Merge remote-tracking branch 'origin/main' into codex-api-server-user-guide

Signed-off-by: hsliu_ustc <hsliu_ustc@noreply.gitcode.com>

# Conflicts:
#	docs/user_guide/examples/online_serving/text_to_audio.md
#	examples/online_serving/stable_audio/README.md

docs/.nav.yml

@@ -5,14 +5,22 @@ nav:
     - getting_started/quickstart.md
     - getting_started/installation/*
   - Serving:
-    - OpenAI-Compatible API:
-      - Chat Completions API: serving/chat_completions_api.md
-      - Image Generation: serving/image_generation_api.md
-      - Image Edit: serving/image_edit_api.md
-      - Text to Speech: serving/speech_api.md
-      - Videos: serving/videos_api.md
-      - Audio Generation: serving/audio_generate_api.md
-      - Streaming Video Input: serving/video_stream_api.md
+    - API Server:
+      - Overview: serving/README.md
+      - Core APIs:
+        - Chat Completions: serving/chat_completions_api.md
+        - Text to Speech: serving/speech_api.md
+        - Audio Generation: serving/audio_generate_api.md
+        - Image Generation: serving/image_generation_api.md
+        - Image Edit: serving/image_edit_api.md
+        - Videos: serving/videos_api.md
+      - Streaming and Realtime:
+        - Realtime Audio: serving/realtime_api.md
+        - Full Duplex: serving/full_duplex_api.md
+        - Streaming Video Input: serving/video_stream_api.md
+        - Streaming Video Output: serving/streaming_video_output_api.md
+        - OpenPI Robot Policy: serving/openpi_api.md
+      - Standalone Experimental Servers: serving/standalone_servers.md
   - Examples:
     - examples/README.md
     - Offline Inference:

docs/examples/README.md

@@ -3,4 +3,6 @@
 vLLM-Omni's examples are split into two categories:
 
 - If you are using vLLM-Omni from within Python code, see the *Offline Inference* section.
-- If you are using vLLM-Omni from an HTTP application or client, see the *Online Serving* section.
+- If you are using vLLM-Omni from an HTTP application or client, start with
+  the [API Server guide](../serving/README.md) to choose an endpoint, then use
+  the *Online Serving* section for model-specific examples.

docs/getting_started/quickstart.md

@@ -91,7 +91,7 @@ if __name__ == "__main__":
 
 For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)
 
-## Online Serving with OpenAI-Completions API
+## Online Serving with the API Server
 
 Text-to-image generation quickstart with vLLM-Omni:
 
@@ -100,20 +100,16 @@ vllm serve Tongyi-MAI/Z-Image-Turbo --omni --port 8091
 ```
 
 ```bash
-curl -s http://localhost:8091/v1/chat/completions \
+curl -s http://localhost:8091/v1/images/generations \
   -H "Content-Type: application/json" \
   -d '{
-    "messages": [
-      {"role": "user", "content": "a cup of coffee on the table"}
-    ],
-    "extra_body": {
-      "height": 1024,
-      "width": 1024,
-      "num_inference_steps": 50,
-      "guidance_scale": 4.0,
-      "seed": 42
-    }
-  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2 | base64 -d > coffee.png
+    "prompt": "a cup of coffee on the table",
+    "size": "1024x1024",
+    "response_format": "b64_json",
+    "seed": 42
+  }' | jq -r '.data[0].b64_json' | base64 -d > coffee.png
 ```
 
-For more details, please refer to [online serving](../user_guide/examples/online_serving/text_to_image.md).
+See the [API Server guide](../serving/README.md) to choose an endpoint. For
+model-specific details, refer to the [text-to-image online serving
+example](../user_guide/examples/online_serving/text_to_image.md).

docs/models/supported_models.md

@@ -21,7 +21,7 @@ th {
 |--------------|--------|-------------------|------------|---------|-----|-----------|--------|
 | `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen3-Omni.md) |
 | `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ |   |   |   | — |
+| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ | | | | — |
 | `BagelForConditionalGeneration` | BAGEL (DiT-only) | `ByteDance-Seed/BAGEL-7B-MoT` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `InternVLAA1Pipeline` | InternVLA-A1 | `InternRobotics/InternVLA-A1-3B` | ✅︎ | ✅︎ | | | — |
 | `Gr00tN1d7Pipeline` | GR00T N1.7 | `nvidia/GR00T-N1.7-3B` | ✅︎ | | | | — |
@@ -62,7 +62,7 @@ th {
 | `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `LongCatVideoAvatarPipeline` | LongCat-Video-Avatar-1.5 A2V/AI2V (native single-speaker and multi-speaker AI2V/AVC) | `meituan-longcat/LongCat-Video-Avatar-1.5` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/meituan-longcat/LongCat-Video-Avatar-1.5.md) |
-| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | ✅︎ | — |
+| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit`, `Boogu/Boogu-Image-0.1-Edit-Turbo` | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Boogu/Boogu-Image.md) |
 | `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ | — |

docs/serving/README.md

@@ -0,0 +1,187 @@
+# API Server
+
+vLLM-Omni exposes OpenAI-compatible HTTP APIs plus vLLM-Omni extensions for
+image, audio, video, realtime, and robot-policy workloads. Use this page to
+choose an endpoint; follow the linked reference page for model-specific fields,
+wire protocols, and response details.
+
+Each server instance hosts one model. An endpoint is usable only when the
+loaded model supports that task.
+
+This guide focuses on vLLM-Omni's primary task endpoints. Compatible models
+may also expose standard routes inherited from vLLM, such as
+`POST /v1/completions`; those routes remain model- and task-dependent.
+
+## Start and Verify the Server
+
+Start a model with the shared serving command:
+
+```bash
+vllm serve <model> --omni --port 8091
+```
+
+Some models require additional flags or a deployment configuration. Use the
+model-specific guide when one is provided.
+
+After the server starts, check its health and served model name:
+
+```bash
+export VLLM_OMNI_BASE_URL=http://localhost:8091
+
+curl "$VLLM_OMNI_BASE_URL/health"
+curl "$VLLM_OMNI_BASE_URL/v1/models" | jq .
+```
+
+Use `http://localhost:8091/v1` as the `base_url` for an OpenAI SDK client. If
+the server was started with `--api-key`, include an `Authorization` header with
+`Bearer <api-key>` in requests.
+
+## Choose a Core HTTP Endpoint
+
+Prefer a task-specific endpoint when one matches your workload. Use Chat
+Completions for conversational or heterogeneous omni pipelines, rather than as
+the default wrapper for every generation task.
+
+| Task | Endpoint | Request | Response | Details |
+|------|----------|---------|----------|---------|
+| Conversation or multimodal understanding/generation | `POST /v1/chat/completions` | JSON | OpenAI-style JSON or SSE | [Chat Completions](chat_completions_api.md) |
+| Text-to-speech | `POST /v1/audio/speech` | JSON | Audio bytes or SSE | [Speech](speech_api.md) |
+| Sound, music, or ambient audio generation | `POST /v1/audio/generate` | JSON | Audio bytes | [Audio Generation](audio_generate_api.md) |
+| Text-to-image | `POST /v1/images/generations` | JSON | Base64 JSON or an image file | [Image Generation](image_generation_api.md) |
+| Image editing | `POST /v1/images/edits` | Multipart form | Base64 JSON or SSE | [Image Edit](image_edit_api.md) |
+| Video generation (recommended) | `POST /v1/videos` | Multipart form | Asynchronous job JSON | [Videos](videos_api.md) |
+| Video generation (blocking) | `POST /v1/videos/sync` | Multipart form | Video bytes | [Videos](videos_api.md#synchronous-response) |
+
+`POST /v1/videos/sync` is intended for tests and simple scripts. Use the
+asynchronous job API for production requests so clients can poll, download,
+and delete generated videos independently.
+
+## Minimal Requests
+
+The examples below assume a compatible model is already running. TTS voices,
+media inputs, generation controls, and output capabilities vary by model.
+
+=== "Chat"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/chat/completions" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "messages": [{"role": "user", "content": "Describe vLLM-Omni briefly."}],
+        "modalities": ["text"]
+      }'
+    ```
+
+=== "Speech"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/audio/speech" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "input": "Hello from vLLM-Omni.",
+        "voice": "vivian"
+      }' \
+      --output speech.wav
+    ```
+
+=== "Audio"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/audio/generate" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "input": "Ocean waves on a quiet beach",
+        "audio_length": 5.0
+      }' \
+      --output audio.wav
+    ```
+
+=== "Image"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/images/generations" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "prompt": "A small robot reading beside a window",
+        "size": "1024x1024",
+        "response_format": "file"
+      }' \
+      --output image.png
+    ```
+
+=== "Image edit"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/images/edits" \
+      -F "image=@input.png" \
+      -F "prompt=Turn the background into a snowy mountain" \
+      | jq -r '.data[0].b64_json' \
+      | base64 --decode > edited.png
+    ```
+
+=== "Video"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/videos" \
+      -F "prompt=A cinematic tracking shot of a mountain lake at sunrise" \
+      -F "size=1280x720" \
+      -F "seconds=5"
+    ```
+
+    The response contains a video job `id`. Poll and download it with:
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/videos/<video_id>"
+    curl -L "$VLLM_OMNI_BASE_URL/v1/videos/<video_id>/content" --output video.mp4
+    ```
+
+## Choose a Streaming or Realtime Endpoint
+
+These WebSocket APIs have different event schemas and cannot be used
+interchangeably.
+
+| Workload | Endpoint | Interaction model | Details |
+|----------|----------|-------------------|---------|
+| Incremental text input for speech synthesis | `WS /v1/audio/speech/stream` | Send text events and receive audio | [Streaming Text to Speech](speech_api.md#streaming-text-input-websocket) |
+| Live video understanding | `WS /v1/video/chat/stream` | Send video frames and receive text/audio | [Streaming Video Input](video_stream_api.md) |
+| Turn-based realtime audio | `WS /v1/realtime` | Stream one audio input and receive transcript/audio events | [Realtime Audio](realtime_api.md) |
+| Continuous speech-to-speech interaction | `WS /v1/realtime?duplex=1` or `WS /v1/duplex` | Listen and speak concurrently with session control | [Full Duplex](full_duplex_api.md) |
+| Generated video chunks | `WS /v1/realtime/video` | Start a diffusion request and receive fragmented MP4 | [Streaming Video Output](streaming_video_output_api.md) |
+| Robot policy inference | `WS /v1/realtime/robot/openpi` | Send MessagePack observations and receive action arrays | [OpenPI Robot Policy](openpi_api.md) |
+
+All six routes are model- or configuration-dependent. In particular,
+`/v1/realtime` is not full duplex unless the client sets `duplex=1` and the
+deployment explicitly enables duplex sessions. Clients should also verify the
+duplex capability payload because the query-parameter form falls back to the
+ordinary realtime handler when duplex is unavailable.
+
+## Related Endpoints
+
+| Purpose | Endpoints | Reference |
+|---------|-----------|-----------|
+| Discovery and readiness | `GET /health`, `GET /v1/models` | This page |
+| Batched conversations | `POST /v1/chat/completions/batch` | [Batch requests](chat_completions_api.md#batch-requests) |
+| Batched speech | `POST /v1/audio/speech/batch` | [Batch speech generation](speech_api.md#batch-speech-generation) |
+| TTS voice management | `GET/POST /v1/audio/voices`, `DELETE /v1/audio/voices/{name}` | [Voices](speech_api.md#voices-endpoint) |
+| Video job lifecycle | `GET /v1/videos`, `GET/DELETE /v1/videos/{video_id}`, `GET /v1/videos/{video_id}/content` | [Video endpoints](videos_api.md#endpoints) |
+| Release and restore stage memory | `POST /v1/omni/sleep`, `POST /v1/omni/wakeup` | [Sleep Mode](../features/sleep_mode.md) |
+
+## Standalone Experimental Servers
+
+PersonaPlex also provides a Moshi-compatible WebSocket server, while JoyVL
+provides a stateful interaction orchestrator in front of another model server.
+These are separate processes with their own routes; they are not additional
+paths on every `vllm serve --omni` instance. See [Standalone Experimental
+Servers](standalone_servers.md) before deploying either one.
+
+## Compatibility Notes
+
+- A registered path does not mean every model supports it. Unsupported tasks
+  return an OpenAI-style error or a WebSocket `error` event.
+- Use JSON for Chat, Speech, Audio Generation, and Image Generation. Use
+  `multipart/form-data` when uploading images, videos, or other files.
+- Generation can take minutes for large diffusion models. Configure client
+  timeouts accordingly; prefer asynchronous video jobs over long blocking
+  requests.
+- Model-specific parameters are vLLM-Omni extensions. Check the endpoint
+  reference before assuming an OpenAI SDK exposes them directly.

docs/serving/audio_generate_api.md

@@ -4,14 +4,14 @@ vLLM-Omni provides an API for text-to-audio generation using diffusion-based mod
 
 Unlike the [Speech API](speech_api.md) which targets text-to-speech synthesis, the Audio Generate API is designed for general-purpose audio generation from text descriptions (sound effects, music, ambient soundscapes, etc.).
 
-Each server instance runs a single model (specified at startup via `vllm-omni serve <model> --omni`).
+Each server instance runs a single model (specified at startup via `vllm serve <model> --omni`).
 
 ## Quick Start
 
 ### Start the Server
 
 ```bash
-vllm-omni serve stabilityai/stable-audio-open-1.0 \
+vllm serve stabilityai/stable-audio-open-1.0 \
     --host 0.0.0.0 \
     --port 8091 \
     --gpu-memory-utilization 0.9 \
@@ -326,7 +326,7 @@ curl http://localhost:8091/health
 Enable debug logging:
 
 ```bash
-vllm-omni serve stabilityai/stable-audio-open-1.0 \
+vllm serve stabilityai/stable-audio-open-1.0 \
     --host 0.0.0.0 \
     --port 8091 \
     --gpu-memory-utilization 0.9 \

docs/serving/chat_completions_api.md

@@ -1,78 +1,110 @@
 # Chat Completions API
 
-vLLM-Omni supports generating and editing images via the `/v1/chat/completions`
-endpoint using diffusion models. This page explains how to pass generation
-parameters (such as `num_inference_steps`, `height`, `width`) to diffusion
-models through this endpoint.
+Use `/v1/chat/completions` for conversational and multimodal pipelines that
+follow the OpenAI Chat API schema. Depending on the loaded model, a request can
+contain text, image, audio, or video input and can return text, audio, images,
+or other model-specific output.
 
-!!! tip
-    For dedicated endpoints that accept generation parameters as top-level
-    fields, see [Image Generation API](image_generation_api.md) and
-    [Image Edit API](image_edit_api.md).
+Some diffusion pipelines also support image generation and editing through
+Chat Completions. Prefer the dedicated [Image Generation
+API](image_generation_api.md) or [Image Edit API](image_edit_api.md) for
+task-specific clients; their request fields and response contracts are more
+direct.
 
-## Passing Generation Parameters
-
-The `/v1/chat/completions` endpoint follows the OpenAI Chat API schema, which
-does not natively include diffusion-specific fields like `num_inference_steps`
-or `height`. How you pass these extra fields depends on your client.
-
-### curl / Python `requests`
-
-Wrap generation parameters inside an `"extra_body"` key in the JSON body:
+## Basic Request
 
 ```bash
-curl -s http://localhost:8091/v1/chat/completions \
+curl http://localhost:8091/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
     "messages": [
-      {"role": "user", "content": "A beautiful landscape painting"}
+      {"role": "user", "content": "Describe vLLM-Omni briefly."}
     ],
-    "extra_body": {
-      "num_inference_steps": 50,
-      "seed": 42
-    }
+    "modalities": ["text"]
   }'
 ```
 
-### OpenAI Python SDK
+Use `stream: true` for Server-Sent Events. Input media syntax, supported output
+`modalities`, and response choices depend on the model; see the
+[model-specific online serving examples](../user_guide/examples/online_serving/qwen3_omni.md).
 
-Use the `extra_body` **keyword argument**. The SDK automatically merges these
-fields into the top-level request body:
+!!! tip
+    Each server hosts one model. Query `GET /v1/models` for its served model
+    name when your client requires the `model` field.
 
-```python
-response = client.chat.completions.create(
-    model="Qwen/Qwen-Image",
-    messages=[{"role": "user", "content": "A beautiful landscape painting"}],
-    extra_body={
-        "num_inference_steps": 50,
-        "seed": 42,
-    },
-)
+## vLLM Extension Parameters
+
+Send standard and vLLM-specific Chat parameters as top-level fields in a
+direct HTTP request. When using the OpenAI Python SDK, pass vLLM-specific
+fields through the SDK's `extra_body` keyword; the SDK merges them into the
+top-level JSON sent to the server.
+
+=== "curl"
+
+    ```bash
+    curl http://localhost:8091/v1/chat/completions \
+      -H "Content-Type: application/json" \
+      -d '{
+        "messages": [{"role": "user", "content": "Write a short haiku."}],
+        "top_k": 40
+      }'
+    ```
+
+=== "OpenAI Python SDK"
+
+    ```python
+    from openai import OpenAI
+
+    client = OpenAI(base_url="http://localhost:8091/v1", api_key="none")
+
+    response = client.chat.completions.create(
+        model="your-served-model",
+        messages=[{"role": "user", "content": "Write a short haiku."}],
+        extra_body={"top_k": 40},
+    )
+    ```
+
+??? note "Diffusion compatibility"
+
+    Diffusion models exposed through Chat Completions can accept supported
+    fields such as `num_inference_steps`, `seed`, `height`, and `width`. Send
+    them at the top level with direct HTTP, or in the SDK's `extra_body`
+    keyword argument.
+
+    A literal nested `"extra_body"` JSON object is accepted for compatibility,
+    but it is not recommended for new direct HTTP clients. Do not provide the
+    same parameter in multiple locations; the server returns a `400` error for
+    duplicate diffusion parameters. Prefer the dedicated [Image Generation
+    API](image_generation_api.md) or [Image Edit API](image_edit_api.md) when
+    either endpoint matches the task.
+
+## Batch Requests
+
+`POST /v1/chat/completions/batch` accepts the same shared generation fields,
+but `messages` is a list of conversations. The response contains one choice
+per conversation in input order.
+
+```bash
+curl http://localhost:8091/v1/chat/completions/batch \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      [{"role": "user", "content": "Summarize vLLM in one sentence."}],
+      [{"role": "user", "content": "Summarize vLLM-Omni in one sentence."}]
+    ],
+    "max_tokens": 64
+  }'
 ```
 
-!!! note "SDK `extra_body` vs. JSON `extra_body`"
-    These two `extra_body` usages look similar but work differently under the
-    hood. The SDK flattens the dict into the top-level request JSON, while the
-    curl/requests approach sends it as a nested `"extra_body"` key. Both are
-    handled correctly by the server.
-
-!!! note "About the `ignored fields` warning"
-    You may see a log message like:
-
-    ```
-    WARNING: The following fields were present in the request but ignored: {'height', 'width', ...}
-    ```
-
-    This is **harmless**. It is emitted by vLLM's request validation layer
-    because these fields are not part of the standard OpenAI
-    `ChatCompletionRequest` schema. The fields are still stored internally
-    and correctly forwarded to the diffusion pipeline.
+Batch Chat Completions does not support streaming, tools, beam search, or
+`n > 1`.
 
 ## Model-Specific Examples
 
-For complete examples with full request/response details, see the model-specific
-guides:
+For complete examples with model-specific inputs and outputs, see:
 
+- [Qwen3-Omni](../user_guide/examples/online_serving/qwen3_omni.md)
+- [Qwen2.5-Omni](../user_guide/examples/online_serving/qwen2_5_omni.md)
 - [Text-to-Image (Qwen-Image)](../user_guide/examples/online_serving/text_to_image.md)
 - [Image-to-Image (Qwen-Image-Edit, Qwen-Image-Layered)](../user_guide/examples/online_serving/image_to_image.md)
 - [GLM-Image](../user_guide/examples/online_serving/glm_image.md)

docs/serving/full_duplex_api.md

@@ -0,0 +1,116 @@
+# Full-Duplex WebSocket API
+
+vLLM-Omni provides an experimental full-duplex runtime for models that can
+continue receiving speech while producing speech. It adds persistent session
+state, model-specific turn policy, overlap handling, playback acknowledgement,
+and optional session resume.
+
+Full duplex is distinct from the turn-based [Realtime Audio API](realtime_api.md).
+
+## Choose an Endpoint
+
+| Endpoint | Protocol | Recommended use |
+|----------|----------|-----------------|
+| `WS /v1/realtime?duplex=1` | OpenAI Realtime-style event projection | Applications and browser clients |
+| `WS /v1/duplex` | Native vLLM-Omni duplex events | Runtime integration and low-level testing |
+
+Both endpoints use the same duplex engine and require the same model-side
+adapter. Prefer the Realtime projection unless the native lifecycle events are
+specifically required.
+
+## Enable Full Duplex
+
+The route becomes usable only when the deployment configuration explicitly
+sets:
+
+```yaml
+session_mode: duplex
+```
+
+The selected model pipeline must also provide a duplex serving adapter.
+Model-native deployments configure the corresponding engine runtime extension
+and control plane as part of their registered pipeline.
+
+!!! warning
+
+    `WS /v1/duplex` fails with `Duplex API is not available` when duplex is not
+    enabled. By contrast, `/v1/realtime?duplex=1` falls back to the ordinary
+    turn-based realtime handler when no duplex handler exists. Confirm that
+    `session.created.session.capabilities` is present before treating the
+    connection as full duplex.
+
+The current unified-runtime integrations are:
+
+- MiniCPM-o 4.5, using `vllm_omni/deploy/minicpmo_4_5_duplex.yaml`;
+- PersonaPlex, whose default `vllm_omni/deploy/personaplex.yaml` enables duplex.
+
+JoyVL is a separate HTTP interaction orchestrator and does not use these
+WebSocket endpoints. See [Standalone Experimental Servers](standalone_servers.md).
+
+## MiniCPM-o Quick Start
+
+Start the duplex deployment:
+
+```bash
+vllm serve openbmb/MiniCPM-o-4_5 --omni \
+  --deploy-config vllm_omni/deploy/minicpmo_4_5_duplex.yaml \
+  --trust-remote-code \
+  --port 8091
+```
+
+Stream a mono, PCM16, 16 kHz WAV file with the provided client:
+
+```bash
+python examples/online_serving/minicpmo/realtime_duplex_demo.py \
+  --url 'ws://localhost:8091/v1/realtime?duplex=1' \
+  --model openbmb/MiniCPM-o-4_5 \
+  --input-wav input_16k_mono.wav \
+  --ref-audio reference_voice.wav \
+  --output-dir /tmp/minicpmo-duplex
+```
+
+## Realtime Event Lifecycle
+
+A typical `/v1/realtime?duplex=1` session follows this lifecycle:
+
+1. Send `session.update` with the model, modalities, audio formats, and session
+   options.
+2. Wait for `session.created`; inspect `session.capabilities` instead of
+   assuming every model supports the same controls.
+3. Send `input_audio_buffer.append` events while microphone audio arrives.
+4. Send `input_audio_buffer.commit` at a user-turn boundary when required by
+   the model policy.
+5. Consume `response.created`, transcript deltas, `response.audio.delta`, and
+   `response.done` or `response.listen` events.
+6. Send `playback.ack` after audio has been played when the session advertises
+   playback acknowledgement support.
+7. Send `session.close` and wait for `session.closed`.
+
+Unlike the turn-based realtime endpoint, input may continue while a response
+is active. The server can emit `overlap.decision` to describe whether input was
+deferred, treated as a short acknowledgement, or used to interrupt output.
+
+## Capabilities and Model Differences
+
+The `session.created` payload includes capability fields such as
+`supports_barge_in`, `supports_playback_ack`, `supports_multi_session`,
+`supports_session_resume`, and `chunk_period_ms`. Treat this payload as the
+runtime contract.
+
+For example, PersonaPlex supports native overlapping speech but currently
+advertises `supports_barge_in=false`; destructive output interruption and
+model-state rewind have not been validated for that integration. Capacity and
+session-resume behavior also depend on the selected deployment configuration.
+
+## Native Protocol
+
+`WS /v1/duplex` exposes lower-level lifecycle names including
+`session.create`, `input_audio_buffer.append`, `turn.signal`, `playback.ack`,
+and `session.close`. It returns native session, input, response, overlap, and
+error events. This protocol is experimental and may evolve with the runtime;
+applications should use the provided Realtime client where possible.
+
+See the [MiniCPM-o example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/minicpmo),
+[PersonaPlex example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/personaplex),
+and [full-duplex runtime design](https://github.com/vllm-project/vllm-omni/blob/main/vllm_omni/experimental/fullduplex/DESIGN.md)
+for model-specific validation and architecture details.

docs/serving/openpi_api.md

@@ -0,0 +1,109 @@
+# OpenPI Robot Policy WebSocket API
+
+Use `WS /v1/realtime/robot/openpi` for low-latency robot-policy inference with
+OpenPI-compatible clients. A persistent WebSocket carries observations to the
+loaded policy model and returns action arrays.
+
+Despite the `/realtime` prefix, this endpoint does not use the OpenAI Realtime
+event schema. It uses binary MessagePack frames with NumPy extensions.
+
+## Availability
+
+The endpoint is available only when the loaded diffusion policy configuration
+contains `policy_server_config`. The server sends that model-specific
+configuration to the client as the first binary frame.
+
+For example, DreamZero declares:
+
+```yaml
+policy_server_config:
+  image_resolution: [180, 320]
+  n_external_cameras: 2
+  needs_wrist_camera: true
+  needs_stereo_camera: false
+  needs_session_id: true
+  action_space: joint_position
+```
+
+Without this configuration, the WebSocket returns `Robot policy not available`
+and closes.
+
+## DreamZero Quick Start
+
+Start the bundled DreamZero deployment:
+
+```bash
+vllm serve GEAR-Dreams/DreamZero-DROID --omni --port 8091 \
+  --served-model-name dreamzero-droid \
+  --deploy-config vllm_omni/deploy/dreamzero_tp1_cfg2.yaml \
+  --enforce-eager --disable-log-stats
+```
+
+Install the optional client dependencies, download the sample camera inputs,
+and run the client:
+
+```bash
+pip install openpi-client websockets opencv-python
+
+hf download YangshenDeng/vllm-omni-dreamzero-assets \
+  --repo-type dataset \
+  --local-dir outputs/dreamzero/assets
+
+python examples/online_serving/dreamzero/openpi_client.py \
+  --host 127.0.0.1 \
+  --port 8091 \
+  --video-dir outputs/dreamzero/assets
+```
+
+## Protocol
+
+The connection is request-response after an initial server handshake:
+
+```text
+connect
+  <- msgpack(policy_server_config)
+
+infer
+  -> msgpack({"endpoint": "infer", "session_id": "...", ...observation})
+  <- msgpack(ndarray | dict[str, ndarray])
+
+reset
+  -> msgpack({"endpoint": "reset"})
+  <- msgpack({"status": "reset successful"})
+```
+
+If `endpoint` is omitted, the server treats the message as `infer`. Each
+inference message produces one action response after the engine request
+completes; action tokens or intermediate tensors are not streamed.
+
+NumPy arrays use the marker format implemented by `openpi-client`. The server
+also accepts the legacy vLLM NumPy marker representation on input. JSON text
+frames are not observation messages.
+
+## Sessions and Reset
+
+- The API layer's current-session and first-call counters are scoped to each
+  WebSocket connection.
+- `session_id` identifies model-side state across observations on that
+  connection. If omitted, it defaults to `default`.
+- The first inference for a session is sent to the model with `reset=true`.
+- Changing `session_id`, or sending the `reset` command, causes the next
+  inference to start with `reset=true`.
+- The policy pipeline owns observation transforms and persistent model state,
+  normally keyed by `session_id`; the API layer forwards the raw observation
+  dictionary.
+
+## Limits and Errors
+
+- Maximum inbound payload size is 64 MiB.
+- The server closes an idle connection after 30 seconds.
+- Invalid binary input returns a MessagePack
+  `{"type":"error","message":"Invalid request payload"}` response.
+- Inference failures return a generic `Internal inference error` without
+  exposing an internal traceback.
+- Unsupported NumPy object, structured, and complex dtypes are rejected.
+
+Observation keys, camera layout, state tensors, and action shapes are defined
+by the loaded policy rather than by this transport. Read the handshake before
+constructing observations. See the [DreamZero example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/dreamzero)
+for a complete OpenPI client and DROID simulation loop.

docs/serving/realtime_api.md

@@ -0,0 +1,82 @@
+# Realtime Audio WebSocket API
+
+Use `WS /v1/realtime` to stream PCM audio into a compatible omni model and
+receive incremental transcription and synthesized response audio. This is a
+turn-based streaming API: it starts one generation over an audio stream and
+finishes after the client closes that input stream.
+
+For a model that listens while it is already speaking, use the experimental
+[Full-Duplex API](full_duplex_api.md) instead.
+
+## Quick Start
+
+Start a compatible model. The currently documented example uses Qwen3-Omni:
+
+```bash
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
+```
+
+Run the provided client with a mono, 16-bit PCM, 16 kHz WAV file:
+
+```bash
+python examples/online_serving/qwen3_omni/openai_realtime_client.py \
+  --url ws://localhost:8091/v1/realtime \
+  --model Qwen/Qwen3-Omni-30B-A3B-Instruct \
+  --input-wav input_16k_mono.wav \
+  --output-wav response.wav \
+  --output-text response.txt
+```
+
+The client dependency is `websockets`.
+
+## Protocol
+
+Messages in both directions are JSON text frames. Audio carried inside an
+event is base64-encoded raw PCM16.
+
+| Direction | Event | Purpose |
+|-----------|-------|---------|
+| Server to client | `session.created` | Confirms that the WebSocket connection is ready |
+| Client to server | `session.update` | Selects and validates the served model |
+| Client to server | `input_audio_buffer.commit` with `final: false` | Starts generation over the incoming stream |
+| Client to server | `input_audio_buffer.append` | Appends base64 PCM16 audio |
+| Client to server | `input_audio_buffer.commit` with `final: true` | Marks the end of input |
+| Server to client | `transcription.delta` | Carries incremental response text |
+| Server to client | `transcription.done` | Carries final text and token usage |
+| Server to client | `response.audio.delta` | Carries incremental PCM16 response audio |
+| Server to client | `response.audio.done` | Marks the end of response audio |
+| Server to client | `error` | Reports an invalid event, model, or audio payload |
+
+A minimal client sends events in this order:
+
+```json
+{"type":"session.update","model":"Qwen/Qwen3-Omni-30B-A3B-Instruct"}
+{"type":"input_audio_buffer.commit","final":false}
+{"type":"input_audio_buffer.append","audio":"<base64-pcm16>"}
+{"type":"input_audio_buffer.commit","final":true}
+```
+
+The initial non-final commit intentionally starts generation before all audio
+has arrived. Continue sending `input_audio_buffer.append` events while the
+engine is consuming the stream.
+
+## Audio Handling
+
+- Input is mono PCM16 at 16 kHz for the Qwen3-Omni example.
+- `response.audio.delta.audio` contains base64-encoded PCM16 bytes.
+- Read `sample_rate_hz` from each audio event instead of assuming an output
+  rate. Qwen3-Omni output is typically 24 kHz.
+- Concatenate audio deltas in receive order to construct the output waveform.
+- Set `input_audio_buffer.commit.final` to `true` after the last input chunk so
+  the server can terminate the streaming request.
+
+## Availability and Limitations
+
+The path is registered on the unified API server, but it is usable only when
+the loaded pipeline implements realtime audio input and produces compatible
+audio output. Unsupported deployments return an `error` event. The endpoint
+does not provide duplex session resume, playback acknowledgement, overlap
+policy, or barge-in controls.
+
+See the [Qwen3-Omni online serving example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen3_omni)
+for concurrency options, chunk pacing, and per-delta audio debugging.

docs/serving/standalone_servers.md

@@ -0,0 +1,109 @@
+# Standalone Experimental Servers
+
+Most vLLM-Omni endpoints run on the unified server started with
+`vllm serve <model> --omni`. The experimental full-duplex package also contains
+two separate user-facing server processes. Their routes are not added to the
+unified API server.
+
+| Server | Transport | Role |
+|--------|-----------|------|
+| PersonaPlex compatibility server | WebSocket | Hosts the official PersonaPlex browser protocol and a raw-PCM alternative |
+| JoyVL interaction server | HTTP | Adds state, memory, proactive decisions, and delegation in front of a separate OpenAI-compatible model backend |
+
+## PersonaPlex Compatibility Server
+
+Use this server when a client requires the official Moshi/PersonaPlex wire
+protocol or the bundled browser UI. For the engine-owned unified duplex path,
+use [`WS /v1/realtime?duplex=1`](full_duplex_api.md) instead.
+
+Start it with:
+
+```bash
+HF_TOKEN=... CUDA_VISIBLE_DEVICES=0 python -m \
+  vllm_omni.experimental.fullduplex.personaplex.serving.server \
+  --port 8091
+```
+
+| Route | Purpose |
+|-------|---------|
+| `GET /` | Official PersonaPlex browser client, when its assets are available |
+| `GET /health` | Readiness check |
+| `WS /api/chat` | Moshi-compatible tagged binary protocol with Opus audio |
+| `WS /v1/audio/duplex` | Simpler float32 raw-PCM protocol |
+
+### `/api/chat` Protocol
+
+The first byte of every binary frame is a message tag:
+
+| Direction | Tag | Payload |
+|-----------|-----|---------|
+| Server to client | `0x00` | Ready handshake |
+| Client to server | `0x01` | Opus microphone audio at 24 kHz |
+| Server to client | `0x01` | Opus response audio |
+| Server to client | `0x02` | UTF-8 inner-monologue text |
+
+The optional `text_prompt` and `voice_prompt` query parameters select a persona
+and voice. The default server admits one conversation; use `--batch-size N` to
+configure multiple lockstep slots. Connections beyond capacity close with
+WebSocket code 1013.
+
+### `/v1/audio/duplex` Protocol
+
+Send `{"type":"open","persona":"...","voice":"..."}` as a JSON text frame,
+then exchange binary float32 PCM frames. The server returns generated float32
+audio as binary frames and text as `{"type":"text","text":"..."}`. Send
+`{"type":"close"}` and wait for `{"type":"done"}` to flush and finish.
+
+See the [PersonaPlex example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/personaplex)
+for the browser and headless clients.
+
+## JoyVL Interaction Server
+
+JoyVL is an orchestration layer, not a model-serving engine. It calls a
+separate OpenAI-compatible backend and adds per-session frame history, memory,
+persona policy, and optional delegation.
+
+Start the model backend and orchestrator separately:
+
+```bash
+vllm serve jdopensource/JoyAI-VL-Interaction-Preview \
+  --served-model-name JoyAI-VL-Interaction-Preview \
+  --port 8092 \
+  --max-model-len 131072 \
+  --enable-prefix-caching \
+  --limit-mm-per-prompt '{"image":256,"video":1}'
+
+python -m vllm_omni.experimental.fullduplex.joyvl.serving.server \
+  --port 8091 \
+  --main-backend-url http://127.0.0.1:8092/v1 \
+  --main-model JoyAI-VL-Interaction-Preview
+```
+
+| Method and route | Purpose |
+|------------------|---------|
+| `GET /health` | Readiness check |
+| `GET /v1/models` | Reports the configured interaction model |
+| `POST /v1/chat/completions` | Processes one frame or interaction tick |
+| `POST /reset` | Resets a session |
+| `POST /v1/streaming/reset` | Alias for `/reset` |
+| `POST /v1/streaming/persona` | Changes the session persona |
+
+Send one multimodal Chat Completions request per video frame, normally around
+one frame per second, and identify the session with `x-session-id`. Responses
+include an `interaction` block whose action is `silence`, `response`, or
+`delegate`.
+
+```bash
+curl http://127.0.0.1:8091/v1/chat/completions \
+  -H 'content-type: application/json' \
+  -H 'x-session-id: demo' \
+  -d '{
+    "messages": [{"role": "user", "content": [
+      {"type": "text", "text": "Alert me if a fire appears"},
+      {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
+    ]}]
+  }'
+```
+
+See the [JoyVL recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/JD/JoyAI-VL-Interaction.md)
+for memory, personas, delegation backends, and the browser UI.

docs/serving/streaming_video_output_api.md

@@ -0,0 +1,92 @@
+# Streaming Video Output WebSocket API
+
+Use `WS /v1/realtime/video` to receive fragmented MP4 chunks while a compatible
+diffusion video model is generating. This endpoint streams generated video
+output; it is unrelated to [`WS /v1/video/chat/stream`](video_stream_api.md),
+which streams live video input into a conversational model.
+
+## Quick Start
+
+Start a model that supports step execution and enable diffusion streaming
+output:
+
+```bash
+vllm serve BestWishYsh/Helios-Distilled --omni \
+  --diffusion-streaming-output \
+  --port 8091
+```
+
+Install the client dependency and run the provided client:
+
+```bash
+pip install av websockets
+
+python examples/online_serving/streaming_video_generation/streaming_video_client.py \
+  --host 127.0.0.1 \
+  --port 8091 \
+  --model BestWishYsh/Helios-Distilled \
+  --prompt "A serene lakeside sunrise with mist over the water" \
+  --output output.mp4
+```
+
+## Protocol
+
+Control events are JSON text frames. Each `video.chunk_metadata` event is
+immediately followed by one binary WebSocket frame containing fragmented MP4
+bytes.
+
+| Direction | Event or frame | Purpose |
+|-----------|----------------|---------|
+| Client to server | `session.start` | Starts generation with a prompt and video parameters |
+| Server to client | `video.start` | Confirms the request ID, format, and accepted configuration |
+| Server to client | `video.chunk_metadata` | Describes the next binary media frame |
+| Server to client | Binary frame | Carries one fragmented MP4 chunk |
+| Client to server | `session.interaction` | Queues an optional prompt update during generation |
+| Server to client | `session.interaction.queued` | Confirms that an interaction was queued |
+| Client to server | `session.ping` | Refreshes the stall clock |
+| Server to client | `session.pong` | Acknowledges a ping |
+| Client to server | `session.stop` | Cancels the active generation |
+| Server to client | `session.done` | Reports completion or cancellation and the chunk count |
+| Server to client | `error` | Reports validation, generation, or timeout failures |
+
+Start a minimal session with:
+
+```json
+{
+  "type": "session.start",
+  "model": "BestWishYsh/Helios-Distilled",
+  "prompt": "A serene lakeside sunrise",
+  "format": "m4s",
+  "width": 640,
+  "height": 384,
+  "fps": 16,
+  "num_frames": 99
+}
+```
+
+Only `m4s` output is currently accepted. Concatenate the binary frames in
+receive order, then remux the fragmented stream if the target player requires
+a progressive MP4 file.
+
+## Mid-Generation Interaction
+
+Models that implement interaction updates can change the active prompt:
+
+```json
+{
+  "type": "session.interaction",
+  "interaction": {
+    "event_id": "sunlight-update",
+    "event": {"prompt": "Sunlight breaks through the mist"},
+    "transition_chunks": 2
+  }
+}
+```
+
+This feature is pipeline-dependent. An enabled endpoint does not imply that
+the loaded model supports midway prompt updates.
+
+The default start timeout is 10 seconds and the server reports a stall after
+about 60 seconds without generation progress or a `session.ping`. See the
+[streaming video generation example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/streaming_video_generation)
+for prompt-update scheduling and browser playback.

examples/online_serving/minicpmo/README.md

@@ -49,7 +49,7 @@ scenario below for live barge-in validation on the target GPU.
 Default:
 
 ```bash
-vllm-omni serve openbmb/MiniCPM-o-4_5 \
+vllm serve openbmb/MiniCPM-o-4_5 \
     --omni \
     --deploy-config vllm_omni/deploy/minicpmo_4_5.yaml \
     --trust-remote-code \

examples/online_serving/streaming_video_generation/README.md

@@ -37,7 +37,7 @@ During generation the client normally sends only `session.start` and then receiv
 ## Install Client Dependency
 
 ```bash
-pip install websockets
+pip install av websockets
 # For the Gradio demo:
 pip install 'vllm-omni[demo]' websockets
 ```

examples/online_serving/streaming_video_generation/streaming_video_client.py

@@ -8,7 +8,7 @@ the received bytes to disk when the session finishes or is interrupted.
 Image/reference input is intentionally not supported in this example yet.
 
 Requirements:
-    pip install websockets
+    pip install av websockets
 """
 
 import argparse

recipes/Boogu/Boogu-Image.md

@@ -1,11 +1,14 @@
 # Boogu-Image
 
-> Text-to-image and image-editing online serving (Boogu-Image-0.1-Base / -Edit)
+> Text-to-image and image-editing online serving
+(Boogu-Image-0.1-Base / -Edit / -Edit-Turbo)
 
 ## Summary
 
 - Vendor: Boogu
-- Model: `Boogu/Boogu-Image-0.1-Base` (text-to-image), `Boogu/Boogu-Image-0.1-Edit` (image editing)
+- Model: `Boogu/Boogu-Image-0.1-Base` (text-to-image),
+  `Boogu/Boogu-Image-0.1-Edit` (image editing), and
+  `Boogu/Boogu-Image-0.1-Edit-Turbo` (four-step image editing)
 - Task: Text-to-image generation and text-guided image editing (TI2I)
 - Mode: Online serving with the OpenAI-compatible API
 - Maintainer: Community
@@ -13,19 +16,23 @@
 ## When to use this recipe
 
 Use this recipe when you want a known-good starting point for serving
-`Boogu/Boogu-Image-0.1-Base` with vLLM-Omni's native pipeline (no
+`Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit`, or
+`Boogu/Boogu-Image-0.1-Edit-Turbo` with vLLM-Omni's native pipeline (no
 `--diffusion-load-format diffusers`, and the upstream `boogu` package is not
 required).
 
 Boogu-Image-0.1 is an Apache-2.0 unified image generation and editing model
-family. This recipe covers the Base text-to-image checkpoint, which pairs a
-Qwen3-VL multimodal encoder with a Diffusion Transformer (DiT) and a flow-match
-Euler scheduler with time-shift. It handles photorealistic generation and
-Chinese/English text rendering.
+family. The Base text-to-image checkpoint pairs a Qwen3-VL multimodal encoder
+with a Diffusion Transformer (DiT) and a flow-match Euler scheduler with
+time-shift. It handles photorealistic generation and Chinese/English text
+rendering. The Edit and Edit-Turbo checkpoints use the same native
+`BooguImagePipeline` for image editing.
 
 ## References
 
 - Upstream model card: <https://huggingface.co/Boogu/Boogu-Image-0.1-Base>
+- Edit model card: <https://huggingface.co/Boogu/Boogu-Image-0.1-Edit>
+- Edit-Turbo 1K hotfix: <https://huggingface.co/Boogu/Boogu-Image-0.1-Edit-Turbo/tree/hotfix-1k-20260708>
 - Project page: <https://boogu.org>
 - GitHub: <https://github.com/boogu-project/Boogu-Image>
 - Related example: [`examples/online_serving/text_to_image/`](../../examples/online_serving/text_to_image/README.md)
@@ -93,7 +100,7 @@ curl -s http://localhost:8091/v1/chat/completions \
 - **Memory usage:** ~34.6 GiB on GPU. Use `--vae-use-slicing --vae-use-tiling`
   to trim peak VRAM if needed.
 - **Key flags:**
-  - `--omni` — enables vLLM-Omni diffusion serving.
+    - `--omni` — enables vLLM-Omni diffusion serving.
 - **Guidance:** Boogu-Image uses `guidance_scale` (mapped to the upstream
   `text_guidance_scale`); the default is `4.0`. Classifier-free guidance is
   active whenever `guidance_scale > 1.0`.
@@ -111,13 +118,13 @@ the image-editing (TI2I) path activates automatically when a request carries a
 reference image. The Base text-to-image path is unaffected (no reference image
 is sent).
 
-#### Command
+### Command
 
 ```bash
 vllm serve Boogu/Boogu-Image-0.1-Edit --omni --port 8091
 ```
 
-#### Verification
+### Verification
 
 Edit an image with `/v1/images/edits` (the model-card example — change a photo
 to a colored-pencil drawing). Diffusion parameters are plain multipart form
@@ -157,14 +164,14 @@ curl -s http://localhost:8091/v1/chat/completions \
   }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > edited.png
 ```
 
-#### Notes
+### Notes
 
 - **Single reference image:** only one input image is supported for now (the
   upstream "Only support 1 reference image for now" limit).
 - **Guidance semantics:**
-  - `guidance_scale` = text guidance (upstream `text_guidance_scale`, default
+    - `guidance_scale` = text guidance (upstream `text_guidance_scale`, default
     `4.0`); `> 1.0` enables text CFG. Editing typically uses `5.0`.
-  - `guidance_scale_2` = image guidance (upstream `image_guidance_scale`,
+    - `guidance_scale_2` = image guidance (upstream `image_guidance_scale`,
     default `1.0` = off). Setting it `> 1.0` enables the double-guidance path
     (3 model predictions per step), steering more strongly toward the reference
     image.
@@ -178,3 +185,69 @@ curl -s http://localhost:8091/v1/chat/completions \
   supports Boogu-Image-Edit directly
   (`--model Boogu/Boogu-Image-0.1-Edit --guidance-scale 5.0`, optional
   `--guidance-scale-2`).
+
+## Fast image editing (Boogu-Image-0.1-Edit-Turbo)
+
+Edit-Turbo is the distilled image-editing checkpoint. Its `model_index.json`
+declares `BooguImagePipeline`, so it uses the same native TI2I path as Edit.
+The upstream 1K hotfix is recommended for stable results and is pinned below
+to avoid silently loading the older checkpoint from the repository's default
+revision.
+
+### Command
+
+```bash
+vllm serve Boogu/Boogu-Image-0.1-Edit-Turbo \
+  --omni \
+  --revision hotfix-1k-20260708 \
+  --port 8091
+```
+
+### Verification
+
+Edit an image with the distilled four-step settings:
+
+```bash
+curl -s http://localhost:8091/v1/images/edits \
+  -F model="Boogu/Boogu-Image-0.1-Edit-Turbo" \
+  -F image="@input.png" \
+  -F prompt="Change the style to a colored pencil drawing." \
+  -F num_inference_steps=4 \
+  -F guidance_scale=1.0 \
+  -F seed=42 \
+  | jq -r '.data[0].b64_json' | base64 -d > edited-turbo.png
+```
+
+Or via chat completions:
+
+```bash
+curl -s http://localhost:8091/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      {"role": "user", "content": [
+        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<BASE64>"}},
+        {"type": "text", "text": "Change the style to a colored pencil drawing."}
+      ]}
+    ],
+    "extra_body": {
+      "num_inference_steps": 4,
+      "guidance_scale": 1.0,
+      "seed": 42
+    }
+  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > edited-turbo.png
+```
+
+### Notes
+
+- **Pinned revision:** use `hotfix-1k-20260708`; upstream recommends the 1K
+  hotfix over the 1.5K variant for more stable results.
+- **Recommended settings:** `num_inference_steps=4` and
+  `guidance_scale=1.0`. Edit-Turbo is guidance-distilled, meaning the guidance
+  behavior is already baked into the distilled checkpoint. A scale of `1.0`
+  avoids applying additional classifier-free guidance; unlike the regular Edit
+  checkpoint, it should not be increased to `5.0`.
+- **Reference images:** the same single-reference and `align_res` behavior as
+  the regular Edit checkpoint applies.
+- **Known limitations:** the same single-GPU limitations as Base and Edit apply;
+  CPU offload, Cache-DiT, and multi-GPU parallelism are not yet validated.

tests/config/test_config_factory.py

@@ -41,6 +41,11 @@ from vllm_omni.engine.arg_utils import SHARED_FIELDS, internal_blacklist_keys
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
+SINGLE_STAGE_PIPE_CFG = (StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),)
+# Validation strings to match against in post init
+NO_STAGES_MATCH_STR = "no stages"
+NO_TERMINAL_STAGE_MATCH_STR = "No terminal stage"
+
 
 @pytest.fixture(autouse=True)
 def _stable_test_platform(monkeypatch):
@@ -618,6 +623,7 @@ class TestPipelineDiscovery:
         p = PipelineConfig(
             model_type="custom_collide",
             hf_architectures=("SomeCollidingArch",),
+            stages=SINGLE_STAGE_PIPE_CFG,
         )
         assert p.hf_architectures == ("SomeCollidingArch",)
 
@@ -639,41 +645,42 @@ class TestStagePipelineConfig:
 
 
 class TestPipelineConfigNew:
+    def test_simple_init(self):
+        # Ensure pipeline config validates in post init.
+        PipelineConfig(
+            model_type="t",
+            model_arch="A",
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+
     def test_frozen(self):
-        p = PipelineConfig(model_type="t", model_arch="A")
+        p = PipelineConfig(
+            model_type="t",
+            model_arch="A",
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
         with pytest.raises(AttributeError):
             p.model_type = "changed"
 
-    def test_validate_valid(self):
-        p = PipelineConfig(
-            model_type="t",
-            model_arch="A",
-            stages=(
-                StagePipelineConfig(stage_id=0, model_stage="a"),
-                StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,), final_output=True),
-            ),
-        )
-        assert p.validate() == []
-
     def test_validate_no_stages(self):
-        p = PipelineConfig(model_type="t", model_arch="A")
-        assert any("no stages" in e.lower() for e in p.validate())
+        with pytest.raises(ValueError, match=NO_STAGES_MATCH_STR):
+            PipelineConfig(model_type="t", model_arch="A")
 
     def test_validate_no_terminal_stage(self):
         """A pipeline with no ``final_output`` stage can never emit a result."""
-        p = PipelineConfig(
-            model_type="t",
-            model_arch="A",
-            stages=(
-                StagePipelineConfig(stage_id=0, model_stage="a"),
-                StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,)),
-            ),
-        )
-        assert any("no terminal stage" in e.lower() for e in p.validate())
+        with pytest.raises(ValueError, match=NO_TERMINAL_STAGE_MATCH_STR):
+            PipelineConfig(
+                model_type="t",
+                model_arch="A",
+                stages=(
+                    StagePipelineConfig(stage_id=0, model_stage="a"),
+                    StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,)),
+                ),
+            )
 
-    def test_validate_terminal_stage_need_not_be_last(self):
-        """The check is cycle-agnostic: any stage may carry ``final_output``."""
-        p = PipelineConfig(
+    def test_pipeline_can_have_final_output_in_any_stage(self):
+        """Ensure any stage may carry ``final_output``."""
+        PipelineConfig(
             model_type="t",
             model_arch="A",
             stages=(
@@ -681,15 +688,20 @@ class TestPipelineConfigNew:
                 StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,)),
             ),
         )
-        assert p.validate() == []
 
 
 class TestPipelineRegistration:
     def test_resolve_pipeline_prefers_deploy_pipeline_key(self, clean_pipeline_registry, tmp_path):
         deploy_key = "deploy_selected_pipeline"
         model_type_key = "hf_model_type_pipeline"
-        deploy_pipe = PipelineConfig(model_type=deploy_key)
-        model_type_pipe = PipelineConfig(model_type=model_type_key)
+        deploy_pipe = PipelineConfig(
+            model_type=deploy_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        model_type_pipe = PipelineConfig(
+            model_type=model_type_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
         register_pipeline(deploy_pipe)
         register_pipeline(model_type_pipe)
 
@@ -715,7 +727,11 @@ class TestPipelineRegistration:
         tmp_path,
     ):
         model_type_key = "registered_model_type_pipeline"
-        register_pipeline(PipelineConfig(model_type=model_type_key))
+        pipeline_cfg = PipelineConfig(
+            model_type=model_type_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        register_pipeline(pipeline_cfg)
 
         deploy_path = tmp_path / "deploy.yaml"
         deploy_path.write_text("pipeline: missing_pipeline\n", encoding="utf-8")
@@ -750,6 +766,7 @@ class TestPipelineRegistration:
         pipe_cfg = PipelineConfig(
             model_type=pipeline_key,
             hf_architectures=("ArchitectureFallbackForTest",),
+            stages=SINGLE_STAGE_PIPE_CFG,
         )
         register_pipeline(pipe_cfg)
 
@@ -780,15 +797,18 @@ class TestPipelineRegistration:
             model_type="rejected_by_predicate",
             hf_architectures=(shared_arch,),
             hf_config_predicate=rejecting_predicate,
+            stages=SINGLE_STAGE_PIPE_CFG,
         )
         raises_cfg = PipelineConfig(
             model_type="raises_in_predicate",
             hf_architectures=(shared_arch,),
             hf_config_predicate=raising_predicate,
+            stages=SINGLE_STAGE_PIPE_CFG,
         )
         accept_cfg = PipelineConfig(
             model_type="accepted_by_predicate",
             hf_architectures=(shared_arch,),
+            stages=SINGLE_STAGE_PIPE_CFG,
         )
         register_pipeline(reject_cfg)
         register_pipeline(raises_cfg)
@@ -822,6 +842,7 @@ class TestPipelineRegistration:
             model_type="predicate_without_arch_match",
             hf_architectures=("DifferentArchitectureForTest",),
             hf_config_predicate=predicate,
+            stages=SINGLE_STAGE_PIPE_CFG,
         )
         register_pipeline(pipe_cfg)
 
@@ -845,6 +866,7 @@ class TestPipelineRegistration:
         resolved_cfg = PipelineConfig(
             model_type="callable_resolved_pipeline",
             hf_architectures=("CallableArchitectureForTest",),
+            stages=SINGLE_STAGE_PIPE_CFG,
         )
         seen_hf_configs = []
 
@@ -918,7 +940,10 @@ class TestPipelineRegistration:
     def test_pipeline_registration(self, clean_pipeline_registry):
         """Ensure that we can register and create a custom pipeline config."""
         new_model_type = "new_model_type"
-        pipe_cfg = PipelineConfig(model_type=new_model_type)
+        pipe_cfg = PipelineConfig(
+            model_type=new_model_type,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
 
         # Register the new PipelineConfig
         assert new_model_type not in OMNI_PIPELINES
@@ -961,7 +986,10 @@ class TestPipelineRegistration:
         def custom_resolver(
             hf_config: FakeConfig,
         ) -> PipelineConfig:
-            return PipelineConfig(model_type=resolved_type)
+            return PipelineConfig(
+                model_type=resolved_type,
+                stages=SINGLE_STAGE_PIPE_CFG,
+            )
 
         # Register the new PipelineConfig
         assert new_model_type not in OMNI_PIPELINES
@@ -1018,7 +1046,7 @@ class TestPipelineRegistration:
         pipe_cfg = PipelineConfig(
             model_type=pipeline_key,
             model_arch="DeployOnlyArch",
-            stages=(StagePipelineConfig(stage_id=0, model_stage="diffusion"),),
+            stages=SINGLE_STAGE_PIPE_CFG,
         )
         register_pipeline(pipe_cfg)
 
@@ -1051,7 +1079,11 @@ class TestPipelineRegistration:
 
     def test_structured_path_loads_explicit_deploy_config_once(self, clean_pipeline_registry, tmp_path):
         pipeline_key = "single_load_pipeline"
-        register_pipeline(PipelineConfig(model_type=pipeline_key))
+        pipeline_cfg = PipelineConfig(
+            model_type=pipeline_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        register_pipeline(pipeline_cfg)
         deploy_path = tmp_path / "single_load.yaml"
         deploy_path.write_text(f"pipeline: {pipeline_key}\n", encoding="utf-8")
 
@@ -1081,6 +1113,7 @@ class TestPipelineRegistration:
         pipeline = PipelineConfig(
             model_type="pipeline_with_default",
             default_deploy_config_name=default_name,
+            stages=SINGLE_STAGE_PIPE_CFG,
         )
 
         with patch(
@@ -1100,8 +1133,16 @@ class TestPipelineRegistration:
             OmniServingCapability.COMPLETIONS,
             "pipeline_a blocks completions",
         )
-        pipe_a = PipelineConfig(model_type="detect_type", endpoint_restrictions=(restriction,))
-        pipe_b = PipelineConfig(model_type="override_type", endpoint_restrictions=())
+        pipe_a = PipelineConfig(
+            model_type="detect_type",
+            endpoint_restrictions=(restriction,),
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        pipe_b = PipelineConfig(
+            model_type="override_type",
+            endpoint_restrictions=(),
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
         register_pipeline(pipe_a)
         register_pipeline(pipe_b)
 
@@ -1641,6 +1682,7 @@ stages:
                     model_stage="ar",
                     execution_type=StageExecutionType.LLM_AR,
                     requires_multimodal_data=True,
+                    final_output=True,
                 ),
             ),
         )
@@ -1661,6 +1703,7 @@ stages:
                     model_stage="ar",
                     execution_type=StageExecutionType.LLM_AR,
                     scheduler_cls=scheduler_cls,
+                    final_output=True,
                 ),
             ),
         )
@@ -1906,7 +1949,6 @@ class TestQwen3OmniPipeline:
         assert isinstance(p, PipelineConfig)
         assert p.model_arch == "Qwen3OmniMoeForConditionalGeneration"
         assert len(p.stages) == 3
-        assert p.validate() == []
 
     def test_thinker(self):
         p = resolve_pipeline_config(
@@ -1960,7 +2002,6 @@ class TestQwen2_5OmniPipeline:
         assert isinstance(p, PipelineConfig)
         assert p.model_arch == "Qwen2_5OmniForConditionalGeneration"
         assert len(p.stages) == 3
-        assert p.validate() == []
 
     def test_thinker(self):
         p = resolve_pipeline_config("qwen2_5_omni")
@@ -2009,7 +2050,6 @@ class TestQwen3TTSPipeline:
         assert p is not None
         assert p.model_arch == "Qwen3TTSTalkerForConditionalGeneration"
         assert len(p.stages) == 2
-        assert p.validate() == []
 
     def test_talker_stage(self):
         p = resolve_pipeline_config("qwen3_tts")
@@ -2089,7 +2129,6 @@ class TestMingFlashOmniPipeline:
         assert isinstance(p, PipelineConfig)
         assert p.model_arch == "MingFlashOmniForConditionalGeneration"
         assert len(p.stages) == 2
-        assert p.validate() == []
 
     def test_thinker_stage(self):
         p = resolve_pipeline_config("ming_flash_omni")
@@ -2146,7 +2185,6 @@ class TestMingFlashOmniPipeline:
         assert isinstance(p, PipelineConfig)
         assert p.model_arch == "MingFlashOmniTalkerForConditionalGeneration"
         assert len(p.stages) == 1
-        assert p.validate() == []
 
     def test_tts_stage(self):
         p = resolve_pipeline_config("ming_flash_omni_tts")
@@ -2203,7 +2241,6 @@ class TestMingFlashOmniPipeline:
         assert isinstance(p, PipelineConfig)
         assert p.model_arch == "MingFlashOmniForConditionalGeneration"
         assert len(p.stages) == 1
-        assert p.validate() == []
 
     def test_thinker_only_stage(self):
         p = resolve_pipeline_config("ming_flash_omni_thinker_only")
@@ -2242,7 +2279,6 @@ class TestMingFlashOmniPipeline:
         assert p is not None
         assert p.model_arch == "MingFlashOmniForConditionalGeneration"
         assert len(p.stages) == 2
-        assert p.validate() == []
 
     def test_image_thinker_stage(self):
         s = resolve_pipeline_config("ming_flash_omni_image").get_stage(0)

tests/config/test_omni_config.py

@@ -45,6 +45,7 @@ from vllm_omni.config.stage_config import (
     PipelineConfig,
     StageDeployConfig,
     StageExecutionType,
+    StagePipelineConfig,
     load_deploy_config,
     merge_pipeline_deploy,
 )
@@ -1039,7 +1040,10 @@ def test_from_pipeline_config_uses_hf_config_for_callable_resolver():
 
 
 def test_from_pipeline_config_accepts_pre_resolved_pipeline():
-    resolved_pipeline = PipelineConfig(model_type="callable_resolved_variant")
+    resolved_pipeline = PipelineConfig(
+        model_type="callable_resolved_variant",
+        stages=(StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),),
+    )
 
     omni_config = VllmOmniConfig.from_pipeline_config(resolved_pipeline)
 
@@ -1072,6 +1076,7 @@ def test_from_pipeline_config_default_deploy_name_ignores_cwd(monkeypatch, tmp_p
     pipeline = PipelineConfig(
         model_type="pipeline_with_default",
         default_deploy_config_name=default_name,
+        stages=(StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),),
     )
     loaded_paths = []
 

tests/config/test_pipeline_registry.py

@@ -1,16 +1,22 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Tests for out of tree registration to OMNI_PIPELINES."""
 
 import pytest
 from transformers import PretrainedConfig
 
 from vllm_omni.config.pipeline_registry import OMNI_PIPELINES, register_pipeline
-from vllm_omni.config.stage_config import PipelineConfig, pipeline_cfg_resolver
+from vllm_omni.config.stage_config import PipelineConfig, StagePipelineConfig, pipeline_cfg_resolver
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
 
+def build_fake_pipeline_config(model_type: str) -> PipelineConfig:
+    return PipelineConfig(
+        model_type=model_type, stages=(StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),)
+    )
+
+
 @pytest.fixture
 def custom_resolver():
     """Build a reusable custom resolver for PipelineConfigs."""
@@ -22,7 +28,7 @@ def custom_resolver():
     def custom_resolver(
         hf_config: CustomConfigType,
     ) -> PipelineConfig:
-        return PipelineConfig(model_type="resolved_type")
+        return build_fake_pipeline_config("resolved_type")
 
     return custom_resolver
 
@@ -30,7 +36,7 @@ def custom_resolver():
 def test_register_pipeline_config(clean_pipeline_registry):
     """Ensure that we can register a custom pipeline config to OMNI_PIPELINES."""
     new_model_type = "new_model_type"
-    pipe_cfg = PipelineConfig(model_type=new_model_type)
+    pipe_cfg = build_fake_pipeline_config(new_model_type)
     assert new_model_type not in OMNI_PIPELINES
     register_pipeline(pipe_cfg)
     assert new_model_type in OMNI_PIPELINES
@@ -41,7 +47,7 @@ def test_register_pipeline_config_with_model_type(clean_pipeline_registry):
     """Ensure that we can register a custom pipeline config with an explicit model_type to OMNI_PIPELINES."""
     new_model_type = "new_model_type"
     unused_model_type = "foo"
-    pipe_cfg = PipelineConfig(model_type=unused_model_type)
+    pipe_cfg = build_fake_pipeline_config(unused_model_type)
     assert new_model_type not in OMNI_PIPELINES
     assert unused_model_type not in OMNI_PIPELINES
 
@@ -73,20 +79,3 @@ def test_minimax_h3_disaggregation_is_explicit_opt_in():
     pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
     assert isinstance(pipeline, PipelineConfig)
     assert pipeline.model_type == "minimax_h3_disaggregated"
-
-
-def test_in_tree_pipelines_pass_topology_validation():
-    """Every statically registered in-tree pipeline must have a valid topology.
-
-    ``PipelineConfig.validate()`` only runs on ``register_pipeline``, which
-    in-tree pipelines never go through, so a shipped topology error would
-    otherwise stay invisible until serving hangs or misroutes.
-    Resolver entries are skipped: they need an ``hf_config`` to produce a
-    ``PipelineConfig``.
-    """
-    failures = {
-        model_type: errors
-        for model_type, pipeline in OMNI_PIPELINES.items()
-        if isinstance(pipeline, PipelineConfig) and (errors := pipeline.validate())
-    }
-    assert not failures, f"in-tree pipelines failed topology validation: {failures}"

tests/diffusion/batching/test_diffusion_batching.py

@@ -7,7 +7,7 @@ End-to-end test for diffusion batching via AsyncOmni.
 This test fires multiple concurrent ``AsyncOmni.generate()`` calls for a
 diffusion model and validates that every caller receives its correct
 individual result.  When the underlying diffusion stage is configured with
-``batch_size > 1`` (via stage config or ``StageDiffusionClient``), the
+``max_num_seqs > 1``, the
 requests will be batched internally.
 
 Even without explicit batching config this test is useful for verifying
@@ -260,7 +260,7 @@ async def compare_single_vs_parallel(
 ) -> None:
     """Run the same prompts sequentially then in parallel and print a comparison."""
 
-    omni = AsyncOmni(model=model, max_num_seqs=batch_size, diffusion_batch_size=batch_size)
+    omni = AsyncOmni(model=model, max_num_seqs=batch_size)
     try:
         await warmup(omni, WARMUP_PROMPTS)
         single_time = await run_single(omni, prompts)
@@ -288,7 +288,7 @@ async def main(model: str, num_prompts: int, mode: str, batch_size: int = 1) ->
         await compare_single_vs_parallel(model, prompts, batch_size=batch_size)
         return
 
-    omni = AsyncOmni(model=model, max_num_seqs=batch_size, diffusion_batch_size=batch_size)
+    omni = AsyncOmni(model=model, max_num_seqs=batch_size)
     try:
         await warmup(omni, WARMUP_PROMPTS)
 
@@ -389,7 +389,7 @@ def test_diffusion_batching_async_concurrent(model_name: str):
     fired via asyncio.gather. Each request_id must appear in the results."""
 
     async def _inner():
-        omni = AsyncOmni(model=model_name, diffusion_batch_size=1)
+        omni = AsyncOmni(model=model_name, max_num_seqs=1)
         try:
             prompts = TEST_PROMPTS[:4]
             sp = _default_sampling_params()
@@ -430,7 +430,7 @@ def test_diffusion_batching_list_prompt_rejected(model_name: str):
     """
 
     async def _inner():
-        omni = AsyncOmni(model=model_name, diffusion_batch_size=4)
+        omni = AsyncOmni(model=model_name, max_num_seqs=4)
         try:
             prompts = TEST_PROMPTS[:4]
             sp = _default_sampling_params()
@@ -528,7 +528,7 @@ if __name__ == "__main__":
         "--batch-size",
         type=int,
         default=1,
-        help="Scheduler in-flight width (--max-num-seqs) and client diffusion_batch_size",
+        help="Scheduler in-flight width (--max-num-seqs)",
     )
     parser.add_argument(
         "--mode",

tests/diffusion/models/boogu_image/test_pipeline_boogu_image.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 """L1 unit tests for the native Boogu-Image pipeline.
 
@@ -133,6 +133,51 @@ def test_constructor_strips_mllm_lm_head(boogu_pipeline, mock_dependencies):
     assert boogu_pipeline.mllm is mock_dependencies["inner_encoder"]
 
 
+def test_constructor_forwards_revision_to_all_component_loaders(mock_dependencies, mocker):
+    from vllm_omni.diffusion.models.boogu_image.pipeline_boogu_image import (
+        BooguImagePipeline,
+    )
+
+    revision = "hotfix-1k-20260708"
+    prefetch = mocker.patch(f"{_MODULE}.prefetch_subfolders")
+    scheduler_loader = mocker.patch(
+        f"{_MODULE}.FlowMatchEulerDiscreteScheduler.from_pretrained",
+        return_value=mock_dependencies["scheduler"],
+    )
+    mllm_loader = mocker.patch(
+        f"{_MODULE}.Qwen3VLForConditionalGeneration.from_pretrained",
+        return_value=mock_dependencies["mllm_wrapper"],
+    )
+    processor_loader = mocker.patch(
+        f"{_MODULE}.Qwen3VLProcessor.from_pretrained",
+        return_value=mock_dependencies["processor"],
+    )
+    vae_loader = mocker.patch(
+        f"{_MODULE}.AutoencoderKL.from_pretrained",
+        return_value=mock_dependencies["vae"],
+    )
+    od_config = OmniDiffusionConfig(
+        model="dummy-boogu",
+        revision=revision,
+        tf_model_config=TransformerConfig(params={}),
+        dtype=torch.float32,
+        num_gpus=1,
+    )
+
+    pipeline = BooguImagePipeline(od_config=od_config)
+
+    (source,) = pipeline.weights_sources
+    assert source.revision == revision
+    prefetch.assert_called_once_with(
+        "dummy-boogu",
+        ["scheduler", "vae", "mllm", "processor"],
+        local_files_only=True,
+        revision=revision,
+    )
+    for loader in (scheduler_loader, mllm_loader, processor_loader, vae_loader):
+        assert loader.call_args.kwargs["revision"] == revision
+
+
 def test_constructor_weights_sources(boogu_pipeline):
     (source,) = boogu_pipeline.weights_sources
     assert source.model_or_path == "dummy-boogu"

tests/diffusion/test_diffusion_streaming_output.py

@@ -441,7 +441,6 @@ class TestPipelineStreamingOutputToEntrypoint:
                     model="test_model",
                     od_config=od_config,
                     metadata=metadata,
-                    batch_size=1,
                 )
 
     @staticmethod
@@ -526,6 +525,7 @@ class TestSupportedPipelines:
         runner = object.__new__(DiffusionModelRunner)
         runner.vllm_config = _make_vllm_config()
         runner.od_config = SimpleNamespace(
+            max_num_seqs=1,
             enable_cpu_offload=False,
             enable_layerwise_offload=False,
             enforce_eager=True,

tests/diffusion/test_inline_stage_diffusion_client.py

@@ -47,7 +47,7 @@ def client(mock_engine):
     with patch.object(InlineStageDiffusionClient, "_enrich_config"):
         od_config = MagicMock(spec=OmniDiffusionConfig)
         od_config.streaming_output = False
-        c = InlineStageDiffusionClient(model="test_model", od_config=od_config, metadata=metadata, batch_size=1)
+        c = InlineStageDiffusionClient(model="test_model", od_config=od_config, metadata=metadata)
         yield c
         c.shutdown()
 
@@ -234,5 +234,4 @@ def test_inline_client_requires_replica_id(mock_engine):
                 model="test_model",
                 od_config=od_config,
                 metadata=metadata,
-                batch_size=1,
             )

tests/diffusion/test_multiproc_engine_concurrency.py

@@ -1171,7 +1171,6 @@ class TestStageDiffusionClientErrorPropagation:
                 metadata,
                 "tcp://req",
                 "tcp://resp",
-                batch_size=1,
             )
 
     @pytest.mark.asyncio

tests/diffusion/test_prompt_update.py

@@ -387,7 +387,6 @@ class TestPromptUpdateIntegration:
                     model="test_model",
                     od_config=od_config,
                     metadata=metadata,
-                    batch_size=1,
                 )
 
     @staticmethod

tests/e2e/online_serving/test_boogu_image_edit.py

@@ -1,15 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 """
-Online serving tests for Boogu-Image-0.1-Edit (image-to-image via chat completions).
+Online serving tests for Boogu-Image-0.1-Edit and Edit-Turbo.
 
-The Edit checkpoint shares the ``BooguImagePipeline`` class with the Base
+Both Edit checkpoints share the ``BooguImagePipeline`` class with the Base
 text-to-image checkpoint; the editing (TI2I) path activates when the request
-carries a reference image. Text guidance rides on ``guidance_scale`` (upstream
-default 4.0), and image guidance rides on ``guidance_scale_2`` (upstream default
-1.0 = off; a value > 1 enables the double-guidance path). Only a single
-reference image is supported.
+carries a reference image. The regular Edit checkpoint uses text guidance via
+``guidance_scale`` and optional image guidance via ``guidance_scale_2``.
+Edit-Turbo is guidance-distilled and uses four steps with guidance scale 1.0.
+Only a single reference image is supported.
 
 - ``test_single_image_to_image_001``: one reference image, text guidance only.
 - ``test_double_guidance_001``: one reference image, text + image guidance.
@@ -40,6 +40,10 @@ pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 EDIT_PROMPT = "Change the style to a colored pencil drawing."
 SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
 
+EDIT_MODEL = "Boogu/Boogu-Image-0.1-Edit"
+EDIT_TURBO_MODEL = "Boogu/Boogu-Image-0.1-Edit-Turbo"
+EDIT_TURBO_REVISION = "hotfix-1k-20260708"
+
 
 def _get_diffusion_feature_cases(model: str):
     """Return one ``default`` ``OmniServerParams`` row for ``model`` (no extra ``server_args``)."""
@@ -54,13 +58,44 @@ def _get_diffusion_feature_cases(model: str):
     ]
 
 
+def _get_single_edit_cases():
+    """Return the regular and distilled Edit server/request configurations."""
+    return [
+        pytest.param(
+            OmniServerParams(model=EDIT_MODEL),
+            {
+                "num_inference_steps": 2,
+                "guidance_scale": 5.0,
+            },
+            id="edit",
+            marks=SINGLE_CARD_FEATURE_MARKS,
+        ),
+        pytest.param(
+            OmniServerParams(
+                model=EDIT_TURBO_MODEL,
+                server_args=["--revision", EDIT_TURBO_REVISION],
+            ),
+            {
+                "num_inference_steps": 4,
+                "guidance_scale": 1.0,
+            },
+            id="edit-turbo-hotfix-1k",
+            marks=SINGLE_CARD_FEATURE_MARKS,
+        ),
+    ]
+
+
 @pytest.mark.parametrize(
-    "omni_server",
-    _get_diffusion_feature_cases("Boogu/Boogu-Image-0.1-Edit"),
-    indirect=True,
+    ("omni_server", "sampling_params"),
+    _get_single_edit_cases(),
+    indirect=["omni_server"],
 )
-def test_single_image_to_image_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):
-    """Single-reference text-guided edit smoke for ``Boogu/Boogu-Image-0.1-Edit``."""
+def test_single_image_to_image_001(
+    omni_server: OmniServer,
+    sampling_params: dict,
+    openai_client: OpenAIClientHandler,
+):
+    """Single-reference text-guided edit smoke for regular and Turbo Edit."""
     image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"
 
     messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=EDIT_PROMPT)
@@ -69,9 +104,7 @@ def test_single_image_to_image_001(omni_server: OmniServer, openai_client: OpenA
         "model": omni_server.model,
         "messages": messages,
         "extra_body": {
-            "num_inference_steps": 2,
-            # Boogu text guidance (upstream default 4.0); > 1 enables CFG.
-            "guidance_scale": 5.0,
+            **sampling_params,
             "seed": 42,
         },
     }
@@ -81,7 +114,7 @@ def test_single_image_to_image_001(omni_server: OmniServer, openai_client: OpenA
 
 @pytest.mark.parametrize(
     "omni_server",
-    _get_diffusion_feature_cases("Boogu/Boogu-Image-0.1-Edit"),
+    _get_diffusion_feature_cases(EDIT_MODEL),
     indirect=True,
 )
 def test_double_guidance_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):
@@ -105,15 +138,20 @@ def test_double_guidance_001(omni_server: OmniServer, openai_client: OpenAIClien
     openai_client.send_diffusion_request(request_config)
 
 
-def _edits_request_config(model: str, *, guidance_scale_2: float | None = None) -> dict:
+def _edits_request_config(
+    model: str,
+    *,
+    num_inference_steps: int = 2,
+    guidance_scale: float = 5.0,
+    guidance_scale_2: float | None = None,
+) -> dict:
     """Multipart ``/v1/images/edits`` config: one synthetic reference + smoke-depth params."""
     image_bytes = base64.b64decode(generate_synthetic_image(512, 512)["base64"])
     data = {
         "model": model,
         "prompt": EDIT_PROMPT,
-        "num_inference_steps": 2,
-        # Boogu text guidance (upstream default 4.0); > 1 enables CFG.
-        "guidance_scale": 5.0,
+        "num_inference_steps": num_inference_steps,
+        "guidance_scale": guidance_scale,
         "seed": 42,
     }
     if guidance_scale_2 is not None:
@@ -133,19 +171,25 @@ def _assert_edits_response_has_image(responses) -> None:
 
 
 @pytest.mark.parametrize(
-    "omni_server",
-    _get_diffusion_feature_cases("Boogu/Boogu-Image-0.1-Edit"),
-    indirect=True,
+    ("omni_server", "sampling_params"),
+    _get_single_edit_cases(),
+    indirect=["omni_server"],
 )
-def test_images_edits_endpoint_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):
-    """Single-reference text-guided edit via the OpenAI-compatible ``/v1/images/edits`` endpoint."""
-    responses = openai_client.send_images_edits_http_request(_edits_request_config(omni_server.model))
+def test_images_edits_endpoint_001(
+    omni_server: OmniServer,
+    sampling_params: dict,
+    openai_client: OpenAIClientHandler,
+):
+    """Single-reference regular and Turbo Edit via ``/v1/images/edits``."""
+    responses = openai_client.send_images_edits_http_request(
+        _edits_request_config(omni_server.model, **sampling_params)
+    )
     _assert_edits_response_has_image(responses)
 
 
 @pytest.mark.parametrize(
     "omni_server",
-    _get_diffusion_feature_cases("Boogu/Boogu-Image-0.1-Edit"),
+    _get_diffusion_feature_cases(EDIT_MODEL),
     indirect=True,
 )
 def test_images_edits_endpoint_double_guidance_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):

tests/e2e/online_serving/test_boogu_image_edit_expansion.py

@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 """
-L4 expansion coverage for ``Boogu/Boogu-Image-0.1-Edit`` (image editing / TI2I).
+L4 expansion coverage for Boogu-Image Edit and Edit-Turbo (image editing / TI2I).
 
 The nightly smoke module (``test_boogu_image_edit.py``) already covers single-image
 text-guided editing and the text+image double-guidance path. This expansion
@@ -16,11 +16,12 @@ module adds the two edit paths smoke does not exercise:
   ``height`` / ``width``: asserts the output resolution follows the reference
   (upstream ``align_res``, on by default for a single reference).
 
-The Edit checkpoint shares ``BooguImagePipeline`` with the Base checkpoint; the
-TI2I path activates when the request carries a reference image. Only a single
-reference image is supported. Cases stay smoke-depth (``num_inference_steps=2``).
-CPU offload, Cache-DiT, and multi-GPU parallelism are not yet supported for this
-model and are intentionally omitted.
+Both Edit checkpoints share ``BooguImagePipeline`` with the Base checkpoint;
+the TI2I path activates when the request carries a reference image. Only a
+single reference image is supported. The regular Edit cases stay smoke-depth
+(``num_inference_steps=2``), while Edit-Turbo uses its tuned four-step
+configuration. CPU offload, Cache-DiT, and multi-GPU parallelism are not yet
+supported for these models and are intentionally omitted.
 
 From ``tests/``::
 
@@ -41,6 +42,8 @@ os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 MODEL = "Boogu/Boogu-Image-0.1-Edit"
+EDIT_TURBO_MODEL = "Boogu/Boogu-Image-0.1-Edit-Turbo"
+EDIT_TURBO_REVISION = "hotfix-1k-20260708"
 EDIT_PROMPT = "Change the style to a colored pencil drawing."
 
 SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
@@ -57,6 +60,33 @@ def _get_diffusion_feature_cases(model: str):
     ]
 
 
+def _get_align_res_cases():
+    """Return regular and Turbo Edit configurations for align-res coverage."""
+    return [
+        pytest.param(
+            OmniServerParams(model=MODEL),
+            {
+                "num_inference_steps": 2,
+                "guidance_scale": 5.0,
+            },
+            id="edit",
+            marks=SINGLE_CARD_FEATURE_MARKS,
+        ),
+        pytest.param(
+            OmniServerParams(
+                model=EDIT_TURBO_MODEL,
+                server_args=["--revision", EDIT_TURBO_REVISION],
+            ),
+            {
+                "num_inference_steps": 4,
+                "guidance_scale": 1.0,
+            },
+            id="edit-turbo-hotfix-1k",
+            marks=SINGLE_CARD_FEATURE_MARKS,
+        ),
+    ]
+
+
 @pytest.mark.parametrize("omni_server", _get_diffusion_feature_cases(MODEL), indirect=True)
 def test_image_only_guidance(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
     """Image-only guidance: text CFG off (1.0), image CFG on (2.0) -> image-only CFG branch."""
@@ -76,8 +106,16 @@ def test_image_only_guidance(omni_server: OmniServer, openai_client: OpenAIClien
     openai_client.send_diffusion_request(request_config)
 
 
-@pytest.mark.parametrize("omni_server", _get_diffusion_feature_cases(MODEL), indirect=True)
-def test_align_res_output_size(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
+@pytest.mark.parametrize(
+    ("omni_server", "sampling_params"),
+    _get_align_res_cases(),
+    indirect=["omni_server"],
+)
+def test_align_res_output_size(
+    omni_server: OmniServer,
+    sampling_params: dict,
+    openai_client: OpenAIClientHandler,
+) -> None:
     """Output resolution follows the reference image (upstream ``align_res``).
 
     No ``height`` / ``width`` is requested. The reference is 512x384 — both
@@ -92,8 +130,7 @@ def test_align_res_output_size(omni_server: OmniServer, openai_client: OpenAICli
         "model": omni_server.model,
         "messages": messages,
         "extra_body": {
-            "num_inference_steps": 2,
-            "guidance_scale": 5.0,
+            **sampling_params,
             "seed": 42,
         },
     }

tests/engine/test_async_omni_engine_stage_init.py

@@ -172,7 +172,6 @@ def _make_stage_runtime() -> StageRuntime:
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=1,
         async_chunk=False,
     )
 
@@ -196,7 +195,6 @@ def test_async_omni_engine_initialize_stages_passes_log_stats_to_runtime(monkeyp
     engine.model = "dummy-model"
     engine.config_path = "dummy-config"
     engine.single_stage_mode = False
-    engine.diffusion_batch_size = 1
     engine.async_chunk = False
     engine.tokenizer = None
     engine._single_stage_id_filter = None
@@ -282,7 +280,6 @@ def test_build_logical_stage_init_plans_handles_stage_without_devices(monkeypatc
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=1,
         async_chunk=False,
     )
 
@@ -371,7 +368,6 @@ def test_initialize_local_diffusion_replica_restores_device_visibility_after_loc
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=1,
         async_chunk=False,
     )
 
@@ -416,7 +412,6 @@ def test_initialize_local_diffusion_replica_passes_stage_init_timeout_and_inline
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=4,
         async_chunk=False,
     )
 
@@ -429,7 +424,6 @@ def test_initialize_local_diffusion_replica_passes_stage_init_timeout_and_inline
     def _capture_launch_diffusion_stage_replica(**kwargs):
         captured["stage_id"] = kwargs["metadata"].stage_id
         captured["stage_init_timeout"] = kwargs["stage_init_timeout"]
-        captured["batch_size"] = kwargs["batch_size"]
         captured["use_inline"] = kwargs["use_inline"]
         captured["omni_master_server"] = kwargs["omni_master_server"]
         return types.SimpleNamespace(), StageReplicaResources()
@@ -441,7 +435,6 @@ def test_initialize_local_diffusion_replica_passes_stage_init_timeout_and_inline
     assert captured == {
         "stage_id": 0,
         "stage_init_timeout": 302,
-        "batch_size": 4,
         "use_inline": expected_inline,
         "omni_master_server": None,
     }
@@ -511,21 +504,20 @@ def test_initialize_local_llm_replica_scopes_runtime_env(monkeypatch):
     assert os.environ[runtime_env_var] == "parent-value"
 
 
-def test_initialize_diffusion_stage_forwards_batch_size_without_writing_max_num_seqs(monkeypatch):
+def test_initialize_diffusion_stage_preserves_configured_max_num_seqs(monkeypatch):
     import vllm_omni.diffusion.stage_diffusion_client as client_mod
     import vllm_omni.engine.stage_init_utils as init_mod
 
-    od_config = types.SimpleNamespace(max_num_seqs=1)
+    od_config = types.SimpleNamespace(max_num_seqs=4)
     captured: dict[str, object] = {}
     monkeypatch.setattr(init_mod, "build_diffusion_config", lambda *args: od_config)
 
-    def _capture_client(model, config, metadata, stage_init_timeout, batch_size, use_inline):
+    def _capture_client(model, config, metadata, stage_init_timeout, use_inline):
         captured.update(
             model=model,
             config=config,
             metadata=metadata,
             stage_init_timeout=stage_init_timeout,
-            batch_size=batch_size,
             use_inline=use_inline,
         )
         return object()
@@ -539,27 +531,25 @@ def test_initialize_diffusion_stage_forwards_batch_size_without_writing_max_num_
         types.SimpleNamespace(),
         metadata,
         stage_init_timeout=12,
-        batch_size=4,
         use_inline=True,
     )
 
-    assert od_config.max_num_seqs == 1
+    assert od_config.max_num_seqs == 4
     assert captured == {
         "model": "dummy-model",
         "config": od_config,
         "metadata": metadata,
         "stage_init_timeout": 12,
-        "batch_size": 4,
         "use_inline": True,
     }
 
 
-def test_launch_diffusion_stage_replica_does_not_write_max_num_seqs_from_batch_size(monkeypatch):
+def test_launch_diffusion_stage_replica_preserves_configured_max_num_seqs(monkeypatch):
     import vllm_omni.diffusion.stage_diffusion_client as client_mod
     import vllm_omni.diffusion.stage_diffusion_proc as proc_mod
     import vllm_omni.engine.stage_engine_startup as startup_mod
 
-    od_config = types.SimpleNamespace(max_num_seqs=1, parallel_config=types.SimpleNamespace(world_size=1))
+    od_config = types.SimpleNamespace(max_num_seqs=4, parallel_config=types.SimpleNamespace(world_size=1))
     monkeypatch.setattr(startup_mod, "build_diffusion_config", lambda *args: od_config)
     monkeypatch.setattr(startup_mod, "acquire_device_locks", lambda *args: [])
     monkeypatch.setattr(
@@ -596,13 +586,12 @@ def test_launch_diffusion_stage_replica_does_not_write_max_num_seqs_from_batch_s
         stage_config=types.SimpleNamespace(),
         metadata=types.SimpleNamespace(stage_id=0),
         stage_init_timeout=12,
-        batch_size=4,
         use_inline=False,
         omni_master_server=omni_master_server,
     )
 
     assert result is sentinel_client
-    assert od_config.max_num_seqs == 1
+    assert od_config.max_num_seqs == 4
     assert resources.manager is proc_manager
 
 
@@ -621,7 +610,6 @@ def test_initialize_diffusion_stage_does_not_write_max_num_seqs(monkeypatch):
         types.SimpleNamespace(),
         metadata,
         stage_init_timeout=12,
-        batch_size=4,
         use_inline=True,
     )
 
@@ -672,7 +660,6 @@ def test_launch_diffusion_stage_replica_preserves_step_execution_max_num_seqs(mo
         stage_config=types.SimpleNamespace(),
         metadata=types.SimpleNamespace(stage_id=0),
         stage_init_timeout=12,
-        batch_size=4,
         use_inline=False,
         omni_master_server=omni_master_server,
     )
@@ -688,7 +675,6 @@ def test_stage_runtime_initializes_stage_pools(monkeypatch):
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=1,
         async_chunk=False,
     )
 
@@ -754,7 +740,6 @@ def test_stage_runtime_passes_log_stats_to_llm_replica_launch(monkeypatch):
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=1,
         async_chunk=False,
         log_stats=True,
     )
@@ -798,7 +783,6 @@ def test_stage_runtime_passes_log_stats_to_output_processor(monkeypatch):
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=1,
         async_chunk=False,
         log_stats=True,
     )
@@ -851,7 +835,6 @@ def test_build_logical_stage_init_plans_applies_replica_device_splits(monkeypatc
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=1,
         async_chunk=False,
     )
 
@@ -892,7 +875,6 @@ def test_initialize_stage_replicas_collects_results_by_stage_and_replica_id(monk
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=123,
-        diffusion_batch_size=1,
         async_chunk=False,
     )
 
@@ -930,7 +912,6 @@ def test_remote_replicas_use_distinct_init_group_keys():
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=123,
-        diffusion_batch_size=1,
         async_chunk=False,
     )
     plan = _make_llm_plan(
@@ -956,7 +937,6 @@ def test_initialize_stages_cleans_up_successful_replicas_after_partial_multi_rep
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=1,
         async_chunk=False,
     )
 
@@ -993,7 +973,6 @@ def test_initialize_stages_cleans_up_late_successful_replicas_after_early_multi_
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=1,
         async_chunk=False,
     )
 
@@ -1032,7 +1011,6 @@ def test_initialize_local_llm_replica_passes_stage_init_timeout_to_complete_stag
         model="dummy-model",
         config_path="dummy-config",
         stage_init_timeout=1,
-        diffusion_batch_size=1,
         async_chunk=False,
     )
     stage_init_timeout = 302

tests/engine/test_single_stage_mode.py

@@ -575,7 +575,6 @@ class TestSingleStageInitialization:
             model="fake-model",
             config_path="/fake/stages.yaml",
             stage_init_timeout=60,
-            diffusion_batch_size=2,
             async_chunk=False,
             single_stage_id_filter=stage_id_filter,
             omni_master_address="127.0.0.1",
@@ -807,7 +806,6 @@ class TestSingleStageInitialization:
             model="fake-model",
             config_path="/fake/stages.yaml",
             stage_init_timeout=60,
-            diffusion_batch_size=2,
             async_chunk=False,
         )
         mocker.patch.object(runtime_mod, "prepare_engine_environment")
@@ -860,7 +858,6 @@ class TestSingleStageReplicaInitialization:
             model="fake-model",
             config_path="/fake/stages.yaml",
             stage_init_timeout=60,
-            diffusion_batch_size=2,
             async_chunk=False,
             single_stage_id_filter=None,
             omni_master_address="127.0.0.1",
@@ -928,7 +925,6 @@ class TestSingleStageReplicaInitialization:
             model="fake-model",
             config_path="/fake/stages.yaml",
             stage_init_timeout=60,
-            diffusion_batch_size=2,
             async_chunk=False,
             single_stage_id_filter=None,
             omni_master_address="127.0.0.1",
@@ -950,7 +946,6 @@ class TestSingleStageReplicaInitialization:
             model="fake-model",
             config_path="/fake/stages.yaml",
             stage_init_timeout=60,
-            diffusion_batch_size=2,
             async_chunk=False,
             single_stage_id_filter=None,
             omni_master_address="127.0.0.1",
@@ -1002,7 +997,6 @@ class TestSingleStageReplicaInitialization:
             model="fake-model",
             config_path="/fake/stages.yaml",
             stage_init_timeout=60,
-            diffusion_batch_size=2,
             async_chunk=False,
             single_stage_id_filter=None,
             omni_master_address="127.0.0.1",
@@ -1068,7 +1062,6 @@ class TestSingleStageReplicaInitialization:
             model="fake-model",
             config_path="/fake/stages.yaml",
             stage_init_timeout=60,
-            diffusion_batch_size=4,
             async_chunk=False,
             single_stage_id_filter=None,
             omni_master_address="127.0.0.1",
@@ -1120,7 +1113,6 @@ class TestSingleStageReplicaInitialization:
             model="fake-model",
             config_path="/fake/stages.yaml",
             stage_init_timeout=60,
-            diffusion_batch_size=4,
             async_chunk=False,
             single_stage_id_filter=None,
             omni_master_address="127.0.0.1",
@@ -1141,7 +1133,7 @@ class TestSingleStageReplicaInitialization:
         runtime._init_visible_devices_baseline = "0"
 
         mocker.patch.object(runtime_mod, "inject_kv_stage_info")
-        od_config = SimpleNamespace(max_num_seqs=None, parallel_config=SimpleNamespace(world_size=1))
+        od_config = SimpleNamespace(max_num_seqs=4, parallel_config=SimpleNamespace(world_size=1))
         mocker.patch("vllm_omni.engine.stage_engine_startup.build_diffusion_config", return_value=od_config)
         mock_register = mocker.patch(
             "vllm_omni.engine.stage_engine_startup.register_stage_with_omni_master",
@@ -1178,7 +1170,7 @@ class TestSingleStageReplicaInitialization:
                 os.environ[device_env_var] = prev_device_env
 
         assert result is sentinel_client
-        assert od_config.max_num_seqs is None
+        assert od_config.max_num_seqs == 4
         mock_register.assert_called_once_with(
             omni_master_address="127.0.0.1",
             omni_master_port=25000,
@@ -1203,7 +1195,6 @@ class TestSingleStageReplicaInitialization:
             request_address="tcp://127.0.0.1:26002",
             response_address="tcp://127.0.0.1:26003",
             proc_manager=mocker.ANY,
-            batch_size=4,
         )
         assert mock_from_addresses.call_args.kwargs["proc_manager"].proc is proc
 
@@ -1216,7 +1207,6 @@ class TestSingleStageReplicaInitialization:
             model="fake-model",
             config_path="/fake/stages.yaml",
             stage_init_timeout=60,
-            diffusion_batch_size=4,
             async_chunk=False,
             single_stage_id_filter=None,
             omni_master_address="127.0.0.1",
@@ -1258,7 +1248,6 @@ class TestSingleStageReplicaInitialization:
             model="fake-model",
             config_path="/fake/stages.yaml",
             stage_init_timeout=60,
-            diffusion_batch_size=4,
             async_chunk=False,
             single_stage_id_filter=None,
             omni_master_address="127.0.0.1",

tests/entrypoints/test_omni_base_profiler.py

@@ -46,6 +46,16 @@ class TestOmniBaseProfiler:
             stage_ids=None,
         )
 
+    def test_removed_diffusion_batch_size_fails_loudly(self, mock_engine, mocker: MockerFixture):
+        """Legacy diffusion_batch_size callers receive an actionable error."""
+        mocker.patch("vllm_omni.entrypoints.omni_base.AsyncOmniEngine", return_value=mock_engine)
+        mocker.patch("vllm_omni.entrypoints.omni_base.omni_snapshot_download", side_effect=lambda x: x)
+        mocker.patch("vllm_omni.entrypoints.omni_base.weakref.finalize")
+        from vllm_omni.entrypoints.omni_base import OmniBase
+
+        with pytest.raises(TypeError, match="diffusion_batch_size.*max_num_seqs"):
+            OmniBase(model="test-model", diffusion_batch_size=8)
+
     def test_start_profile_with_prefix(self, omni_base_instance, mock_engine):
         """Test that start_profile passes profile_prefix to collective_rpc."""
         omni_base_instance.start_profile(profile_prefix="test_trace")

tests/entrypoints/test_utils.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """Unit tests for vllm_omni.entrypoints.utils module."""
 
 import os
@@ -420,6 +423,7 @@ class TestLoadAndResolveStageConfigs:
         """Ensure that kwargs survive default stage creation."""
         engine_backend = "vllm_omni.experimental.ar_diffusion.engine.ARDiffusionEngine"
         kwargs = {"dtype": torch.float32, "engine_backend": engine_backend}
+        kwargs["revision"] = "pinned-revision"
         mocker.patch("vllm_omni.entrypoints.utils.resolve_model_config_path", return_value=None)
         mocker.patch("vllm_omni.entrypoints.utils.load_stage_configs_from_model", return_value=([], None))
 
@@ -433,6 +437,7 @@ class TestLoadAndResolveStageConfigs:
         assert len(stage_configs) == 1
         assert "dtype" in stage_configs[0]["engine_args"]
         assert stage_configs[0]["engine_args"]["engine_backend"] == engine_backend
+        assert stage_configs[0]["engine_args"]["revision"] == "pinned-revision"
 
     def test_deploy_config_preserves_cli_overrides_and_replicas(self, tmp_path, mocker: MockerFixture):
         deploy_path = tmp_path / "qwen3_multi.yaml"

tests/model_executor/models/minicpmo_4_5/test_pipeline.py

@@ -26,6 +26,7 @@ from vllm_omni.config.pipeline_registry import OMNI_PIPELINES
 from vllm_omni.config.stage_config import (
     PipelineConfig,
     StageExecutionType,
+    StagePipelineConfig,
     _apply_platform_overrides,
     load_deploy_config,
     merge_pipeline_deploy,
@@ -65,7 +66,9 @@ class TestRegistryDeclaration:
         assert not hasattr(pipeline, "max_native_duplex_sessions")
 
     def test_ordinary_pipeline_defaults_to_no_duplex_control(self) -> None:
-        pipeline = PipelineConfig(model_type="ordinary")
+        pipeline = PipelineConfig(
+            model_type="ordinary", stages=(StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),)
+        )
         assert pipeline.duplex_control_enabled is False
         assert pipeline.duplex_serving_adapter is None
         assert not hasattr(pipeline, "max_native_duplex_sessions")
@@ -80,10 +83,6 @@ class TestPipelineTopology:
         assert len(pipeline.stages) == 3
         assert [s.stage_id for s in pipeline.stages] == [0, 1, 2]
 
-    def test_topology_validates(self, pipeline: PipelineConfig) -> None:
-        # ``validate`` returns a list of structural errors; empty == valid.
-        assert pipeline.validate() == []
-
     def test_thinker_stage(self, pipeline: PipelineConfig) -> None:
         thinker = pipeline.get_stage(0)
         assert thinker is not None

tests/model_executor/models/test_nemotron_voicechat_registration.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """L1 registration + config-shim tests for NemotronVoiceChat (CPU, no weights)."""
 
 import subprocess
@@ -59,7 +59,6 @@ def test_pipeline_registered_with_three_stages() -> None:
     assert code2wav.execution_type == StageExecutionType.LLM_GENERATION
     assert code2wav.final_output and code2wav.final_output_type == "audio"
     assert code2wav.input_sources == (1,)
-    assert pipeline.validate() == []
 
 
 def test_architectures_registered() -> None:

tests/utils/test_tracking_parser.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """Tests for TrackingArgumentParser and related utilities."""
 
 import argparse
@@ -35,7 +38,7 @@ _TEST_PIPELINE = PipelineConfig(
     stages=(
         StagePipelineConfig(stage_id=0, model_stage="stage0"),
         StagePipelineConfig(stage_id=1, model_stage="stage1"),
-        StagePipelineConfig(stage_id=2, model_stage="stage2"),
+        StagePipelineConfig(stage_id=2, model_stage="stage2", final_output=True),
     ),
 )
 

vllm_omni/config/pipeline_registry.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Pipeline registry and factory for vllm-omni.
 
 ``OMNI_PIPELINES`` maps each ``model_type`` to either a ``PipelineConfig``
@@ -196,18 +196,13 @@ def register_pipeline(pipeline: PipelineConfig | PipelineResolverFunc, model_typ
     since resolvers can return multiple different PipelineConfigs depending on the
     consumed config.
     """
-    errors: list[str] = []
     if isinstance(pipeline, PipelineConfig):
-        errors = pipeline.validate()
         model_type = model_type if model_type is not None else pipeline.model_type
-    else:
-        if model_type is None:
-            raise ValueError("Model type must be explicitly provided when registering a pipeline resolver")
+    elif model_type is None:
+        raise ValueError("Model type must be explicitly provided when registering a pipeline resolver")
 
     if model_type in OMNI_PIPELINES:
-        errors.append(f"Model type {model_type} is already registered; the old mapping will be clobbered")
-    if errors:
-        logger.warning("Registration for pipeline of type %s produced the following issues: %s", model_type, errors)
+        logger.warning(f"Model type {model_type} is already registered; the old mapping will be clobbered")
     OMNI_PIPELINES[model_type] = pipeline
 
 

vllm_omni/config/stage_config.py

@@ -302,6 +302,17 @@ class PipelineConfig:
     # Global CLI spelling -> (stage id, stage-local spelling).
     stage_cli_aliases: dict[str, tuple[int, str]] = field(default_factory=dict)
 
+    def __post_init__(self) -> None:
+        errors = self.get_validation_errors()
+
+        if errors:
+            # If we have multiple errors, put them on separate indented lines for readability
+            multi_sep = "\n\t"
+            error_str = multi_sep.join(errors)
+            if len(errors) > 1:
+                error_str = f"{multi_sep}{error_str}"
+            raise ValueError(f"PipelineConfig initialization failed with the following error(s): {error_str}")
+
     def get_stage(self, stage_id: int) -> StagePipelineConfig | None:
         """Look up a stage by its ID."""
         for stage in self.stages:
@@ -309,7 +320,7 @@ class PipelineConfig:
                 return stage
         return None
 
-    def validate(self) -> list[str]:
+    def get_validation_errors(self) -> list[str]:
         """Return list of topology errors (empty if valid)."""
         errors: list[str] = []
         if not self.stages:

vllm_omni/deploy/wan2_2_ti2v.yaml

@@ -1,7 +1,6 @@
 # Wan2.2 TI2V-5B deploy.
 #
 # Single GPU by default. Increase tensor_parallel_size and devices for A14B MoE variants.
-# Request-level batching uses scheduler max_num_seqs, not diffusion_batch_size.
 async_chunk: false
 trust_remote_code: true
 distributed_executor_backend: mp

vllm_omni/diffusion/inline_stage_diffusion_client.py

@@ -48,7 +48,6 @@ class InlineStageDiffusionClient(StageClientBase):
         model: str,
         od_config: OmniDiffusionConfig,
         metadata: StageMetadata,
-        batch_size: int = 1,
     ) -> None:
         self.model = model
         self.od_config = od_config
@@ -63,7 +62,6 @@ class InlineStageDiffusionClient(StageClientBase):
         self.prompt_transform_func = None
         self.prompt_expand_func = None
         self.engine_input_source = metadata.engine_input_source
-        self.batch_size = batch_size
 
         self._enrich_config()
         self._engine = DiffusionEngine.make_engine(self.od_config)
@@ -79,10 +77,10 @@ class InlineStageDiffusionClient(StageClientBase):
         self._engine.executor.register_failure_callback(self._mark_engine_dead)
 
         logger.info(
-            "[InlineStageDiffusionClient] stage-%s [rep-%s] initialized inline (batch_size=%d)",
+            "[InlineStageDiffusionClient] stage-%s [rep-%s] initialized inline (max_num_seqs=%d)",
             self.stage_id,
             self.replica_id,
-            self.batch_size,
+            self.od_config.max_num_seqs,
         )
 
     def _enrich_config(self) -> None:

vllm_omni/diffusion/models/boogu_image/pipeline_boogu_image.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 """Native vLLM-Omni pipeline for Boogu-Image-0.1.
 
@@ -220,7 +220,7 @@ class BooguImagePipeline(nn.Module, ProgressBarMixin, SupportsComponentDiscovery
             DiffusersPipelineLoader.ComponentSource(
                 model_or_path=od_config.model,
                 subfolder="transformer",
-                revision=None,
+                revision=od_config.revision,
                 prefix="transformer.",
                 fall_back_to_pt=True,
             )
@@ -233,10 +233,18 @@ class BooguImagePipeline(nn.Module, ProgressBarMixin, SupportsComponentDiscovery
         # See ``hub_prefetch.py`` for the transformers v5 multi-worker subfolder
         # race; prefetch the whole component set before any from_pretrained.
         boogu_subfolders = ["scheduler", "vae", "mllm", "processor"]
-        prefetch_subfolders(model, boogu_subfolders, local_files_only=local_files_only)
+        prefetch_subfolders(
+            model,
+            boogu_subfolders,
+            local_files_only=local_files_only,
+            revision=od_config.revision,
+        )
 
         self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
-            model, subfolder="scheduler", local_files_only=local_files_only
+            model,
+            subfolder="scheduler",
+            local_files_only=local_files_only,
+            revision=od_config.revision,
         )
 
         mllm = from_pretrained_with_prefetch(
@@ -246,6 +254,7 @@ class BooguImagePipeline(nn.Module, ProgressBarMixin, SupportsComponentDiscovery
             prefetch_list=boogu_subfolders,
             local_files_only=local_files_only,
             torch_dtype=od_config.dtype,
+            revision=od_config.revision,
         )
         # Upstream reuses the full VLM as an optional instruction rewriter and
         # encodes with its inner model (no ``lm_head``); the rewriter is not
@@ -255,7 +264,10 @@ class BooguImagePipeline(nn.Module, ProgressBarMixin, SupportsComponentDiscovery
         self.mllm = mllm.to(self._execution_device)
 
         self.processor = Qwen3VLProcessor.from_pretrained(
-            model, subfolder="processor", local_files_only=local_files_only
+            model,
+            subfolder="processor",
+            local_files_only=local_files_only,
+            revision=od_config.revision,
         )
 
         self.vae = from_pretrained_with_prefetch(
@@ -264,6 +276,7 @@ class BooguImagePipeline(nn.Module, ProgressBarMixin, SupportsComponentDiscovery
             subfolder="vae",
             prefetch_list=boogu_subfolders,
             local_files_only=local_files_only,
+            revision=od_config.revision,
         ).to(self._execution_device)
 
         self.transformer = BooguImageTransformer2DModel(od_config=od_config)

vllm_omni/diffusion/stage_diffusion_client.py

@@ -49,14 +49,13 @@ def create_diffusion_client(
     od_config: OmniDiffusionConfig,
     metadata: StageMetadata,
     stage_init_timeout: int,
-    batch_size: int = 1,
     use_inline: bool = False,
 ) -> Any:
     """Factory to create either an inline or out-of-process diffusion client."""
     if use_inline:
         from vllm_omni.diffusion.inline_stage_diffusion_client import InlineStageDiffusionClient
 
-        return InlineStageDiffusionClient(model, od_config, metadata, batch_size=batch_size)
+        return InlineStageDiffusionClient(model, od_config, metadata)
     proc_manager = StageDiffusionProcManager(
         model=model,
         od_config=od_config,
@@ -67,7 +66,6 @@ def create_diffusion_client(
         request_address=proc_manager.addresses.inputs[0],
         response_address=proc_manager.addresses.outputs[0],
         proc_manager=proc_manager,
-        batch_size=batch_size,
     )
 
 
@@ -91,14 +89,12 @@ class StageDiffusionClient(StageClientBase):
         response_address: str,
         *,
         proc_manager: StageDiffusionProcManager | None = None,
-        batch_size: int = 1,
     ) -> None:
         self._initialize_client(
             metadata,
             request_address,
             response_address,
             proc_manager=proc_manager,
-            batch_size=batch_size,
         )
 
     @classmethod
@@ -109,7 +105,6 @@ class StageDiffusionClient(StageClientBase):
         response_address: str,
         *,
         proc_manager: StageDiffusionProcManager | None = None,
-        batch_size: int = 1,
     ) -> StageDiffusionClient:
         """Create a client for an already-running diffusion subprocess."""
         return cls(
@@ -117,7 +112,6 @@ class StageDiffusionClient(StageClientBase):
             request_address,
             response_address,
             proc_manager=proc_manager,
-            batch_size=batch_size,
         )
 
     def _initialize_client(
@@ -127,7 +121,6 @@ class StageDiffusionClient(StageClientBase):
         response_address: str,
         *,
         proc_manager: StageDiffusionProcManager | None = None,
-        batch_size: int,
     ) -> None:
         self._set_stage_metadata(metadata)
         self._proc_manager = proc_manager
@@ -144,11 +137,10 @@ class StageDiffusionClient(StageClientBase):
             self._start_proc_monitor()
 
         logger.info(
-            "[StageDiffusionClient] stage-%s [rep-%s] initialized (owns_process=%s, batch_size=%d)",
+            "[StageDiffusionClient] stage-%s [rep-%s] initialized (owns_process=%s)",
             self.stage_id,
             self.replica_id,
             self._proc_manager is not None,
-            batch_size,
         )
 
     def _set_stage_metadata(self, metadata: StageMetadata) -> None:

vllm_omni/engine/async_omni_engine.py

@@ -129,7 +129,6 @@ class AsyncOmniEngine:
         model: str,
         stage_init_timeout: int = 300,
         init_timeout: int = 600,
-        diffusion_batch_size: int = 1,
         single_stage_mode: bool = False,
         transfer_emitter: Any = None,
         prom_metrics: Any = None,
@@ -140,7 +139,6 @@ class AsyncOmniEngine:
     ) -> None:
         self.model = model
         self.tokenizer = tokenizer
-        self.diffusion_batch_size = diffusion_batch_size
         # Cached by get_diffusion_od_config().
         self._diffusion_od_config_view: Any = None
         startup_timeout = int(init_timeout)
@@ -320,7 +318,6 @@ class AsyncOmniEngine:
             config_path=self.config_path,
             single_stage_mode=self.single_stage_mode,
             stage_init_timeout=stage_init_timeout,
-            diffusion_batch_size=self.diffusion_batch_size,
             async_chunk=self.async_chunk,
             tokenizer=self.tokenizer,
             single_stage_id_filter=self._single_stage_id_filter,

vllm_omni/engine/stage_engine_startup.py

@@ -1546,7 +1546,6 @@ def launch_diffusion_stage_replica(
     stage_config: Any,
     metadata: Any,
     stage_init_timeout: int,
-    batch_size: int,
     use_inline: bool,
     replica_id: int = 0,
     omni_master_server: OmniMasterServer | None = None,
@@ -1565,7 +1564,6 @@ def launch_diffusion_stage_replica(
             stage_config,
             metadata,
             stage_init_timeout=stage_init_timeout,
-            batch_size=batch_size,
             use_inline=use_inline,
         )
         return client, StageReplicaResources()
@@ -1624,7 +1622,6 @@ def launch_diffusion_stage_replica(
             request_address=proc_manager.addresses.inputs[0],
             response_address=proc_manager.addresses.outputs[0],
             proc_manager=proc_manager,
-            batch_size=batch_size,
         )
         return client, StageReplicaResources(
             manager=proc_manager,

vllm_omni/engine/stage_init_utils.py

@@ -1813,7 +1813,6 @@ def initialize_diffusion_stage(
     stage_cfg: Any,
     metadata: StageMetadata,
     stage_init_timeout: int,
-    batch_size: int = 1,
     use_inline: bool = False,
 ) -> Any:
     """Build a diffusion stage client.
@@ -1823,15 +1822,12 @@ def initialize_diffusion_stage(
         stage_cfg: Stage configuration.
         metadata: Extracted stage metadata.
         stage_init_timeout: Timeout in seconds for stage initialization handshake
-        batch_size: Client-side request batch width. Does not set scheduler
-            ``max_num_seqs``; pass ``--max-num-seqs`` or stage YAML for that.
-            Forwarded to ``StageDiffusionClient``.
         use_inline: If True, uses the inline diffusion client instead of subprocess.
     """
     from vllm_omni.diffusion.stage_diffusion_client import create_diffusion_client
 
     od_config = build_diffusion_config(model, stage_cfg, metadata)
-    return create_diffusion_client(model, od_config, metadata, stage_init_timeout, batch_size, use_inline)
+    return create_diffusion_client(model, od_config, metadata, stage_init_timeout, use_inline)
 
 
 def _stage_declares_cfg_pairs(model_config: Any) -> bool:

vllm_omni/engine/stage_runtime.py

@@ -87,7 +87,6 @@ class StageRemoteFactoryContext:
     base_metadata: Any
     vllm_config: Any | None = None
     executor_class: type | None = None
-    diffusion_batch_size: int = 1
 
 
 def _build_load_balancer_factory(policy: str) -> Callable[[], LoadBalancer]:
@@ -124,7 +123,6 @@ class StageRuntime:
         config_path: str,
         *,
         stage_init_timeout: int,
-        diffusion_batch_size: int,
         async_chunk: bool,
         tokenizer: str | None = None,
         log_stats: bool = False,
@@ -133,7 +131,6 @@ class StageRuntime:
         self._model = model
         self._config_path = config_path
         self._stage_init_timeout = stage_init_timeout
-        self._diffusion_batch_size = diffusion_batch_size
         self._async_chunk = async_chunk
         self._tokenizer = tokenizer
         self._log_stats = log_stats
@@ -672,7 +669,6 @@ class StageRuntime:
                     stage_config=plan.stage_cfg,
                     metadata=plan.metadata,
                     stage_init_timeout=stage_init_timeout,
-                    batch_size=self._diffusion_batch_size,
                     use_inline=plan.num_replicas == 1
                     and bool(self._num_stages == 1 or inline_diffusion or custom_pipeline_args),
                     replica_id=plan.replica_id,
@@ -681,10 +677,9 @@ class StageRuntime:
                 )
 
             logger.info(
-                "[StageRuntime] Stage %s replica %s initialized (diffusion, batch_size=%d)",
+                "[StageRuntime] Stage %s replica %s initialized (diffusion)",
                 plan.metadata.stage_id,
                 plan.replica_id,
-                self._diffusion_batch_size,
             )
             return cast(StagePoolClient, client)
         except Exception:
@@ -768,7 +763,6 @@ class DistStageRuntime(StageRuntime):
         config_path: str,
         *,
         stage_init_timeout: int,
-        diffusion_batch_size: int,
         async_chunk: bool,
         single_stage_id_filter: int | None,
         omni_master_address: str,
@@ -785,7 +779,6 @@ class DistStageRuntime(StageRuntime):
             model=model,
             config_path=config_path,
             stage_init_timeout=stage_init_timeout,
-            diffusion_batch_size=diffusion_batch_size,
             async_chunk=async_chunk,
             tokenizer=tokenizer,
             log_stats=log_stats,
@@ -914,7 +907,6 @@ class DistStageRuntime(StageRuntime):
             base_metadata=metadata,
             vllm_config=plan.stage_vllm_config,
             executor_class=plan.executor_class,
-            diffusion_batch_size=self._diffusion_batch_size,
         )
         return self._create_remote_replica_client(ctx, plan.replica_id)
 
@@ -977,7 +969,6 @@ class DistStageRuntime(StageRuntime):
                 base_metadata=template.metadata,
                 vllm_config=template.stage_vllm_config,
                 executor_class=template.executor_class,
-                diffusion_batch_size=self._diffusion_batch_size,
             )
         return contexts
 
@@ -1045,7 +1036,6 @@ class DistStageRuntime(StageRuntime):
                 metadata,
                 request_address=resources.addresses.inputs[0],
                 response_address=resources.addresses.outputs[0],
-                batch_size=ctx.diffusion_batch_size,
             )
             logger.info(
                 "[DistStageRuntime] Remote diffusion replica attached stage=%d replica=%d",
@@ -1109,7 +1099,6 @@ def create_stage_runtime(
     *,
     single_stage_mode: bool,
     stage_init_timeout: int,
-    diffusion_batch_size: int,
     async_chunk: bool,
     tokenizer: str | None = None,
     # Distributed-only params:
@@ -1131,7 +1120,6 @@ def create_stage_runtime(
             model=model,
             config_path=config_path,
             stage_init_timeout=stage_init_timeout,
-            diffusion_batch_size=diffusion_batch_size,
             async_chunk=async_chunk,
             tokenizer=tokenizer,
             log_stats=log_stats,
@@ -1148,7 +1136,6 @@ def create_stage_runtime(
         model=model,
         config_path=config_path,
         stage_init_timeout=stage_init_timeout,
-        diffusion_batch_size=diffusion_batch_size,
         async_chunk=async_chunk,
         tokenizer=tokenizer,
         log_stats=log_stats,

vllm_omni/entrypoints/omni_base.py

@@ -187,7 +187,9 @@ class OmniBase(PDDisaggregationMixin):
         # override the deploy YAML's ``async_chunk: true`` default.
         async_chunk = kwargs.get("async_chunk")
         output_modalities = kwargs.pop("output_modalities", None)
-        diffusion_batch_size: int = kwargs.pop("diffusion_batch_size", 1)
+
+        if "diffusion_batch_size" in kwargs:
+            raise TypeError("`diffusion_batch_size` has been removed in Omni/AsyncOmni. Use `max_num_seqs`.")
 
         if "log_requests" in kwargs:
             raise TypeError("`log_requests` has been removed in Omni/AsyncOmni. Use `log_stats`.")
@@ -213,7 +215,6 @@ class OmniBase(PDDisaggregationMixin):
             model=model,
             init_timeout=init_timeout,
             stage_init_timeout=stage_init_timeout,
-            diffusion_batch_size=diffusion_batch_size,
             transfer_emitter=self.transfer_metrics,
             prom_metrics=self.prom_metrics,
             log_stats=log_stats,

vllm_omni/entrypoints/utils.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 import json
 import os
 import types
@@ -600,6 +603,18 @@ def load_and_resolve_stage_configs(
             stage_configs = []
 
     stage_configs = filter_stages(config_path, stage_configs, kwargs)
+
+    # ``revision`` selects the model source for every stage. Keep the global
+    # CLI pin on each stage's engine args so diffusion config construction can
+    # pass it through to all component loaders. The registry-to-legacy-stage
+    # conversion currently drops this inherited vLLM EngineArgs field.
+    revision = (kwargs or {}).get("revision")
+    if revision is not None:
+        for stage_config in stage_configs:
+            engine_args = stage_config.get("engine_args") if hasattr(stage_config, "get") else None
+            if engine_args is not None and engine_args.get("revision") is None:
+                engine_args["revision"] = revision
+
     logger.debug(f"stage_configs: {stage_configs}")
 
     return config_path, stage_configs, omni_lb_policy