/entrypoint_module_design.md
   - Docs Guide: contributing/DOCS_GUIDE.md
 - API Reference:
   - api/README.md

docs/README.md

@@ -59,4 +59,6 @@ vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, in
 - Multi-modality generation models (e.g. Qwen-Image)
 
 For more information, checkout the following:
+
+- [vllm-omni architecture design and recent roadmaps](https://docs.google.com/presentation/d/1Y7t2Zm3BIISPN-_X_sDpxZMNCWir2rAx/edit?usp=drive_link&ouid=110473603432222024453&rtpof=true&sd=true)
 - [vllm-omni announcement blogpost](https://blog.vllm.ai/2025/11/30/vllm-omni.html)

docs/api/README.md

@@ -49,10 +49,9 @@ Engine classes for offline and online inference.
 Core scheduling and caching components.
 
 - [vllm_omni.core.dit_cache_manager.DiTCacheManager][]
-- [vllm_omni.core.sched.diffusion_scheduler.DiffusionScheduler][]
-- [vllm_omni.core.sched.generation_scheduler.GenerationScheduler][]
+- [vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler][]
+- [vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler][]
 - [vllm_omni.core.sched.output.OmniNewRequestData][]
-- [vllm_omni.core.sched.scheduler.OmniScheduler][]
 
 ## Model Executor
 
@@ -108,13 +107,11 @@ Worker classes and model runners for distributed inference.
 - [vllm_omni.diffusion.worker.npu.npu_worker.NPUWorkerProc][]
 - [vllm_omni.worker.gpu_ar_model_runner.GPUARModelRunner][]
 - [vllm_omni.worker.gpu_ar_worker.GPUARWorker][]
-- [vllm_omni.worker.gpu_diffusion_model_runner.GPUDiffusionModelRunner][]
-- [vllm_omni.worker.gpu_diffusion_worker.GPUDiffusionWorker][]
 - [vllm_omni.worker.gpu_generation_model_runner.GPUGenerationModelRunner][]
 - [vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker][]
 - [vllm_omni.worker.gpu_model_runner.OmniGPUModelRunner][]
 - [vllm_omni.worker.npu.npu_ar_model_runner.NPUARModelRunner][]
 - [vllm_omni.worker.npu.npu_ar_worker.NPUARWorker][]
-- [vllm_omni.worker.npu.npu_diffusion_model_runner.NPUDiffusionModelRunner][]
-- [vllm_omni.worker.npu.npu_diffusion_worker.NPUDiffusionWorker][]
+- [vllm_omni.worker.npu.npu_generation_model_runner.NPUGenerationModelRunner][]
+- [vllm_omni.worker.npu.npu_generation_worker.NPUGenerationWorker][]
 - [vllm_omni.worker.npu.npu_model_runner.OmniNPUModelRunner][]

docs/community/contact_us.md

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

docs/community/volunteers.md

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

docs/configuration/stage_configs/qwen2_5_omni.yaml

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

docs/contributing/README.md

@@ -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]"
 ```
 

docs/design/feature/omni_connector_design.md

@@ -0,0 +1 @@
+## OmniConnector for fully disaggregated serving (update soon)

docs/design/index.md

@@ -6,4 +6,14 @@ This section contains design documents and architecture specifications for vLLM-
 
 - [Architecture Overview](architecture_overview.md)
 - [vLLM-Omni Software Design Document](vllm_omni_design.md)
-- [Multi-Request Streaming](mrs_design.md)
+
+## Feature Design Documents
+
+- [Multi-Request Streaming](feature/multi_request_streaming_design.md)
+- [Omni Connector Design](feature/omni_connector_design.md)
+
+## Module Design Documents
+
+- [AR Module Design](module/ar_module_design.md)
+- [DIT Module Design](module/dit_module_design.md)
+- [Entrypoint Module Design](module/entrypoint_module_design.md)

docs/design/module/ar_module_design.md

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

docs/design/module/dit_module_design.md

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

docs/design/module/entrypoint_module_design.md

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

docs/design/vllm_omni_design.md

@@ -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 --> OutputProcessoer (New one for final multimodal output)
+ --> ModelRunner (New one for AR hiddenstate, New one for DiT) --> RequestState --> OutputProcessor (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(requesy_id, prompt_str, tokenization_kwargs)
+            stage_engine.add_request(request_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:

docs/getting_started/installation/README.md

@@ -1,6 +1,6 @@
 # Installation
 
-vLLM supports the following hardware platforms:
+vLLM-Omni supports the following hardware platforms:
 
 - [GPU](gpu.md)
     - [NVIDIA CUDA](gpu.md)

docs/getting_started/installation/gpu.md

@@ -37,4 +37,7 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr
 ## Set up using Docker
 
 ### Pre-built images
-To be released... Stay tuned!
+
+=== "NVIDIA CUDA"
+
+    --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:pre-built-images"

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

@@ -44,7 +44,7 @@ uv pip install vllm==0.11.0 --torch-backend=auto
 Install additional requirements for vLLM-Omni
 ```bash
 git clone https://github.com/vllm-project/vllm-omni.git
-cd vllm_omni
+cd vllm-omni
 uv pip install -e .
 ```
 
@@ -73,3 +73,24 @@ uv pip install --no-build-isolation --editable .
 </details>
 
 # --8<-- [end:build-wheel-from-source]
+
+
+# --8<-- [start:pre-built-images]
+
+vLLM-Omni offers an official docker image for deployment. These images are built on top of vLLM docker images and available on Docker Hub as [vllm/vllm-omni](https://hub.docker.com/r/vllm/vllm-omni/tags). The version of vLLM-Omni indicates which release of vLLM it is based on.
+
+Here's an example deployment command that has been verified on 2 x H100's:
+```bash
+docker run --runtime nvidia --gpus 2 \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HF_TOKEN=$HF_TOKEN" \
+    -p 8091:8091 \
+    --ipc=host \
+    vllm/vllm-omni:v0.11.0rc1 \
+    --model Qwen/Qwen3-Omni-30B-A3B-Instruct --port 8091
+```
+
+!!! tip
+    You can use this docker image to serve models the same way you would with in vLLM! To do so, make sure you overwrite the default entrypoint (`vllm serve --omni`) which works only for models supported in the vLLM-Omni project.
+
+# --8<-- [end:pre-built-images]

docs/getting_started/quickstart.md

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

docs/mkdocs/hooks/generate_examples.py

@@ -173,7 +173,7 @@ class Example:
             gh_file = gh_file.relative_to(ROOT_DIR)
 
             # Make GitHub URL
-            url = "https://github.com/vllm-project/vllm/"
+            url = "https://github.com/vllm-project/vllm-omni/"
             url += "tree/main" if self.path.is_dir() else "blob/main"
             gh_url = f"{url}/{gh_file}"
 
@@ -183,7 +183,7 @@ class Example:
 
     def generate(self) -> str:
         content = f"# {self.title}\n\n"
-        url = "https://github.com/vllm-project/vllm/"
+        url = "https://github.com/vllm-project/vllm-omni/"
         url += "tree/main" if self.path.is_dir() else "blob/main"
         content += f"Source <{url}/{self.path.relative_to(ROOT_DIR)}>.\n\n"
 

docs/mkdocs/stylesheets/extra.css

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

docs/models/supported_models.md

@@ -4,7 +4,7 @@ vLLM-Omni supports unified multimodal comprehension and generation models across
 
 ## Model Implementation
 
-If vLLM-Omni natively supports a model, its implementation can be found in <gh-file:vllm-omni/model_executor/models> and <gh-file:vllm_omni/diffusion/models>.
+If vLLM-Omni natively supports a model, its implementation can be found in <gh-file:vllm_omni/model_executor/models> and <gh-file:vllm_omni/diffusion/models>.
 
 ## List of Supported Models for Nvidia GPU
 
@@ -20,9 +20,11 @@ th {
 | `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` |
 | `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` |
 | `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` |
+| `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` |
+|`ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` |
 
 
-## List of Supported Models for Ascend NPU
+## List of Supported Models for NPU
 
 <style>
 th {

docs/usage/faq.md

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

docs/user_guide/examples/offline_inference/qwen2_5_omni.md

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

docs/user_guide/examples/offline_inference/qwen3_omni.md

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

docs/user_guide/examples/offline_inference/qwen_image.md

@@ -1,6 +1,6 @@
 # Qwen-Image Offline Inference
 
-Source <https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen_image>.
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen_image>.
 
 
 This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Image` using vLLM-Omni:

docs/user_guide/examples/online_serving/qwen2_5_omni.md

@@ -1,15 +1,16 @@
 # Online serving Example of vLLM-Omni for Qwen2.5-Omni
 
-Source <https://github.com/vllm-project/vllm/tree/main/examples/online_serving/qwen2_5_omni>.
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen2_5_omni>.
 
 
 ## 🛠️ Installation
 
-Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/README.md)
+Please refer to [README.md](https://github.com/vllm-project/vllm-omni/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
 ```
@@ -19,29 +20,59 @@ If you have custom stage configs file, launch the server with command below
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
 ```
 
+### Send Multi-modal Request
+
 Get into the example folder
 ```bash
 cd examples/online_serving/qwen2_5_omni
 ```
 
-Send request via python
+####  Send request via python
+
 ```bash
 python openai_chat_completion_client_for_multimodal_generation.py --query-type mixed_modalities
 ```
 
-Send request via curl
+The Python client supports the following command-line arguments:
+
+- `--query-type` (or `-q`): Query type (default: `mixed_modalities`)
+  - Options: `mixed_modalities`, `use_audio_in_video`, `multi_audios`, `text`
+- `--video-path` (or `-v`): Path to local video file or URL
+  - If not provided and query-type uses video, uses default video URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Example: `--video-path /path/to/video.mp4` or `--video-path https://example.com/video.mp4`
+- `--image-path` (or `-i`): Path to local image file or URL
+  - If not provided and query-type uses image, uses default image URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common image formats: JPEG, PNG, GIF, WebP
+  - Example: `--image-path /path/to/image.jpg` or `--image-path https://example.com/image.png`
+- `--audio-path` (or `-a`): Path to local audio file or URL
+  - If not provided and query-type uses audio, uses default audio URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common audio formats: MP3, WAV, OGG, FLAC, M4A
+  - Example: `--audio-path /path/to/audio.wav` or `--audio-path https://example.com/audio.mp3`
+- `--prompt` (or `-p`): Custom text prompt/question
+  - If not provided, uses default prompt for the selected query type
+  - Example: `--prompt "What are the main activities shown in this video?"`
+
+
+For example, to use mixed modalities with all local files:
+
+```bash
+python openai_chat_completion_client_for_multimodal_generation.py \
+    --query-type mixed_modalities \
+    --video-path /path/to/your/video.mp4 \
+    --image-path /path/to/your/image.jpg \
+    --audio-path /path/to/your/audio.wav \
+    --prompt "Analyze all the media content and provide a comprehensive summary."
+```
+
+####  Send request via curl
+
 ```bash
 bash run_curl_multimodal_generation.sh mixed_modalities
 ```
 
-### FAQ
-
-If you encounter error about backend of librosa, try to install ffmpeg with command below.
-```
-sudo apt update
-sudo apt install ffmpeg
-```
-
 ## Run Local Web UI Demo
 
 This Web UI demo allows users to interact with the model through a web browser.
@@ -56,24 +87,21 @@ python gradio_demo.py  --model Qwen/Qwen2.5-Omni-7B --port 7861
 
 Then open `http://localhost:7861/` on your local browser to interact with the web UI.
 
+The gradio script supports the following arguments:
 
-### Options
+- `--model`: Model name
+- `--ip`: Host/IP for Gradio server (default: 127.0.0.1)
+- `--port`: Port for Gradio server (default: 7861)
+- `--stage-configs-path`: Path to custom stage configs YAML file (optional)
+- `--share`: Share the Gradio demo publicly (creates a public link)
 
-You can customize its basic launch parameters:
+### FAQ
 
-```bash
-python gradio_demo.py \
-    --model Qwen/Qwen2.5-Omni-7B \
-    --ip 127.0.0.1 \
-    --port 7861 \
-    --stage-configs-path /path/to/stage_configs.yaml
+If you encounter error about backend of librosa, try to install ffmpeg with command below.
+```
+sudo apt update
+sudo apt install ffmpeg
 ```
-
-- `--model`: Local model checkpoint to load (default `Qwen/Qwen2.5-Omni-7B`).
-- `--ip`: Host/IP for the Gradio server (default `127.0.0.1`).
-- `--port`: Port for the Gradio server (default `7861`).
-- `--stage-configs-path`: Optional path to custom stage configs YAML.
-- `--share`: Set to expose a temporary public link via Gradio.
 
 ## Example materials
 

docs/user_guide/examples/online_serving/qwen3_omni.md

@@ -1,15 +1,16 @@
 # Online serving Example of vLLM-Omni for Qwen3-omni
 
-Source <https://github.com/vllm-project/vllm/tree/main/examples/online_serving/qwen3_omni>.
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen3_omni>.
 
 
 ## 🛠️ Installation
 
-Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/README.md)
+Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
 
 ## Run examples (Qwen3-Omni)
 
-Launch the server
+### Launch the Server
+
 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
@@ -19,21 +20,59 @@ If you have custom stage configs file, launch the server with command below
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
 ```
 
+### Send Multi-modal Request
+
 Get into the example folder
 ```bash
 cd examples/online_serving/qwen3_omni
 ```
 
-Send request via python
+####  Send request via python
+
 ```bash
 python openai_chat_completion_client_for_multimodal_generation.py --query-type use_image
 ```
 
-Send request via curl
+The Python client supports the following command-line arguments:
+
+- `--query-type` (or `-q`): Query type (default: `use_video`)
+  - Options: `text`, `use_audio`, `use_image`, `use_video`
+- `--model` (or `-m`): Model name/path (default: `Qwen/Qwen3-Omni-30B-A3B-Instruct`)
+- `--video-path` (or `-v`): Path to local video file or URL
+  - If not provided and query-type is `use_video`, uses default video URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Example: `--video-path /path/to/video.mp4` or `--video-path https://example.com/video.mp4`
+- `--image-path` (or `-i`): Path to local image file or URL
+  - If not provided and query-type is `use_image`, uses default image URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common image formats: JPEG, PNG, GIF, WebP
+  - Example: `--image-path /path/to/image.jpg` or `--image-path https://example.com/image.png`
+- `--audio-path` (or `-a`): Path to local audio file or URL
+  - If not provided and query-type is `use_audio`, uses default audio URL
+  - Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs
+  - Supports common audio formats: MP3, WAV, OGG, FLAC, M4A
+  - Example: `--audio-path /path/to/audio.wav` or `--audio-path https://example.com/audio.mp3`
+- `--prompt` (or `-p`): Custom text prompt/question
+  - If not provided, uses default prompt for the selected query type
+  - Example: `--prompt "What are the main activities shown in this video?"`
+
+
+For example, to use a local video file with custom prompt:
+
+```bash
+python openai_chat_completion_client_for_multimodal_generation.py \
+    --query-type use_video \
+    --video-path /path/to/your/video.mp4 \
+    --prompt "What are the main activities shown in this video?"
+```
+
+####  Send request via curl
+
 ```bash
 bash run_curl_multimodal_generation.sh use_image
 ```
 
+
 ### FAQ
 
 If you encounter error about backend of librosa, try to install ffmpeg with command below.
@@ -57,23 +96,9 @@ python gradio_demo.py  --model Qwen/Qwen3-Omni-30B-A3B-Instruct --port 7861
 Then open `http://localhost:7861/` on your local browser to interact with the web UI.
 
 
-### Options
-
-The gradio demo also supports running with an existing API server and can be customized with the following arguments.
-
-
-```bash
-python gradio_demo.py \
-    --model Qwen/Qwen3-Omni-30B-A3B-Instruct \
-    --use-api-server \
-    --api-base http://localhost:8091/v1 \
-    --ip 127.0.0.1 \
-    --port 7861
-```
+The gradio script supports the following arguments:
 
 - `--model`: Model name
-- `--use-api-server`: If set, connect to an existing vLLM HTTP API server instead of running AsyncOmni locally.
-- `--api-base`: Base URL for vllm serve (only used when `use-api-server` is set, default: http://localhost:8091/v1)
 - `--ip`: Host/IP for Gradio server (default: 127.0.0.1)
 - `--port`: Port for Gradio server (default: 7861)
 - `--stage-configs-path`: Path to custom stage configs YAML file (optional)
@@ -89,6 +114,10 @@ python gradio_demo.py \
     ``````py
     --8<-- "examples/online_serving/qwen3_omni/openai_chat_completion_client_for_multimodal_generation.py"
     ``````
+??? abstract "qwen3_omni_moe_thinking.yaml"
+    ``````yaml
+    --8<-- "examples/online_serving/qwen3_omni/qwen3_omni_moe_thinking.yaml"
+    ``````
 ??? abstract "run_curl_multimodal_generation.sh"
     ``````sh
     --8<-- "examples/online_serving/qwen3_omni/run_curl_multimodal_generation.sh"

examples/offline_inference/qwen2_5_omni/README.md

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

examples/offline_inference/qwen2_5_omni/end2end.py

@@ -6,12 +6,15 @@ with the correct prompt format on Qwen2.5-Omni
 """
 
 import os
-from typing import NamedTuple
+from typing import NamedTuple, Optional
 
+import librosa
+import numpy as np
 import soundfile as sf
+from PIL import Image
 from vllm.assets.audio import AudioAsset
 from vllm.assets.image import ImageAsset
-from vllm.assets.video import VideoAsset
+from vllm.assets.video import VideoAsset, video_to_ndarrays
 from vllm.multimodal.image import convert_image_mode
 from vllm.sampling_params import SamplingParams
 from vllm.utils import FlexibleArgumentParser
@@ -54,7 +57,13 @@ def get_text_query(question: str = None) -> QueryResult:
     )
 
 
-def get_mixed_modalities_query() -> QueryResult:
+def get_mixed_modalities_query(
+    video_path: Optional[str] = None,
+    image_path: Optional[str] = None,
+    audio_path: Optional[str] = None,
+    num_frames: int = 16,
+    sampling_rate: int = 16000,
+) -> QueryResult:
     question = "What is recited in the audio? What is the content of this image? Why is this video funny?"
     prompt = (
         f"<|im_start|>system\n{default_system}<|im_end|>\n"
@@ -64,20 +73,49 @@ def get_mixed_modalities_query() -> QueryResult:
         f"{question}<|im_end|>\n"
         f"<|im_start|>assistant\n"
     )
+
+    # Load video
+    if video_path:
+        if not os.path.exists(video_path):
+            raise FileNotFoundError(f"Video file not found: {video_path}")
+        video_frames = video_to_ndarrays(video_path, num_frames=num_frames)
+    else:
+        video_frames = VideoAsset(name="baby_reading", num_frames=num_frames).np_ndarrays
+
+    # Load image
+    if image_path:
+        if not os.path.exists(image_path):
+            raise FileNotFoundError(f"Image file not found: {image_path}")
+        pil_image = Image.open(image_path)
+        image_data = convert_image_mode(pil_image, "RGB")
+    else:
+        image_data = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
+
+    # Load audio
+    if audio_path:
+        if not os.path.exists(audio_path):
+            raise FileNotFoundError(f"Audio file not found: {audio_path}")
+        audio_signal, sr = librosa.load(audio_path, sr=sampling_rate)
+        audio_data = (audio_signal.astype(np.float32), sr)
+    else:
+        audio_data = AudioAsset("mary_had_lamb").audio_and_sample_rate
+
     return QueryResult(
         inputs={
             "prompt": prompt,
             "multi_modal_data": {
-                "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
-                "image": convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB"),
-                "video": VideoAsset(name="baby_reading", num_frames=16).np_ndarrays,
+                "audio": audio_data,
+                "image": image_data,
+                "video": video_frames,
             },
         },
         limit_mm_per_prompt={"audio": 1, "image": 1, "video": 1},
     )
 
 
-def get_use_audio_in_video_query() -> QueryResult:
+def get_use_audio_in_video_query(
+    video_path: Optional[str] = None, num_frames: int = 16, sampling_rate: int = 16000
+) -> QueryResult:
     question = "Describe the content of the video, then convert what the baby say into text."
     prompt = (
         f"<|im_start|>system\n{default_system}<|im_end|>\n"
@@ -85,14 +123,24 @@ def get_use_audio_in_video_query() -> QueryResult:
         f"{question}<|im_end|>\n"
         f"<|im_start|>assistant\n"
     )
-    asset = VideoAsset(name="baby_reading", num_frames=16)
-    audio = asset.get_audio(sampling_rate=16000)
+
+    if video_path:
+        if not os.path.exists(video_path):
+            raise FileNotFoundError(f"Video file not found: {video_path}")
+        video_frames = video_to_ndarrays(video_path, num_frames=num_frames)
+        # Extract audio from video file
+        audio_signal, sr = librosa.load(video_path, sr=sampling_rate)
+        audio = (audio_signal.astype(np.float32), sr)
+    else:
+        asset = VideoAsset(name="baby_reading", num_frames=num_frames)
+        video_frames = asset.np_ndarrays
+        audio = asset.get_audio(sampling_rate=sampling_rate)
 
     return QueryResult(
         inputs={
             "prompt": prompt,
             "multi_modal_data": {
-                "video": asset.np_ndarrays,
+                "video": video_frames,
                 "audio": audio,
             },
             "mm_processor_kwargs": {
@@ -103,7 +151,7 @@ def get_use_audio_in_video_query() -> QueryResult:
     )
 
 
-def get_multi_audios_query() -> QueryResult:
+def get_multi_audios_query(audio_path: Optional[str] = None, sampling_rate: int = 16000) -> QueryResult:
     question = "Are these two audio clips the same?"
     prompt = (
         f"<|im_start|>system\n{default_system}<|im_end|>\n"
@@ -112,14 +160,28 @@ def get_multi_audios_query() -> QueryResult:
         f"{question}<|im_end|>\n"
         f"<|im_start|>assistant\n"
     )
+
+    if audio_path:
+        if not os.path.exists(audio_path):
+            raise FileNotFoundError(f"Audio file not found: {audio_path}")
+        audio_signal, sr = librosa.load(audio_path, sr=sampling_rate)
+        audio_data = (audio_signal.astype(np.float32), sr)
+        # Use the provided audio as the first audio, default as second
+        audio_list = [
+            audio_data,
+            AudioAsset("mary_had_lamb").audio_and_sample_rate,
+        ]
+    else:
+        audio_list = [
+            AudioAsset("winning_call").audio_and_sample_rate,
+            AudioAsset("mary_had_lamb").audio_and_sample_rate,
+        ]
+
     return QueryResult(
         inputs={
             "prompt": prompt,
             "multi_modal_data": {
-                "audio": [
-                    AudioAsset("winning_call").audio_and_sample_rate,
-                    AudioAsset("mary_had_lamb").audio_and_sample_rate,
-                ],
+                "audio": audio_list,
             },
         },
         limit_mm_per_prompt={
@@ -128,17 +190,135 @@ def get_multi_audios_query() -> QueryResult:
     )
 
 
+def get_image_query(question: str = None, image_path: Optional[str] = None) -> QueryResult:
+    if question is None:
+        question = "What is the content of this image?"
+    prompt = (
+        f"<|im_start|>system\n{default_system}<|im_end|>\n"
+        "<|im_start|>user\n<|vision_bos|><|IMAGE|><|vision_eos|>"
+        f"{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+
+    if image_path:
+        if not os.path.exists(image_path):
+            raise FileNotFoundError(f"Image file not found: {image_path}")
+        pil_image = Image.open(image_path)
+        image_data = convert_image_mode(pil_image, "RGB")
+    else:
+        image_data = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
+
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+            "multi_modal_data": {
+                "image": image_data,
+            },
+        },
+        limit_mm_per_prompt={"image": 1},
+    )
+
+
+def get_video_query(question: str = None, video_path: Optional[str] = None, num_frames: int = 16) -> QueryResult:
+    if question is None:
+        question = "Why is this video funny?"
+    prompt = (
+        f"<|im_start|>system\n{default_system}<|im_end|>\n"
+        "<|im_start|>user\n<|vision_bos|><|VIDEO|><|vision_eos|>"
+        f"{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+
+    if video_path:
+        if not os.path.exists(video_path):
+            raise FileNotFoundError(f"Video file not found: {video_path}")
+        video_frames = video_to_ndarrays(video_path, num_frames=num_frames)
+    else:
+        video_frames = VideoAsset(name="baby_reading", num_frames=num_frames).np_ndarrays
+
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+            "multi_modal_data": {
+                "video": video_frames,
+            },
+        },
+        limit_mm_per_prompt={"video": 1},
+    )
+
+
+def get_audio_query(question: str = None, audio_path: Optional[str] = None, sampling_rate: int = 16000) -> QueryResult:
+    if question is None:
+        question = "What is the content of this audio?"
+    prompt = (
+        f"<|im_start|>system\n{default_system}<|im_end|>\n"
+        "<|im_start|>user\n<|audio_bos|><|AUDIO|><|audio_eos|>"
+        f"{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+
+    if audio_path:
+        if not os.path.exists(audio_path):
+            raise FileNotFoundError(f"Audio file not found: {audio_path}")
+        audio_signal, sr = librosa.load(audio_path, sr=sampling_rate)
+        audio_data = (audio_signal.astype(np.float32), sr)
+    else:
+        audio_data = AudioAsset("mary_had_lamb").audio_and_sample_rate
+
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+            "multi_modal_data": {
+                "audio": audio_data,
+            },
+        },
+        limit_mm_per_prompt={"audio": 1},
+    )
+
+
 query_map = {
     "mixed_modalities": get_mixed_modalities_query,
     "use_audio_in_video": get_use_audio_in_video_query,
     "multi_audios": get_multi_audios_query,
+    "use_image": get_image_query,
+    "use_video": get_video_query,
+    "use_audio": get_audio_query,
     "text": get_text_query,
 }
 
 
 def main(args):
     model_name = "Qwen/Qwen2.5-Omni-7B"
-    query_result = query_map[args.query_type]()
+
+    # Get paths from args
+    video_path = getattr(args, "video_path", None)
+    image_path = getattr(args, "image_path", None)
+    audio_path = getattr(args, "audio_path", None)
+    num_frames = getattr(args, "num_frames", 16)
+    sampling_rate = getattr(args, "sampling_rate", 16000)
+
+    # Get the query function and call it with appropriate parameters
+    query_func = query_map[args.query_type]
+    if args.query_type == "mixed_modalities":
+        query_result = query_func(
+            video_path=video_path,
+            image_path=image_path,
+            audio_path=audio_path,
+            num_frames=num_frames,
+            sampling_rate=sampling_rate,
+        )
+    elif args.query_type == "use_audio_in_video":
+        query_result = query_func(video_path=video_path, num_frames=num_frames, sampling_rate=sampling_rate)
+    elif args.query_type == "multi_audios":
+        query_result = query_func(audio_path=audio_path, sampling_rate=sampling_rate)
+    elif args.query_type == "use_image":
+        query_result = query_func(image_path=image_path)
+    elif args.query_type == "use_video":
+        query_result = query_func(video_path=video_path, num_frames=num_frames)
+    elif args.query_type == "use_audio":
+        query_result = query_func(audio_path=audio_path, sampling_rate=sampling_rate)
+    else:
+        query_result = query_func()
 
     omni_llm = Omni(
         model=model_name,
@@ -283,6 +463,39 @@ def parse_args():
         default=None,
         help="Path to a .txt file with one prompt per line (preferred).",
     )
+    parser.add_argument(
+        "--video-path",
+        "-v",
+        type=str,
+        default=None,
+        help="Path to local video file. If not provided, uses default video asset.",
+    )
+    parser.add_argument(
+        "--image-path",
+        "-i",
+        type=str,
+        default=None,
+        help="Path to local image file. If not provided, uses default image asset.",
+    )
+    parser.add_argument(
+        "--audio-path",
+        "-a",
+        type=str,
+        default=None,
+        help="Path to local audio file. If not provided, uses default audio asset.",
+    )
+    parser.add_argument(
+        "--num-frames",
+        type=int,
+        default=16,
+        help="Number of frames to extract from video (default: 16).",
+    )
+    parser.add_argument(
+        "--sampling-rate",
+        type=int,
+        default=16000,
+        help="Sampling rate for audio loading (default: 16000).",
+    )
 
     return parser.parse_args()
 

examples/offline_inference/qwen3_omni/README.md

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

examples/offline_inference/qwen3_omni/end2end.py

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

examples/offline_inference/qwen_image/README.md

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

examples/offline_inference/qwen_image/gradio_demo.py

@@ -105,7 +105,6 @@ def build_demo(args: argparse.Namespace) -> gr.Blocks:
             generator=generator,
             true_cfg_scale=float(cfg_scale_value),
             num_inference_steps=num_steps,
-            num_images_per_prompt=num_images,
             num_outputs_per_prompt=num_images,
         )
         return [img for img in images[:num_images]]

examples/offline_inference/qwen_image/image_edit.py

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

examples/offline_inference/qwen_image/text_to_image.py

@@ -65,7 +65,6 @@ def main():
         generator=generator,
         true_cfg_scale=args.cfg_scale,
         num_inference_steps=args.num_inference_steps,
-        num_images_per_prompt=args.num_images_per_prompt,
         num_outputs_per_prompt=args.num_images_per_prompt,
     )
 

examples/online_serving/qwen3_omni/qwen3_omni_moe_thinking.yaml

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

pyproject.toml

@@ -156,6 +156,7 @@ markers = [
     "integration: Integration tests",
     "benchmark: Benchmark tests",
     "slow: Slow tests",
+    "core_model: enable this model test in each PR instead of only nightly",
 ]
 
 [tool.typos.default]

tests/conftest.py

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

tests/multi_stages/conftest.py

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

tests/multi_stages/stage_configs/qwen2_5_omni_ci.yaml

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

tests/multi_stages/test_qwen2_5_omni.py

@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+E2E tests for Qwen2.5-Omni model with mixed modality inputs and audio output.
+"""
+
+from pathlib import Path
+
+import pytest
+from vllm.assets.audio import AudioAsset
+from vllm.assets.image import ImageAsset
+from vllm.assets.video import VideoAsset
+from vllm.multimodal.image import convert_image_mode
+
+from .conftest import OmniRunner
+from .utils import create_new_process_for_each_test
+
+models = ["Qwen/Qwen2.5-Omni-3B"]
+
+# CI stage config optimized for 24GB GPU (L4/RTX3090)
+CI_STAGE_CONFIG_PATH = str(Path(__file__).parent / "stage_configs" / "qwen2_5_omni_ci.yaml")
+
+
+@pytest.mark.core_model
+@pytest.mark.parametrize("model", models)
+@create_new_process_for_each_test()
+def test_mixed_modalities_to_audio(omni_runner: type[OmniRunner], model: str) -> None:
+    """Test processing audio, image, and video together, generating audio output."""
+    with omni_runner(model, seed=42, stage_configs_path=CI_STAGE_CONFIG_PATH) as runner:
+        # Prepare multimodal inputs
+        question = "What is recited in the audio? What is in this image? Describe the video briefly."
+        audio = AudioAsset("mary_had_lamb").audio_and_sample_rate
+        audio = (audio[0][: 16000 * 5], audio[1])  # Trim to first 5 seconds
+        image = convert_image_mode(ImageAsset("cherry_blossom").pil_image.resize((128, 128)), "RGB")
+        video = VideoAsset(name="baby_reading", num_frames=4).np_ndarrays
+
+        outputs = runner.generate_multimodal(
+            prompts=question,
+            audios=audio,
+            images=image,
+            videos=video,
+        )
+
+        # Verify we got outputs from multiple stages
+        assert len(outputs) > 0
+
+        # Find and verify text output (thinker stage)
+        text_output = None
+        for stage_output in outputs:
+            if stage_output.final_output_type == "text":
+                text_output = stage_output
+                break
+
+        assert text_output is not None
+        assert len(text_output.request_output) > 0
+        text_content = text_output.request_output[0].outputs[0].text
+        assert text_content is not None
+        assert len(text_content.strip()) > 0
+
+        # Find and verify audio output (code2wav stage)
+        audio_output = None
+        for stage_output in outputs:
+            if stage_output.final_output_type == "audio":
+                audio_output = stage_output
+                break
+
+        assert audio_output is not None
+        assert len(audio_output.request_output) > 0
+
+        # Verify audio tensor exists and has content
+        audio_tensor = audio_output.request_output[0].multimodal_output["audio"]
+        assert audio_tensor is not None
+        assert audio_tensor.numel() > 0

tests/multi_stages/utils.py

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

tests/multi_stages_h100/conftest.py

@@ -0,0 +1,14 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Pytest configuration for multi_stages_h100 tests.
+
+This conftest imports fixtures from the parent multi_stages conftest.
+"""
+
+# Import the omni_runner fixture from the parent directory's conftest
+# This makes the fixture available to tests in this directory
+from ..multi_stages.conftest import omni_runner  # noqa: F401
+
+# Explicitly expose the fixture for pytest discovery
+__all__ = ["omni_runner"]

tests/multi_stages_h100/stage_configs/qwen3_omni_ci.yaml

@@ -0,0 +1,98 @@
+# Stage config for running Qwen3-Omni-MoE with 3-stage architecture
+# Stage 0: Thinker (multimodal understanding + text generation)
+# Stage 1: Talker (text embeddings → 8-layer RVQ codec codes)
+# Stage 2: Code2Wav (8-layer RVQ codes → audio waveform)
+
+# The following config has been verified on 2x H100-80G GPUs.
+stage_args:
+  - stage_id: 0
+    runtime:
+      devices: "0,1"
+      max_batch_size: 1
+    engine_args:
+      model_stage: thinker
+      model_arch: Qwen3OmniMoeForConditionalGeneration
+      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      gpu_memory_utilization: 0.6
+      enforce_eager: true
+      trust_remote_code: true
+      engine_output_type: latent  # Output hidden states for talker
+      distributed_executor_backend: "mp"
+      enable_prefix_caching: false
+      hf_config_name: thinker_config
+      tensor_parallel_size: 2
+      load_format: dummy
+    final_output: true
+    final_output_type: text
+    is_comprehension: true
+    default_sampling_params:
+      temperature: 0.4
+      top_p: 0.9
+      top_k: 1
+      max_tokens: 100
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.05
+
+  - stage_id: 1
+    runtime:
+       devices: "1"
+       max_batch_size: 1
+    engine_args:
+       model_stage: talker
+       model_arch: Qwen3OmniMoeForConditionalGeneration
+       worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
+       scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+       gpu_memory_utilization: 0.5
+       enforce_eager: true
+       trust_remote_code: true
+       engine_output_type: latent  # Output codec codes for code2wav
+      #  tensor_parallel_size: 2
+       enable_prefix_caching: false
+       distributed_executor_backend: "mp"
+       hf_config_name: talker_config
+       load_format: dummy
+    engine_input_source: [0]
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker
+    # final_output: true
+    # final_output_type: text
+    default_sampling_params:
+      temperature: 0.9
+      top_k: 50
+      max_tokens: 100
+      seed: 42
+      detokenize: False
+      repetition_penalty: 1.05
+      stop_token_ids: [2150]
+
+  - stage_id: 2
+    runtime:
+      devices: "0"
+      max_batch_size: 1
+    engine_args:
+      model_stage: code2wav
+      model_arch: Qwen3OmniMoeForConditionalGeneration
+      worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
+      enforce_eager: true
+      trust_remote_code: true
+      enable_prefix_caching: false
+      engine_output_type: audio  # Final output: audio waveform
+      gpu_memory_utilization: 0.1
+      distributed_executor_backend: "mp"
+      max_num_batched_tokens: 1000000
+      hf_config_name: thinker_config
+      load_format: dummy
+    engine_input_source: [1]
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav
+    final_output: true
+    final_output_type: audio
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 200
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.1

tests/multi_stages_h100/test_qwen3_omni.py

@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+E2E tests for Qwen3-Omni model with audio/video/image input and audio output.
+"""
+
+import os
+from pathlib import Path
+
+import pytest
+from vllm.assets.video import VideoAsset
+
+from ..multi_stages.conftest import OmniRunner
+
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+models = ["Qwen/Qwen3-Omni-30B-A3B-Instruct"]
+
+# CI stage config for 2xH100-80G GPUs
+CI_STAGE_CONFIG_PATH = str(Path(__file__).parent / "stage_configs" / "qwen3_omni_ci.yaml")
+
+
+@pytest.mark.parametrize("model", models)
+def test_video_to_audio(omni_runner: type[OmniRunner], model: str) -> None:
+    """Test processing video, generating audio output."""
+    with omni_runner(model, seed=42, stage_configs_path=CI_STAGE_CONFIG_PATH) as runner:
+        # Prepare inputs
+        question = "Describe the video briefly."
+        video = VideoAsset(name="baby_reading", num_frames=4).np_ndarrays
+
+        outputs = runner.generate_multimodal(
+            prompts=question,
+            videos=video,
+        )
+
+        # Verify we got outputs from multiple stages
+        assert len(outputs) > 0
+
+        # Find and verify text output (thinker stage)
+        text_output = None
+        for stage_output in outputs:
+            if stage_output.final_output_type == "text":
+                text_output = stage_output
+                break
+
+        assert text_output is not None
+        assert len(text_output.request_output) > 0
+        text_content = text_output.request_output[0].outputs[0].text
+        assert text_content is not None
+        assert len(text_content.strip()) > 0
+
+        # Find and verify audio output (code2wav stage)
+        audio_output = None
+        for stage_output in outputs:
+            if stage_output.final_output_type == "audio":
+                audio_output = stage_output
+                break
+
+        assert audio_output is not None
+        assert len(audio_output.request_output) > 0
+
+        # Verify audio tensor exists and has content
+        audio_tensor = audio_output.request_output[0].multimodal_output["audio"]
+        assert audio_tensor is not None
+        assert audio_tensor.numel() > 0

tests/single_stage/test_diffusion_model.py

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

tests/utils.py

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

vllm_omni/core/sched/__init__.py

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

vllm_omni/core/sched/diffusion_scheduler.py

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

vllm_omni/core/sched/omni_ar_scheduler.py

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

vllm_omni/core/sched/omni_generation_scheduler.py

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

vllm_omni/diffusion/attention/__init__.py

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

vllm_omni/diffusion/attention/backends/__init__.py

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

vllm_omni/diffusion/attention/backends/abstract.py

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

vllm_omni/diffusion/attention/backends/flash_attn.py

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

vllm_omni/diffusion/attention/backends/sdpa.py

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

vllm_omni/diffusion/attention/layer.py

@@ -0,0 +1,43 @@
+# 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

vllm_omni/diffusion/attention/selector.py

@@ -0,0 +1,53 @@
+# 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

vllm_omni/diffusion/data.py

@@ -14,6 +14,32 @@ from vllm_omni.diffusion.utils.network_utils import is_port_available
 logger = init_logger(__name__)
 
 
+@dataclass
+class TransformerConfig:
+    """Container for raw transformer configuration dictionaries."""
+
+    params: dict[str, Any] = field(default_factory=dict)
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "TransformerConfig":
+        if not isinstance(data, dict):
+            raise TypeError(f"Expected transformer config dict, got {type(data)!r}")
+        return cls(params=dict(data))
+
+    def to_dict(self) -> dict[str, Any]:
+        return dict(self.params)
+
+    def get(self, key: str, default: Any | None = None) -> Any:
+        return self.params.get(key, default)
+
+    def __getattr__(self, item: str) -> Any:
+        params = object.__getattribute__(self, "params")
+        try:
+            return params[item]
+        except KeyError as exc:
+            raise AttributeError(item) from exc
+
+
 @dataclass
 class OmniDiffusionConfig:
     # Model and path configuration (for convenience)
@@ -21,6 +47,10 @@ class OmniDiffusionConfig:
 
     model_class_name: str | None = None
 
+    dtype: torch.dtype = torch.bfloat16
+
+    tf_model_config: TransformerConfig = field(default_factory=TransformerConfig)
+
     # Attention
     # attention_backend: str = None
 
@@ -212,3 +242,7 @@ class AttentionBackendEnum(enum.Enum):
 
     def __str__(self):
         return self.name.lower()
+
+
+# Special message broadcast via scheduler queues to signal worker shutdown.
+SHUTDOWN_MESSAGE = {"type": "shutdown"}

vllm_omni/diffusion/diffusion_engine.py

@@ -2,11 +2,12 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import multiprocessing as mp
+import time
 
 from vllm.logger import init_logger
 
-from vllm_omni.diffusion.data import OmniDiffusionConfig
-from vllm_omni.diffusion.registry import get_diffusion_post_process_func
+from vllm_omni.diffusion.data import SHUTDOWN_MESSAGE, OmniDiffusionConfig
+from vllm_omni.diffusion.registry import get_diffusion_post_process_func, get_diffusion_pre_process_func
 from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.scheduler import scheduler
 from vllm_omni.utils.platform_utils import get_diffusion_worker_class
@@ -26,16 +27,32 @@ class DiffusionEngine:
         self.od_config = od_config
 
         self.post_process_func = get_diffusion_post_process_func(od_config)
+        self.pre_process_func = get_diffusion_pre_process_func(od_config)
 
+        self._processes: list[mp.Process] = []
+        self._closed = False
         self._make_client()
 
     def step(self, requests: list[OmniDiffusionRequest]):
         try:
+            # Apply pre-processing if available
+            if self.pre_process_func is not None:
+                preprocess_start_time = time.time()
+                requests = self.pre_process_func(requests)
+                preprocess_time = time.time() - preprocess_start_time
+                logger.info(f"Pre-processing completed in {preprocess_time:.4f} seconds")
+
             output = self.add_req_and_wait_for_response(requests)
             if output.error:
                 raise Exception(f"{output.error}")
             logger.info("Generation completed successfully.")
-            return self.post_process_func(output.output)
+
+            postprocess_start_time = time.time()
+            result = self.post_process_func(output.output)
+            postprocess_time = time.time() - postprocess_start_time
+            logger.info(f"Post-processing completed in {postprocess_time:.4f} seconds")
+
+            return result
         except Exception as e:
             logger.error(f"Generation failed: {e}")
             return None
@@ -68,6 +85,8 @@ class DiffusionEngine:
         else:
             logger.error("Failed to get result queue handle from workers")
 
+        self._processes = processes
+
     def _launch_workers(self, broadcast_handle):
         od_config = self.od_config
         logger.info("Starting server...")
@@ -131,3 +150,32 @@ class DiffusionEngine:
 
     def add_req_and_wait_for_response(self, requests: list[OmniDiffusionRequest]):
         return scheduler.add_req(requests)
+
+    def close(self, *, timeout_s: float = 30.0) -> None:
+        if self._closed:
+            return
+        self._closed = True
+
+        # Send shutdown signal to worker processes via broadcast queue
+        try:
+            if getattr(scheduler, "mq", None) is not None:
+                for _ in range(self.od_config.num_gpus or 1):
+                    scheduler.mq.enqueue(SHUTDOWN_MESSAGE)
+        except Exception as exc:  # pragma: no cover - best effort cleanup
+            logger.warning("Failed to send shutdown signal: %s", exc)
+
+        # Join all worker processes, terminate if they refuse to exit
+        for proc in self._processes:
+            if not proc.is_alive():
+                continue
+            proc.join(timeout_s)
+            if proc.is_alive():
+                logger.warning("Terminating diffusion worker %s after timeout", proc.name)
+                proc.terminate()
+                proc.join(timeout_s)
+
+        scheduler.close()
+        self._processes = []
+
+    def __del__(self):  # pragma: no cover - best effort cleanup
+        self.close()

vllm_omni/diffusion/model_loader/diffusers_loader.py

@@ -0,0 +1,228 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import dataclasses
+import glob
+import os
+import time
+from collections.abc import Generator, Iterable
+from pathlib import Path
+from typing import cast
+
+import torch
+from torch import nn
+from vllm.config import ModelConfig
+from vllm.config.load import LoadConfig
+from vllm.logger import init_logger
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+from vllm.model_executor.model_loader.weight_utils import (
+    download_safetensors_index_file_from_hf,
+    download_weights_from_hf,
+    filter_duplicate_safetensors_files,
+    filter_files_not_needed_for_inference,
+    maybe_download_from_modelscope,
+    safetensors_weights_iterator,
+)
+
+from vllm_omni.diffusion.data import OmniDiffusionConfig
+from vllm_omni.diffusion.registry import initialize_model
+
+logger = init_logger(__name__)
+
+
+MODEL_INDEX = "model_index.json"
+DIFFUSION_MODEL_WEIGHTS_INDEX = "diffusion_pytorch_model.safetensors.index.json"
+
+
+class DiffusersPipelineLoader:
+    """Model loader that can load diffusers pipeline components from disk."""
+
+    # default number of thread when enable multithread weight loading
+    DEFAULT_NUM_THREADS = 8
+
+    @dataclasses.dataclass
+    class ComponentSource:
+        """A source for weights."""
+
+        model_or_path: str
+        """The model ID or path."""
+
+        subfolder: str | None
+        """The subfolder inside the model repo."""
+
+        revision: str | None
+        """The optional model revision."""
+
+        prefix: str = ""
+        """A prefix to prepend to all weights."""
+
+        fall_back_to_pt: bool = True
+        """Whether .pt weights can be used."""
+
+        allow_patterns_overrides: list[str] | None = None
+        """If defined, weights will load exclusively using these patterns."""
+
+    counter_before_loading_weights: float = 0.0
+    counter_after_loading_weights: float = 0.0
+
+    def __init__(self, load_config: LoadConfig):
+        self.load_config = load_config
+
+        # TODO(Isotr0py): Enable multithreaded weight loading
+        # extra_config = load_config.model_loader_extra_config
+        # allowed_keys = {"enable_multithread_load", "num_threads"}
+        # unexpected_keys = set(extra_config.keys()) - allowed_keys
+
+        # if unexpected_keys:
+        #     raise ValueError(
+        #         f"Unexpected extra config keys for load format {load_config.load_format}: {unexpected_keys}"
+        #     )
+
+    def _prepare_weights(
+        self,
+        model_name_or_path: Path,
+        subfolder: str | None,
+        revision: str | None,
+        fall_back_to_pt: bool,
+        allow_patterns_overrides: list[str] | None,
+    ) -> tuple[str, list[str], bool]:
+        """Prepare weights for the model.
+
+        If the model is not local, it will be downloaded."""
+        model_name_or_path = maybe_download_from_modelscope(model_name_or_path, revision) or model_name_or_path
+
+        is_local = os.path.isdir(model_name_or_path)
+        load_format = self.load_config.load_format
+        use_safetensors = False
+        index_file = DIFFUSION_MODEL_WEIGHTS_INDEX
+
+        # only hf is supported currently
+        if load_format == "auto":
+            load_format = "hf"
+
+        # Some quantized models use .pt files for storing the weights.
+        if load_format == "hf":
+            allow_patterns = ["*.safetensors", "*.bin"]
+        else:
+            raise ValueError(f"Unknown load_format: {load_format}")
+
+        if fall_back_to_pt:
+            allow_patterns += ["*.pt"]
+
+        if allow_patterns_overrides is not None:
+            allow_patterns = allow_patterns_overrides
+
+        if subfolder is not None:
+            allow_patterns = [f"{subfolder}/{pattern}" for pattern in allow_patterns]
+
+        if not is_local:
+            hf_folder = download_weights_from_hf(
+                model_name_or_path,
+                self.load_config.download_dir,
+                allow_patterns,
+                revision,
+                ignore_patterns=self.load_config.ignore_patterns,
+            )
+        else:
+            hf_folder = model_name_or_path
+
+        hf_weights_files: list[str] = []
+        for pattern in allow_patterns:
+            hf_weights_files += glob.glob(os.path.join(hf_folder, pattern))
+            if len(hf_weights_files) > 0:
+                if pattern == "*.safetensors":
+                    use_safetensors = True
+                break
+
+        if use_safetensors:
+            # For models like Mistral-7B-Instruct-v0.3
+            # there are both sharded safetensors files and a consolidated
+            # safetensors file. Using both breaks.
+            # Here, we download the `model.safetensors.index.json` and filter
+            # any files not found in the index.
+            if not is_local:
+                download_safetensors_index_file_from_hf(
+                    model_name_or_path,
+                    index_file,
+                    self.load_config.download_dir,
+                    revision,
+                )
+            hf_weights_files = filter_duplicate_safetensors_files(hf_weights_files, hf_folder, index_file)
+        else:
+            hf_weights_files = filter_files_not_needed_for_inference(hf_weights_files)
+
+        if len(hf_weights_files) == 0:
+            raise RuntimeError(f"Cannot find any model weights with `{model_name_or_path}`")
+
+        return hf_folder, hf_weights_files, use_safetensors
+
+    def _get_weights_iterator(self, source: "ComponentSource") -> Generator[tuple[str, torch.Tensor], None, None]:
+        """Get an iterator for the model weights based on the load format."""
+        hf_folder, hf_weights_files, use_safetensors = self._prepare_weights(
+            source.model_or_path,
+            source.subfolder,
+            source.revision,
+            source.fall_back_to_pt,
+            source.allow_patterns_overrides,
+        )
+        weights_iterator = safetensors_weights_iterator(
+            hf_weights_files,
+            self.load_config.use_tqdm_on_load,
+            self.load_config.safetensors_load_strategy,
+        )
+
+        if self.counter_before_loading_weights == 0.0:
+            self.counter_before_loading_weights = time.perf_counter()
+        # Apply the prefix.
+        return ((source.prefix + name, tensor) for (name, tensor) in weights_iterator)
+
+    def get_all_weights(
+        self,
+        model: nn.Module,
+    ) -> Generator[tuple[str, torch.Tensor], None, None]:
+        sources = cast(
+            Iterable[DiffusersPipelineLoader.ComponentSource],
+            getattr(model, "weights_sources", ()),
+        )
+        for source in sources:
+            yield from self._get_weights_iterator(source)
+
+    def download_model(self, model_config: ModelConfig) -> None:
+        self._prepare_weights(
+            model_config.model,
+            model_config.revision,
+            fall_back_to_pt=True,
+            allow_patterns_overrides=None,
+        )
+
+    def load_model(self, od_config: OmniDiffusionConfig, load_device: str) -> nn.Module:
+        """Load a model with the given configurations."""
+        target_device = torch.device(load_device)
+        with set_default_torch_dtype(od_config.dtype):
+            with target_device:
+                model = initialize_model(od_config)
+
+            logger.debug("Loading weights on %s ...", load_device)
+            # Quantization does not happen in `load_weights` but after it
+            self.load_weights(model)
+        return model.eval()
+
+    def load_weights(self, model: nn.Module) -> None:
+        weights_to_load = {name for name, _ in model.named_parameters()}
+        loaded_weights = model.load_weights(self.get_all_weights(model))
+
+        self.counter_after_loading_weights = time.perf_counter()
+        logger.info_once(
+            "Loading weights took %.2f seconds",
+            self.counter_after_loading_weights - self.counter_before_loading_weights,
+        )
+        # TODO(Isotr0py): Enable weights loading check after decoupling
+        # all components' weights loading (AutoModel.from_pretrained etc).
+        # We only enable strict check for non-quantized models
+        # that have loaded weights tracking currently.
+        if loaded_weights is not None:
+            _ = weights_to_load - loaded_weights
+        #     if weights_not_loaded:
+        #         raise ValueError(
+        #             "Following weights were not initialized from "
+        #             f"checkpoint: {weights_not_loaded}"
+        #         )

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py

@@ -6,6 +6,7 @@ import json
 import logging
 import math
 import os
+from collections.abc import Iterable
 from typing import Any, Optional, Union
 
 import numpy as np
@@ -20,10 +21,11 @@ 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.model_executor.models.utils import AutoWeightsLoader
 
 from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
 from vllm_omni.diffusion.distributed.utils import get_local_device
+from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
 from vllm_omni.diffusion.models.qwen_image.qwen_image_transformer import (
     QwenImageTransformer2DModel,
 )
@@ -236,37 +238,37 @@ class QwenImagePipeline(
     def __init__(
         self,
         *,
-        od_config: OmniDiffusionConfig = None,
+        od_config: OmniDiffusionConfig,
         prefix: str = "",
     ):
         super().__init__()
         self.od_config = od_config
+        self.weights_sources = [
+            DiffusersPipelineLoader.ComponentSource(
+                model_or_path=od_config.model,
+                subfolder="transformer",
+                revision=None,
+                prefix="transformer.",
+                fall_back_to_pt=True,
+            )
+        ]
+
         self.device = get_local_device()
         model = od_config.model
-
         # Check if model is a local path
         local_files_only = os.path.exists(model)
 
         self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
             model, subfolder="scheduler", local_files_only=local_files_only
         )
-        logger.info("Loaded Qwen-Image scheduler successfully")
-
-        with set_default_torch_dtype(torch.bfloat16):
-            self.text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-                model, subfolder="text_encoder", local_files_only=local_files_only
-            )
-            logger.info("Loaded Qwen-Image text encoder successfully")
-            self.vae = AutoencoderKLQwenImage.from_pretrained(
-                model, subfolder="vae", local_files_only=local_files_only
-            ).to(self.device)
-            logger.info("Loaded Qwen-Image VAE successfully")
-            self.transformer = QwenImageTransformer2DModel()
-            logger.info("Initialized Qwen-Image transformer successfully.")
-            self.tokenizer = Qwen2Tokenizer.from_pretrained(
-                model, subfolder="tokenizer", local_files_only=local_files_only
-            )
-            logger.info("Loaded Qwen-Image tokenizer successfully.")
+        self.text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            model, subfolder="text_encoder", local_files_only=local_files_only
+        )
+        self.vae = AutoencoderKLQwenImage.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to(
+            self.device
+        )
+        self.transformer = QwenImageTransformer2DModel(od_config=od_config)
+        self.tokenizer = Qwen2Tokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
 
         self.stage = None
 
@@ -741,52 +743,6 @@ class QwenImagePipeline(
 
         return DiffusionOutput(output=image)
 
-    def load_weights(self):
-        self.load_transformer()
-
-    # TODO: find a better way to load
-    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 hasattr(self, "od_config") else "Qwen/Qwen-Image"
-            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
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit.py

@@ -0,0 +1,829 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import inspect
+import json
+import logging
+import math
+import os
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import numpy as np
+import PIL.Image
+import torch
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.autoencoders.autoencoder_kl_qwenimage import (
+    AutoencoderKLQwenImage,
+)
+from diffusers.schedulers.scheduling_flow_match_euler_discrete import (
+    FlowMatchEulerDiscreteScheduler,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from torch import nn
+from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, Qwen2VLProcessor
+from vllm.model_executor.models.utils import AutoWeightsLoader
+
+from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.distributed.utils import get_local_device
+from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
+from vllm_omni.diffusion.models.qwen_image.pipeline_qwen_image import calculate_shift
+from vllm_omni.diffusion.models.qwen_image.qwen_image_transformer import (
+    QwenImageTransformer2DModel,
+)
+from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.model_executor.model_loader.weight_utils import (
+    download_weights_from_hf_specific,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def get_qwen_image_edit_pre_process_func(
+    od_config: OmniDiffusionConfig,
+):
+    """Pre-processing function for QwenImageEditPipeline."""
+    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["temporal_downsample"]) if "temporal_downsample" in vae_config else 8
+
+    image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor * 2)
+    latent_channels = vae_config.get("z_dim", 16)
+
+    def pre_process_func(
+        requests: list[OmniDiffusionRequest],
+    ):
+        """Pre-process requests for QwenImageEditPipeline."""
+        for req in requests:
+            image = req.pil_image
+
+            image_size = image[0].size if isinstance(image, list) else image.size
+            calculated_width, calculated_height = calculate_dimensions(1024 * 1024, image_size[0] / image_size[1])
+            height = req.height or calculated_height
+            width = req.width or calculated_width
+
+            # Ensure dimensions are multiples of vae_scale_factor * 2
+            multiple_of = vae_scale_factor * 2
+            height = height // multiple_of * multiple_of
+            width = width // multiple_of * multiple_of
+
+            # Store calculated dimensions in request
+            req.calculated_height = calculated_height
+            req.calculated_width = calculated_width
+            req.height = height
+            req.width = width
+
+            # Preprocess image
+            if image is not None and not (
+                isinstance(image, torch.Tensor) and len(image.shape) > 1 and image.shape[1] == latent_channels
+            ):
+                image = image_processor.resize(image, height, width)
+                prompt_image = image
+                image = image_processor.preprocess(image, height, width)
+                image = image.unsqueeze(2)
+
+                # Store preprocessed image and prompt image in request
+                req.preprocessed_image = image
+                req.prompt_image = prompt_image
+
+        return requests
+
+    return pre_process_func
+
+
+def get_qwen_image_edit_post_process_func(
+    od_config: OmniDiffusionConfig,
+):
+    """Post-processing function for QwenImageEditPipeline."""
+    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["temporal_downsample"]) if "temporal_downsample" 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
+
+
+def calculate_dimensions(target_area: float, ratio: float):
+    """Calculate width and height from target area and aspect ratio."""
+    width = math.sqrt(target_area * ratio)
+    height = width / ratio
+
+    width = round(width / 32) * 32
+    height = round(height / 32) * 32
+
+    return width, height
+
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[list[int]] = None,
+    sigmas: Optional[list[float]] = 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`.
+    """
+    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
+
+
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "argmax"
+):
+    """Retrieve latents from VAE encoder output."""
+    if hasattr(encoder_output, "latent_dist"):
+        return (
+            encoder_output.latent_dist.mode()
+            if sample_mode == "argmax"
+            else encoder_output.latent_dist.sample(generator)
+        )
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class QwenImageEditPipeline(
+    nn.Module,
+):
+    def __init__(
+        self,
+        *,
+        od_config: OmniDiffusionConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.od_config = od_config
+        self.weights_sources = [
+            DiffusersPipelineLoader.ComponentSource(
+                model_or_path=od_config.model,
+                subfolder="transformer",
+                revision=None,
+                prefix="transformer.",
+                fall_back_to_pt=True,
+            )
+        ]
+        self.device = get_local_device()
+        model = od_config.model
+
+        # Check if model is a local path
+        local_files_only = os.path.exists(model)
+
+        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)
+        self.processor = Qwen2VLProcessor.from_pretrained(
+            model, subfolder="processor", local_files_only=local_files_only
+        )
+
+        self.stage = None
+
+        self.vae_scale_factor = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
+        self.latent_channels = self.vae.config.z_dim if getattr(self, "vae", None) else 16
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
+        self.tokenizer_max_length = 1024
+        # Edit prompt template - different from generation template
+        self.prompt_template_encode = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"  # noqa: E501
+        self.prompt_template_encode_start_idx = 64
+        self.default_sample_size = 128
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        image=None,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_embeds_mask=None,
+        negative_prompt_embeds_mask=None,
+        callback_on_step_end_tensor_inputs=None,
+        max_sequence_length=None,
+    ):
+        if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
+            logger.warning(
+                f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} "
+                f"but are {height} and {width}. Dimensions will be resized accordingly"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and prompt_embeds_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. "
+                "Make sure to generate `prompt_embeds_mask` from the same text encoder "
+                "that was used to generate `prompt_embeds`."
+            )
+        if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. "
+                "Make sure to generate `negative_prompt_embeds_mask` from the same text encoder "
+                "that was used to generate `negative_prompt_embeds`."
+            )
+
+        if max_sequence_length is not None and max_sequence_length > 1024:
+            raise ValueError(f"`max_sequence_length` cannot be greater than 1024 but is {max_sequence_length}")
+
+    def _extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor):
+        bool_mask = mask.bool()
+        valid_lengths = bool_mask.sum(dim=1)
+        selected = hidden_states[bool_mask]
+        split_result = torch.split(selected, valid_lengths.tolist(), dim=0)
+
+        return split_result
+
+    def _get_qwen_prompt_embeds(
+        self,
+        prompt: Union[str, list[str]] = None,
+        image: Optional[torch.Tensor] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        template = self.prompt_template_encode
+        drop_idx = self.prompt_template_encode_start_idx
+        txt = [template.format(e) for e in prompt]
+
+        model_inputs = self.processor(
+            text=txt,
+            images=image,
+            padding=True,
+            return_tensors="pt",
+        ).to(self.device)
+
+        outputs = self.text_encoder(
+            input_ids=model_inputs.input_ids,
+            attention_mask=model_inputs.attention_mask,
+            pixel_values=model_inputs.pixel_values,
+            image_grid_thw=model_inputs.image_grid_thw,
+            output_hidden_states=True,
+        )
+
+        hidden_states = outputs.hidden_states[-1]
+        split_hidden_states = self._extract_masked_hidden(hidden_states, model_inputs.attention_mask)
+        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
+        attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states]
+        max_seq_len = max([e.size(0) for e in split_hidden_states])
+        prompt_embeds = torch.stack(
+            [torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states]
+        )
+        encoder_attention_mask = torch.stack(
+            [torch.cat([u, u.new_zeros(max_seq_len - u.size(0))]) for u in attn_mask_list]
+        )
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=self.device)
+
+        return prompt_embeds, encoder_attention_mask
+
+    def _get_qwen_prompt_embeds(
+        self,
+        prompt: Union[str, list[str]] = None,
+        image: Optional[Union[PIL.Image.Image, torch.Tensor]] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        """Get prompt embeddings with image support for editing."""
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        template = self.prompt_template_encode
+        drop_idx = self.prompt_template_encode_start_idx
+        txt = [template.format(e) for e in prompt]
+
+        # Use processor to handle both text and image inputs
+        model_inputs = self.processor(
+            text=txt,
+            images=image,
+            padding=True,
+            return_tensors="pt",
+        ).to(self.device)
+
+        outputs = self.text_encoder(
+            input_ids=model_inputs.input_ids,
+            attention_mask=model_inputs.attention_mask,
+            pixel_values=model_inputs.pixel_values,
+            image_grid_thw=model_inputs.image_grid_thw,
+            output_hidden_states=True,
+        )
+
+        hidden_states = outputs.hidden_states[-1]
+        split_hidden_states = self._extract_masked_hidden(hidden_states, model_inputs.attention_mask)
+        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
+        attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states]
+        max_seq_len = max([e.size(0) for e in split_hidden_states])
+        prompt_embeds = torch.stack(
+            [torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states]
+        )
+        encoder_attention_mask = torch.stack(
+            [torch.cat([u, u.new_zeros(max_seq_len - u.size(0))]) for u in attn_mask_list]
+        )
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype)
+
+        return prompt_embeds, encoder_attention_mask
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, list[str]],
+        image: Optional[torch.Tensor] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 1024,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `list[str]`, *optional*):
+                prompt to be encoded
+            image (`torch.Tensor`, *optional*):
+                image to be encoded
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            prompt_embeds (`torch.Tensor`, *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.
+        """
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt) if prompt_embeds is None else prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(prompt, image)
+
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+        prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
+
+        return prompt_embeds, prompt_embeds_mask
+
+    @staticmethod
+    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
+        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+        latents = latents.permute(0, 2, 4, 1, 3, 5)
+        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+        return latents
+
+    @staticmethod
+    def _unpack_latents(latents, height, width, vae_scale_factor):
+        batch_size, num_patches, channels = latents.shape
+
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // (vae_scale_factor * 2))
+        width = 2 * (int(width) // (vae_scale_factor * 2))
+
+        latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
+        latents = latents.permute(0, 3, 1, 4, 2, 5)
+
+        latents = latents.reshape(batch_size, channels // (2 * 2), 1, height, width)
+
+        return latents
+
+    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+        if isinstance(generator, list):
+            image_latents = [
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i], sample_mode="argmax")
+                for i in range(image.shape[0])
+            ]
+            image_latents = torch.cat(image_latents, dim=0)
+        else:
+            image_latents = retrieve_latents(self.vae.encode(image), generator=generator, sample_mode="argmax")
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean)
+            .view(1, self.latent_channels, 1, 1, 1)
+            .to(image_latents.device, image_latents.dtype)
+        )
+        latents_std = (
+            torch.tensor(self.vae.config.latents_std)
+            .view(1, self.latent_channels, 1, 1, 1)
+            .to(image_latents.device, image_latents.dtype)
+        )
+        image_latents = (image_latents - latents_mean) / latents_std
+
+        return image_latents
+
+    def prepare_latents(
+        self,
+        image,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        width = 2 * (int(width) // (self.vae_scale_factor * 2))
+
+        shape = (batch_size, 1, num_channels_latents, height, width)
+
+        image_latents = None
+        if image is not None:
+            image = image.to(device=device, dtype=dtype)
+            if image.shape[1] != self.latent_channels:
+                image_latents = self._encode_vae_image(image=image, generator=generator)
+            else:
+                image_latents = image
+            if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
+                # expand init_latents for batch_size
+                additional_image_per_prompt = batch_size // image_latents.shape[0]
+                image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
+            elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
+                raise ValueError(
+                    f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
+                )
+            else:
+                image_latents = torch.cat([image_latents], dim=0)
+
+            image_latent_height, image_latent_width = image_latents.shape[3:]
+            image_latents = self._pack_latents(
+                image_latents, batch_size, num_channels_latents, image_latent_height, image_latent_width
+            )
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
+        else:
+            latents = latents.to(device=device, dtype=dtype)
+
+        return latents, image_latents
+
+    def prepare_timesteps(self, num_inference_steps, sigmas, image_seq_len):
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
+        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),
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            sigmas=sigmas,
+            mu=mu,
+        )
+        return timesteps, num_inference_steps
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    def diffuse(
+        self,
+        prompt_embeds,
+        prompt_embeds_mask,
+        negative_prompt_embeds,
+        negative_prompt_embeds_mask,
+        latents,
+        image_latents,
+        img_shapes,
+        txt_seq_lens,
+        negative_txt_seq_lens,
+        timesteps,
+        do_true_cfg,
+        guidance,
+        true_cfg_scale,
+    ):
+        """Diffusion loop with optional image conditioning."""
+        self.scheduler.set_begin_index(0)
+        for i, t in enumerate(timesteps):
+            if self.interrupt:
+                continue
+            self._current_timestep = t
+            # broadcast to batch dimension and place on same device/dtype as latents
+            timestep = t.expand(latents.shape[0]).to(device=latents.device, dtype=latents.dtype)
+
+            # Concatenate image latents with noise latents if available
+            latent_model_input = latents
+            if image_latents is not None:
+                latent_model_input = torch.cat([latents, image_latents], dim=1)
+
+            noise_pred = self.transformer(
+                hidden_states=latent_model_input,
+                timestep=timestep / 1000,
+                guidance=guidance,
+                encoder_hidden_states_mask=prompt_embeds_mask,
+                encoder_hidden_states=prompt_embeds,
+                img_shapes=img_shapes,
+                txt_seq_lens=txt_seq_lens,
+                attention_kwargs=self.attention_kwargs,
+                return_dict=False,
+            )[0]
+            noise_pred = noise_pred[:, : latents.size(1)]
+
+            if do_true_cfg:
+                neg_noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    encoder_hidden_states_mask=negative_prompt_embeds_mask,
+                    encoder_hidden_states=negative_prompt_embeds,
+                    img_shapes=img_shapes,
+                    txt_seq_lens=negative_txt_seq_lens,
+                    attention_kwargs=self.attention_kwargs,
+                    return_dict=False,
+                )[0]
+                neg_noise_pred = neg_noise_pred[:, : latents.size(1)]
+                comb_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
+                cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
+                noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True)
+                noise_pred = comb_pred * (cond_norm / noise_norm)
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+        return latents
+
+    def forward(
+        self,
+        req: OmniDiffusionRequest,
+        prompt: Union[str, list[str]] = "",
+        negative_prompt: Union[str, list[str]] = "",
+        image: Optional[Union[PIL.Image.Image, torch.Tensor]] = None,
+        true_cfg_scale: float = 4.0,
+        height: int | None = None,
+        width: int | None = None,
+        num_inference_steps: int = 50,
+        sigmas: Optional[list[float]] = None,
+        guidance_scale: float = 1.0,
+        num_images_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, list[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_mask: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        attention_kwargs: Optional[dict[str, Any]] = None,
+        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ) -> DiffusionOutput:
+        """Forward pass for image editing."""
+        prompt = req.prompt if req.prompt is not None else prompt
+        negative_prompt = req.negative_prompt if req.negative_prompt is not None else negative_prompt
+
+        # Get preprocessed image from request (pre-processing is done in DiffusionEngine)
+        if hasattr(req, "preprocessed_image"):
+            prompt_image = req.prompt_image
+            image = req.preprocessed_image
+            calculated_height = req.calculated_height
+            calculated_width = req.calculated_width
+            height = req.height
+            width = req.width
+        else:
+            # fallback to run pre-processing in pipeline (debug only)
+            image_size = image[0].size if isinstance(image, list) else image.size
+            calculated_width, calculated_height, _ = calculate_dimensions(1024 * 1024, image_size[0] / image_size[1])
+            height = height or calculated_height
+            width = width or calculated_width
+
+            multiple_of = self.vae_scale_factor * 2
+            width = width // multiple_of * multiple_of
+            height = height // multiple_of * multiple_of
+
+            if image is not None and not (isinstance(image, torch.Tensor) and image.size(1) == self.latent_channels):
+                image = self.image_processor.resize(image, calculated_height, calculated_width)
+                prompt_image = image
+                image = self.image_processor.preprocess(image, calculated_height, calculated_width)
+                image = image.unsqueeze(2)
+
+        num_inference_steps = req.num_inference_steps or num_inference_steps
+        generator = req.generator or generator
+        true_cfg_scale = req.true_cfg_scale or true_cfg_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
+
+        # 1. check inputs
+        # 2. encode prompts
+        # 3. prepare latents and timesteps
+        # 4. diffusion process
+        # 5. decode latents
+        # 6. post-process outputs
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            image,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            prompt_embeds_mask,
+            negative_prompt_embeds_mask,
+            callback_on_step_end_tensor_inputs,
+            max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        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 = prompt_embeds.shape[0]
+
+        has_neg_prompt = negative_prompt is not None or (
+            negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
+        )
+
+        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
+        prompt_embeds, prompt_embeds_mask = self.encode_prompt(
+            prompt=prompt,
+            image=prompt_image,  # Use resized image for prompt encoding
+            prompt_embeds=prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+        )
+
+        if do_true_cfg:
+            negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt(
+                prompt=negative_prompt,
+                image=prompt_image,  # Use same resized image for negative prompt encoding
+                prompt_embeds=negative_prompt_embeds,
+                prompt_embeds_mask=negative_prompt_embeds_mask,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+            )
+
+        num_channels_latents = self.transformer.in_channels // 4
+        # random noise latents, and image latents encoded by vae
+        latents, image_latents = self.prepare_latents(
+            image,
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            self.device,
+            generator,
+            latents,
+        )
+        img_shapes = [
+            [
+                (1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2),
+                (1, calculated_height // self.vae_scale_factor // 2, calculated_width // self.vae_scale_factor // 2),
+            ]
+        ] * batch_size
+
+        timesteps, num_inference_steps = self.prepare_timesteps(num_inference_steps, sigmas, latents.shape[1])
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0])
+        else:
+            guidance = None
+
+        if self.attention_kwargs is None:
+            self._attention_kwargs = {}
+
+        txt_seq_lens = prompt_embeds_mask.sum(dim=1).tolist() if prompt_embeds_mask is not None else None
+        negative_txt_seq_lens = (
+            negative_prompt_embeds_mask.sum(dim=1).tolist() if negative_prompt_embeds_mask is not None else None
+        )
+
+        latents = self.diffuse(
+            prompt_embeds,
+            prompt_embeds_mask,
+            negative_prompt_embeds,
+            negative_prompt_embeds_mask,
+            latents,
+            image_latents,
+            img_shapes,
+            txt_seq_lens,
+            negative_txt_seq_lens,
+            timesteps,
+            do_true_cfg,
+            guidance,
+            true_cfg_scale,
+        )
+
+        self._current_timestep = None
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = latents.to(self.vae.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
+            latents = latents / latents_std + latents_mean
+            image = self.vae.decode(latents, return_dict=False)[0][:, :, 0]
+
+        return DiffusionOutput(output=image)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)

vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py

@@ -8,7 +8,6 @@ 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
@@ -17,6 +16,10 @@ from diffusers.models.normalization import AdaLayerNormContinuous
 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.model_executor.model_loader.weight_utils import default_weight_loader
+
+from vllm_omni.diffusion.attention.layer import Attention
+from vllm_omni.diffusion.data import OmniDiffusionConfig
 
 logger = init_logger(__name__)
 
@@ -262,12 +265,12 @@ 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,
-        #     scale=1.0,  # softmax_scale
-        #     attn_type=AttentionType.ENCODER,
-        # )
+        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,
@@ -316,20 +319,11 @@ class QwenImageCrossAttention(nn.Module):
         joint_value = torch.cat([txt_value, img_value], dim=1)
 
         # Compute joint attention
-        # joint_hidden_states = self.attn(
-        #     joint_query,
-        #     joint_key,
-        #     joint_value,
-        # )
-        joint_hidden_states = dispatch_attention_fn(
+        joint_hidden_states = self.attn(
             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)
 
@@ -368,19 +362,6 @@ 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,
@@ -506,6 +487,7 @@ class QwenImageTransformer2DModel(nn.Module):
     # _repeated_blocks = ["QwenImageTransformerBlock"]
     def __init__(
         self,
+        od_config: OmniDiffusionConfig,
         patch_size: int = 2,
         in_channels: int = 64,
         out_channels: Optional[int] = 16,
@@ -517,7 +499,8 @@ class QwenImageTransformer2DModel(nn.Module):
         axes_dims_rope: tuple[int, int, int] = (16, 56, 56),
     ):
         super().__init__()
-        self.config = None
+        model_config = od_config.tf_model_config
+        num_layers = model_config.num_layers
         self.in_channels = in_channels
         self.out_channels = out_channels or in_channels
         self.inner_dim = num_attention_heads * attention_head_dim
@@ -624,79 +607,39 @@ class QwenImageTransformer2DModel(nn.Module):
 
         return Transformer2DModelOutput(sample=output)
 
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
-        # Collect incoming weights into a dict for flexible mapping and grouping
-        weights_map: dict[str, torch.Tensor] = {name: tensor for name, tensor in weights}
-
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
-            (
-                "to_qkv",
-                [
-                    {"aliases": ["q_proj", "to_q", "q", "query"], "order": 0},
-                    {"aliases": ["k_proj", "to_k", "k", "key"], "order": 1},
-                    {"aliases": ["v_proj", "to_v", "v", "value"], "order": 2},
-                ],
-            ),
-            (
-                "add_kv_proj",
-                [
-                    {"aliases": ["add_q_proj", "add_q"], "order": 0},
-                    {"aliases": ["add_k_proj", "add_k"], "order": 1},
-                    {"aliases": ["add_v_proj", "add_v"], "order": 2},
-                ],
-            ),
+            # (param_name, shard_name, shard_id)
+            # self-attn
+            (".to_qkv", ".to_q", "q"),
+            (".to_qkv", ".to_k", "k"),
+            (".to_qkv", ".to_v", "v"),
+            # cross-attn
+            (".add_kv_proj", ".add_q_proj", "q"),
+            (".add_kv_proj", ".add_k_proj", "k"),
+            (".add_kv_proj", ".add_v_proj", "v"),
         ]
 
-        # Helper that attempts to gather shards per base key using aliases and merges them
-        for target, shards in stacked_params_mapping:
-            base_candidates: dict[str, dict[str, dict[int, tuple[str, torch.Tensor]]]] = {}
-            for key in list(weights_map.keys()):
-                for shard in shards:
-                    for alias in shard["aliases"]:
-                        weight_suffix = f".{alias}.weight"
-                        bias_suffix = f".{alias}.bias"
-                        if key.endswith(weight_suffix):
-                            base = key[: -len(weight_suffix)]
-                            base_entry = base_candidates.setdefault(base, {"weight": {}, "bias": {}})
-                            base_entry["weight"][shard["order"]] = (
-                                key,
-                                weights_map[key],
-                            )
-                            break
-                        if key.endswith(bias_suffix):
-                            base = key[: -len(bias_suffix)]
-                            base_entry = base_candidates.setdefault(base, {"weight": {}, "bias": {}})
-                            base_entry["bias"][shard["order"]] = (key, weights_map[key])
-                            break
-
-            for base, entry in base_candidates.items():
-                if len(entry["weight"]) == len(shards):
-                    ordered = [entry["weight"][order] for order in sorted(entry["weight"])]
-                    combined = torch.cat([tensor for _, tensor in ordered], dim=0)
-                    for key, _ in ordered:
-                        weights_map.pop(key, None)
-                    weights_map[base + f".{target}.weight"] = combined
-                if len(entry["bias"]) == len(shards):
-                    ordered_bias = [entry["bias"][order] for order in sorted(entry["bias"])]
-                    bcombined = torch.cat([tensor for _, tensor in ordered_bias], dim=0)
-                    for key, _ in ordered_bias:
-                        weights_map.pop(key, None)
-                    weights_map[base + f".{target}.bias"] = bcombined
-
-        # Now perform the parameter copy using the possibly-updated weights_map
         params_dict = dict(self.named_parameters())
-        for name, loaded_weight in weights_map.items():
-            # Handle potential name mismatches for FeedForward layers
-            if name.endswith(".proj.weight") and name not in params_dict:
-                mapped_name = name.replace(".proj.weight", ".weight")
-            elif name.endswith(".proj.bias") and name not in params_dict:
-                mapped_name = name.replace(".proj.bias", ".bias")
-            else:
-                mapped_name = name
 
-            if mapped_name in params_dict:
-                param = params_dict[mapped_name]
-                with torch.no_grad():
-                    param.copy_(loaded_weight)
+        # we need to load the buffers for beta and eps (XIELU)
+        for name, buffer in self.named_buffers():
+            if name.endswith(".beta") or name.endswith(".eps"):
+                params_dict[name] = buffer
+
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
             else:
-                logger.warning(f"Weight {name} (mapped to {mapped_name}) not found in model params.")
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params

vllm_omni/diffusion/models/z_image/pipeline_z_image.py

@@ -0,0 +1,615 @@
+# 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 collections.abc import Iterable
+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.model_executor.models.utils import AutoWeightsLoader
+
+from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.distributed.utils import get_local_device
+from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
+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,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.od_config = od_config
+        self.weights_sources = [
+            DiffusersPipelineLoader.ComponentSource(
+                model_or_path=od_config.model,
+                subfolder="transformer",
+                revision=None,
+                prefix="transformer.",
+                fall_back_to_pt=True,
+            )
+        ]
+        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, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)

vllm_omni/diffusion/models/z_image/z_image_transformer.py

@@ -0,0 +1,683 @@
+# 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
+from torch.nn.utils.rnn import pad_sequence
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+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_qkv = QKVParallelLinear(
+            hidden_size=dim,
+            head_size=self.head_dim,
+            total_num_heads=num_heads,
+            disable_tp=True,
+            bias=False,
+        )
+
+        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,
+    ):
+        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.w13 = MergedColumnParallelLinear(
+            dim,
+            [hidden_dim] * 2,
+            bias=False,
+            disable_tp=True,
+            return_bias=False,
+        )
+        self.act = SiluAndMul()
+        self.w2 = RowParallelLinear(
+            hidden_dim,
+            dim,
+            bias=False,
+            disable_tp=True,
+            return_bias=False,
+        )
+
+    def forward(self, x):
+        return self.w2(self.act(self.w13(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]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            # self-attn
+            (".to_qkv", ".to_q", "q"),
+            (".to_qkv", ".to_k", "k"),
+            (".to_qkv", ".to_v", "v"),
+            # ffn
+            (".w13", ".w1", 0),
+            (".w13", ".w3", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+
+        loaded_params = set[str]()
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params

vllm_omni/diffusion/omni_diffusion.py

@@ -7,7 +7,7 @@ from dataclasses import fields
 from vllm.logger import init_logger
 from vllm.transformers_utils.config import get_hf_file_to_dict
 
-from vllm_omni.diffusion.data import OmniDiffusionConfig
+from vllm_omni.diffusion.data import OmniDiffusionConfig, TransformerConfig
 from vllm_omni.diffusion.diffusion_engine import DiffusionEngine
 from vllm_omni.diffusion.request import OmniDiffusionRequest
 
@@ -53,6 +53,11 @@ class OmniDiffusion:
             od_config.model,
         )
         od_config.model_class_name = config_dict.get("_class_name", None)
+        tf_config_dict = get_hf_file_to_dict(
+            "transformer/config.json",
+            od_config.model,
+        )
+        od_config.tf_model_config = TransformerConfig.from_dict(tf_config_dict)
 
         self.engine: DiffusionEngine = DiffusionEngine.make_engine(od_config)
 
@@ -82,3 +87,12 @@ class OmniDiffusion:
 
     def _run_engine(self, requests: list[OmniDiffusionRequest]):
         return self.engine.step(requests)
+
+    def close(self) -> None:
+        self.engine.close()
+
+    def __del__(self):  # pragma: no cover - best effort cleanup
+        try:
+            self.close()
+        except Exception:
+            pass

vllm_omni/diffusion/registry.py

@@ -3,6 +3,8 @@
 
 import importlib
 
+from vllm.model_executor.models.registry import _LazyRegisteredModel, _ModelRegistry
+
 from vllm_omni.diffusion.data import OmniDiffusionConfig
 
 _DIFFUSION_MODELS = {
@@ -12,25 +14,41 @@ _DIFFUSION_MODELS = {
         "pipeline_qwen_image",
         "QwenImagePipeline",
     ),
+    "QwenImageEditPipeline": (
+        "qwen_image",
+        "pipeline_qwen_image_edit",
+        "QwenImageEditPipeline",
+    ),
+    "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,
 ):
-    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)
-
+    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)
         # 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.")
@@ -41,18 +59,38 @@ _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",
+    "QwenImageEditPipeline": "get_qwen_image_edit_post_process_func",
+    "ZImagePipeline": "get_post_process_func",
+}
+
+_DIFFUSION_PRE_PROCESS_FUNCS = {
+    # arch: pre_process_func
+    # `pre_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
+    "QwenImageEditPipeline": "get_qwen_image_edit_pre_process_func",
 }
 
 
+def _load_process_func(od_config: OmniDiffusionConfig, func_name: str):
+    """Load and return a process function from the appropriate module."""
+    mod_folder, mod_relname, _ = _DIFFUSION_MODELS[od_config.model_class_name]
+    module_name = f"vllm_omni.diffusion.models.{mod_folder}.{mod_relname}"
+    module = importlib.import_module(module_name)
+    func = getattr(module, func_name)
+    return func(od_config)
+
+
 def get_diffusion_post_process_func(od_config: OmniDiffusionConfig):
-    if od_config.model_class_name in _DIFFUSION_POST_PROCESS_FUNCS:
-        mod_folder, mod_relname, _ = _DIFFUSION_MODELS[od_config.model_class_name]
-        func_name = _DIFFUSION_POST_PROCESS_FUNCS[od_config.model_class_name]
-        module_name = f"vllm_omni.diffusion.models.{mod_folder}.{mod_relname}"
-        module = importlib.import_module(module_name)
-        post_process_func = getattr(module, func_name)
-        return post_process_func(od_config)
-    else:
+    if od_config.model_class_name not in _DIFFUSION_POST_PROCESS_FUNCS:
         raise ValueError(
             f"Post process function for model class {od_config.model_class_name} not found in diffusion model registry."
         )
+    func_name = _DIFFUSION_POST_PROCESS_FUNCS[od_config.model_class_name]
+    return _load_process_func(od_config, func_name)
+
+
+def get_diffusion_pre_process_func(od_config: OmniDiffusionConfig):
+    if od_config.model_class_name not in _DIFFUSION_PRE_PROCESS_FUNCS:
+        return None  # Return None if no pre-processing function is registered (for backward compatibility)
+    func_name = _DIFFUSION_PRE_PROCESS_FUNCS[od_config.model_class_name]
+    return _load_process_func(od_config, func_name)

vllm_omni/diffusion/scheduler.py

@@ -20,7 +20,8 @@ class Scheduler:
         return cls._instance
 
     def initialize(self, od_config: OmniDiffusionConfig):
-        if hasattr(self, "context") and not self.context.closed:
+        existing_context = getattr(self, "context", None)
+        if existing_context is not None and not existing_context.closed:
             logger.warning("SyncSchedulerClient is already initialized. Re-initializing.")
             self.close()
 
@@ -65,6 +66,9 @@ class Scheduler:
         """Closes the socket and terminates the context."""
         if hasattr(self, "context"):
             self.context.term()
+        self.context = None
+        self.mq = None
+        self.result_mq = None
 
 
 # Singleton instance for easy access

vllm_omni/diffusion/utils/hf_utils.py

@@ -1,12 +1,13 @@
 from functools import lru_cache
 
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from vllm.logger import init_logger
 
 logger = init_logger(__name__)
 
 
 def load_diffusers_config(model_name) -> dict:
+    from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+
     config = DiffusionPipeline.load_config(model_name)
     return config
 

vllm_omni/diffusion/worker/gpu_worker.py

@@ -2,19 +2,25 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import multiprocessing as mp
 import os
+import time
 
 import torch
 import zmq
-from vllm.config import VllmConfig, set_current_vllm_config
+from vllm.config import LoadConfig, VllmConfig, set_current_vllm_config
 from vllm.distributed.device_communicators.shm_broadcast import MessageQueue
 from vllm.distributed.parallel_state import (
     init_distributed_environment,
     initialize_model_parallel,
 )
 from vllm.logger import init_logger
+from vllm.utils import DeviceMemoryProfiler, GiB_bytes
 
-from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
-from vllm_omni.diffusion.registry import initialize_model
+from vllm_omni.diffusion.data import (
+    SHUTDOWN_MESSAGE,
+    DiffusionOutput,
+    OmniDiffusionConfig,
+)
+from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
 from vllm_omni.diffusion.request import OmniDiffusionRequest
 
 logger = init_logger(__name__)
@@ -59,12 +65,23 @@ class GPUWorker:
 
         init_distributed_environment(world_size=world_size, rank=rank)
         initialize_model_parallel(tensor_model_parallel_size=world_size)
+        logger.info(f"Worker {self.rank}: Initialized device and distributed environment.")
 
-        with device:
-            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.")
+        load_config = LoadConfig()
+        model_loader = DiffusersPipelineLoader(load_config)
+        time_before_load = time.perf_counter()
+        with DeviceMemoryProfiler() as m:
+            self.pipeline = model_loader.load_model(
+                od_config=self.od_config,
+                load_device=f"cuda:{rank}",
+            )
+        time_after_load = time.perf_counter()
+
+        logger.info(
+            "Model loading took %.4f GiB and %.6f seconds",
+            m.consumed_memory / GiB_bytes,
+            time_after_load - time_before_load,
+        )
         logger.info(f"Worker {self.rank}: Model loaded successfully.")
 
     @torch.inference_mode()
@@ -78,6 +95,14 @@ class GPUWorker:
         output = self.pipeline.forward(req)
         return output
 
+    def shutdown(self) -> None:
+        if torch.distributed.is_initialized():
+            try:
+                torch.distributed.destroy_process_group()
+                logger.info("Worker %s: Destroyed process group", self.rank)
+            except Exception as exc:  # pragma: no cover - best effort cleanup
+                logger.warning("Worker %s: Failed to destroy process group: %s", self.rank, exc)
+
 
 class WorkerProc:
     """Wrapper that runs one Worker in a separate process."""
@@ -149,6 +174,14 @@ class WorkerProc:
                 )
                 continue
 
+            if reqs == SHUTDOWN_MESSAGE:
+                logger.info("Worker %s: Received shutdown message", self.gpu_id)
+                self._running = False
+                continue
+            if reqs is None:
+                logger.warning("Worker %s: Received empty payload, ignoring", self.gpu_id)
+                continue
+
             # 2: execute, make sure a reply is always sent
             try:
                 output = self.worker.execute_model(reqs, self.od_config)
@@ -167,6 +200,10 @@ class WorkerProc:
                 continue
 
         logger.info("event loop terminated.")
+        try:
+            self.worker.shutdown()
+        except Exception as exc:  # pragma: no cover - best effort cleanup
+            logger.warning("Worker %s: Shutdown encountered an error: %s", self.gpu_id, exc)
         # if self.result_sender is not None:
         #     self.result_sender.close()
         self.context.term()

vllm_omni/diffusion/worker/npu/npu_worker.py

@@ -2,20 +2,26 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import multiprocessing as mp
 import os
+import time
 
 import torch
 import zmq
 from transformers import PretrainedConfig
-from vllm.config import ModelConfig, VllmConfig, set_current_vllm_config
+from vllm.config import LoadConfig, ModelConfig, VllmConfig, set_current_vllm_config
 from vllm.distributed.device_communicators.shm_broadcast import MessageQueue
 from vllm.distributed.parallel_state import (
     init_distributed_environment,
     initialize_model_parallel,
 )
 from vllm.logger import init_logger
+from vllm.utils import DeviceMemoryProfiler, GiB_bytes
 
-from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
-from vllm_omni.diffusion.registry import initialize_model
+from vllm_omni.diffusion.data import (
+    SHUTDOWN_MESSAGE,
+    DiffusionOutput,
+    OmniDiffusionConfig,
+)
+from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
 from vllm_omni.diffusion.request import OmniDiffusionRequest
 
 logger = init_logger(__name__)
@@ -59,18 +65,27 @@ class NPUWorker:
             def __getattr__(self, name):
                 return "fake"
 
-        vllm_config = VllmConfig(model_config=ModelConfig(hf_config=_FakePretrainedConfig()))
+        vllm_config = VllmConfig(model_config=ModelConfig(hf_config=_FakePretrainedConfig()), load_config=LoadConfig())
         vllm_config.parallel_config.tensor_parallel_size = self.od_config.num_gpus
         set_current_vllm_config(vllm_config)
 
         init_distributed_environment(world_size=world_size, rank=rank)
         initialize_model_parallel(tensor_model_parallel_size=world_size)
 
-        with device:
-            self.pipeline = initialize_model(self.od_config)
-            self.pipeline.load_weights()
-            self.pipeline.eval()
-        logger.info(f"Worker {self.rank}: Initialized device, model, and distributed environment.")
+        model_loader = DiffusersPipelineLoader(vllm_config.load_config)
+        time_before_load = time.perf_counter()
+        with DeviceMemoryProfiler() as m:
+            self.pipeline = model_loader.load_model(
+                od_config=self.od_config,
+                load_device=f"npu:{rank}",
+            )
+        time_after_load = time.perf_counter()
+
+        logger.info(
+            "Model loading took %.4f GiB and %.6f seconds",
+            m.consumed_memory / GiB_bytes,
+            time_after_load - time_before_load,
+        )
         logger.info(f"Worker {self.rank}: Model loaded successfully.")
 
     @torch.inference_mode()
@@ -84,6 +99,14 @@ class NPUWorker:
         output = self.pipeline.forward(req)
         return output
 
+    def shutdown(self) -> None:
+        if torch.distributed.is_initialized():
+            try:
+                torch.distributed.destroy_process_group()
+                logger.info("Worker %s: Destroyed process group", self.rank)
+            except Exception as exc:  # pragma: no cover - best effort cleanup
+                logger.warning("Worker %s: Failed to destroy process group: %s", self.rank, exc)
+
 
 class NPUWorkerProc:
     """Wrapper that runs one Worker in a separate process."""
@@ -155,6 +178,14 @@ class NPUWorkerProc:
                 )
                 continue
 
+            if reqs == SHUTDOWN_MESSAGE:
+                logger.info("Worker %s: Received shutdown message", self.gpu_id)
+                self._running = False
+                continue
+            if reqs is None:
+                logger.warning("Worker %s: Received empty payload, ignoring", self.gpu_id)
+                continue
+
             # 2: execute, make sure a reply is always sent
             try:
                 output = self.worker.execute_model(reqs, self.od_config)
@@ -173,6 +204,10 @@ class NPUWorkerProc:
                 continue
 
         logger.info("event loop terminated.")
+        try:
+            self.worker.shutdown()
+        except Exception as exc:  # pragma: no cover - best effort cleanup
+            logger.warning("Worker %s: Shutdown encountered an error: %s", self.gpu_id, exc)
         # if self.result_sender is not None:
         #     self.result_sender.close()
         self.context.term()

vllm_omni/engine/arg_utils.py

@@ -51,6 +51,11 @@ class OmniEngineArgs(EngineArgs):
         # Create OmniModelConfig by copying all base config attributes
         # and adding the new omni-specific fields
         config_dict = base_config.__dict__.copy()
+        # FIXME(Isotr0py): This is a temporary workaround for multimodal_config
+        config_dict = {
+            **(getattr(mm := config_dict.pop("multimodal_config", None), "__dict__", mm or {})),
+            **config_dict,
+        }
 
         # Add the new omni-specific fields
         config_dict["stage_id"] = self.stage_id

vllm_omni/entrypoints/async_omni.py

@@ -3,6 +3,7 @@ import multiprocessing as mp
 import os
 import socket
 import time
+from argparse import Namespace
 from collections.abc import AsyncGenerator, Iterable, Mapping, Sequence
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Any, Optional, Union
@@ -61,21 +62,23 @@ class AsyncOmni(EngineClient):
 
     Args:
         model: Model name or path to load
-        stage_configs_path: Optional path to YAML file containing stage
-            configurations. If None, configurations are loaded from the model.
-        log_stats: Whether to enable statistics logging
-        log_file: Optional path prefix for log files. If provided, logs will
-            be written to files with stage-specific suffixes.
-        init_sleep_seconds: Number of seconds to sleep between starting
-            each stage process during initialization
-        shm_threshold_bytes: Threshold in bytes for using shared memory
-            for IPC. Objects larger than this threshold will use shared memory.
-        batch_timeout: Timeout in seconds for batching requests within a stage
-        init_timeout: Timeout in seconds for waiting for all stages to initialize
+        cli_args: Namespace object containing command-line arguments.
+            Expected attributes include:
+            - stage_configs_path: Optional path to YAML file containing stage
+              configurations. If None, configurations are loaded from the model.
+            - log_stats: Whether to enable statistics logging
+            - log_file: Optional path prefix for log files. If provided, logs will
+              be written to files with stage-specific suffixes.
+            - init_sleep_seconds: Number of seconds to sleep between starting
+              each stage process during initialization
+            - shm_threshold_bytes: Threshold in bytes for using shared memory
+              for IPC. Objects larger than this threshold will use shared memory.
+            - batch_timeout: Timeout in seconds for batching requests within a stage
+            - init_timeout: Timeout in seconds for waiting for all stages to initialize
         **kwargs: Additional keyword arguments passed to stage engines
 
     Example:
-        >>> async_llm = AsyncOmni(model="Qwen/Qwen2.5-Omni-7B")
+        >>> async_llm = AsyncOmni(model="Qwen/Qwen2.5-Omni-7B", cli_args=args)
         >>> async for output in async_llm.generate(
         ...     prompt="Hello",
         ...     request_id="req-1",
@@ -87,33 +90,29 @@ class AsyncOmni(EngineClient):
     def __init__(
         self,
         model: str,
-        stage_configs_path: Optional[str] = None,
-        log_stats: bool = False,
-        log_file: Optional[str] = None,
-        init_sleep_seconds: int = 30,
-        shm_threshold_bytes: int = 65536,
-        batch_timeout: int = 10,
-        init_timeout: int = 60000,
+        cli_args: Namespace,
         **kwargs: Any,
     ):
-        self.batch_timeout = batch_timeout
-        self._enable_stats: bool = bool(log_stats)
+        self.batch_timeout = cli_args.batch_timeout
+        self._enable_stats: bool = bool(cli_args.log_stats)
 
-        if stage_configs_path is None:
-            self.stage_configs = load_stage_configs_from_model(model)
+        base_engine_args = AsyncOmniEngineArgs.from_cli_args(cli_args).__dict__.copy()
+
+        if cli_args.stage_configs_path is None:
+            self.stage_configs = load_stage_configs_from_model(model, base_engine_args)
         else:
-            self.stage_configs = load_stage_configs_from_yaml(stage_configs_path)
+            self.stage_configs = load_stage_configs_from_yaml(cli_args.stage_configs_path, base_engine_args)
 
         self.stage_list: list[OmniStage] = []
         self.default_sampling_params_list: list[SamplingParams] = []
         # Optional file handler for orchestrator
-        self._log_file = log_file
+        self._log_file = cli_args.log_file
         if self._log_file:
             remove_old_logs(self._log_file, len(self.stage_configs))
             configure_orchestrator_logger(logger, self._log_file)
 
         self._stats_file, self._overall_stats_file = init_stats_paths(self._enable_stats, self._log_file)
-        self._initialize_stages(model, init_sleep_seconds, shm_threshold_bytes, init_timeout)
+        self._initialize_stages(model, cli_args.init_sleep_seconds, cli_args.shm_threshold_bytes, cli_args.init_timeout)
 
     def _initialize_stages(
         self,

vllm_omni/entrypoints/cli/serve.py

@@ -60,6 +60,41 @@ class OmniServeCommand(CLISubcommand):
             default=None,
             help="Path to the stage configs file. If not specified, the stage configs will be loaded from the model.",
         )
+        serve_parser.add_argument(
+            "--init-sleep-seconds",
+            type=int,
+            default=30,
+            help="The number of seconds to sleep before initializing the stages.",
+        )
+        serve_parser.add_argument(
+            "--init-timeout",
+            type=int,
+            default=60000,
+            help="The timeout for initializing the stages.",
+        )
+        serve_parser.add_argument(
+            "--shm-threshold-bytes",
+            type=int,
+            default=65536,
+            help="The threshold for the shared memory size.",
+        )
+        serve_parser.add_argument(
+            "--log-stats",
+            action="store_true",
+            help="Enable logging the stats.",
+        )
+        serve_parser.add_argument(
+            "--log-file",
+            type=str,
+            default=None,
+            help="The path to the log file.",
+        )
+        serve_parser.add_argument(
+            "--batch-timeout",
+            type=int,
+            default=10,
+            help="The timeout for the batch.",
+        )
         return serve_parser
 
 

vllm_omni/entrypoints/omni.py

@@ -47,3 +47,14 @@ class Omni:
         if hasattr(self.instance, "generate"):
             return getattr(self.instance, "generate")(*args, **kwargs)
         raise AttributeError(f"'{self.instance.__class__.__name__}' has no attribute 'generate'")
+
+    def close(self) -> None:
+        close_method = getattr(self.instance, "close", None)
+        if callable(close_method):
+            close_method()
+
+    def __del__(self):  # pragma: no cover - best effort cleanup
+        try:
+            self.close()
+        except Exception:
+            pass

vllm_omni/entrypoints/omni_stage.py

@@ -735,7 +735,7 @@ async def _stage_worker_async(
         stage_id,
         list(engine_args.keys()),
     )
-    omni_engine_args = AsyncOmniEngineArgs(model=model, **engine_args)
+    omni_engine_args = AsyncOmniEngineArgs(**engine_args)
     usage_context = UsageContext.OPENAI_API_SERVER
     vllm_config = omni_engine_args.create_engine_config(usage_context=usage_context)
     stage_engine = AsyncOmniStageLLM.from_vllm_config(

vllm_omni/entrypoints/openai/api_server.py

@@ -185,10 +185,7 @@ async def build_async_omni_from_stage_config(
     async_omni: Optional[EngineClient] = None
 
     try:
-        if getattr(args, "stage_configs_path", None):
-            async_omni = AsyncOmni(model=args.model, stage_configs_path=args.stage_configs_path)
-        else:
-            async_omni = AsyncOmni(model=args.model)
+        async_omni = AsyncOmni(model=args.model, cli_args=args)
 
         # # Don't keep the dummy data in memory
         # await async_llm.reset_mm_cache()

vllm_omni/entrypoints/utils.py

@@ -1,5 +1,8 @@
 import os
+from collections import Counter
+from dataclasses import asdict, is_dataclass
 from pathlib import Path
+from typing import Any
 
 from omegaconf import OmegaConf
 from vllm.transformers_utils.config import get_config
@@ -10,7 +13,56 @@ from vllm_omni.utils import detect_device_type
 PROJECT_ROOT = Path(__file__).parent.parent.parent
 
 
-def load_stage_configs_from_model(model: str) -> list:
+def _convert_dataclasses_to_dict(obj: Any) -> Any:
+    """Recursively convert non-serializable objects to OmegaConf-compatible types.
+
+    This is needed because OmegaConf cannot handle:
+    - Dataclass objects with Literal type annotations (e.g., StructuredOutputsConfig)
+    - Counter objects (from collections or vllm.utils)
+    - Set objects
+    - Other non-primitive types
+    """
+    # IMPORTANT: Check Counter BEFORE dict, since Counter is a subclass of dict
+    # Handle Counter objects (convert to dict)
+    # Check by class name first to catch both collections.Counter and vllm.utils.Counter
+    if hasattr(obj, "__class__") and obj.__class__.__name__ == "Counter":
+        try:
+            return dict(obj)
+        except (TypeError, ValueError):
+            # If Counter can't be converted to dict, return empty dict
+            return {}
+    # Also check isinstance for collections.Counter (must be before dict check)
+    if isinstance(obj, Counter):
+        return dict(obj)
+    # Handle set objects (convert to list)
+    if isinstance(obj, set):
+        return list(obj)
+    # Handle dataclass objects
+    # Note: asdict() recursively converts nested dataclasses but not Counter objects,
+    # so we need to recursively process the result
+    if is_dataclass(obj):
+        result = asdict(obj)
+        # Recursively process the result to convert any Counter objects
+        return _convert_dataclasses_to_dict(result)
+    # Handle dictionaries (recurse into values)
+    # Note: This must come AFTER Counter check since Counter is a dict subclass
+    if isinstance(obj, dict):
+        return {k: _convert_dataclasses_to_dict(v) for k, v in obj.items()}
+    # Handle lists and tuples (recurse into items)
+    if isinstance(obj, (list, tuple)):
+        return type(obj)(_convert_dataclasses_to_dict(item) for item in obj)
+    # Try to convert any dict-like object (has keys/values methods) to dict
+    if hasattr(obj, "keys") and hasattr(obj, "values") and not isinstance(obj, (str, bytes)):
+        try:
+            return {k: _convert_dataclasses_to_dict(v) for k, v in obj.items()}
+        except (TypeError, ValueError, AttributeError):
+            # If conversion fails, return as-is
+            return obj
+    # Primitive types and other objects that OmegaConf can handle
+    return obj
+
+
+def load_stage_configs_from_model(model: str, base_engine_args: dict = {}) -> list:
     """Load stage configurations from model's default config file.
 
     Loads stage configurations based on the model type and device type.
@@ -35,7 +87,9 @@ def load_stage_configs_from_model(model: str) -> list:
         device_config_file = f"vllm_omni/model_executor/stage_configs/{device_type}/{model_type}.yaml"
         device_config_path = PROJECT_ROOT / device_config_file
         if os.path.exists(device_config_path):
-            stage_configs = load_stage_configs_from_yaml(config_path=str(device_config_path))
+            stage_configs = load_stage_configs_from_yaml(
+                config_path=str(device_config_path), base_engine_args=base_engine_args
+            )
             return stage_configs
 
     # Fall back to default config
@@ -43,11 +97,11 @@ def load_stage_configs_from_model(model: str) -> list:
     stage_config_path = PROJECT_ROOT / stage_config_file
     if not os.path.exists(stage_config_path):
         raise FileNotFoundError(f"Stage config file {stage_config_path} not found")
-    stage_configs = load_stage_configs_from_yaml(config_path=str(stage_config_path))
+    stage_configs = load_stage_configs_from_yaml(config_path=str(stage_config_path), base_engine_args=base_engine_args)
     return stage_configs
 
 
-def load_stage_configs_from_yaml(config_path: str) -> list:
+def load_stage_configs_from_yaml(config_path: str, base_engine_args: dict = {}) -> list:
     """Load stage configurations from a YAML file.
 
     Args:
@@ -57,4 +111,14 @@ def load_stage_configs_from_yaml(config_path: str) -> list:
         List of stage configuration dictionaries from the file's stage_args
     """
     config_data = OmegaConf.load(config_path)
-    return config_data.stage_args
+    stage_args = config_data.stage_args
+    # Convert any nested dataclass objects to dicts before creating OmegaConf
+    base_engine_args = _convert_dataclasses_to_dict(base_engine_args)
+    base_engine_args = OmegaConf.create(base_engine_args)
+    for stage_arg in stage_args:
+        base_engine_args_tmp = base_engine_args.copy()
+        # Update base_engine_args with stage-specific engine_args if they exist
+        if hasattr(stage_arg, "engine_args") and stage_arg.engine_args is not None:
+            base_engine_args_tmp = OmegaConf.merge(base_engine_args_tmp, stage_arg.engine_args)
+        stage_arg.engine_args = base_engine_args_tmp
+    return stage_args

vllm_omni/model_executor/model_loader/weight_utils.py

@@ -3,10 +3,15 @@ from pathlib import Path
 from typing import Optional, Union
 
 import huggingface_hub
-from huggingface_hub import snapshot_download
+import vllm.envs as envs
 from vllm.logger import init_logger
 from vllm.model_executor.model_loader.weight_utils import DisabledTqdm, get_lock
 
+if envs.VLLM_USE_MODELSCOPE:
+    from modelscope.hub.snapshot_download import snapshot_download
+else:
+    from huggingface_hub import snapshot_download
+
 logger = init_logger(__name__)
 
 
@@ -37,6 +42,7 @@ def download_weights_from_hf_specific(
     """
     assert len(allow_patterns) > 0
     local_only = huggingface_hub.constants.HF_HUB_OFFLINE
+    download_kwargs = {"tqdm_class": DisabledTqdm} if not envs.VLLM_USE_MODELSCOPE else {}
 
     logger.info("Using model weights format %s", allow_patterns)
     # Use file lock to prevent multiple processes from
@@ -49,9 +55,9 @@ def download_weights_from_hf_specific(
                 allow_patterns=allow_pattern,
                 ignore_patterns=ignore_patterns,
                 cache_dir=cache_dir,
-                tqdm_class=DisabledTqdm,
                 revision=revision,
                 local_files_only=local_only,
+                **download_kwargs,
             )
             # If we have downloaded weights for this allow_pattern,
             # we don't need to check the rest.

vllm_omni/model_executor/models/qwen3_omni/qwen3_omni.py

@@ -156,6 +156,9 @@ class Qwen3OmniMoeForConditionalGeneration(
                 ]
             )
 
+            # for CI: Initialize special tokens embeddings early to avoid AttributeError when loading dummy weights
+            self._init_special_tokens_embeddings()
+
         elif self.model_stage == "code2wav":
             self.thinker = None
             self.talker = None
@@ -342,9 +345,9 @@ class Qwen3OmniMoeForConditionalGeneration(
                     and thinker_tts_embeds.shape[1] == 3
                 ):
                     bos_eos_pad = thinker_tts_embeds.to(text_hidden_states.device).chunk(3, dim=1)  # 3 * [1,1,H]
-                    multimodal_outputs["tts_bos_embed"] = bos_eos_pad[0]
-                    multimodal_outputs["tts_eos_embed"] = bos_eos_pad[1]
-                    multimodal_outputs["tts_pad_embed"] = bos_eos_pad[2]
+                    multimodal_outputs["tts_bos_embed"] = [bos_eos_pad[0]]
+                    multimodal_outputs["tts_eos_embed"] = [bos_eos_pad[1]]
+                    multimodal_outputs["tts_pad_embed"] = [bos_eos_pad[2]]
             except Exception:
                 # Best-effort; absence will be handled by talker with fallbacks
                 pass

vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_thinker.py

@@ -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(inputs_embeds.size(0))
+            deepstack_input_embeds = self._get_deepstack_input_embeds(input_ids.size(1))
         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(inputs_embeds.size(0))
+            self._clear_deepstack_input_embeds(input_ids.size(1))
 
         return hidden_states.unsqueeze(0), captured_hidden_states
 

vllm_omni/model_executor/stage_configs/npu/qwen2_5_omni.yaml

@@ -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.scheduler.OmniScheduler
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
       gpu_memory_utilization: 0.8
       enforce_eager: true  # Now we only support eager mode
       trust_remote_code: true
@@ -35,7 +35,7 @@ stage_args:
       model_stage: talker
       model_arch: Qwen2_5OmniForConditionalGeneration
       worker_cls: vllm_omni.worker.npu.npu_ar_worker.NPUARWorker
-      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
       gpu_memory_utilization: 0.8
       enforce_eager: true
       trust_remote_code: true
@@ -60,8 +60,8 @@ stage_args:
     engine_args:
       model_stage: code2wav
       model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_cls: vllm_omni.worker.npu.npu_diffusion_worker.NPUDiffusionWorker
-      scheduler_cls: vllm_omni.core.sched.diffusion_scheduler.DiffusionScheduler
+      worker_cls: vllm_omni.worker.npu.npu_generation_worker.NPUGenerationWorker
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
       gpu_memory_utilization: 0.15
       enforce_eager: true
       trust_remote_code: true

vllm_omni/model_executor/stage_configs/qwen2_5_omni.yaml

@@ -11,12 +11,13 @@ stage_args:
       model_stage: thinker
       model_arch: Qwen2_5OmniForConditionalGeneration
       worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
       gpu_memory_utilization: 0.8
       enforce_eager: true  # Now we only support eager mode
       trust_remote_code: true
       engine_output_type: latent
       enable_prefix_caching: false
+      max_num_batched_tokens: 32768
     is_comprehension: true
     final_output: true
     final_output_type: text
@@ -37,11 +38,12 @@ stage_args:
       model_stage: talker
       model_arch: Qwen2_5OmniForConditionalGeneration
       worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
-      scheduler_cls: vllm_omni.core.sched.scheduler.OmniScheduler
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
       gpu_memory_utilization: 0.8
       enforce_eager: true
       trust_remote_code: true
       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
@@ -62,12 +64,13 @@ stage_args:
     engine_args:
       model_stage: code2wav
       model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_cls: vllm_omni.worker.gpu_diffusion_worker.GPUDiffusionWorker
-      scheduler_cls: vllm_omni.core.sched.diffusion_scheduler.DiffusionScheduler
+      worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
       gpu_memory_utilization: 0.15
       enforce_eager: true
       trust_remote_code: true
       enable_prefix_caching: false
+      max_num_batched_tokens: 32768
       engine_output_type: audio
     engine_input_source: [1]
     final_output: true

vllm_omni/model_executor/stage_configs/qwen3_omni_moe.yaml

@@ -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.scheduler.OmniScheduler
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
       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.scheduler.OmniScheduler
-       gpu_memory_utilization: 0.3
+       scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+       gpu_memory_utilization: 0.5
        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.generation_scheduler.GenerationScheduler
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
       enforce_eager: true
       trust_remote_code: true
       enable_prefix_caching: false

vllm_omni/worker/gpu_ar_model_runner.py

@@ -291,6 +291,11 @@ class GPUARModelRunner(OmniGPUModelRunner):
                         # Case 1: tensor aligned on token dimension
                         if isinstance(v, torch.Tensor) and v.shape[0] == hidden_states_cpu.shape[0]:
                             mm_payload[k] = v.detach().to("cpu")[prev_logits_index : logits_index + 1].contiguous()
+                        elif isinstance(v, torch.Tensor) and v.shape[0] != hidden_states_cpu.shape[0]:
+                            logger.error(
+                                f"Error in merge multimodal outputs: Tensor dimension mismatch, \
+                                          {v.shape} != {hidden_states_cpu.shape} for {k}"
+                            )
                         # Case 2: nested dict of tensors aligned on token dimension (e.g., selected_hidden_layers)
                         elif isinstance(v, dict):
                             sub_dict: dict[str, torch.Tensor] = {}
@@ -302,7 +307,9 @@ class GPUARModelRunner(OmniGPUModelRunner):
                             if sub_dict:
                                 mm_payload[k] = sub_dict
                         elif isinstance(v, list):
-                            element: torch.Tensor = v[0]
+                            element = v[0]
+                            if isinstance(element, torch.Tensor):
+                                element = element.detach().to("cpu").contiguous()
                             multimodal_outputs[k] = v[1:] if len(v) > 1 else v
                             mm_payload[k] = element
                     except Exception as e:

vllm_omni/worker/gpu_diffusion_model_runner.py

@@ -1,576 +0,0 @@
-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()