Dev/debug qwen tts (#903)

Signed-off-by: Taichang Zhou <tzhouam@connect.ust.hk>

.buildkite/pipeline.yml

@@ -41,23 +41,6 @@ steps:
           volumes:
             - "/fsx/hf_cache:/fsx/hf_cache"
 
-  - label: "Diffusion Images API LoRA E2E"
-    timeout_in_minutes: 20
-    depends_on: image-build
-    commands:
-      - pytest -s -v tests/e2e/online_serving/test_images_generations_lora.py
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
   - label: "Diffusion Model CPU offloading Test"
     timeout_in_minutes: 20
     depends_on: image-build
@@ -187,10 +170,7 @@ steps:
     depends_on: image-build
     commands:
       - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-      - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-      - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
-      - pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py
-      - pytest -s -v tests/e2e/online_serving/test_async_omni.py
+      - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/online_serving/test_async_omni.py
     agents:
       queue: "mithril-h100-pool"
     plugins:

.buildkite/scripts/simple_test.sh

@@ -52,7 +52,6 @@ VENV_PYTHON="${VENV_DIR}/bin/python"
 
 "${VENV_PYTHON}" -m pytest -v -s tests/entrypoints/
 "${VENV_PYTHON}" -m pytest -v -s tests/diffusion/cache/
-"${VENV_PYTHON}" -m pytest -v -s tests/diffusion/lora/
 "${VENV_PYTHON}" -m pytest -v -s tests/model_executor/models/qwen2_5_omni/test_audio_length.py
 "${VENV_PYTHON}" -m pytest -v -s tests/worker/
 "${VENV_PYTHON}" -m pytest -v -s tests/distributed/omni_connectors/test_kv_flow.py

.pre-commit-config.yaml

@@ -29,10 +29,7 @@ repos:
         # only for staged files
 
   - repo: https://github.com/rhysd/actionlint
-    # v1.7.8+ sets `go 1.24.0` in go.mod, which older Go toolchains (and most
-    # current CI images) cannot parse. Pin to v1.7.7 until actionlint fixes the
-    # go.mod directive.
-    rev: v1.7.7
+    rev: v1.7.9
     hooks:
       - id: actionlint
         files: ^\.github/workflows/.*\.ya?ml$

docker/Dockerfile.rocm

@@ -17,10 +17,6 @@ RUN mkdir -p ${COMMON_WORKDIR}/vllm-omni
 COPY . ${COMMON_WORKDIR}/vllm-omni
 RUN cd ${COMMON_WORKDIR}/vllm-omni && uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" --no-cache-dir ".[dev]"
 
-# When we are installing onnxruntime-rocm, we need to uninstall the system-installed onnxruntime first.
-# These are the dependencies of Qwen3-TTS.
-RUN uv pip uninstall onnxruntime --system && uv pip install --no-cache-dir onnxruntime-rocm sox --system
-
 RUN ln -sf /usr/bin/python3 /usr/bin/python
 
 CMD ["/bin/bash"]
@@ -29,4 +25,4 @@ ENTRYPOINT []
 
 #Set entrypoint for vllm-openai official images
 FROM final AS vllm-openai
-ENTRYPOINT ["vllm", "serve", "--omni"]
+ENTRYPOINT ["vllm", "serve"]

docs/api/README.md

@@ -53,10 +53,8 @@ Engine classes for offline and online inference.
 
 Core scheduling and caching components.
 
-- [vllm_omni.core.sched.omni_ar_scheduler.KVCacheTransferData][]
 - [vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler][]
 - [vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler][]
-- [vllm_omni.core.sched.output.OmniCachedRequestData][]
 - [vllm_omni.core.sched.output.OmniNewRequestData][]
 - [vllm_omni.model_executor.models.qwen3_tts.tokenizer_25hz.vq.core_vq.DistributedGroupResidualVectorQuantization][]
 - [vllm_omni.model_executor.models.qwen3_tts.tokenizer_25hz.vq.core_vq.DistributedResidualVectorQuantization][]

docs/contributing/model/adding_diffusion_model.md

@@ -44,7 +44,7 @@ vllm_omni/
 
 # Step-by-step-implementation
 ## Step 1: Model Implementation
-The diffusion pipeline’s implementation follows **HuggingFace Diffusers**.
+The diffusion pipeline’s implementation follows **HuggingFace Diffusers**, and components that do not need modification can be imported directly.
 ### 1.1 Define the Pipeline Class
 Define the pipeline class, e.g., `QwenImageEditPipeline`, and initialize all required submodules, either from HuggingFace `diffusers` or custom implementations. In `QwenImageEditPipeline`, only `QwenImageTransformer2DModel` is re-implemented to support optimizations such as Ulysses-SP. When adding new models in the future, you can either reuse this re-implemented `QwenImageTransformer2DModel` or extend it as needed.
 
@@ -73,75 +73,10 @@ def get_qwen_image_edit_post_process_func(
 The forward function of `QwenImageEditPipeline` follows the HuggingFace `diffusers` design for the most part. The key differences are:
 + As described in the overview, arguments are passed through `OnniDiffusionRequest`, so we need to get user parameters from it accordingly.
 ```python
-prompt = req.prompt
+prompt = req.prompt if req.prompt is not None else prompt
 ```
 + pre/post-processing are handled by the framework elsewhere, so skip them.
 
-### 1.4 Replace some ops or layers in DiT component
-
-vLLM-Omni provides a set of optimized operators with better performance and built-in support for parallelism, including attention, rotary embeddings (RoPE), and linear layers.
-
-Below is an example showing how to replace standard Transformer attention and FFN layers with vLLM-Omni implementations:
-
-```python
-from vllm_omni.diffusion.attention.layer import Attention
-from vllm_omni.diffusion.layers.rope import RotaryEmbedding
-
-class MyAttention(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.attn = Attention()
-        self.to_qkv = QKVParallelLinear()
-        self.to_out = RowParallelLinear()
-        self.rope = RotaryEmbedding(is_neox_style=False)
-
-    def forward(self, hidden_states):
-        qkv, _ = self.to_qkv(hidden_states)
-        q, k, v = qkv.split(...)
-        q, k = self.rope(...)
-        attn_output = self.attn(q, k, v)
-        output = self.to_out(attn_output)
-
-class MyFFN(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.fc1 = ColumnParallelLinear()
-        self.fc2 = RowParallelLinear()
-        self.act = F.gelu
-
-    def forward(self, hidden_states):
-        hidden, _ = self.fc1(hidden_states)
-        hidden = self.act(hidden)
-        output = self.fc2(hidden)
-        return output
-```
-
-In this example:
-
-+ Attention uses vLLM-Omni’s optimized attention kernel together with parallel QKV projection and RoPE.
-
-+ Linear layers are replaced with column- and row-parallel variants to enable tensor parallelism.
-
-+ The FFN follows a standard two-layer structure and can be further optimized (e.g., using fused or merged projections) if needed.
-
-
-### 1.5 Provide a `_repeated_blocks` in DiT model
-`_repeated_blocks` is the small and frequently-repeated block(s) of a model -- typically a transformer layer.
-
-It's used for torch compile optimizations.
-```python
-_repeated_blocks = ["QwenImageTransformerBlock"]
-```
-
-
-### 1.6 (Optional) implement sequence parallelism
-vLLM-Omni has a non-intrusive `_sp_plan` that enable sequence parallel without modifying `forward()` logic.
-You can refer to [How to parallelize a new model](../../user_guide/diffusion/parallelism_acceleration.md)
-
-
-### 1.7 (Optional) integrate with Cache-Dit
-vLLM-Omni supports acceleration via [Cache-Dit](../../user_guide/diffusion/cache_dit_acceleration.md). Most models compatible with Diffusers can use Cache-Dit seamlessly. For new models, you can extend support by modifying`cache_dit_backend.py`
-
 ## Step 2: Extend OmniDiffusionRequest Fields
 User-provided inputs are ultimately passed to the model’s forward method through OmniDiffusionRequest, so we add the required fields here to support the new model.
 ```python
@@ -204,21 +139,6 @@ Key point for writing the example:
 
 + Save or display the generated results so users can validate the integration.
 
-## Step 5: Open a Pull Request
-
-When submitting a pull request to add support for a new model, please include the following information in the PR description:
-
-+ Output verification: provide generation outputs to verify correctness and model behavior.
-
-+ Inference speed: provide a comparison with the corresponding implementation in Diffusers.
-
-+ Parallelism support: specify the supported parallel sizes and any relevant limitations.
-
-+ Cache acceleration: check whether the model can be accelerated using Cache-Dit or not.
-
-
-Providing these details helps reviewers evaluate correctness, performance improvements, and parallel scalability of the new model integration.
-
 # Testing
 For comprehensive testing guidelines, please refer to the [Test File Structure and Style Guide](../ci/tests_style.md).
 

docs/design/feature/disaggregated_inference.md

@@ -1,35 +1,27 @@
 # Disaggregated Inference for Omni-Modality Models
 
-This guide explains how to configure and use distributed connectors
-(`vllm_omni/distributed/omni_connectors`) in vllm-omni for multi-stage pipelines.
+This guide explains how to configure and use distributed connectors (vllm_omni/distributed/connectors) in vllm-omni for multi-stage pipelines.
 
-Backend-specific setup lives in separate docs:
-
-- [SharedMemoryConnector](omni_connectors/shared_memory_connector.md)
-- [MooncakeConnector](omni_connectors/mooncake_connector.md)
-- [YuanrongConnector](omni_connectors/yuanrong_connector.md)
-
-## Overview
+## 1. Overview
 
 Connectors enable data transfer between pipeline stages (e.g., Thinker -> Talker).
-Current connectors operate in D2H2D (device to host to device) mode.
+Currently supported connectors operate in **D2H2D (Device-to-Host-to-Device)** mode:
+1. **SharedMemoryConnector**: Uses system shared memory.
+2. **MooncakeConnector**: Uses [Mooncake](https://github.com/kvcache-ai/Mooncake).
 
-## Connector Choices
+*   **SharedMemoryConnector (Default)**: Zero-copy (on host), lowest latency. Best for **single-node** deployments. Auto-configured if no connectors are specified.
+*   **MooncakeConnector**: TCP/RDMA based. Best for **multi-node** distributed deployments. Requires a Mooncake Master service.
 
-| Use Case | Recommended Connector | Notes |
-| :--- | :--- | :--- |
-| Single node | SharedMemoryConnector | Auto-configured if no connector is specified. |
-| Multi node (Mooncake) | MooncakeConnector | Requires Mooncake Master + metadata server. |
-| Multi node (Yuanrong) | YuanrongConnector | Requires Yuanrong Datasystem + etcd. |
+## 2. API Design
 
-## Core API
+The connector system is built around the `OmniConnectorBase` abstraction, which decouples data transport from stage logic.
 
-The connector system is built around `OmniConnectorBase`.
+### Core Interface
 
 ```python
 class OmniConnectorBase(ABC):
     @abstractmethod
-    def put(self, from_stage: str, to_stage: str, put_key: str, data: Any) -> tuple[bool, int, Optional[dict]]:
+    def put(self, from_stage: str, to_stage: str, request_id: str, data: Any) -> tuple[bool, int, Optional[dict]]:
         """
         Store data.
         Returns: (success, serialized_size, metadata)
@@ -37,23 +29,145 @@ class OmniConnectorBase(ABC):
         pass
 
     @abstractmethod
-    def get(self, from_stage: str, to_stage: str, get_key: str, metadata: Optional[dict] = None) -> Optional[tuple[Any, int]]:
+    def get(self, from_stage: str, to_stage: str, request_id: str, metadata: Optional[dict] = None) -> Optional[tuple[Any, int]]:
         """
         Retrieve data.
-        Args: metadata - transport-specific handles returned by put() (e.g., SHM name).
+        Args: metadata - Transport-specific handles returned by put() (e.g., SHM name).
         Returns: (object, serialized_size)
         """
         pass
 ```
 
-### Metadata Passing
+### Key Concept: Metadata Passing
+Unlike a pure key-value store, some connectors (like `SharedMemoryConnector`) generate transient resources (e.g., a shared memory block name) during `put()`. This `metadata` **must be passed via the control plane** (e.g., HTTP headers, queue messages) from the producer stage to the consumer stage so `get()` can locate the data.
 
-Some connectors (e.g., SharedMemoryConnector) generate transient resources during `put()`.
-This `metadata` must be passed through the control plane so `get()` can locate the data.
+## 3. Backends & Use Cases
 
-## Configuration Model
+### 3.1 SharedMemoryConnector
+**Best for:** Single-node, high-performance IPC.
 
-Define connectors in runtime:
+*   **Mechanism:**
+    *   **Small Payloads (< Threshold)**: Data is serialized and passed directly "inline" within the `metadata` dictionary. This avoids the overhead of creating SHM blocks for tiny messages.
+    *   **Large Payloads (>= Threshold)**: Data is written to a named System Shared Memory block. The block name is returned in `metadata`.
+*   **Configuration:**
+    *   `shm_threshold_bytes`: Size in bytes to switch from inline to SHM (default: 64KB).
+
+### 3.2 MooncakeConnector
+**Best for:** Multi-node distributed inference.
+
+*   **Mechanism:** Uses Mooncake's distributed KVCache store.
+    *   **Data Plane**: TCP or RDMA for high-bandwidth transfer.
+    *   **Control Plane**: Uses a centralized Mooncake Master and Metadata Server.
+    *   **Keying**: Deterministic keys based on `request_id/from_stage_to_stage`.
+*   **Requirements**: Requires a running Mooncake Master service.
+
+## 4. Relationship with vLLM
+
+vLLM provides specialized distributed mechanisms for specific artifacts:
+*   **KV Transfer** (`vllm.distributed.kv_transfer`): Optimized for transferring KV caches between prefill and decode instances (using NCCL, Mooncake, etc.).
+*   **EC Transfer** (`vllm.distributed.ec_transfer`): Optimized for sharing encoder embeddings.
+*   **Device Communicators** (`vllm.distributed.device_communicators`): Low-level primitives (NCCL, SHM) for Tensor/Pipeline Parallelism.
+
+`vllm-omni` complements this by introducing a **Generalized Connector Abstraction** (`OmniConnector`) for multimodal pipelines. While vLLM's connectors are artifact-specific, `vllm-omni`:
+
+1.  **Unifies Transport**: Provides a single API (`put`/`get`) to transport *any* stage artifact (Input Embeddings, Hidden States, Audio/Image Tensors, KV Cache, Final Output) between arbitrary pipeline stages (e.g., AudioEncoder -> LLM -> AudioGenerator).
+2.  **Extends Connectivity**: Enables flexible construction of complex DAGs (Directed Acyclic Graphs) where stages can run in the same process, same node, or across nodes, using the most appropriate backend (SHM, Mooncake, etc.) for each edge.
+3.  **Wraps & Adapts**: Can internally utilize vLLM's specialized `kv_transfer` for KV paths while using generic transports (SHM/Mooncake) for other data types, presenting a consistent interface to the application layer.
+
+## 5. Installation (Mooncake)
+
+If using `MooncakeConnector`, install the library first:
+
+```bash
+# For CUDA-enabled systems (Recommended)
+pip install mooncake-transfer-engine
+
+# For non-CUDA systems
+pip install mooncake-transfer-engine-non-cuda
+```
+
+## 6. Using MooncakeConnector
+
+### 6.1 Start Mooncake Master
+
+Start the master service on your primary node:
+
+```bash
+# if you use mooncake SSD storage
+mkdir -p ./mc_storage
+
+mooncake_master \
+  --rpc_port=50051 \
+  --enable_http_metadata_server=true \
+  --http_metadata_server_host=0.0.0.0 \
+  --http_metadata_server_port=8080 \
+  --metrics_port=9003 \
+  --root_fs_dir=./mc_storage/ \
+  --cluster_id=mc-local-1 &
+```
+
+### 6.2 Configuration (YAML)
+
+Edit your stage config (e.g., `qwen2_5_omni.yaml`).
+
+**Step 1: Define Connector in Global Runtime**
+
+```yaml
+runtime:
+  connectors:
+    connector_of_mooncake:
+      name: MooncakeConnector
+      extra:
+        host: "127.0.0.1"           # Local Worker IP
+        metadata_server: "http://<MASTER_IP>:8080/metadata"
+        master: "<MASTER_IP>:50051"
+        segment: 512000000          # 512MB segment
+        localbuf: 64000000          # 64MB buffer
+        proto: "tcp"                # "tcp" or "rdma"
+    ```
+
+**Mooncake Configuration Parameters:**
+
+*   **host**: The hostname or IP address of the local machine (worker). Mooncake uses this to register itself in the metadata server so other nodes can find it.
+*   **metadata_server**: The URL of the metadata server. This is used for service discovery and connection establishment (e.g., exchanging QP information for RDMA).
+*   **master**: The address of the Mooncake Master Server (e.g., `<MASTER_IP>:50051`). This is used for global state management and control plane operations.
+*   **segment**: The size of the global memory segment in bytes (default: ~512MB). This defines the shared memory region accessible by Mooncake for data transfer.
+*   **localbuf**: The size of the local buffer in bytes (default: ~64MB). Used for local data buffering during transfer operations.
+*   **proto**: The transport protocol to use. Options:
+    *   `tcp`: Standard TCP/IP (easier setup, universal compatibility).
+    *   `rdma`: Remote Direct Memory Access (higher performance, requires RDMA-capable hardware).
+
+For more details, refer to the [Mooncake Repository](https://github.com/kvcache-ai/Mooncake).
+
+    **Step 2: Reference in Stages**
+
+Explicitly link stages using `input_connectors` and `output_connectors`:
+
+```yaml
+stage_args:
+  - stage_id: 0
+    # ...
+    output_connectors:
+      to_stage_1: connector_of_mooncake
+
+  - stage_id: 1
+    # ...
+    input_connectors:
+      from_stage_0: connector_of_mooncake
+```
+
+## 7. Using SharedMemoryConnector (Auto-Mode)
+
+**Best for single-node.**
+
+The system will automatically create `SharedMemoryConnector`s for any pipeline edge that does not have an explicit connector defined. This is inferred from:
+1.  `runtime.edges` list in the config.
+2.  `engine_input_source` dependencies defined in `stage_args`.
+
+### Threshold Configuration
+By default, payloads larger than **64KB** (default threshold) are transferred via shared memory, while smaller ones use the control queue (inline).
+
+To adjust this threshold (e.g., to 1GB), add the following to your `runtime.connectors`:
 
 ```yaml
 runtime:
@@ -61,46 +175,26 @@ runtime:
     connector_of_shared_memory:
       name: SharedMemoryConnector
       extra:
-        shm_threshold_bytes: 65536
+        shm_threshold_bytes: 1024 # 1KB threshold
 ```
 
-Wire stages to connectors:
+## 8. Summary
 
-```yaml
-stage_args:
-  - stage_id: 0
-    output_connectors:
-      to_stage_1: connector_of_shared_memory
+| Use Case | Recommended Connector | Configuration |
+| :--- | :--- | :--- |
+| **Single Node** | `SharedMemoryConnector` | **None** (Automatic) or Custom Threshold |
+| **Multi Node** | `MooncakeConnector` | Explicit YAML + Mooncake Master |
 
-  - stage_id: 1
-    input_connectors:
-      from_stage_0: connector_of_shared_memory
-```
+## 9. Operational Notes (important)
 
-If a pipeline edge has no explicit connector, the system auto-creates a
-SharedMemoryConnector for that edge.
+- **Fail-fast config validation**: the loader raises if any expected edge is missing a connector. Define `input_connectors`/`output_connectors` or rely on auto-SHM filling; otherwise startup aborts.
+- **Missing payloads halt the stage**: workers expect connector payloads; if metadata or connector config is missing, the stage raises and stops. Verify connector wiring and metadata propagation before production.
 
-## Relationship with vLLM
+## 10. Future Roadmap: Device-to-Device (D2D) Transport
 
-vLLM provides specialized distributed mechanisms for specific artifacts:
+The current implementations (`SharedMemoryConnector`, `MooncakeConnector`) utilize a **D2H2D (Device-to-Host-to-Device)** data path. Tensors are moved to CPU memory (Host) for transport, which incurs PCIe overhead.
 
-- KV Transfer (`vllm.distributed.kv_transfer`): optimized for KV caches.
-- EC Transfer (`vllm.distributed.ec_transfer`): optimized for encoder embeddings.
-- Device Communicators (`vllm.distributed.device_communicators`): low-level primitives (NCCL, SHM).
+As outlined in the design RFC, future versions will introduce **D2D (Device-to-Device)** connectors:
 
-vllm-omni complements this with a generalized connector abstraction:
-
-1. Unifies transport via a single `put`/`get` API for any stage artifact.
-2. Enables DAG-style pipelines across processes or nodes with per-edge transports.
-3. Can wrap vLLM-specific transfers for KV paths while keeping a consistent interface.
-
-## Operational Notes
-
-- Fail-fast config validation: missing expected edges cause startup failures.
-- Missing payloads halt stages: verify connector wiring and metadata propagation.
-
-## Future Roadmap: D2D Transport
-
-Current connectors use D2H2D paths. Future versions will introduce direct
-device-to-device connectors (NCCL, UCX, IPC) to reduce latency for large
-tensor payloads.
+*   **Goal**: Direct GPU-to-GPU transfer (via NCCL, UCX, or IPC) to minimize latency for large tensor payloads.
+*   **Mechanism**: The `OmniConnector` API allows `put()` to initiate a transfer and return a lightweight handle (metadata) via the control plane, while the heavy payload flows directly between devices.

docs/design/feature/omni_connectors/mooncake_connector.md

@@ -1,74 +0,0 @@
-# MooncakeConnector
-
-## When to Use
-
-Best for multi-node distributed inference using Mooncake.
-
-## Installation
-
-```bash
-# For CUDA-enabled systems (recommended)
-pip install mooncake-transfer-engine
-
-# For non-CUDA systems
-pip install mooncake-transfer-engine-non-cuda
-```
-
-## Start Mooncake Master
-
-```bash
-# If you use Mooncake SSD storage
-mkdir -p ./mc_storage
-
-mooncake_master \
-  --rpc_port=50051 \
-  --enable_http_metadata_server=true \
-  --http_metadata_server_host=0.0.0.0 \
-  --http_metadata_server_port=8080 \
-  --metrics_port=9003 \
-  --root_fs_dir=./mc_storage/ \
-  --cluster_id=mc-local-1 &
-```
-
-## Configuration
-
-Define the connector in runtime:
-
-```yaml
-runtime:
-  connectors:
-    connector_of_mooncake:
-      name: MooncakeConnector
-      extra:
-        host: "127.0.0.1"
-        metadata_server: "http://<MASTER_IP>:8080/metadata"
-        master: "<MASTER_IP>:50051"
-        segment: 512000000
-        localbuf: 64000000
-        proto: "tcp"
-```
-
-Wire stages to the connector:
-
-```yaml
-stage_args:
-  - stage_id: 0
-    output_connectors:
-      to_stage_1: connector_of_mooncake
-
-  - stage_id: 1
-    input_connectors:
-      from_stage_0: connector_of_mooncake
-```
-
-Parameters:
-
-- host: local worker IP registered in the metadata server.
-- metadata_server: metadata server URL for discovery and setup.
-- master: Mooncake Master address.
-- segment: global memory segment size in bytes.
-- localbuf: local buffer size in bytes.
-- proto: transport protocol ("tcp" or "rdma").
-
-For more details, refer to the
-[Mooncake repository](https://github.com/kvcache-ai/Mooncake).

docs/design/feature/omni_connectors/shared_memory_connector.md

@@ -1,27 +0,0 @@
-# SharedMemoryConnector
-
-## When to Use
-
-Best for single-node deployments where stages run on the same host. It is
-auto-configured when no explicit connector is specified for an edge.
-
-## How It Works
-
-- Small payloads (< threshold): serialized and passed inline in metadata.
-- Large payloads (>= threshold): stored in shared memory; the SHM name is
-  returned in metadata.
-
-## Configuration
-
-```yaml
-runtime:
-  connectors:
-    connector_of_shared_memory:
-      name: SharedMemoryConnector
-      extra:
-        shm_threshold_bytes: 65536
-```
-
-## Notes
-
-- Auto-mode uses SharedMemoryConnector if no connector is declared for an edge.

docs/design/feature/omni_connectors/yuanrong_connector.md

@@ -1,101 +0,0 @@
-# YuanrongConnector
-
-## When to Use
-
-Best for multi-node distributed inference using Yuanrong Datasystem.
-
-## Mechanism
-
-Uses Yuanrong Datasystem's distributed KV store (`datasystem.kv_client`).
-
-- Data Plane: TCP or RDMA for high-bandwidth transfer.
-- Control Plane: Yuanrong Datasystem workers and etcd.
-- Keying: deterministic keys based on `put_key` (often composed as `request_id:fromStage_toStage`).
-
-## Installation
-
-```bash
-pip install openyuanrong-datasystem
-```
-
-## Start etcd
-
-```bash
-# Download and install etcd (v3.5.12 or higher)
-ETCD_VERSION="v3.5.12"
-ETCD_ARCH="linux-arm64"
-wget https://github.com/etcd-io/etcd/releases/download/${ETCD_VERSION}/etcd-${ETCD_VERSION}-${ETCD_ARCH}.tar.gz
-tar -xvf etcd-${ETCD_VERSION}-${ETCD_ARCH}.tar.gz
-cd etcd-${ETCD_VERSION}-${ETCD_ARCH}
-sudo cp etcd etcdctl /usr/local/bin/
-
-# Start etcd
-etcd \
-  --name etcd-single \
-  --data-dir /tmp/etcd-data \
-  --listen-client-urls http://0.0.0.0:2379 \
-  --advertise-client-urls http://0.0.0.0:2379 \
-  --listen-peer-urls http://0.0.0.0:2380 \
-  --initial-advertise-peer-urls http://0.0.0.0:2380 \
-  --initial-cluster etcd-single=http://0.0.0.0:2380 &
-
-# Verify etcd is running
-etcdctl --endpoints "127.0.0.1:2379" put key "value"
-etcdctl --endpoints "127.0.0.1:2379" get key
-```
-
-For production environments, refer to the
-[official etcd clustering documentation](https://etcd.io/docs/current/op-guide/clustering/).
-
-## Start Datasystem Worker
-
-```bash
-# Replace ${ETCD_IP} with etcd node IP, ${WORKER_IP} with local node IP
-dscli start -w \
-  --worker_address "${WORKER_IP}:31501" \
-  --etcd_address "${ETCD_IP}:2379" \
-  --shared_memory_size_mb 20480
-```
-
-To stop the worker:
-
-```bash
-dscli stop --worker_address "${WORKER_IP}:31501"
-```
-
-## Configuration
-
-Define the connector in runtime:
-
-```yaml
-runtime:
-  connectors:
-    connector_of_yuanrong:
-      name: YuanrongConnector
-      extra:
-        host: "127.0.0.1"
-        port: 31501
-        get_sub_timeout_ms: 1000
-```
-
-Wire stages to the connector:
-
-```yaml
-stage_args:
-  - stage_id: 0
-    output_connectors:
-      to_stage_1: connector_of_yuanrong
-
-  - stage_id: 1
-    input_connectors:
-      from_stage_0: connector_of_yuanrong
-```
-
-Parameters:
-
-- host: datasystem worker host.
-- port: datasystem worker port.
-- get_sub_timeout_ms: get timeout in milliseconds (0 for no timeout).
-
-For more details, refer to the
-[Yuanrong Datasystem repository](https://atomgit.com/openeuler/yuanrong-datasystem).

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

@@ -17,7 +17,7 @@ Therefore, it is recommended to install vLLM and vLLM-Omni with a **fresh new**
 # --8<-- [start:pre-built-wheels]
 
 #### Installation of vLLM
-Note: Pre-built wheels are currently only available for vLLM-Omni 0.11.0rc1, 0.12.0rc1, 0.14.0rc1. For the latest version, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).
+Note: Pre-built wheels are currently only available for vLLM-Omni 0.11.0rc1 and 0.12.0rc1. For the latest version, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).
 
 
 vLLM-Omni is built based on vLLM. Please install it with command below.
@@ -93,7 +93,7 @@ docker run --runtime nvidia --gpus 2 \
     --env "HF_TOKEN=$HF_TOKEN" \
     -p 8091:8091 \
     --ipc=host \
-    vllm/vllm-omni:v0.14.0rc1 \
+    vllm/vllm-omni:v0.14.0 \
     --model Qwen/Qwen3-Omni-30B-A3B-Instruct --port 8091
 ```
 

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

@@ -23,6 +23,15 @@ vLLM-Omni current recommends the steps in under setup through Docker Images.
 DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm -t vllm-omni-rocm .
 ```
 
+If you want to specify which GPU Arch to build for to cutdown build time:
+
+```bash
+DOCKER_BUILDKIT=1 docker build \
+  -f docker/Dockerfile.rocm \
+  --build-arg PYTORCH_ROCM_ARCH="gfx942;gfx950" \
+  -t vllm-omni-rocm .
+```
+
 #### Launch the docker image
 
 ##### Launch with OpenAI API Server
@@ -80,7 +89,7 @@ docker run --rm \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HF_TOKEN=$HF_TOKEN" \
   -p 8091:8091 \
-  vllm/vllm-omni-rocm:v0.14.0rc1 \
+  vllm/vllm-omni-rocm:v0.14.0 \
   --model Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
 
@@ -99,7 +108,7 @@ docker run --rm -it \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HF_TOKEN=$HF_TOKEN" \
   --entrypoint bash \
-  vllm/vllm-omni-rocm:v0.14.0rc1
+  vllm/vllm-omni-rocm:v0.14.0
 ```
 
 # --8<-- [end:pre-built-images]

docs/getting_started/quickstart.md

@@ -46,46 +46,6 @@ if __name__ == "__main__":
     images[0].save("coffee.png")
 ```
 
-You can pass a list of prompts and wait for them to process altogether, shown below.
-
-!!! info
-
-    However, it is not currently recommended to do so
-    because not all models support batch inference,
-    and batch requesting mostly does not provide significant performance improvement (despite the impression that it does).
-    This feature is primarily for the sake of interface compatibility with vLLM and to allow for future improvements.
-
-```python
-from vllm_omni.entrypoints.omni import Omni
-
-if __name__ == "__main__":
-    omni = Omni(
-        model="Tongyi-MAI/Z-Image-Turbo",
-        # stage_configs_path="./stage-config.yaml",  # See below
-    )
-    prompts = [
-        "a cup of coffee on a table",
-        "a toy dinosaur on a sandy beach",
-        "a fox waking up in bed and yawning",
-    ]
-    omni_outputs = omni.generate(prompts)
-    for i_prompt, prompt_output in enumerate(omni_outputs):
-        this_request_output = prompt_output.request_output[0]
-        this_images = this_request_output.images
-        for i_image, image in enumerate(this_images):
-            image.save(f"p{i_prompt}-img{i_image}.jpg")
-            print("saved to", f"p{i_prompt}-img{i_image}.jpg")
-            # saved to p0-img0.jpg
-            # saved to p1-img0.jpg
-            # saved to p2-img0.jpg
-```
-
-!!! info
-
-    For diffusion pipelines, the stage config field `stage_args.[].runtime.max_batch_size` is 1 by default, and the input
-    list is sliced into single-item requests before feeding into the diffusion pipeline. For models that do internally support
-    batched inputs, you can [modify this configuration](../configuration/stage_configs.md) to let the model accept a longer batch of prompts.
-
 For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)
 
 ## Online Serving with OpenAI-Completions API

docs/models/supported_models.md

@@ -33,7 +33,6 @@ th {
 |`LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` |
 |`StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` |
 |`Flux2KleinPipeline` | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B`, `black-forest-labs/FLUX.2-klein-9B` |
-|`FluxPipeline` | FLUX.1-dev | `black-forest-labs/FLUX.1-dev` |
 |`StableAudioPipeline` | Stable-Audio-Open | `stabilityai/stable-audio-open-1.0` |
 |`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` |
 |`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` |

docs/user_guide/diffusion/cache_dit_acceleration.md

@@ -18,7 +18,6 @@ Enable cache-dit acceleration by simply setting `cache_backend="cache_dit"`. Cac
 
 ```python
 from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 # Simplest way: just enable cache-dit with default parameters
 omni = Omni(
@@ -28,7 +27,7 @@ omni = Omni(
 
 images = omni.generate(
     "a beautiful landscape",
-    OmniDiffusionSamplingParams(num_inference_steps=50),
+    num_inference_steps=50,
 )
 ```
 

docs/user_guide/diffusion/parallelism_acceleration.md

@@ -24,13 +24,11 @@ The following table shows which models are currently supported by parallelism me
 | **LongCat-Image-Edit** | `meituan-longcat/LongCat-Image-Edit` | ✅ | ✅ | ❌ | ❌ |
 | **Ovis-Image** | `OvisAI/Ovis-Image` | ❌ | ❌ | ❌ | ❌ |
 | **Qwen-Image** | `Qwen/Qwen-Image` | ✅ | ✅ | ✅ | ✅ |
-| **Qwen-Image-Edit** | `Qwen/Qwen-Image-Edit` | ✅ | ✅ | ✅ | ✅ |
-| **Qwen-Image-Edit-2509** | `Qwen/Qwen-Image-Edit-2509` | ✅ | ✅ | ✅ | ✅ |
-| **Qwen-Image-Layered** | `Qwen/Qwen-Image-Layered` | ✅ | ✅ | ✅ | ✅ |
+| **Qwen-Image-Edit** | `Qwen/Qwen-Image-Edit` | ✅ | ✅ | ✅ | ❌ |
+| **Qwen-Image-Edit-2509** | `Qwen/Qwen-Image-Edit-2509` | ✅ | ✅ | ✅ | ❌ |
+| **Qwen-Image-Layered** | `Qwen/Qwen-Image-Layered` | ✅ | ✅ | ✅ | ❌ |
 | **Z-Image** | `Tongyi-MAI/Z-Image-Turbo` | ✅ | ✅ | ❌ | ✅ (TP=2 only) |
 | **Stable-Diffusion3.5** | `stabilityai/stable-diffusion-3.5` | ❌ | ❌ | ❌ | ❌ |
-| **FLUX.2-klein** | `black-forest-labs/FLUX.2-klein-4B` | ❌ | ❌ | ❌ | ✅ |
-| **FLUX.1-dev** | `black-forest-labs/FLUX.1-dev` | ❌ | ❌ | ❌ | ✅ |
 
 
 !!! note "TP Limitations for Diffusion Models"
@@ -50,7 +48,7 @@ The following table shows which models are currently supported by parallelism me
 
 | Model | Model Identifier | Ulysses-SP | Ring-SP | Tensor-Parallel |
 |-------|------------------|------------|---------|--------------------------|
-| **Wan2.2** | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | ✅ | ✅ | ❌ |
+| **Wan2.2** | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | ❌ | ❌ | ❌ |
 
 ### Tensor Parallelism
 
@@ -68,12 +66,10 @@ omni = Omni(
 )
 
 outputs = omni.generate(
-    "a cat reading a book",
-    OmniDiffusionSamplingParams(
-        num_inference_steps=9,
-        width=512,
-        height=512,
-    ),
+    prompt="a cat reading a book",
+    num_inference_steps=9,
+    width=512,
+    height=512,
 )
 ```
 
@@ -86,7 +82,6 @@ outputs = omni.generate(
 An example of offline inference script using [Ulysses-SP](https://arxiv.org/pdf/2309.14509) is shown below:
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 ulysses_degree = 2
 
@@ -95,10 +90,7 @@ omni = Omni(
     parallel_config=DiffusionParallelConfig(ulysses_degree=2)
 )
 
-outputs = omni.generate(
-    "A cat sitting on a windowsill",
-    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
-)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50, width=2048, height=2048)
 ```
 
 See `examples/offline_inference/text_to_image/text_to_image.py` for a complete working example.
@@ -140,7 +132,6 @@ Ring-Attention ([arxiv paper](https://arxiv.org/abs/2310.01889)) splits the inpu
 An example of offline inference script using Ring-Attention is shown below:
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 ring_degree = 2
 
@@ -149,10 +140,7 @@ omni = Omni(
     parallel_config=DiffusionParallelConfig(ring_degree=2)
 )
 
-outputs = omni.generate(
-    "A cat sitting on a windowsill",
-    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
-)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50, width=2048, height=2048)
 ```
 
 See `examples/offline_inference/text_to_image/text_to_image.py` for a complete working example.
@@ -194,7 +182,6 @@ You can combine both Ulysses-SP and Ring-Attention for larger scale parallelism.
 
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 
 # Hybrid: 2 Ulysses × 2 Ring = 4 GPUs total
@@ -203,10 +190,7 @@ omni = Omni(
     parallel_config=DiffusionParallelConfig(ulysses_degree=2, ring_degree=2)
 )
 
-outputs = omni.generate(
-    "A cat sitting on a windowsill",
-    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
-)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50, width=2048, height=2048)
 ```
 
 ##### Online Serving
@@ -389,15 +373,11 @@ omni = Omni(
 )
 
 outputs = omni.generate(
-    {
-        "prompt": "turn this cat to a dog",
-        "negative_prompt": "low quality, blurry",
-    },
-    OmniDiffusionSamplingParams(
-        true_cfg_scale=4.0,
-        pil_image=input_image,
-        num_inference_steps=50,
-    ),
+    prompt="turn this cat to a dog",
+    negative_prompt="low quality, blurry",
+    true_cfg_scale=4.0,
+    pil_image=input_image,
+    num_inference_steps=50,
 )
 ```
 

docs/user_guide/diffusion/teacache.md

@@ -8,7 +8,6 @@ Enable TeaCache by setting `cache_backend` to `"tea_cache"`:
 
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 # Simple configuration - model_type is automatically extracted from pipeline.__class__.__name__
 omni = Omni(
@@ -18,12 +17,7 @@ omni = Omni(
         "rel_l1_thresh": 0.2  # Optional, defaults to 0.2
     }
 )
-outputs = omni.generate(
-    "A cat sitting on a windowsill",
-    OmniDiffusionSamplingParams(
-        num_inference_steps=50,
-    ),
-)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)
 ```
 
 ### Using Environment Variable
@@ -74,19 +68,13 @@ Controls the balance between speed and quality. Lower values prioritize quality,
 
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 omni = Omni(
     model="Qwen/Qwen-Image",
     cache_backend="tea_cache",
     cache_config={"rel_l1_thresh": 0.2}
 )
-outputs = omni.generate(
-    "A cat sitting on a windowsill",
-    OmniDiffusionSamplingParams(
-        num_inference_steps=50,
-    ),
-)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)
 ```
 
 ## Performance Tuning
@@ -124,7 +112,6 @@ th {
 | `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` |
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` |
 | `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` |
-| `BagelForConditionalGeneration` | BAGEL (DiT-only) | `ByteDance-Seed/BAGEL-7B-MoT` |
 
 ### VideoGen
 

docs/user_guide/diffusion_acceleration.md

@@ -49,7 +49,7 @@ The following table shows which models are currently supported by each accelerat
 | **Qwen-Image-Layered** | `Qwen/Qwen-Image-Layered` | ❌ | ✅ | ✅ | ✅ | ✅ |
 | **Z-Image** | `Tongyi-MAI/Z-Image-Turbo` | ✅ | ✅ | ❌ | ❌ | ❌ |
 | **Stable-Diffusion3.5** | `stabilityai/stable-diffusion-3.5` | ❌ | ✅ | ❌ | ❌ | ❌ |
-| **Bagel** | `ByteDance-Seed/BAGEL-7B-MoT` | ✅ | ✅ | ❌ | ❌ | ❌ |
+| **Bagel** | `ByteDance-Seed/BAGEL-7B-MoT` | ❌ | ✅ | ❌ | ❌ | ❌ |
 
 ### VideoGen
 
@@ -96,7 +96,6 @@ To measure the parallelism methods, we run benchmarks with **Qwen/Qwen-Image** m
 
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 omni = Omni(
     model="Qwen/Qwen-Image",
@@ -104,19 +103,13 @@ omni = Omni(
     cache_config={"rel_l1_thresh": 0.2}  # Optional, defaults to 0.2
 )
 
-outputs = omni.generate(
-    "A cat sitting on a windowsill",
-    OmniDiffusionSamplingParams(
-        num_inference_steps=50,
-    ),
-)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)
 ```
 
 ### Using Cache-DiT
 
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 omni = Omni(
     model="Qwen/Qwen-Image",
@@ -130,12 +123,7 @@ omni = Omni(
     }
 )
 
-outputs = omni.generate(
-    "A cat sitting on a windowsill",
-    OmniDiffusionSamplingParams(
-        num_inference_steps=50,
-    ),
-)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)
 ```
 
 ### Using Ulysses-SP
@@ -143,7 +131,6 @@ outputs = omni.generate(
 Run text-to-image:
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 ulysses_degree = 2
 
@@ -152,17 +139,13 @@ omni = Omni(
     parallel_config=DiffusionParallelConfig(ulysses_degree=ulysses_degree)
 )
 
-outputs = omni.generate(
-    "A cat sitting on a windowsill",
-    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
-)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50, width=2048, height=2048)
 ```
 
 
 Run image-to-image:
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 ulysses_degree = 2
 
@@ -171,13 +154,8 @@ omni = Omni(
     parallel_config=DiffusionParallelConfig(ulysses_degree=ulysses_degree)
 )
 
-outputs = omni.generate(
-    {
-        "prompt": "turn this cat to a dog",
-        "multi_modal_data": {"image": input_image}
-    },
-    OmniDiffusionSamplingParams(num_inference_steps=50),
-)
+outputs = omni.generate(prompt="turn this cat to a dog",
+        pil_image=input_image, num_inference_steps=50)
 ```
 
 ### Using Ring-Attention
@@ -185,7 +163,6 @@ outputs = omni.generate(
 Run text-to-image:
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 ring_degree = 2
 
@@ -194,10 +171,7 @@ omni = Omni(
     parallel_config=DiffusionParallelConfig(ring_degree=2)
 )
 
-outputs = omni.generate(
-    "A cat sitting on a windowsill",
-    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
-)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50, width=2048, height=2048)
 ```
 
 ### Using CFG-Parallel
@@ -208,7 +182,6 @@ CFG-Parallel splits the CFG positive/negative branches across GPUs. Use it when
 
 ```python
 from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 cfg_parallel_size = 2
 
@@ -217,13 +190,8 @@ omni = Omni(
     parallel_config=DiffusionParallelConfig(cfg_parallel_size=cfg_parallel_size)
 )
 
-outputs = omni.generate(
-    {
-        "prompt": "turn this cat to a dog",
-        "multi_modal_data": {"image": input_image}
-    },
-    OmniDiffusionSamplingParams(num_inference_steps=50, true_cfg_scale=4.0),
-)
+outputs = omni.generate(prompt="turn this cat to a dog",
+        pil_image=input_image, num_inference_steps=50, true_cfg_scale=4.0)
 ```
 
 ## Documentation

docs/user_guide/examples/offline_inference/lora_inference.md

@@ -1,107 +0,0 @@
-# LoRA-Inference
-
-Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/lora_inference>.
-
-This contains examples for using LoRA (Low-Rank Adaptation) adapters with vLLM-omni diffusion models for offline inference.
-The example uses the  `stabilityai/stable-diffusion-3.5-medium` as the default model, but you can replace it with other models in vLLM-omni.
-
-## Overview
-
-Similar to vLLM, vLLM-omni uses a unified LoRA handling mechanism:
-
-- **Pre-loaded LoRA**: Loaded at initialization via `--lora-path` (pre-loaded into cache)
-- **Per-request LoRA**: Loaded on-demand. In the example, the LoRA is loaded via `--lora-request-path` in each request
-
-Both approaches use the same underlying mechanism - all LoRA adapters are handled uniformly through `set_active_adapter()`. If no LoRA request is provided in a request, all adapters are deactivated.
-
-## Usage
-
-### Pre-loaded LoRA (via --lora-path)
-
-Load a LoRA adapter at initialization. This adapter is pre-loaded into the cache and can be activated by requests:
-
-```bash
-python -m examples.offline_inference.lora_inference.lora_inference \
-    --prompt "A piece of cheesecake" \
-    --lora-path /path/to/lora/ \
-    --lora-scale 1.0 \
-    --num_inference_steps 50 \
-    --height 1024 \
-    --width 1024 \
-    --output output_preloaded.png
-```
-
-**Note**: When using `--lora-path`, the adapter is loaded at init time with a stable ID derived from the adapter path. This example activates it automatically for the request.
-
-### Per-request LoRA (via --lora-request-path)
-
-Load a LoRA adapter on-demand for each request:
-
-```bash
-python -m examples.offline_inference.lora_inference.lora_inference \
-    --prompt "A piece of cheesecake" \
-    --lora-request-path /path/to/lora/ \
-    --lora-scale 1.0 \
-    --num_inference_steps 50 \
-    --height 1024 \
-    --width 1024 \
-    --output output_per_request.png
-```
-
-### No LoRA
-
-If no LoRA request is provided, we will use the base model without any LoRA adapters:
-
-```bash
-python -m examples.offline_inference.lora_inference.lora_inference \
-    --prompt "A piece of cheesecake" \
-    --num_inference_steps 50 \
-    --height 1024 \
-    --width 1024 \
-    --output output_no_lora.png
-```
-
-## Parameters
-
-### LoRA Parameters
-
-- `--lora-path`: Path to LoRA adapter folder to pre-load at initialization (loads into cache with a stable ID derived from the path)
-- `--lora-request-path`: Path to LoRA adapter folder for per-request loading
-- `--lora-request-id`: Integer ID for the LoRA adapter (optional). If not provided and `--lora-request-path` is set, will derive a stable ID from the path.
-- `--lora-scale`: Scale factor for LoRA weights (default: 1.0). Higher values increase the influence of the LoRA adapter.
-
-### Standard Parameters
-
-- `--prompt`: Text prompt for image generation (required)
-- `--seed`: Random seed for reproducibility (default: 42)
-- `--height`: Image height in pixels (default: 1024)
-- `--width`: Image width in pixels (default: 1024)
-- `--num_inference_steps`: Number of denoising steps (default: 50)
-- `--output`: Output file path (default: `lora_output.png`)
-
-## How LoRA Works
-
-All LoRA adapters are handled uniformly:
-
-1. **Initialization**: If `--lora-path` is provided, the adapter is loaded into cache with a stable ID derived from the adapter path
-2. **Per-request**: If `--lora-request-path` is provided, the adapter is loaded/activated for that request
-3. **No LoRA**: If no LoRA request is provided (`req.lora_request` is None), all adapters are deactivated
-
-The system uses LRU cache management - adapters are cached and evicted when the cache is full (unless pinned).
-
-## LoRA Adapter Format
-
-LoRA adapters must be in PEFT (Parameter-Efficient Fine-Tuning) format. A typical LoRA adapter directory structure:
-
-```
-lora_adapter/
-├── adapter_config.json
-└── adapter_model.safetensors
-```
-
-## Example materials
-
-??? abstract "lora_inference.py"
-    ``````py
-    --8<-- "examples/offline_inference/lora_inference/lora_inference.py"
-    ``````

docs/user_guide/examples/offline_inference/text_to_image.md

@@ -23,7 +23,7 @@ if __name__ == "__main__":
     images[0].save("coffee.png")
 ```
 
-Or put more than one prompt in a request.
+Or put more than one prompt in a request, processing them sequentially.
 
 ```python
 from vllm_omni.entrypoints.omni import Omni
@@ -40,41 +40,6 @@ if __name__ == "__main__":
       image = output.request_output[0].images[0].save(f"{i}.jpg")
 ```
 
-!!! info
-
-    However, it is not currently recommended to do so
-    because not all models support batch inference,
-    and batch requesting mostly does not provide significant performance improvement (despite the impression that it does).
-    This feature is primarily for the sake of interface compatibility with vLLM and to allow for future improvements.
-
-!!! info
-
-    For diffusion pipelines, the stage config field `stage_args.[].runtime.max_batch_size` is 1 by default, and the input
-    list is sliced into single-item requests before feeding into the diffusion pipeline. For models that do internally support
-    batched inputs, you can [modify this configuration](../../../configuration/stage_configs.md) to let the model accept a longer batch of prompts.
-
-Apart from string prompt, vLLM-Omni also supports dictionary prompts in the same style as vLLM.
-This is useful for models that support negative prompts.
-
-```python
-from vllm_omni.entrypoints.omni import Omni
-
-if __name__ == "__main__":
-    omni = Omni(model="Qwen/Qwen-Image")
-    outputs = omni.generate([
-      {
-        "prompt": "a cup of coffee on a table"，
-        "negative_prompt": "low resolution"
-      },
-      {
-        "prompt": "a toy dinosaur on a sandy beach"，
-        "negative_prompt": "cinematic, realistic"
-      }
-    ])
-    for i, output in enumerate(outputs):
-      image = output.request_output[0].images[0].save(f"{i}.jpg")
-```
-
 ## Local CLI Usage
 
 ```bash

docs/user_guide/examples/online_serving/lora_inference.md

@@ -1,69 +0,0 @@
-# LoRA-Inference
-
-Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/lora_inference>.
-
-This example shows how to use **per-request LoRA** with vLLM-Omni diffusion models via the OpenAI-compatible Chat Completions API.
-
-> Note: The LoRA adapter path must be readable on the **server** machine (usually a local path or a mounted directory).
-> Note: This example uses `/v1/chat/completions`. LoRA payloads for other OpenAI endpoints are not implemented here.
-
-## Start Server
-
-```bash
-# Pick a diffusion model (examples)
-# export MODEL=stabilityai/stable-diffusion-3.5-medium
-# export MODEL=Qwen/Qwen-Image
-
-bash run_server.sh
-```
-
-## Call API (curl)
-
-```bash
-# Required: local LoRA folder on the server
-export LORA_PATH=/path/to/lora_adapter
-
-# Optional
-export SERVER=http://localhost:8091
-export PROMPT="A piece of cheesecake"
-export LORA_NAME=my_lora
-export LORA_SCALE=1.0
-# Optional: if omitted, the server derives a stable id from LORA_PATH.
-# export LORA_INT_ID=123
-
-bash run_curl_lora_inference.sh
-```
-
-## Call API (Python)
-
-```bash
-python openai_chat_client.py \
-  --prompt "A piece of cheesecake" \
-  --lora-path /path/to/lora_adapter \
-  --lora-name my_lora \
-  --lora-scale 1.0 \
-  --output output.png
-```
-
-## LoRA Format
-
-LoRA adapters should be in PEFT format, for example:
-
-```
-lora_adapter/
-├── adapter_config.json
-└── adapter_model.safetensors
-```
-
-??? abstract "openai_chat_client.py"
-    ``````py
-    --8<-- "examples/online_serving/lora_inference/openai_chat_client.py"
-    ``````
-??? abstract "run_curl_lora_inference.sh"
-    ``````py
-    --8<-- "examples/online_serving/lora_inference/run_curl_lora_inference.sh"
-    ``````
-??? abstract "run_server.sh"
-    ``````py
-    --8<-- "examples/online_serving/lora_inference/run_server.sh"
-    ``````

docs/user_guide/examples/online_serving/qwen3_omni.md

@@ -15,12 +15,6 @@ Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
 
-If you want to open async chunking for qwen3-omni, launch the server with command below
-
-```bash
-vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /vllm_omni/model_executor/stage_configs/qwen3_omni_moe_async_chunk.yaml
-```
-
 If you have custom stage configs file, launch the server with command below
 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /path/to/stage_configs_file

examples/offline_inference/bagel/end2end.py

@@ -1,8 +1,5 @@
 import argparse
 import os
-from typing import cast
-
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniPromptType
 
 
 def parse_args():
@@ -53,24 +50,21 @@ def parse_args():
 def main():
     args = parse_args()
     model_name = args.model
-    prompts: list[OmniPromptType] = []
     try:
         # Preferred: load from txt file (one prompt per line)
         if getattr(args, "txt_prompts", None) and args.prompt_type == "text":
             with open(args.txt_prompts, encoding="utf-8") as f:
                 lines = [ln.strip() for ln in f.readlines()]
-            prompts = [ln for ln in lines if ln != ""]
-            print(f"[Info] Loaded {len(prompts)} prompts from {args.txt_prompts}")
-        else:
-            prompts = args.prompts
+            args.prompts = [ln for ln in lines if ln != ""]
+            print(f"[Info] Loaded {len(args.prompts)} prompts from {args.txt_prompts}")
     except Exception as e:
         print(f"[Error] Failed to load prompts: {e}")
         raise
 
-    if not prompts:
+    if args.prompts is None:
         # Default prompt for text2img test if none provided
-        prompts = ["<|im_start|>A cute cat<|im_end|>"]
-        print(f"[Info] No prompts provided, using default: {prompts}")
+        args.prompts = ["<|im_start|>A cute cat<|im_end|>"]
+        print(f"[Info] No prompts provided, using default: {args.prompts}")
     omni_outputs = []
 
     from PIL import Image
@@ -83,27 +77,21 @@ def main():
         print("[Info] Running in img2img mode (Stage 1 only)")
         client = OmniDiffusion(model=model_name)
 
+        generate_kwargs = {
+            "prompt": args.prompts,
+            "seed": 52,
+            "need_kv_receive": False,
+            "num_inference_steps": args.steps,
+        }
+
         if args.image_path:
             if os.path.exists(args.image_path):
                 loaded_image = Image.open(args.image_path).convert("RGB")
-                prompts = [
-                    {
-                        "prompt": cast(str, p),
-                        "multi_modal_data": {"image": loaded_image},
-                    }
-                    for p in prompts
-                ]
+                generate_kwargs["pil_image"] = loaded_image
             else:
                 print(f"[Warning] Image path {args.image_path} does not exist.")
 
-        result = client.generate(
-            prompts,
-            OmniDiffusionSamplingParams(
-                seed=52,
-                need_kv_receive=False,
-                num_inference_steps=args.steps,
-            ),
-        )
+        result = client.generate(**generate_kwargs)
 
         # Ensure result is a list for iteration
         if not isinstance(result, list):
@@ -112,6 +100,8 @@ def main():
             omni_outputs = result
 
     else:
+        import copy
+
         from vllm_omni.entrypoints.omni import Omni
 
         omni_kwargs = {}
@@ -154,11 +144,11 @@ def main():
                 prompt_dict = {"prompt": final_prompt_text, "modalities": ["image"]}
                 formatted_prompts.append(prompt_dict)
 
-        params_list = omni.default_sampling_params_list
+        params_list = copy.deepcopy(omni.default_sampling_params_list)
         if args.modality == "text2img":
-            params_list[0].max_tokens = 1  # type: ignore # The first stage is a SamplingParam (vllm)
+            params_list[0]["max_tokens"] = 1
             if len(params_list) > 1:
-                params_list[1].num_inference_steps = args.steps  # type: ignore # The second stage is an OmniDiffusionSamplingParam
+                params_list[1]["num_inference_steps"] = args.steps
 
         omni_outputs = list(omni.generate(prompts=formatted_prompts, sampling_params_list=params_list))
 

examples/offline_inference/image_to_image/image_edit.py

@@ -79,7 +79,6 @@ from PIL import Image
 
 from vllm_omni.diffusion.data import DiffusionParallelConfig, logger
 from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.utils.platform_utils import detect_device_type, is_npu
 
@@ -280,11 +279,6 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help="Disable torch.compile and force eager execution.",
     )
-    parser.add_argument(
-        "--enable-cpu-offload",
-        action="store_true",
-        help="Enable CPU offloading for diffusion models.",
-    )
     return parser.parse_args()
 
 
@@ -350,7 +344,6 @@ def main():
         cache_config=cache_config,
         parallel_config=parallel_config,
         enforce_eager=args.enforce_eager,
-        enable_cpu_offload=args.enable_cpu_offload,
     )
     print("Pipeline loaded")
 
@@ -375,28 +368,25 @@ def main():
     print(f"{'=' * 60}\n")
 
     generation_start = time.perf_counter()
+    # Generate edited image
+    generate_kwargs = {
+        "prompt": args.prompt,
+        "pil_image": input_image,
+        "negative_prompt": args.negative_prompt,
+        "generator": generator,
+        "true_cfg_scale": args.cfg_scale,
+        "guidance_scale": args.guidance_scale,
+        "num_inference_steps": args.num_inference_steps,
+        "num_outputs_per_prompt": args.num_outputs_per_prompt,
+        "layers": args.layers,
+        "resolution": args.resolution,
+    }
 
     if profiler_enabled:
         print("[Profiler] Starting profiling...")
         omni.start_profile()
 
-    # Generate edited image
-    outputs = omni.generate(
-        {
-            "prompt": args.prompt,
-            "negative_prompt": args.negative_prompt,
-            "multi_modal_data": {"image": input_image},
-        },
-        OmniDiffusionSamplingParams(
-            generator=generator,
-            true_cfg_scale=args.cfg_scale,
-            guidance_scale=args.guidance_scale,
-            num_inference_steps=args.num_inference_steps,
-            num_outputs_per_prompt=args.num_outputs_per_prompt,
-            layers=args.layers,
-            resolution=args.resolution,
-        ),
-    )
+    outputs = omni.generate(**generate_kwargs)
     generation_end = time.perf_counter()
     generation_time = generation_end - generation_start
 

examples/offline_inference/image_to_image/image_to_image.md

@@ -47,4 +47,3 @@ Key arguments:
 - `--guidance_scale`: guidance scale for guidance-distilled models (default: 1.0, disabled). Unlike classifier-free guidance (--cfg_scale), guidance-distilled models take the guidance scale directly as an input parameter. Enabled when guidance_scale > 1. Ignored when not using guidance-distilled models.
 - `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
 - `--output`: path to save the generated PNG.
-- `--enable-cpu-offload`: enable CPU offloading for diffusion models.

examples/offline_inference/image_to_video/README.md

@@ -54,4 +54,3 @@ Key arguments:
 - `--num_inference_steps`: Number of denoising steps (default 50).
 - `--fps`: Frames per second for the saved MP4 (requires `diffusers` export_to_video).
 - `--output`: Path to save the generated video.
-- `--enable-cpu-offload`: enable CPU offloading for diffusion models.

examples/offline_inference/image_to_video/image_to_video.py

@@ -27,7 +27,6 @@ import PIL.Image
 import torch
 
 from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.utils.platform_utils import detect_device_type, is_npu
 
@@ -59,11 +58,6 @@ def parse_args() -> argparse.Namespace:
     )
     parser.add_argument("--output", type=str, default="i2v_output.mp4", help="Path to save the video (mp4).")
     parser.add_argument("--fps", type=int, default=16, help="Frames per second for the output video.")
-    parser.add_argument(
-        "--enable-cpu-offload",
-        action="store_true",
-        help="Enable CPU offloading for diffusion models.",
-    )
     return parser.parse_args()
 
 
@@ -111,7 +105,6 @@ def main():
         vae_use_tiling=vae_use_tiling,
         boundary_ratio=args.boundary_ratio,
         flow_shift=args.flow_shift,
-        enable_cpu_offload=args.enable_cpu_offload,
     )
 
     if profiler_enabled:
@@ -120,20 +113,16 @@ def main():
 
     # omni.generate() returns Generator[OmniRequestOutput, None, None]
     frames = omni.generate(
-        {
-            "prompt": args.prompt,
-            "negative_prompt": args.negative_prompt,
-            "multi_modal_data": {"image": image},
-        },
-        OmniDiffusionSamplingParams(
-            height=height,
-            width=width,
-            generator=generator,
-            guidance_scale=args.guidance_scale,
-            guidance_scale_2=args.guidance_scale_high,
-            num_inference_steps=args.num_inference_steps,
-            num_frames=args.num_frames,
-        ),
+        args.prompt,
+        negative_prompt=args.negative_prompt,
+        pil_image=image,
+        height=height,
+        width=width,
+        generator=generator,
+        guidance_scale=args.guidance_scale,
+        guidance_scale_2=args.guidance_scale_high,
+        num_inference_steps=args.num_inference_steps,
+        num_frames=args.num_frames,
     )
 
     # Extract video frames from OmniRequestOutput

examples/offline_inference/lora_inference/README.md

@@ -1,98 +0,0 @@
-# LoRA Inference Examples
-
-This directory contains examples for using LoRA (Low-Rank Adaptation) adapters with vLLM-omni diffusion models for offline inference.
-The example uses the  `stabilityai/stable-diffusion-3.5-medium` as the default model, but you can replace it with other models in vLLM-omni.
-
-## Overview
-
-Similar to vLLM, vLLM-omni uses a unified LoRA handling mechanism:
-
-- **Pre-loaded LoRA**: Loaded at initialization via `--lora-path` (pre-loaded into cache)
-- **Per-request LoRA**: Loaded on-demand. In the example, the LoRA is loaded via `--lora-request-path` in each request
-
-Both approaches use the same underlying mechanism - all LoRA adapters are handled uniformly through `set_active_adapter()`. If no LoRA request is provided in a request, all adapters are deactivated.
-
-## Usage
-
-### Pre-loaded LoRA (via --lora-path)
-
-Load a LoRA adapter at initialization. This adapter is pre-loaded into the cache and can be activated by requests:
-
-```bash
-python -m examples.offline_inference.lora_inference.lora_inference \
-    --prompt "A piece of cheesecake" \
-    --lora-path /path/to/lora/ \
-    --lora-scale 1.0 \
-    --num_inference_steps 50 \
-    --height 1024 \
-    --width 1024 \
-    --output output_preloaded.png
-```
-
-**Note**: When using `--lora-path`, the adapter is loaded at init time with a stable ID derived from the adapter path. This example activates it automatically for the request.
-
-### Per-request LoRA (via --lora-request-path)
-
-Load a LoRA adapter on-demand for each request:
-
-```bash
-python -m examples.offline_inference.lora_inference.lora_inference \
-    --prompt "A piece of cheesecake" \
-    --lora-request-path /path/to/lora/ \
-    --lora-scale 1.0 \
-    --num_inference_steps 50 \
-    --height 1024 \
-    --width 1024 \
-    --output output_per_request.png
-```
-
-### No LoRA
-
-If no LoRA request is provided, we will use the base model without any LoRA adapters:
-
-```bash
-python -m examples.offline_inference.lora_inference.lora_inference \
-    --prompt "A piece of cheesecake" \
-    --num_inference_steps 50 \
-    --height 1024 \
-    --width 1024 \
-    --output output_no_lora.png
-```
-
-## Parameters
-
-### LoRA Parameters
-
-- `--lora-path`: Path to LoRA adapter folder to pre-load at initialization (loads into cache with a stable ID derived from the path)
-- `--lora-request-path`: Path to LoRA adapter folder for per-request loading
-- `--lora-request-id`: Integer ID for the LoRA adapter (optional). If not provided and `--lora-request-path` is set, will derive a stable ID from the path.
-- `--lora-scale`: Scale factor for LoRA weights (default: 1.0). Higher values increase the influence of the LoRA adapter.
-
-### Standard Parameters
-
-- `--prompt`: Text prompt for image generation (required)
-- `--seed`: Random seed for reproducibility (default: 42)
-- `--height`: Image height in pixels (default: 1024)
-- `--width`: Image width in pixels (default: 1024)
-- `--num_inference_steps`: Number of denoising steps (default: 50)
-- `--output`: Output file path (default: `lora_output.png`)
-
-## How LoRA Works
-
-All LoRA adapters are handled uniformly:
-
-1. **Initialization**: If `--lora-path` is provided, the adapter is loaded into cache with a stable ID derived from the adapter path
-2. **Per-request**: If `--lora-request-path` is provided, the adapter is loaded/activated for that request
-3. **No LoRA**: If no LoRA request is provided (`req.lora_request` is None), all adapters are deactivated
-
-The system uses LRU cache management - adapters are cached and evicted when the cache is full (unless pinned).
-
-## LoRA Adapter Format
-
-LoRA adapters must be in PEFT (Parameter-Efficient Fine-Tuning) format. A typical LoRA adapter directory structure:
-
-```
-lora_adapter/
-├── adapter_config.json
-└── adapter_model.safetensors
-```

examples/offline_inference/lora_inference/lora_inference.py

@@ -1,147 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import argparse
-from pathlib import Path
-
-from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-from vllm_omni.lora.request import LoRARequest
-from vllm_omni.lora.utils import stable_lora_int_id
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Generate images with LoRA adapters.")
-    parser.add_argument("--model", default="stabilityai/stable-diffusion-3.5-medium", help="Model name or path.")
-    parser.add_argument("--prompt", required=True, help="Text prompt for image generation.")
-    parser.add_argument("--seed", type=int, default=42, help="Random seed for deterministic results.")
-    parser.add_argument("--height", type=int, default=1024, help="Height of generated image.")
-    parser.add_argument("--width", type=int, default=1024, help="Width of generated image.")
-    parser.add_argument(
-        "--num_inference_steps",
-        type=int,
-        default=50,
-        help="Number of denoising steps for the diffusion sampler.",
-    )
-    parser.add_argument(
-        "--output",
-        type=str,
-        default="lora_output.png",
-        help="Path to save the generated image (PNG).",
-    )
-    parser.add_argument(
-        "--lora-path",
-        type=str,
-        default=None,
-        help="Path to LoRA adapter folder to pre-load at initialization (PEFT format). "
-        "Note: pre-loading populates the cache; you still need to pass a lora_request to activate it.",
-    )
-    parser.add_argument(
-        "--lora-request-path",
-        type=str,
-        default=None,
-        help="Path to LoRA adapter folder for per-request activation (dynamic LoRA). "
-        "If --lora-request-id is not provided, a stable ID will be derived from this path.",
-    )
-    parser.add_argument(
-        "--lora-request-id",
-        type=int,
-        default=None,
-        help="Integer ID for the LoRA adapter (for dynamic LoRA). "
-        "If not provided and --lora-request-path is set, will derive a stable ID from the path.",
-    )
-    parser.add_argument(
-        "--lora-scale",
-        type=float,
-        default=1.0,
-        help="Scale factor for LoRA weights (default: 1.0).",
-    )
-    return parser.parse_args()
-
-
-def main():
-    args = parse_args()
-
-    model = args.model
-
-    omni_kwargs = {}
-
-    if args.lora_path:
-        omni_kwargs["lora_path"] = args.lora_path
-        print(f"Using static LoRA from: {args.lora_path}")
-
-    omni = Omni(model=model, **omni_kwargs)
-
-    lora_request = None
-    if args.lora_request_path:
-        if args.lora_request_id is None:
-            lora_request_id = stable_lora_int_id(args.lora_request_path)
-        else:
-            lora_request_id = args.lora_request_id
-
-        lora_name = Path(args.lora_request_path).stem
-        lora_request = LoRARequest(
-            lora_name=lora_name,
-            lora_int_id=lora_request_id,
-            lora_path=args.lora_request_path,
-        )
-        print(f"Using per-request LoRA: name={lora_name}, id={lora_request_id}, scale={args.lora_scale}")
-    elif args.lora_path:
-        # pre-loaded LoRA
-        lora_request_id = stable_lora_int_id(args.lora_path)
-        lora_request = LoRARequest(
-            lora_name="preloaded",
-            lora_int_id=lora_request_id,
-            lora_path=args.lora_path,
-        )
-        print(f"Activating pre-loaded LoRA: id={lora_request_id}, scale={args.lora_scale}")
-
-    sampling_params = OmniDiffusionSamplingParams(
-        height=args.height,
-        width=args.width,
-        num_inference_steps=args.num_inference_steps,
-    )
-
-    if lora_request:
-        sampling_params.lora_request = lora_request
-        sampling_params.lora_scale = args.lora_scale
-
-    outputs = omni.generate(args.prompt, sampling_params)
-
-    if not outputs or len(outputs) == 0:
-        raise ValueError("No output generated from omni.generate()")
-
-    if isinstance(outputs, list):
-        first_output = outputs[0]
-    else:
-        first_output = outputs
-
-    images = None
-    if hasattr(first_output, "images") and first_output.images:
-        images = first_output.images
-    elif hasattr(first_output, "request_output") and first_output.request_output:
-        req_out = first_output.request_output
-        if isinstance(req_out, list) and len(req_out) > 0:
-            req_out = req_out[0]
-        if hasattr(req_out, "images") and req_out.images:
-            images = req_out.images
-
-    if not images:
-        raise ValueError("No images found in request_output")
-
-    output_path = Path(args.output)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    suffix = output_path.suffix or ".png"
-    stem = output_path.stem or "lora_output"
-    if len(images) <= 1:
-        images[0].save(output_path)
-        print(f"Saved generated image to {output_path}")
-    else:
-        for idx, img in enumerate(images):
-            save_path = output_path.parent / f"{stem}_{idx}{suffix}"
-            img.save(save_path)
-            print(f"Saved generated image to {save_path}")
-
-
-if __name__ == "__main__":
-    main()

examples/offline_inference/qwen3_tts/README.md

@@ -13,15 +13,6 @@ Qwen3 TTS provides multiple task variants for speech generation:
 ## Setup
 Please refer to the [stage configuration documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/configuration/stage_configs/) to configure memory allocation appropriately for your hardware setup.
 
-### ROCm Dependencies
-
-You will need to install these two dependencies `onnxruntime-rocm` and `sox`.
-
-```
-pip uninstall onnxruntime # should be removed before we can install onnxruntime-rocm
-pip install onnxruntime-rocm sox
-```
-
 ## Quick Start
 
 Run a single sample for a task:

examples/offline_inference/text_to_audio/README.md

@@ -1,37 +0,0 @@
-# Text-To-Audio
-
-The `stabilityai/stable-audio-open-1.0` pipeline generates audio from text prompts.
-
-## Prerequisites
-
-If you use a gated model (e.g., `stabilityai/stable-audio-open-1.0`), ensure you have access:
-
-1. **Accept Model License**: Visit the model page on Hugging Face (e.g., [stabilityai/stable-audio-open-1.0]) and accept the user agreement.
-2. **Authenticate**: Log in to Hugging Face locally to access the gated model.
-   ```bash
-   huggingface-cli login
-   ```
-
-## Local CLI Usage
-
-```bash
-python text_to_audio.py \
-  --model stabilityai/stable-audio-open-1.0 \
-  --prompt "The sound of a hammer hitting a wooden surface" \
-  --negative_prompt "Low quality" \
-  --seed 42 \
-  --guidance_scale 7.0 \
-  --audio_length 10.0 \
-  --num_inference_steps 100 \
-  --output stable_audio_output.wav
-```
-
-Key arguments:
-
-- `--prompt`: text description (string).
-- `--negative_prompt`: negative prompt for classifier-free guidance.
-- `--seed`: integer seed for deterministic generation.
-- `--guidance_scale`: classifier-free guidance scale.
-- `--audio_length`: audio duration in seconds.
-- `--num_inference_steps`: diffusion sampling steps.(more steps = higher quality, slower).
-- `--output`: path to save the generated WAV file.

examples/offline_inference/text_to_audio/text_to_audio.py

@@ -21,7 +21,6 @@ import numpy as np
 import torch
 
 from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.utils.platform_utils import detect_device_type
 
 
@@ -143,21 +142,17 @@ def main():
     generation_start = time.perf_counter()
 
     # Generate audio
-    outputs = omni.generate(
-        {
-            "prompt": args.prompt,
-            "negative_prompt": args.negative_prompt,
+    audio = omni.generate(
+        args.prompt,
+        negative_prompt=args.negative_prompt,
+        generator=generator,
+        guidance_scale=args.guidance_scale,
+        num_inference_steps=args.num_inference_steps,
+        num_outputs_per_prompt=args.num_waveforms,
+        extra={
+            "audio_start_in_s": args.audio_start,
+            "audio_end_in_s": audio_end_in_s,
         },
-        OmniDiffusionSamplingParams(
-            generator=generator,
-            guidance_scale=args.guidance_scale,
-            num_inference_steps=args.num_inference_steps,
-            num_outputs_per_prompt=args.num_waveforms,
-            extra_args={
-                "audio_start_in_s": args.audio_start,
-                "audio_end_in_s": audio_end_in_s,
-            },
-        ),
     )
 
     generation_end = time.perf_counter()
@@ -171,21 +166,6 @@ def main():
     suffix = output_path.suffix or ".wav"
     stem = output_path.stem or "stable_audio_output"
 
-    # Extract audio from omni.generate() outputs
-    if not outputs:
-        raise ValueError("No output generated from omni.generate()")
-
-    output = outputs[0]
-    if not hasattr(output, "request_output") or not output.request_output:
-        raise ValueError("No request_output found in OmniRequestOutput")
-    request_output = output.request_output[0]
-    if not hasattr(request_output, "multimodal_output"):
-        raise ValueError("No multimodal_output found in request_output")
-
-    audio = request_output.multimodal_output.get("audio")
-    if audio is None:
-        raise ValueError("No audio output found in request_output")
-
     # Handle different output formats
     if isinstance(audio, torch.Tensor):
         audio = audio.cpu().float().numpy()

examples/offline_inference/text_to_image/README.md

@@ -20,7 +20,7 @@ if __name__ == "__main__":
     images[0].save("coffee.png")
 ```
 
-Or put more than one prompt in a request.
+Or put more than one prompt in a request, processing them sequentially.
 
 ```python
 from vllm_omni.entrypoints.omni import Omni
@@ -37,41 +37,6 @@ if __name__ == "__main__":
       image = output.request_output[0].images[0].save(f"{i}.jpg")
 ```
 
-!!! info
-
-    However, it is not currently recommended to do so
-    because not all models support batch inference,
-    and batch requesting mostly does not provide significant performance improvement (despite the impression that it does).
-    This feature is primarily for the sake of interface compatibility with vLLM and to allow for future improvements.
-
-!!! info
-
-    For diffusion pipelines, the stage config field `stage_args.[].runtime.max_batch_size` is 1 by default, and the input
-    list is sliced into single-item requests before feeding into the diffusion pipeline. For models that do internally support
-    batched inputs, you can [modify this configuration](../../../configuration/stage_configs.md) to let the model accept a longer batch of prompts.
-
-Apart from string prompt, vLLM-Omni also supports dictionary prompts in the same style as vLLM.
-This is useful for models that support negative prompts.
-
-```python
-from vllm_omni.entrypoints.omni import Omni
-
-if __name__ == "__main__":
-    omni = Omni(model="Qwen/Qwen-Image")
-    outputs = omni.generate([
-      {
-        "prompt": "a cup of coffee on a table"，
-        "negative_prompt": "low resolution"
-      },
-      {
-        "prompt": "a toy dinosaur on a sandy beach"，
-        "negative_prompt": "cinematic, realistic"
-      }
-    ])
-    for i, output in enumerate(outputs):
-      image = output.request_output[0].images[0].save(f"{i}.jpg")
-```
-
 ## Local CLI Usage
 
 ```bash
@@ -96,7 +61,6 @@ Key arguments:
 - `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
 - `--height/--width`: output resolution (defaults 1024x1024).
 - `--output`: path to save the generated PNG.
-- `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 
 > ℹ️ Qwen-Image currently publishes best-effort presets at `1328x1328`, `1664x928`, `928x1664`, `1472x1140`, `1140x1472`, `1584x1056`, and `1056x1584`. Adjust `--height/--width` accordingly for the most reliable outcomes.
 

examples/offline_inference/text_to_image/gradio_demo.py

@@ -5,7 +5,6 @@ import gradio as gr
 import torch
 
 from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.utils.platform_utils import detect_device_type, is_npu
 
@@ -102,14 +101,12 @@ def build_demo(args: argparse.Namespace) -> gr.Blocks:
         generator = torch.Generator(device=device).manual_seed(seed)
         outputs = omni.generate(
             prompt.strip(),
-            OmniDiffusionSamplingParams(
-                height=height,
-                width=width,
-                generator=generator,
-                true_cfg_scale=float(cfg_scale_value),
-                num_inference_steps=num_steps,
-                num_outputs_per_prompt=num_images,
-            ),
+            height=height,
+            width=width,
+            generator=generator,
+            true_cfg_scale=float(cfg_scale_value),
+            num_inference_steps=num_steps,
+            num_outputs_per_prompt=num_images,
         )
         images_outputs = []
         for output in outputs:

examples/offline_inference/text_to_image/text_to_image.py

@@ -10,7 +10,6 @@ import torch
 
 from vllm_omni.diffusion.data import DiffusionParallelConfig, logger
 from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.utils.platform_utils import detect_device_type, is_npu
 
@@ -97,11 +96,6 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help="Disable torch.compile and force eager execution.",
     )
-    parser.add_argument(
-        "--enable-cpu-offload",
-        action="store_true",
-        help="Enable CPU offloading for diffusion models.",
-    )
     parser.add_argument(
         "--tensor_parallel_size",
         type=int,
@@ -168,7 +162,6 @@ def main():
         cache_config=cache_config,
         parallel_config=parallel_config,
         enforce_eager=args.enforce_eager,
-        enable_cpu_offload=args.enable_cpu_offload,
     )
 
     if profiler_enabled:
@@ -190,19 +183,15 @@ def main():
 
     generation_start = time.perf_counter()
     outputs = omni.generate(
-        {
-            "prompt": args.prompt,
-            "negative_prompt": args.negative_prompt,
-        },
-        OmniDiffusionSamplingParams(
-            height=args.height,
-            width=args.width,
-            generator=generator,
-            true_cfg_scale=args.cfg_scale,
-            guidance_scale=args.guidance_scale,
-            num_inference_steps=args.num_inference_steps,
-            num_outputs_per_prompt=args.num_images_per_prompt,
-        ),
+        args.prompt,
+        negative_prompt=args.negative_prompt,
+        height=args.height,
+        width=args.width,
+        generator=generator,
+        true_cfg_scale=args.cfg_scale,
+        guidance_scale=args.guidance_scale,
+        num_inference_steps=args.num_inference_steps,
+        num_outputs_per_prompt=args.num_images_per_prompt,
     )
     generation_end = time.perf_counter()
     generation_time = generation_end - generation_start

examples/offline_inference/text_to_video/text_to_video.md

@@ -9,11 +9,10 @@ python text_to_video.py \
   --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
   --negative_prompt "<optional quality filter>" \
   --height 480 \
-  --width 832 \
+  --width 640 \
   --num_frames 33 \
   --guidance_scale 4.0 \
   --guidance_scale_high 3.0 \
-  --flow_shift 12.0 \
   --num_inference_steps 40 \
   --fps 16 \
   --output t2v_out.mp4
@@ -22,11 +21,10 @@ python text_to_video.py \
 Key arguments:
 
 - `--prompt`: text description (string).
-- `--height/--width`: output resolution (defaults 480x832, i.e. 480P). Dimensions should align with Wan VAE downsampling (multiples of 8).
+- `--height/--width`: output resolution (defaults 720x1280). Dimensions should align with Wan VAE downsampling (multiples of 8).
 - `--num_frames`: Number of frames (Wan default is 81).
-- `--guidance_scale` and `--guidance_scale_high`: CFG scale (applied to low/high).
+- `--guidance_scale` and `--guidance_scale_high`: CFG scale (applied to low/high)..
 - `--negative_prompt`: optional list of artifacts to suppress (the PR demo used a long Chinese string).
 - `--boundary_ratio`: Boundary split ratio for low/high DiT.
 - `--fps`: frames per second for the saved MP4 (requires `diffusers` export_to_video).
 - `--output`: path to save the generated video.
-- `--enable-cpu-offload`: enable CPU offloading for diffusion models.

examples/offline_inference/text_to_video/text_to_video.py

@@ -3,15 +3,12 @@
 
 import argparse
 import os
-import time
 from pathlib import Path
 
 import numpy as np
 import torch
 
-from vllm_omni.diffusion.data import DiffusionParallelConfig
 from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.utils.platform_utils import detect_device_type, is_npu
 
@@ -38,28 +35,6 @@ def parse_args() -> argparse.Namespace:
     )
     parser.add_argument("--output", type=str, default="wan22_output.mp4", help="Path to save the video (mp4).")
     parser.add_argument("--fps", type=int, default=24, help="Frames per second for the output video.")
-    parser.add_argument(
-        "--enable-cpu-offload",
-        action="store_true",
-        help="Enable CPU offloading for diffusion models.",
-    )
-    parser.add_argument(
-        "--ulysses_degree",
-        type=int,
-        default=1,
-        help="Number of GPUs used for ulysses sequence parallelism.",
-    )
-    parser.add_argument(
-        "--ring_degree",
-        type=int,
-        default=1,
-        help="Number of GPUs used for ring sequence parallelism.",
-    )
-    parser.add_argument(
-        "--enforce_eager",
-        action="store_true",
-        help="Disable torch.compile and force eager execution.",
-    )
     return parser.parse_args()
 
 
@@ -72,13 +47,6 @@ def main():
     vae_use_slicing = is_npu()
     vae_use_tiling = is_npu()
 
-    # Configure parallel settings (only SP is supported for Wan)
-    # Note: cfg_parallel and tensor_parallel are not implemented for Wan models
-    parallel_config = DiffusionParallelConfig(
-        ulysses_degree=args.ulysses_degree,
-        ring_degree=args.ring_degree,
-    )
-
     # Check if profiling is requested via environment variable
     profiler_enabled = bool(os.getenv("VLLM_TORCH_PROFILER_DIR"))
 
@@ -88,46 +56,23 @@ def main():
         vae_use_tiling=vae_use_tiling,
         boundary_ratio=args.boundary_ratio,
         flow_shift=args.flow_shift,
-        enable_cpu_offload=args.enable_cpu_offload,
-        parallel_config=parallel_config,
-        enforce_eager=args.enforce_eager,
     )
 
     if profiler_enabled:
         print("[Profiler] Starting profiling...")
         omni.start_profile()
 
-    # Print generation configuration
-    print(f"\n{'=' * 60}")
-    print("Generation Configuration:")
-    print(f"  Model: {args.model}")
-    print(f"  Inference steps: {args.num_inference_steps}")
-    print(f"  Frames: {args.num_frames}")
-    print(f"  Parallel configuration: ulysses_degree={args.ulysses_degree}, ring_degree={args.ring_degree}")
-    print(f"  Video size: {args.width}x{args.height}")
-    print(f"{'=' * 60}\n")
-
-    generation_start = time.perf_counter()
     frames = omni.generate(
-        {
-            "prompt": args.prompt,
-            "negative_prompt": args.negative_prompt,
-        },
-        OmniDiffusionSamplingParams(
-            height=args.height,
-            width=args.width,
-            generator=generator,
-            guidance_scale=args.guidance_scale,
-            guidance_scale_2=args.guidance_scale_high,
-            num_inference_steps=args.num_inference_steps,
-            num_frames=args.num_frames,
-        ),
+        args.prompt,
+        negative_prompt=args.negative_prompt,
+        height=args.height,
+        width=args.width,
+        generator=generator,
+        guidance_scale=args.guidance_scale,
+        guidance_scale_2=args.guidance_scale_high,
+        num_inference_steps=args.num_inference_steps,
+        num_frames=args.num_frames,
     )
-    generation_end = time.perf_counter()
-    generation_time = generation_end - generation_start
-
-    # Print profiling results
-    print(f"Total generation time: {generation_time:.4f} seconds ({generation_time * 1000:.2f} ms)")
 
     # Extract video frames from OmniRequestOutput
     if isinstance(frames, list) and len(frames) > 0:

examples/online_serving/lora_inference/README.md

@@ -1,54 +0,0 @@
-# Online LoRA Inference (Diffusion)
-
-This example shows how to use **per-request LoRA** with vLLM-Omni diffusion models via the OpenAI-compatible Chat Completions API.
-
-> Note: The LoRA adapter path must be readable on the **server** machine (usually a local path or a mounted directory).
-> Note: This example uses `/v1/chat/completions`. LoRA payloads for other OpenAI endpoints are not implemented here.
-
-## Start Server
-
-```bash
-# Pick a diffusion model (examples)
-# export MODEL=stabilityai/stable-diffusion-3.5-medium
-# export MODEL=Qwen/Qwen-Image
-
-bash run_server.sh
-```
-
-## Call API (curl)
-
-```bash
-# Required: local LoRA folder on the server
-export LORA_PATH=/path/to/lora_adapter
-
-# Optional
-export SERVER=http://localhost:8091
-export PROMPT="A piece of cheesecake"
-export LORA_NAME=my_lora
-export LORA_SCALE=1.0
-# Optional: if omitted, the server derives a stable id from LORA_PATH.
-# export LORA_INT_ID=123
-
-bash run_curl_lora_inference.sh
-```
-
-## Call API (Python)
-
-```bash
-python openai_chat_client.py \
-  --prompt "A piece of cheesecake" \
-  --lora-path /path/to/lora_adapter \
-  --lora-name my_lora \
-  --lora-scale 1.0 \
-  --output output.png
-```
-
-## LoRA Format
-
-LoRA adapters should be in PEFT format, for example:
-
-```
-lora_adapter/
-├── adapter_config.json
-└── adapter_model.safetensors
-```

examples/online_serving/lora_inference/openai_chat_client.py

@@ -1,124 +0,0 @@
-#!/usr/bin/env python3
-"""
-OpenAI-compatible chat client for diffusion LoRA inference.
-
-Example:
-  python openai_chat_client.py \
-    --server http://localhost:8091 \
-    --prompt "A piece of cheesecake" \
-    --lora-path /path/to/lora_adapter \
-    --lora-name my_lora \
-    --lora-scale 1.0 \
-    --output output.png
-"""
-
-import argparse
-import base64
-from pathlib import Path
-
-import requests
-
-
-def generate_image(
-    *,
-    prompt: str,
-    server_url: str,
-    height: int | None,
-    width: int | None,
-    num_inference_steps: int | None,
-    seed: int | None,
-    lora_name: str | None,
-    lora_path: str | None,
-    lora_scale: float | None,
-    lora_int_id: int | None,
-) -> bytes | None:
-    messages = [{"role": "user", "content": prompt}]
-
-    extra_body: dict = {}
-    if height is not None:
-        extra_body["height"] = height
-    if width is not None:
-        extra_body["width"] = width
-    if num_inference_steps is not None:
-        extra_body["num_inference_steps"] = num_inference_steps
-    if seed is not None:
-        extra_body["seed"] = seed
-
-    if lora_path:
-        lora_body: dict = {
-            "local_path": lora_path,
-            "name": lora_name or Path(lora_path).stem,
-        }
-        if lora_scale is not None:
-            lora_body["scale"] = float(lora_scale)
-        if lora_int_id is not None:
-            lora_body["int_id"] = int(lora_int_id)
-        extra_body["lora"] = lora_body
-
-    payload = {"messages": messages}
-    if extra_body:
-        payload["extra_body"] = extra_body
-
-    response = requests.post(
-        f"{server_url}/v1/chat/completions",
-        headers={"Content-Type": "application/json"},
-        json=payload,
-        timeout=300,
-    )
-    response.raise_for_status()
-    data = response.json()
-
-    content = data["choices"][0]["message"]["content"]
-    if isinstance(content, list) and content:
-        image_url = content[0].get("image_url", {}).get("url", "")
-        if image_url.startswith("data:image"):
-            _, b64_data = image_url.split(",", 1)
-            return base64.b64decode(b64_data)
-
-    raise RuntimeError(f"Unexpected response format: {content!r}")
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Diffusion LoRA OpenAI chat client")
-    parser.add_argument("--server", default="http://localhost:8091", help="Server URL")
-    parser.add_argument("--prompt", default="A piece of cheesecake", help="Text prompt")
-    parser.add_argument("--output", default="lora_online_output.png", help="Output image path")
-
-    parser.add_argument("--height", type=int, default=1024, help="Image height")
-    parser.add_argument("--width", type=int, default=1024, help="Image width")
-    parser.add_argument("--steps", type=int, default=50, help="num_inference_steps")
-    parser.add_argument("--seed", type=int, default=42, help="Random seed")
-
-    parser.add_argument("--lora-path", default=None, help="Server-local LoRA adapter folder (PEFT format)")
-    parser.add_argument("--lora-name", default=None, help="LoRA name (optional)")
-    parser.add_argument("--lora-scale", type=float, default=1.0, help="LoRA scale")
-    parser.add_argument(
-        "--lora-int-id",
-        type=int,
-        default=None,
-        help="LoRA integer id (cache key). If omitted, the server derives a stable id from lora_path.",
-    )
-
-    args = parser.parse_args()
-
-    image_bytes = generate_image(
-        prompt=args.prompt,
-        server_url=args.server,
-        height=args.height,
-        width=args.width,
-        num_inference_steps=args.steps,
-        seed=args.seed,
-        lora_name=args.lora_name,
-        lora_path=args.lora_path,
-        lora_scale=args.lora_scale if args.lora_path else None,
-        lora_int_id=args.lora_int_id if args.lora_path else None,
-    )
-
-    out_path = Path(args.output)
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    out_path.write_bytes(image_bytes)
-    print(f"Saved: {out_path} ({len(image_bytes) / 1024:.1f} KiB)")
-
-
-if __name__ == "__main__":
-    main()

examples/online_serving/lora_inference/run_curl_lora_inference.sh

@@ -1,61 +0,0 @@
-#!/bin/bash
-# Online diffusion LoRA inference via OpenAI-compatible chat API.
-
-SERVER="${SERVER:-http://localhost:8091}"
-PROMPT="${PROMPT:-A piece of cheesecake}"
-
-LORA_PATH="${LORA_PATH:-}"
-LORA_NAME="${LORA_NAME:-lora}"
-LORA_SCALE="${LORA_SCALE:-1.0}"
-LORA_INT_ID="${LORA_INT_ID:-}"
-
-HEIGHT="${HEIGHT:-1024}"
-WIDTH="${WIDTH:-1024}"
-NUM_INFERENCE_STEPS="${NUM_INFERENCE_STEPS:-50}"
-SEED="${SEED:-42}"
-
-CURRENT_TIME=$(date +%Y%m%d%H%M%S)
-OUTPUT="${OUTPUT:-lora_online_output_${CURRENT_TIME}.png}"
-
-if [ -z "$LORA_PATH" ]; then
-  echo "ERROR: LORA_PATH is required (must be a server-local path)."
-  exit 1
-fi
-
-echo "Generating image with LoRA..."
-echo "Server: $SERVER"
-echo "Prompt: $PROMPT"
-echo "LoRA: name=$LORA_NAME id=${LORA_INT_ID:-auto} scale=$LORA_SCALE path=$LORA_PATH"
-echo "Output: $OUTPUT"
-
-LORA_INT_ID_FIELD=""
-if [ -n "$LORA_INT_ID" ]; then
-  LORA_INT_ID_FIELD=", \"int_id\": $LORA_INT_ID"
-fi
-
-curl -s "$SERVER/v1/chat/completions" \
-  -H "Content-Type: application/json" \
-  -d "{
-    \"messages\": [
-      {\"role\": \"user\", \"content\": \"$PROMPT\"}
-    ],
-    \"extra_body\": {
-      \"height\": $HEIGHT,
-      \"width\": $WIDTH,
-      \"num_inference_steps\": $NUM_INFERENCE_STEPS,
-      \"seed\": $SEED,
-      \"lora\": {
-        \"name\": \"$LORA_NAME\",
-        \"local_path\": \"$LORA_PATH\",
-        \"scale\": $LORA_SCALE$LORA_INT_ID_FIELD
-      }
-    }
-  }" | jq -r '.choices[0].message.content[0].image_url.url' | sed 's/^data:image[^,]*,\s*//' | base64 -d > "$OUTPUT"
-
-if [ -f "$OUTPUT" ]; then
-  echo "Image saved to: $OUTPUT"
-  echo "Size: $(du -h "$OUTPUT" | cut -f1)"
-else
-  echo "Failed to generate image"
-  exit 1
-fi

examples/online_serving/lora_inference/run_server.sh

@@ -1,20 +0,0 @@
-#!/bin/bash
-# Online diffusion serving with vLLM-Omni (OpenAI-compatible API).
-
-MODEL="${MODEL:-stabilityai/stable-diffusion-3.5-medium}"
-PORT="${PORT:-8091}"
-
-echo "Starting vLLM-Omni diffusion server..."
-echo "Model: $MODEL"
-echo "Port: $PORT"
-
-if [ -z "${VLLM_BIN:-}" ]; then
-  if command -v vllm-omni >/dev/null 2>&1; then
-    VLLM_BIN="vllm-omni"
-  else
-    VLLM_BIN="vllm"
-  fi
-fi
-
-"$VLLM_BIN" serve "$MODEL" --omni \
-  --port "$PORT"

examples/online_serving/qwen3_omni/README.md

@@ -12,12 +12,6 @@ Please refer to [README.md](../../../README.md)
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
 
-If you want to open async chunking for qwen3-omni, launch the server with command below
-
-```bash
-vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /vllm_omni/model_executor/stage_configs/qwen3_omni_moe_async_chunk.yaml
-```
-
 If you have custom stage configs file, launch the server with command below
 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /path/to/stage_configs_file

examples/online_serving/qwen3_omni/openai_chat_completion_client_for_multimodal_generation.py

@@ -1,5 +1,4 @@
 import base64
-import concurrent.futures
 import os
 from typing import NamedTuple
 
@@ -415,67 +414,51 @@ def run_multimodal_generation(args) -> None:
     else:
         output_modalities = None
 
-    # Test multiple concurrent completions
-    num_concurrent_requests = args.num_concurrent_requests
+    chat_completion = client.chat.completions.create(
+        messages=[
+            get_system_prompt(),
+            prompt,
+        ],
+        model=model_name,
+        modalities=output_modalities,
+        extra_body=extra_body,
+        stream=args.stream,
+    )
 
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_concurrent_requests) as executor:
-        # Submit multiple completion requests concurrently
-        futures = [
-            executor.submit(
-                client.chat.completions.create,
-                messages=[
-                    get_system_prompt(),
-                    prompt,
-                ],
-                model=model_name,
-                modalities=output_modalities,
-                extra_body=extra_body,
-                stream=args.stream,
-            )
-            for _ in range(num_concurrent_requests)
-        ]
-
-        # Wait for all requests to complete and collect results
-        chat_completions = [future.result() for future in concurrent.futures.as_completed(futures)]
-
-    assert len(chat_completions) == num_concurrent_requests
     count = 0
     if not args.stream:
-        # Verify all completions succeeded
-        for chat_completion in chat_completions:
-            for choice in chat_completion.choices:
-                if choice.message.audio:
-                    audio_data = base64.b64decode(choice.message.audio.data)
+        for choice in chat_completion.choices:
+            if choice.message.audio:
+                audio_data = base64.b64decode(choice.message.audio.data)
+                audio_file_path = f"audio_{count}.wav"
+                with open(audio_file_path, "wb") as f:
+                    f.write(audio_data)
+                print(f"Audio saved to {audio_file_path}")
+                count += 1
+            elif choice.message.content:
+                print("Chat completion output from text:", choice.message.content)
+    else:
+        printed_content = False
+        for chunk in chat_completion:
+            for choice in chunk.choices:
+                if hasattr(choice, "delta"):
+                    content = getattr(choice.delta, "content", None)
+                else:
+                    content = None
+
+                if getattr(chunk, "modality", None) == "audio" and content:
+                    audio_data = base64.b64decode(content)
                     audio_file_path = f"audio_{count}.wav"
                     with open(audio_file_path, "wb") as f:
                         f.write(audio_data)
-                    print(f"Audio saved to {audio_file_path}")
+                    print(f"\nAudio saved to {audio_file_path}")
                     count += 1
-                elif choice.message.content:
-                    print("Chat completion output from text:", choice.message.content)
-    else:
-        printed_content = False
-        for chat_completion in chat_completions:
-            for chunk in chat_completion:
-                for choice in chunk.choices:
-                    if hasattr(choice, "delta"):
-                        content = getattr(choice.delta, "content", None)
-                    else:
-                        content = None
 
-                    if getattr(chunk, "modality", None) == "audio" and content:
-                        audio_data = base64.b64decode(content)
-                        audio_file_path = f"audio_{count}.wav"
-                        with open(audio_file_path, "wb") as f:
-                            f.write(audio_data)
-                        print(f"\nAudio saved to {audio_file_path}")
-                        count += 1
-
-                    elif getattr(chunk, "modality", None) == "text":
-                        if not printed_content:
-                            printed_content = True
-                            print("\ncontent:", end="", flush=True)
-                        print(content, end="", flush=True)
+                elif getattr(chunk, "modality", None) == "text":
+                    if not printed_content:
+                        printed_content = True
+                        print("\ncontent:", end="", flush=True)
+                    print(content, end="", flush=True)
 
 
 def parse_args():
@@ -534,12 +517,6 @@ def parse_args():
         action="store_true",
         help="Stream the response.",
     )
-    parser.add_argument(
-        "--num-concurrent-requests",
-        type=int,
-        default=1,
-        help="Number of concurrent requests to send. Default is 1.",
-    )
 
     return parser.parse_args()
 

examples/online_serving/qwen3_tts/README.md

@@ -1,170 +0,0 @@
-# Qwen3-TTS Online Serving
-
-This directory contains examples for running Qwen3-TTS models with vLLM-Omni's online serving API.
-
-## Supported Models
-
-| Model | Task Type | Description |
-|-------|-----------|-------------|
-| `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | CustomVoice | Predefined speaker voices with optional style control |
-| `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | VoiceDesign | Natural language voice style description |
-| `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | Base | Voice cloning from reference audio |
-
-## Quick Start
-
-### 1. Start the Server
-
-```bash
-# CustomVoice model (default)
-./run_server.sh
-
-# Or specify task type
-./run_server.sh CustomVoice
-./run_server.sh VoiceDesign
-./run_server.sh Base
-```
-
-### 2. Run the Client
-
-```bash
-# CustomVoice: Use predefined speaker
-python openai_speech_client.py \
-    --text "你好，我是通义千问" \
-    --voice Vivian \
-    --language Chinese
-
-# CustomVoice with style instruction
-python openai_speech_client.py \
-    --text "今天天气真好" \
-    --voice Ryan \
-    --instructions "用开心的语气说"
-
-# VoiceDesign: Describe the voice style
-python openai_speech_client.py \
-    --task-type VoiceDesign \
-    --text "哥哥，你回来啦" \
-    --instructions "体现撒娇稚嫩的萝莉女声，音调偏高"
-
-# Base: Voice cloning
-python openai_speech_client.py \
-    --task-type Base \
-    --text "Hello, this is a cloned voice" \
-    --ref-audio /path/to/reference.wav \
-    --ref-text "Original transcript of the reference audio"
-```
-
-### 3. Using curl
-
-```bash
-# Simple TTS request
-curl -X POST http://localhost:8000/v1/audio/speech \
-    -H "Content-Type: application/json" \
-    -d '{
-        "input": "Hello, how are you?",
-        "voice": "Vivian",
-        "language": "English"
-    }' --output output.wav
-
-# With style instruction
-curl -X POST http://localhost:8000/v1/audio/speech \
-    -H "Content-Type: application/json" \
-    -d '{
-        "input": "I am so excited!",
-        "voice": "Vivian",
-        "instructions": "Speak with great enthusiasm"
-    }' --output excited.wav
-```
-
-## API Reference
-
-### Endpoint
-
-```
-POST /v1/audio/speech
-```
-
-This endpoint follows the [OpenAI Audio Speech API](https://platform.openai.com/docs/api-reference/audio/createSpeech) format with additional Qwen3-TTS parameters.
-
-### Request Body
-
-```json
-{
-    "input": "Text to synthesize",
-    "voice": "Vivian",
-    "response_format": "wav",
-    "task_type": "CustomVoice",
-    "language": "Auto",
-    "instructions": "Optional style instructions",
-    "ref_audio": "URL or base64 for voice cloning",
-    "ref_text": "Reference audio transcript",
-    "x_vector_only_mode": false,
-    "max_new_tokens": 2048
-}
-```
-
-> **Note:** The `model` field is optional when serving a single model, as the server already knows which model is loaded.
-
-### Response
-
-Returns audio data in the requested format (default: WAV).
-
-## Parameters
-
-### Standard OpenAI Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `input` | string | required | Text to synthesize |
-| `voice` | string | "Vivian" | Speaker/voice name |
-| `response_format` | string | "wav" | Audio format: wav, mp3, flac, pcm, aac, opus |
-| `speed` | float | 1.0 | Playback speed (0.25-4.0) |
-| `model` | string | optional | Model name (optional when serving single model) |
-
-### Qwen3-TTS Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `task_type` | string | "CustomVoice" | Task: CustomVoice, VoiceDesign, or Base |
-| `language` | string | "Auto" | Language: Auto, Chinese, English, Japanese, Korean |
-| `instructions` | string | "" | Voice style/emotion instructions |
-| `max_new_tokens` | int | 2048 | Maximum tokens to generate |
-
-### Voice Clone Parameters (Base task)
-
-| Parameter | Type | Required | Description |
-|-----------|------|----------|-------------|
-| `ref_audio` | string | Yes* | Reference audio (file path, URL, or base64) |
-| `ref_text` | string | No | Transcript of reference audio (for ICL mode) |
-| `x_vector_only_mode` | bool | false | Use speaker embedding only (no ICL) |
-
-## Python Usage
-
-```python
-import httpx
-
-# Simple request
-response = httpx.post(
-    "http://localhost:8000/v1/audio/speech",
-    json={
-        "model": "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
-        "input": "Hello world",
-        "voice": "Vivian",
-    },
-    timeout=300.0,
-)
-
-with open("output.wav", "wb") as f:
-    f.write(response.content)
-```
-
-## Limitations
-
-- **No streaming**: Audio is generated completely before being returned. Streaming will be supported after the pipeline is disaggregated (see RFC #938).
-- **Single request**: Batch processing is not yet optimized for online serving.
-
-## Troubleshooting
-
-1. **Connection refused**: Make sure the server is running on the correct port
-2. **Out of memory**: Reduce `--gpu-memory-utilization` in run_server.sh
-3. **Unsupported speaker**: Check supported speakers via model documentation
-4. **Voice clone fails**: Ensure you're using the Base model variant for voice cloning

examples/online_serving/qwen3_tts/openai_speech_client.py

@@ -1,235 +0,0 @@
-"""OpenAI-compatible client for Qwen3-TTS via /v1/audio/speech endpoint.
-
-This script demonstrates how to use the OpenAI-compatible speech API
-to generate audio from text using Qwen3-TTS models.
-
-Examples:
-    # CustomVoice task (predefined speaker)
-    python openai_speech_client.py --text "Hello, how are you?" --voice Vivian
-
-    # CustomVoice with emotion instruction
-    python openai_speech_client.py --text "I'm so happy!" --voice Vivian \
-        --instructions "Speak with excitement"
-
-    # VoiceDesign task (voice from description)
-    python openai_speech_client.py --text "Hello world" \
-        --task-type VoiceDesign \
-        --instructions "A warm, friendly female voice"
-
-    # Base task (voice cloning)
-    python openai_speech_client.py --text "Hello world" \
-        --task-type Base \
-        --ref-audio "https://example.com/reference.wav" \
-        --ref-text "This is the reference transcript"
-"""
-
-import argparse
-import base64
-import os
-
-import httpx
-
-# Default server configuration
-DEFAULT_API_BASE = "http://localhost:8000"
-DEFAULT_API_KEY = "EMPTY"
-
-
-def encode_audio_to_base64(audio_path: str) -> str:
-    """Encode a local audio file to base64 data URL."""
-    if not os.path.exists(audio_path):
-        raise FileNotFoundError(f"Audio file not found: {audio_path}")
-
-    # Detect MIME type from extension
-    audio_path_lower = audio_path.lower()
-    if audio_path_lower.endswith(".wav"):
-        mime_type = "audio/wav"
-    elif audio_path_lower.endswith((".mp3", ".mpeg")):
-        mime_type = "audio/mpeg"
-    elif audio_path_lower.endswith(".flac"):
-        mime_type = "audio/flac"
-    elif audio_path_lower.endswith(".ogg"):
-        mime_type = "audio/ogg"
-    else:
-        mime_type = "audio/wav"  # Default
-
-    with open(audio_path, "rb") as f:
-        audio_bytes = f.read()
-    audio_b64 = base64.b64encode(audio_bytes).decode("utf-8")
-    return f"data:{mime_type};base64,{audio_b64}"
-
-
-def run_tts_generation(args) -> None:
-    """Run TTS generation via OpenAI-compatible /v1/audio/speech API."""
-
-    # Build request payload
-    payload = {
-        "model": args.model,
-        "input": args.text,
-        "voice": args.voice,
-        "response_format": args.response_format,
-    }
-
-    # Add optional parameters
-    if args.instructions:
-        payload["instructions"] = args.instructions
-    if args.task_type:
-        payload["task_type"] = args.task_type
-    if args.language:
-        payload["language"] = args.language
-    if args.max_new_tokens:
-        payload["max_new_tokens"] = args.max_new_tokens
-
-    # Voice clone parameters (Base task)
-    if args.ref_audio:
-        if args.ref_audio.startswith(("http://", "https://")):
-            payload["ref_audio"] = args.ref_audio
-        else:
-            payload["ref_audio"] = encode_audio_to_base64(args.ref_audio)
-    if args.ref_text:
-        payload["ref_text"] = args.ref_text
-    if args.x_vector_only:
-        payload["x_vector_only_mode"] = True
-
-    print(f"Task type: {args.task_type or 'CustomVoice'}")
-    print(f"Text: {args.text}")
-    print(f"Voice: {args.voice}")
-    print("Generating audio...")
-
-    # Make the API call
-    api_url = f"{args.api_base}/v1/audio/speech"
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {args.api_key}",
-    }
-
-    with httpx.Client(timeout=300.0) as client:
-        response = client.post(api_url, json=payload, headers=headers)
-
-    if response.status_code != 200:
-        print(f"Error: {response.status_code}")
-        print(response.text)
-        return
-
-    # Save audio response
-    output_path = args.output or "tts_output.wav"
-    with open(output_path, "wb") as f:
-        f.write(response.content)
-    print(f"Audio saved to: {output_path}")
-
-
-def parse_args():
-    """Parse command line arguments."""
-    parser = argparse.ArgumentParser(
-        description="OpenAI-compatible client for Qwen3-TTS via /v1/audio/speech",
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-        epilog=__doc__,
-    )
-
-    # Server configuration
-    parser.add_argument(
-        "--api-base",
-        type=str,
-        default=DEFAULT_API_BASE,
-        help=f"API base URL (default: {DEFAULT_API_BASE})",
-    )
-    parser.add_argument(
-        "--api-key",
-        type=str,
-        default=DEFAULT_API_KEY,
-        help="API key (default: EMPTY)",
-    )
-    parser.add_argument(
-        "--model",
-        "-m",
-        type=str,
-        default="Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
-        help="Model name/path",
-    )
-
-    # Task configuration
-    parser.add_argument(
-        "--task-type",
-        "-t",
-        type=str,
-        default=None,
-        choices=["CustomVoice", "VoiceDesign", "Base"],
-        help="TTS task type (default: CustomVoice)",
-    )
-
-    # Input text
-    parser.add_argument(
-        "--text",
-        type=str,
-        required=True,
-        help="Text to synthesize",
-    )
-
-    # Voice/speaker
-    parser.add_argument(
-        "--voice",
-        type=str,
-        default="Vivian",
-        help="Speaker/voice name (default: Vivian). Options: Vivian, Ryan, etc.",
-    )
-    parser.add_argument(
-        "--language",
-        type=str,
-        default=None,
-        help="Language: Auto, Chinese, English, etc.",
-    )
-    parser.add_argument(
-        "--instructions",
-        type=str,
-        default=None,
-        help="Voice style/emotion instructions",
-    )
-
-    # Base (voice clone) parameters
-    parser.add_argument(
-        "--ref-audio",
-        type=str,
-        default=None,
-        help="Reference audio file path or URL for voice cloning (Base task)",
-    )
-    parser.add_argument(
-        "--ref-text",
-        type=str,
-        default=None,
-        help="Reference audio transcript for voice cloning (Base task)",
-    )
-    parser.add_argument(
-        "--x-vector-only",
-        action="store_true",
-        help="Use x-vector only mode for voice cloning (no ICL)",
-    )
-
-    # Generation parameters
-    parser.add_argument(
-        "--max-new-tokens",
-        type=int,
-        default=None,
-        help="Maximum new tokens to generate",
-    )
-
-    # Output
-    parser.add_argument(
-        "--response-format",
-        type=str,
-        default="wav",
-        choices=["wav", "mp3", "flac", "pcm", "aac", "opus"],
-        help="Audio output format (default: wav)",
-    )
-    parser.add_argument(
-        "--output",
-        "-o",
-        type=str,
-        default=None,
-        help="Output audio file path (default: tts_output.wav)",
-    )
-
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    run_tts_generation(args)

examples/online_serving/qwen3_tts/run_server.sh

@@ -1,40 +0,0 @@
-#!/bin/bash
-# Launch vLLM-Omni server for Qwen3-TTS models
-#
-# Usage:
-#   ./run_server.sh                           # Default: CustomVoice model
-#   ./run_server.sh CustomVoice               # CustomVoice model
-#   ./run_server.sh VoiceDesign               # VoiceDesign model
-#   ./run_server.sh Base                      # Base (voice clone) model
-
-set -e
-
-TASK_TYPE="${1:-CustomVoice}"
-
-case "$TASK_TYPE" in
-    CustomVoice)
-        MODEL="Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice"
-        ;;
-    VoiceDesign)
-        MODEL="Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign"
-        ;;
-    Base)
-        MODEL="Qwen/Qwen3-TTS-12Hz-1.7B-Base"
-        ;;
-    *)
-        echo "Unknown task type: $TASK_TYPE"
-        echo "Supported: CustomVoice, VoiceDesign, Base"
-        exit 1
-        ;;
-esac
-
-echo "Starting Qwen3-TTS server with model: $MODEL"
-
-vllm-omni serve "$MODEL" \
-    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts.yaml \
-    --host 0.0.0.0 \
-    --port 8000 \
-    --gpu-memory-utilization 0.9 \
-    --trust-remote-code \
-    --enforce-eager \
-    --omni

examples/online_serving/text_to_image/openai_chat_client.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Qwen-Image OpenAI-compatible image generation client.
+Qwen-Image OpenAI-compatible chat client for image generation.
 
 Usage:
     python openai_chat_client.py --prompt "A beautiful landscape" --output output.png
@@ -25,14 +25,14 @@ def generate_image(
     negative_prompt: str | None = None,
     num_outputs_per_prompt: int = 1,
 ) -> bytes | None:
-    """Generate an image using the images generation API.
+    """Generate an image using the chat completions API.
 
     Args:
         prompt: Text description of the image
         server_url: Server URL
         height: Image height in pixels
         width: Image width in pixels
-        steps: Number of diffusion steps
+        steps: Number of inference steps
         true_cfg_scale: Qwen-Image CFG scale
         seed: Random seed
         negative_prompt: Negative prompt
@@ -41,31 +41,33 @@ def generate_image(
     Returns:
         Image bytes or None if failed
     """
-    payload: dict[str, object] = {
-        "prompt": prompt,
-        "response_format": "b64_json",
-        "n": num_outputs_per_prompt,
-    }
-
-    if width is not None and height is not None:
-        payload["size"] = f"{width}x{height}"
-    elif width is not None:
-        payload["size"] = f"{width}x{width}"
-    elif height is not None:
-        payload["size"] = f"{height}x{height}"
+    messages = [{"role": "user", "content": prompt}]
 
+    # Build extra_body with generation parameters
+    extra_body = {}
+    if height is not None:
+        extra_body["height"] = height
+    if width is not None:
+        extra_body["width"] = width
     if steps is not None:
-        payload["num_inference_steps"] = steps
+        extra_body["num_inference_steps"] = steps
     if true_cfg_scale is not None:
-        payload["true_cfg_scale"] = true_cfg_scale
-    if negative_prompt:
-        payload["negative_prompt"] = negative_prompt
+        extra_body["true_cfg_scale"] = true_cfg_scale
     if seed is not None:
-        payload["seed"] = seed
+        extra_body["seed"] = seed
+    if negative_prompt:
+        extra_body["negative_prompt"] = negative_prompt
+    extra_body["num_outputs_per_prompt"] = num_outputs_per_prompt
 
+    # Build request payload
+    payload = {"messages": messages}
+    if extra_body:
+        payload["extra_body"] = extra_body
+
+    # Send request
     try:
         response = requests.post(
-            f"{server_url}/v1/images/generations",
+            f"{server_url}/v1/chat/completions",
             headers={"Content-Type": "application/json"},
             json=payload,
             timeout=300,
@@ -73,13 +75,15 @@ def generate_image(
         response.raise_for_status()
         data = response.json()
 
-        items = data.get("data")
-        if isinstance(items, list) and items:
-            first = items[0].get("b64_json") if isinstance(items[0], dict) else None
-            if isinstance(first, str):
-                return base64.b64decode(first)
+        # Extract image from response
+        content = data["choices"][0]["message"]["content"]
+        if isinstance(content, list) and len(content) > 0:
+            image_url = content[0].get("image_url", {}).get("url", "")
+            if image_url.startswith("data:image"):
+                _, b64_data = image_url.split(",", 1)
+                return base64.b64decode(b64_data)
 
-        print(f"Unexpected response format: {data}")
+        print(f"Unexpected response format: {content}")
         return None
 
     except Exception as e:

examples/online_serving/text_to_image/run_curl_text_to_image.sh

@@ -1,10 +1,35 @@
 #!/bin/bash
 # Qwen-Image text-to-image curl example
 
-curl -X POST http://localhost:8091/v1/images/generations \
+SERVER="${SERVER:-http://localhost:8091}"
+PROMPT="${PROMPT:-a good boy in the ocean}"
+CURRENT_TIME=$(date +%Y%m%d%H%M%S)
+OUTPUT="${OUTPUT:-qwen_image_output_${CURRENT_TIME}.png}"
+
+echo "Generating image..."
+echo "Prompt: $PROMPT"
+echo "Output: $OUTPUT"
+
+curl -s "$SERVER/v1/chat/completions" \
   -H "Content-Type: application/json" \
-  -d '{
-    "prompt": "a dragon laying over the spine of the Green Mountains of Vermont",
-    "size": "1024x1024",
-    "seed": 42
-  }' | jq -r '.data[0].b64_json' | base64 -d > dragon.png
+  -d "{
+    \"messages\": [
+      {\"role\": \"user\", \"content\": \"$PROMPT\"}
+    ],
+    \"extra_body\": {
+      \"height\": 1024,
+      \"width\": 1024,
+      \"num_inference_steps\": 100,
+      \"true_cfg_scale\": 4.0,
+      \"seed\": 42,
+      \"num_outputs_per_prompt\": 1
+    }
+  }" | jq -r '.choices[0].message.content[0].image_url.url' | sed 's/^data:image[^,]*,\s*//' | base64 -d > "$OUTPUT"
+
+if [ -f "$OUTPUT" ]; then
+    echo "Image saved to: $OUTPUT"
+    echo "Size: $(du -h "$OUTPUT" | cut -f1)"
+else
+    echo "Failed to generate image"
+    exit 1
+fi

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "vllm-omni"
-version = "0.14.0rc1"
+version = "0.12.0rc1"
 description = "A framework for efficient model inference with omni-modality models"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
@@ -42,8 +42,6 @@ dependencies = [
     "fa3-fwd", # flash attention 3, maintained by @ZJY0516
     "openai-whisper>=20250625",
     "imageio[ffmpeg]>=2.37.2",
-    "onnxruntime>=1.19.0",
-    "sox>=1.5.0",
     # "vllm==0.14.0",  # TODO: fix the entrypoints overwrite problem
 ]
 
@@ -202,5 +200,4 @@ extend-ignore-identifiers-re = [
     ".*nothink.*",
     ".*NOTHINK.*",
     ".*nin.*",
-    "Ono_Anna",
 ]

tests/conftest.py

@@ -22,7 +22,6 @@ import psutil
 import pytest
 import torch
 import yaml
-from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
 from vllm.logger import init_logger
 from vllm.utils.network_utils import get_open_port
 
@@ -53,118 +52,31 @@ def default_vllm_config():
 
 @pytest.fixture(autouse=True)
 def clean_gpu_memory_between_tests():
-    print("\n=== PRE-TEST GPU CLEANUP ===")
-    _run_pre_test_cleanup()
-    yield
-    _run_post_test_cleanup()
-
-
-def _run_pre_test_cleanup():
     if os.getenv("VLLM_TEST_CLEAN_GPU_MEMORY", "0") != "1":
-        print("GPU cleanup disabled")
+        yield
         return
 
-    print("Pre-test GPU status:")
-    _print_simple_gpu_status()
+    # Wait for GPU memory to be cleared before starting the test
+    import gc
+
+    from tests.utils import wait_for_gpu_memory_to_clear
 
     num_gpus = torch.cuda.device_count()
     if num_gpus > 0:
         try:
-            from tests.utils import wait_for_gpu_memory_to_clear
-
             wait_for_gpu_memory_to_clear(
                 devices=list(range(num_gpus)),
                 threshold_ratio=0.1,
             )
-        except Exception as e:
-            print(f"Pre-test cleanup note: {e}")
+        except ValueError as e:
+            logger.info("Failed to clean GPU memory: %s", e)
 
+    yield
 
-def _run_post_test_cleanup():
-    if os.getenv("VLLM_TEST_CLEAN_GPU_MEMORY", "0") != "1":
-        return
-
-    import gc
-
+    # Clean up GPU memory after the test
     if torch.cuda.is_available():
-        gc.collect()
         torch.cuda.empty_cache()
-
-        print("Post-test GPU status:")
-        _print_simple_gpu_status()
-        _print_gpu_processes()
-
-
-def _print_simple_gpu_status():
-    """Print simple GPU memory status"""
-    if not torch.cuda.is_available():
-        print("  CUDA not available")
-        return
-
-    num_devices = torch.cuda.device_count()
-    for device_id in range(num_devices):
-        try:
-            torch.cuda.set_device(device_id)
-            allocated = torch.cuda.memory_allocated(device_id) / (1024**2)
-            reserved = torch.cuda.memory_reserved(device_id) / (1024**2)
-            print(f"  GPU {device_id}: Allocated: {allocated:.1f}MB, Reserved: {reserved:.1f}MB")
-        except Exception:
-            print(f"  GPU {device_id}: Error reading status")
-
-
-def _print_gpu_processes():
-    """Print GPU information including nvidia-smi and system processes"""
-
-    print("\n" + "=" * 80)
-    print("NVIDIA GPU Information (nvidia-smi)")
-    print("=" * 80)
-
-    try:
-        nvidia_result = subprocess.run(
-            ["nvidia-smi"],
-            capture_output=True,
-            text=True,
-            timeout=5,
-        )
-
-        if nvidia_result.returncode == 0:
-            lines = nvidia_result.stdout.strip().split("\n")
-            for line in lines[:20]:
-                print(line)
-
-            if len(lines) > 20:
-                print(f"... (showing first 20 of {len(lines)} lines)")
-        else:
-            print("nvidia-smi command failed")
-
-    except (subprocess.TimeoutExpired, FileNotFoundError):
-        print("nvidia-smi not available or timed out")
-    except Exception as e:
-        print(f"Error running nvidia-smi: {e}")
-
-    print("\n" + "=" * 80)
-    print("Detailed GPU Processes (nvidia-smi pmon)")
-    print("=" * 80)
-
-    try:
-        pmon_result = subprocess.run(
-            ["nvidia-smi", "pmon", "-c", "1"],
-            capture_output=True,
-            text=True,
-            timeout=3,
-        )
-
-        if pmon_result.returncode == 0 and pmon_result.stdout.strip():
-            print(pmon_result.stdout)
-        else:
-            print("No active GPU processes found via nvidia-smi pmon")
-
-    except Exception:
-        print("nvidia-smi pmon not available")
-
-    print("\n" + "=" * 80)
-    print("System Processes with GPU keywords")
-    print("=" * 80)
+        gc.collect()
 
 
 def dummy_messages_from_mix_data(
@@ -616,6 +528,7 @@ def convert_audio_to_text(audio_data):
         temperature=0.0,
         word_timestamps=True,
         condition_on_previous_text=False,
+        initial_prompt="Please transcribe with proper word spacing.",
     )["text"]
     if text:
         return text
@@ -625,238 +538,75 @@ def convert_audio_to_text(audio_data):
 
 def modify_stage_config(
     yaml_path: str,
-    updates: dict[str, Any],
-    deletes: dict[str, Any] = None,
+    stage_updates: dict[int, dict[str, Any]],
 ) -> str:
     """
-    Modify configurations in a YAML file, supporting both top-level and stage-specific modifications,
-    including addition, modification, and deletion of configurations.
+    Batch modify configurations for multiple stages in a YAML file.
 
     Args:
         yaml_path: Path to the YAML configuration file.
-        updates: Dictionary containing both top-level and stage-specific modifications to add or update.
-                Format: {
-                    'async_chunk': True,
-                    'stage_args': {
-                        0: {'engine_args.max_model_len': 5800},
-                        1: {'runtime.max_batch_size': 2}
-                    }
-                }
-        deletes: Dictionary containing configurations to delete.
-                Format: {
-                    'old_config': None,  # Delete entire key
-                    'stage_args': {
-                        0: ['engine_args.old_param'],
-                        1: ['runtime.unused_setting']
-                    }
-                }
+        stage_updates: Dictionary where keys are stage IDs and values are dictionaries of
+                      modifications for that stage. Each modification dictionary uses
+                      dot-separated paths as keys and new configuration values as values.
+                      Example: {
+                          0: {'engine_args.max_model_len': 5800},
+                          1: {'runtime.max_batch_size': 2}
+                      }
 
     Returns:
         str: Path to the newly created modified YAML file with timestamp suffix.
+
+    Example:
+        >>> output_file = modify_stage_config(
+        ...     'config.yaml',
+        ...     {
+        ...         0: {'engine_args.max_model_len': 5800},
+        ...         1: {'runtime.max_batch_size': 2}
+        ...     }
+        ... )
+        >>> print(f"Modified configuration saved to: {output_file}")
+        Modified configuration saved to: config_1698765432.yaml
     """
     path = Path(yaml_path)
     if not path.exists():
         raise FileNotFoundError(f"yaml does not exist: {path}")
-
     try:
         with open(yaml_path, encoding="utf-8") as f:
             config = yaml.safe_load(f) or {}
     except Exception as e:
         raise ValueError(f"Cannot parse YAML file: {e}")
 
-    # Helper function to apply update
-    def apply_update(config_dict: dict, key_path: str, value: Any) -> None:
-        """Apply update to dictionary using dot-separated path."""
-        # Handle direct list assignment (e.g., engine_input_source: [1, 2])
-        if "." not in key_path:
-            # Simple key, set directly
-            config_dict[key_path] = value
-            return
+    stage_args = config.get("stage_args", [])
+    if not stage_args:
+        raise ValueError("the stage_args does not exist")
 
-        current = config_dict
-        keys = key_path.split(".")
+    for stage_id, config_dict in stage_updates.items():
+        target_stage = None
+        for stage in stage_args:
+            if stage.get("stage_id") == stage_id:
+                target_stage = stage
+                break
 
-        for i in range(len(keys) - 1):
-            key = keys[i]
+        if target_stage is None:
+            available_ids = [s.get("stage_id") for s in stage_args if "stage_id" in s]
+            raise KeyError(f"Stage ID {stage_id} is not exist, available IDs: {available_ids}")
 
-            # Handle list indices
-            if key.isdigit() and isinstance(current, list):
-                index = int(key)
-                if index < 0:
-                    raise ValueError(f"Negative list index not allowed: {index}")
-                if index >= len(current):
-                    # Expand list if needed
-                    while len(current) <= index:
-                        # If we need to go deeper (more keys after this), create a dict
-                        # Otherwise, create None placeholder
-                        current.append({} if i < len(keys) - 2 else None)
-                current = current[index]
-            elif isinstance(current, dict):
-                # Handle dictionary keys
+        for key_path, value in config_dict.items():
+            current = target_stage
+            keys = key_path.split(".")
+            for i in range(len(keys) - 1):
+                key = keys[i]
                 if key not in current:
-                    # If there are more keys after this, create appropriate structure
-                    if i < len(keys) - 1:
-                        # Check if next key is a digit (list index) or string (dict key)
-                        if keys[i + 1].isdigit():
-                            current[key] = []
-                        else:
-                            current[key] = {}
-                    else:
-                        # This is the last key, create based on value type
-                        current[key] = [] if isinstance(value, list) else {}
-                elif not isinstance(current[key], (dict, list)) and i < len(keys) - 1:
-                    # If current value is not dict/list but we need to go deeper, replace it
-                    if keys[i + 1].isdigit():
-                        current[key] = []
-                    else:
-                        current[key] = {}
+                    raise KeyError(f"the {'.'.join(keys[: i + 1])} does not exist")
+
+                elif not isinstance(current[key], dict) and i < len(keys) - 2:
+                    raise ValueError(f"{'.'.join(keys[: i + 1])}' cannot continue deeper because it's not a dict")
                 current = current[key]
-            else:
-                # Current is not a dict or list, cannot traverse further
-                raise TypeError(
-                    f"Cannot access {'.'.join(keys[: i + 1])} as a dict/list. It's a {type(current).__name__}"
-                )
-
-        # Set the final value
-        last_key = keys[-1]
-        if isinstance(current, list) and last_key.isdigit():
-            # Setting a value in a list by index
-            index = int(last_key)
-            if index < 0:
-                raise ValueError(f"Negative list index not allowed: {index}")
-            if index >= len(current):
-                # Expand list if needed
-                while len(current) <= index:
-                    current.append(None)
-            current[index] = value
-        elif isinstance(current, dict):
-            # Special case: if the value is a list and we're setting a top-level key
-            # Example: updating engine_input_source with [1, 2]
-            current[last_key] = value
-        else:
-            # Current is not a dict, cannot set key
-            raise TypeError(f"Cannot set value at {key_path}. Current type is {type(current).__name__}, expected dict.")
-
-    # Helper function to delete by path
-    def delete_by_path(config_dict: dict, path: str) -> None:
-        """Delete configuration by dot-separated path."""
-        if not path:
-            return
-
-        current = config_dict
-        keys = path.split(".")
-
-        # Traverse to the parent
-        for i in range(len(keys) - 1):
-            key = keys[i]
-
-            # Handle list indices
-            if key.isdigit() and isinstance(current, list):
-                index = int(key)
-                if index < 0 or index >= len(current):
-                    raise KeyError(f"List index {index} out of bounds")
-                current = current[index]
-            elif isinstance(current, dict):
-                if key not in current:
-                    raise KeyError(f"Path {'.'.join(keys[: i + 1])} does not exist")
-                current = current[key]
-            else:
-                raise TypeError(
-                    f"Cannot access {'.'.join(keys[: i + 1])} as a dict/list. It's a {type(current).__name__}"
-                )
-
-        # Delete the item
-        last_key = keys[-1]
-
-        if isinstance(current, list) and last_key.isdigit():
-            index = int(last_key)
-            if index < 0 or index >= len(current):
-                raise KeyError(f"List index {index} out of bounds")
-            del current[index]
-        elif isinstance(current, dict) and last_key in current:
-            del current[last_key]
-        else:
-            raise KeyError(f"Path {path} does not exist")
-
-    # Apply deletions first
-    if deletes:
-        for key, value in deletes.items():
-            if key == "stage_args":
-                if value and isinstance(value, dict):
-                    stage_args = config.get("stage_args", [])
-                    if not stage_args:
-                        raise ValueError("stage_args does not exist in config")
-
-                    for stage_id, delete_paths in value.items():
-                        if not delete_paths:
-                            continue
-
-                        # Find stage by ID
-                        target_stage = None
-                        for stage in stage_args:
-                            if stage.get("stage_id") == stage_id:
-                                target_stage = stage
-                                break
-
-                        if target_stage is None:
-                            available_ids = [s.get("stage_id") for s in stage_args if "stage_id" in s]
-                            raise KeyError(f"Stage ID {stage_id} not found, available: {available_ids}")
-
-                        # Delete specified paths in this stage
-                        for path in delete_paths:
-                            if path:  # Skip empty paths
-                                delete_by_path(target_stage, path)
-            elif "." in key:
-                # Delete using dot-separated path
-                delete_by_path(config, key)
-            elif value is None and key in config:
-                # Delete entire key
-                del config[key]
-
-    # Apply updates
-    for key, value in updates.items():
-        if key == "stage_args":
-            if value and isinstance(value, dict):
-                stage_args = config.get("stage_args", [])
-                if not stage_args:
-                    raise ValueError("stage_args does not exist in config")
-
-                for stage_id, stage_updates in value.items():
-                    # Find stage by ID
-                    target_stage = None
-                    for stage in stage_args:
-                        if stage.get("stage_id") == stage_id:
-                            target_stage = stage
-                            break
-
-                    if target_stage is None:
-                        available_ids = [s.get("stage_id") for s in stage_args if "stage_id" in s]
-                        raise KeyError(f"Stage ID {stage_id} not found, available: {available_ids}")
-
-                    # Apply updates to this stage
-                    for path, val in stage_updates.items():
-                        # Check if this is a simple key (not dot-separated)
-                        # Example: 'engine_input_source' vs 'engine_args.max_model_len'
-                        if "." not in path:
-                            # Direct key assignment (e.g., updating a list value)
-                            target_stage[path] = val
-                        else:
-                            # Dot-separated path (e.g., nested dict access)
-                            apply_update(target_stage, path, val)
-        elif "." in key:
-            # Apply using dot-separated path
-            apply_update(config, key, value)
-        else:
-            # Direct top-level key
-            config[key] = value
-
-    # Save to new file with timestamp
-    timestamp = int(time.time())
-    base_name = yaml_path.rsplit(".", 1)[0] if "." in yaml_path else yaml_path
-    output_path = f"{base_name}_{timestamp}.yaml"
+            current[keys[-1]] = value
 
+    output_path = f"{yaml_path.split('.')[0]}_{int(time.time())}.yaml"
     with open(output_path, "w", encoding="utf-8") as f:
-        yaml.dump(config, f, default_flow_style=None, sort_keys=False, allow_unicode=True, indent=2)
+        yaml.dump(config, f, default_flow_style=False, sort_keys=False, allow_unicode=True, indent=2)
 
     return output_path
 
@@ -923,32 +673,24 @@ class OmniServer:
         raise RuntimeError(f"Server failed to start within {max_wait} seconds")
 
     def _kill_process_tree(self, pid):
-        """kill process and its children with verification"""
+        """kill process and its children"""
         try:
             parent = psutil.Process(pid)
             children = parent.children(recursive=True)
-
-            # Get all PIDs first
-            all_pids = [pid] + [child.pid for child in children]
-
-            # Terminate children
             for child in children:
                 try:
                     child.terminate()
                 except psutil.NoSuchProcess:
                     pass
 
-            # Wait for children
             gone, still_alive = psutil.wait_procs(children, timeout=10)
 
-            # Kill remaining children
             for child in still_alive:
                 try:
                     child.kill()
                 except psutil.NoSuchProcess:
                     pass
 
-            # Terminate parent
             try:
                 parent.terminate()
                 parent.wait(timeout=10)
@@ -958,24 +700,6 @@ class OmniServer:
                 except psutil.NoSuchProcess:
                     pass
 
-            # VERIFICATION: Check if all processes are gone
-            time.sleep(1)  # Give system time
-            alive_processes = []
-            for check_pid in all_pids:
-                if psutil.pid_exists(check_pid):
-                    alive_processes.append(check_pid)
-
-            if alive_processes:
-                print(f"Warning: Processes still alive: {alive_processes}")
-                # Optional: Try system kill
-                import subprocess
-
-                for alive_pid in alive_processes:
-                    try:
-                        subprocess.run(["kill", "-9", str(alive_pid)], timeout=2)
-                    except Exception as e:
-                        print(f"Cleanup failed: {e}")
-
         except psutil.NoSuchProcess:
             pass
 
@@ -985,6 +709,31 @@ class OmniServer:
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         if self.proc:
-            self._kill_process_tree(self.proc.pid)
-        _run_post_test_cleanup()
-        cleanup_dist_env_and_memory()
+            try:
+                parent = psutil.Process(self.proc.pid)
+                children = parent.children(recursive=True)
+                for child in children:
+                    try:
+                        child.terminate()
+                    except psutil.NoSuchProcess:
+                        pass
+
+                gone, still_alive = psutil.wait_procs(children, timeout=10)
+
+                for child in still_alive:
+                    try:
+                        child.kill()
+                    except psutil.NoSuchProcess:
+                        pass
+
+                try:
+                    parent.terminate()
+                    parent.wait(timeout=10)
+                except (psutil.NoSuchProcess, psutil.TimeoutExpired):
+                    try:
+                        parent.kill()
+                    except psutil.NoSuchProcess:
+                        pass
+
+            except psutil.NoSuchProcess:
+                pass

tests/diffusion/lora/test_base_linear.py

@@ -1,171 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-import torch
-
-from vllm_omni.diffusion.lora.layers.base_linear import DiffusionBaseLinearLayerWithLoRA
-
-
-@dataclass
-class _DummyLoRAConfig:
-    fully_sharded_loras: bool = False
-
-
-class _DummyQuantMethod:
-    def __init__(self, weight: torch.Tensor):
-        self._weight = weight
-
-    def apply(self, _base_layer, x: torch.Tensor, bias: torch.Tensor | None):
-        y = x @ self._weight.t()
-        if bias is not None:
-            y = y + bias
-        return y
-
-
-def test_diffusion_base_linear_apply_multi_slice():
-    # Build a fake diffusion LoRA layer with 2 slices and rank=2.
-    layer = DiffusionBaseLinearLayerWithLoRA.__new__(DiffusionBaseLinearLayerWithLoRA)
-    layer.tp_size = 1
-    layer.lora_config = _DummyLoRAConfig()
-
-    in_dim = 3
-    out_slices = (2, 1)
-    rank = 2
-
-    # Base weight: identity-ish mapping to make base output easy to reason about.
-    base_weight = torch.tensor(
-        [
-            [1.0, 0.0, 0.0],
-            [0.0, 1.0, 0.0],
-            [0.0, 0.0, 1.0],
-        ]
-    )
-    layer.base_layer = type("Base", (), {})()
-    layer.base_layer.quant_method = _DummyQuantMethod(base_weight)
-
-    # Allocate stacked weights: (max_loras=1, 1, rank, in_dim) and (1, 1, out_slice, rank)
-    a0 = torch.zeros((1, 1, rank, in_dim))
-    b0 = torch.zeros((1, 1, out_slices[0], rank))
-    a1 = torch.zeros((1, 1, rank, in_dim))
-    b1 = torch.zeros((1, 1, out_slices[1], rank))
-
-    # Slice 0: delta0 = (x @ A0.T) @ B0.T
-    A0 = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])  # (2, 3)
-    B0 = torch.tensor([[1.0, 0.0], [0.0, 1.0]])  # (2, 2)
-    a0[0, 0, :, :] = A0
-    b0[0, 0, :, :] = B0
-
-    # Slice 1: delta1 = (x @ A1.T) @ B1.T
-    A1 = torch.tensor([[0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])  # (2, 3)
-    B1 = torch.tensor([[2.0, 0.0]])  # (1, 2)
-    a1[0, 0, :, :] = A1
-    b1[0, 0, :, :] = B1
-
-    layer.lora_a_stacked = (a0, a1)
-    layer.lora_b_stacked = (b0, b1)
-    layer.output_slices = out_slices
-
-    x = torch.tensor([[1.0, 2.0, 3.0]])
-    out = layer.apply(x)
-
-    # Base output is identity: [1,2,3]
-    base_out = x @ base_weight.t()
-    # delta0:
-    # (x @ A0.T) = [1,2]
-    # [1,2] @ B0.T = [1,2]
-    delta0 = torch.tensor([[1.0, 2.0]])
-    # delta1:
-    # (x @ A1.T) = [3,1]
-    # [3,1] @ B1.T = [6]
-    delta1 = torch.tensor([[6.0]])
-    expected = torch.cat([base_out[:, :2] + delta0, base_out[:, 2:3] + delta1], dim=-1)
-    assert torch.allclose(out, expected)
-
-
-def test_diffusion_base_linear_reset_lora_disables_fast_path(monkeypatch):
-    # Verify that after reset_lora(), apply() skips LoRA matmuls even if the
-    # LoRA tensors are still allocated and non-empty.
-    from vllm.lora.layers.base_linear import BaseLinearLayerWithLoRA
-
-    layer = DiffusionBaseLinearLayerWithLoRA.__new__(DiffusionBaseLinearLayerWithLoRA)
-    layer.tp_size = 1
-    layer.lora_config = _DummyLoRAConfig()
-
-    in_dim = 2
-    out_dim = 2
-    rank = 1
-
-    base_weight = torch.eye(in_dim)
-    layer.base_layer = type("Base", (), {})()
-    layer.base_layer.quant_method = _DummyQuantMethod(base_weight)
-
-    a = torch.ones((1, 1, rank, in_dim))
-    b = torch.tensor([[[[1.0], [2.0]]]])  # (1,1,out_dim,rank)
-
-    layer.lora_a_stacked = (a,)
-    layer.lora_b_stacked = (b,)
-    layer.output_slices = (out_dim,)
-    layer._diffusion_lora_active_slices = (True,)
-
-    x = torch.tensor([[1.0, 2.0]])
-    out_active = layer.apply(x)
-    assert torch.allclose(out_active, torch.tensor([[4.0, 8.0]]))
-
-    monkeypatch.setattr(BaseLinearLayerWithLoRA, "reset_lora", lambda self, index: None)
-    layer.reset_lora(0)
-
-    assert layer._diffusion_lora_active_slices == (False,)
-    out_inactive = layer.apply(x)
-    assert torch.allclose(out_inactive, x)
-
-
-def test_diffusion_base_linear_apply_respects_inactive_slices():
-    # Build a fake diffusion LoRA layer with 2 slices and rank=2.
-    layer = DiffusionBaseLinearLayerWithLoRA.__new__(DiffusionBaseLinearLayerWithLoRA)
-    layer.tp_size = 1
-    layer.lora_config = _DummyLoRAConfig()
-
-    in_dim = 3
-    out_slices = (2, 1)
-    rank = 2
-
-    base_weight = torch.tensor(
-        [
-            [1.0, 0.0, 0.0],
-            [0.0, 1.0, 0.0],
-            [0.0, 0.0, 1.0],
-        ]
-    )
-    layer.base_layer = type("Base", (), {})()
-    layer.base_layer.quant_method = _DummyQuantMethod(base_weight)
-
-    a0 = torch.zeros((1, 1, rank, in_dim))
-    b0 = torch.zeros((1, 1, out_slices[0], rank))
-    a1 = torch.zeros((1, 1, rank, in_dim))
-    b1 = torch.zeros((1, 1, out_slices[1], rank))
-
-    A0 = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])  # (2, 3)
-    B0 = torch.tensor([[1.0, 0.0], [0.0, 1.0]])  # (2, 2)
-    a0[0, 0, :, :] = A0
-    b0[0, 0, :, :] = B0
-
-    A1 = torch.tensor([[0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])  # (2, 3)
-    B1 = torch.tensor([[2.0, 0.0]])  # (1, 2)
-    a1[0, 0, :, :] = A1
-    b1[0, 0, :, :] = B1
-
-    layer.lora_a_stacked = (a0, a1)
-    layer.lora_b_stacked = (b0, b1)
-    layer.output_slices = out_slices
-    layer._diffusion_lora_active_slices = (True, False)
-
-    x = torch.tensor([[1.0, 2.0, 3.0]])
-    out = layer.apply(x)
-
-    # Only the first slice should be adapted.
-    expected = torch.tensor([[2.0, 4.0, 3.0]])
-    assert torch.allclose(out, expected)

tests/diffusion/lora/test_lora_manager.py

@@ -1,343 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-import torch
-from vllm.lora.lora_weights import LoRALayerWeights
-from vllm.lora.utils import get_supported_lora_modules
-from vllm.model_executor.layers.linear import LinearBase
-
-from vllm_omni.diffusion.lora.manager import DiffusionLoRAManager
-from vllm_omni.lora.request import LoRARequest
-
-
-class _DummyLoRALayer:
-    def __init__(self, n_slices: int, output_slices: tuple[int, ...]):
-        self.n_slices = n_slices
-        self.output_slices = output_slices
-        self.set_calls: list[
-            tuple[list[torch.Tensor | None] | torch.Tensor, list[torch.Tensor | None] | torch.Tensor]
-        ] = []
-        self.reset_calls: int = 0
-
-    def set_lora(self, index: int, lora_a, lora_b):
-        assert index == 0
-        self.set_calls.append((lora_a, lora_b))
-
-    def reset_lora(self, index: int):
-        assert index == 0
-        self.reset_calls += 1
-
-
-class _FakeLinearBase(LinearBase):
-    def __init__(self):
-        torch.nn.Module.__init__(self)
-
-
-def test_lora_manager_supported_modules_are_stable_with_wrapped_layers(monkeypatch):
-    # Simulate a pipeline that already contains LoRA wrappers where the original
-    # LinearBase is nested under ".base_layer".
-    import vllm_omni.diffusion.lora.manager as manager_mod
-
-    class _DummyBaseLayerWithLoRA(torch.nn.Module):
-        def __init__(self, base_layer: torch.nn.Module):
-            super().__init__()
-            self.base_layer = base_layer
-
-    monkeypatch.setattr(manager_mod, "BaseLayerWithLoRA", _DummyBaseLayerWithLoRA)
-
-    pipeline = torch.nn.Module()
-    pipeline.transformer = torch.nn.Module()
-    pipeline.transformer.foo = _DummyBaseLayerWithLoRA(_FakeLinearBase())
-
-    # vLLM helper would see only the nested LinearBase and yield "base_layer".
-    assert get_supported_lora_modules(pipeline) == ["base_layer"]
-
-    manager = DiffusionLoRAManager(
-        pipeline=pipeline,
-        device=torch.device("cpu"),
-        dtype=torch.bfloat16,
-        max_cached_adapters=1,
-    )
-
-    assert "foo" in manager._supported_lora_modules
-    assert "base_layer" not in manager._supported_lora_modules
-
-
-def test_lora_manager_replace_layers_does_not_rewrap_base_layer(monkeypatch):
-    import vllm_omni.diffusion.lora.manager as manager_mod
-
-    class _DummyBaseLayerWithLoRA(torch.nn.Module):
-        def __init__(self, base_layer: torch.nn.Module):
-            super().__init__()
-            self.base_layer = base_layer
-
-    monkeypatch.setattr(manager_mod, "BaseLayerWithLoRA", _DummyBaseLayerWithLoRA)
-
-    def _fake_from_layer_diffusion(*, layer: torch.nn.Module, **_kwargs):
-        if isinstance(layer, _FakeLinearBase):
-            return _DummyBaseLayerWithLoRA(layer)
-        return layer
-
-    replace_calls: list[str] = []
-
-    def _fake_replace_submodule(root: torch.nn.Module, module_name: str, submodule: torch.nn.Module):
-        replace_calls.append(module_name)
-        setattr(root, module_name, submodule)
-
-    monkeypatch.setattr(manager_mod, "from_layer_diffusion", _fake_from_layer_diffusion)
-    monkeypatch.setattr(manager_mod, "replace_submodule", _fake_replace_submodule)
-
-    pipeline = torch.nn.Module()
-    pipeline.transformer = torch.nn.Module()
-    pipeline.transformer.foo = _FakeLinearBase()
-
-    manager = DiffusionLoRAManager(
-        pipeline=pipeline,
-        device=torch.device("cpu"),
-        dtype=torch.bfloat16,
-        max_cached_adapters=1,
-    )
-
-    peft_helper = type("_PH", (), {"r": 1})()
-
-    manager._replace_layers_with_lora(peft_helper)
-    manager._replace_layers_with_lora(peft_helper)
-
-    # Only the top-level layer should have been replaced; nested ".base_layer"
-    # must be skipped to avoid nesting LoRA wrappers.
-    assert replace_calls == ["foo"]
-
-
-def test_lora_manager_replaces_packed_layer_when_targeting_sublayers(monkeypatch):
-    import vllm_omni.diffusion.lora.manager as manager_mod
-
-    class _DummyBaseLayerWithLoRA(torch.nn.Module):
-        def __init__(self, base_layer: torch.nn.Module):
-            super().__init__()
-            self.base_layer = base_layer
-
-    monkeypatch.setattr(manager_mod, "BaseLayerWithLoRA", _DummyBaseLayerWithLoRA)
-
-    def _fake_from_layer_diffusion(*, layer: torch.nn.Module, **_kwargs):
-        return _DummyBaseLayerWithLoRA(layer)
-
-    replace_calls: list[str] = []
-
-    def _fake_replace_submodule(root: torch.nn.Module, module_name: str, submodule: torch.nn.Module):
-        replace_calls.append(module_name)
-        setattr(root, module_name, submodule)
-
-    monkeypatch.setattr(manager_mod, "from_layer_diffusion", _fake_from_layer_diffusion)
-    monkeypatch.setattr(manager_mod, "replace_submodule", _fake_replace_submodule)
-
-    pipeline = torch.nn.Module()
-    pipeline.packed_modules_mapping = {"to_qkv": ["to_q", "to_k", "to_v"]}
-    pipeline.transformer = torch.nn.Module()
-    pipeline.transformer.to_qkv = _FakeLinearBase()
-
-    manager = DiffusionLoRAManager(
-        pipeline=pipeline,
-        device=torch.device("cpu"),
-        dtype=torch.bfloat16,
-        max_cached_adapters=1,
-    )
-
-    # Treat the dummy layer as a packed 3-slice projection so the manager uses
-    # `packed_modules_mapping` to decide replacement based on target_modules.
-    monkeypatch.setattr(manager, "_get_packed_modules_list", lambda _module: ["q", "k", "v"])
-
-    peft_helper = type("_PH", (), {"r": 1, "target_modules": ["to_q"]})()
-    manager._replace_layers_with_lora(peft_helper)
-
-    assert replace_calls == ["to_qkv"]
-
-
-def test_lora_manager_activates_fused_lora_on_packed_layer():
-    manager = DiffusionLoRAManager(
-        pipeline=torch.nn.Module(),
-        device=torch.device("cpu"),
-        dtype=torch.bfloat16,
-        max_cached_adapters=1,
-    )
-
-    packed_layer = _DummyLoRALayer(n_slices=3, output_slices=(2, 1, 1))
-    manager._lora_modules = {"transformer.blocks.0.attn.to_qkv": packed_layer}
-
-    rank = 2
-    A = torch.ones((rank, 4))
-    B = torch.arange(0, sum(packed_layer.output_slices) * rank, dtype=torch.bfloat16).view(-1, rank)
-    lora = LoRALayerWeights(
-        module_name="transformer.blocks.0.attn.to_qkv",
-        rank=rank,
-        lora_alpha=rank,
-        lora_a=A,
-        lora_b=B,
-    )
-    manager._registered_adapters = {
-        7: type(
-            "LM",
-            (),
-            {
-                "id": 7,
-                "loras": {"transformer.blocks.0.attn.to_qkv": lora},
-                "get_lora": lambda self, k: self.loras.get(k),
-            },
-        )()
-    }
-    manager._adapter_scales = {7: 0.5}
-
-    manager._activate_adapter(7)
-
-    assert packed_layer.reset_calls == 0
-    assert len(packed_layer.set_calls) == 1
-    lora_a_list, lora_b_list = packed_layer.set_calls[0]
-    assert isinstance(lora_a_list, list)
-    assert isinstance(lora_b_list, list)
-    assert len(lora_a_list) == 3
-    assert len(lora_b_list) == 3
-    assert all(torch.allclose(a, A) for a in lora_a_list)
-    # B should be split into 3 slices and scaled.
-    b0, b1, b2 = lora_b_list
-    assert b0.shape[0] == 2 and b1.shape[0] == 1 and b2.shape[0] == 1
-    assert torch.allclose(torch.cat([b0, b1, b2], dim=0), B * 0.5)
-
-
-def test_lora_manager_activates_packed_lora_from_sublayers():
-    pipeline = torch.nn.Module()
-    pipeline.packed_modules_mapping = {"to_qkv": ["to_q", "to_k", "to_v"]}
-    manager = DiffusionLoRAManager(
-        pipeline=pipeline,
-        device=torch.device("cpu"),
-        dtype=torch.bfloat16,
-        max_cached_adapters=1,
-    )
-
-    packed_layer = _DummyLoRALayer(n_slices=3, output_slices=(2, 1, 1))
-    manager._lora_modules = {"transformer.blocks.0.attn.to_qkv": packed_layer}
-
-    rank = 2
-    loras: dict[str, LoRALayerWeights] = {}
-    for name, out_dim in zip(["to_q", "to_k", "to_v"], [2, 1, 1]):
-        loras[f"transformer.blocks.0.attn.{name}"] = LoRALayerWeights(
-            module_name=f"transformer.blocks.0.attn.{name}",
-            rank=rank,
-            lora_alpha=rank,
-            lora_a=torch.ones((rank, 4)) * (1 if name == "to_q" else 2),
-            lora_b=torch.ones((out_dim, rank)) * (3 if name == "to_q" else 4),
-        )
-
-    manager._registered_adapters = {
-        1: type("LM", (), {"id": 1, "loras": loras, "get_lora": lambda self, k: self.loras.get(k)})()
-    }
-    manager._adapter_scales = {1: 2.0}
-
-    manager._activate_adapter(1)
-
-    assert packed_layer.reset_calls == 0
-    assert len(packed_layer.set_calls) == 1
-    lora_a_list, lora_b_list = packed_layer.set_calls[0]
-    assert isinstance(lora_a_list, list)
-    assert isinstance(lora_b_list, list)
-    assert len(lora_a_list) == 3
-    assert len(lora_b_list) == 3
-    # Scale should apply to B only.
-    assert torch.allclose(lora_b_list[0], torch.ones((2, rank)) * 3 * 2.0)
-    assert torch.allclose(lora_b_list[1], torch.ones((1, rank)) * 4 * 2.0)
-    assert torch.allclose(lora_b_list[2], torch.ones((1, rank)) * 4 * 2.0)
-
-
-def _dummy_lora_request(adapter_id: int) -> LoRARequest:
-    return LoRARequest(
-        lora_name=f"adapter_{adapter_id}",
-        lora_int_id=adapter_id,
-        lora_path=f"/tmp/adapter_{adapter_id}",
-    )
-
-
-def test_lora_manager_evicts_lru_adapter_when_cache_full(monkeypatch):
-    manager = DiffusionLoRAManager(
-        pipeline=torch.nn.Module(),
-        device=torch.device("cpu"),
-        dtype=torch.bfloat16,
-        max_cached_adapters=2,
-    )
-
-    def _fake_load(_req: LoRARequest):
-        lora_model = type("LM", (), {"id": _req.lora_int_id})()
-        peft_helper = type("PH", (), {})()
-        return lora_model, peft_helper
-
-    monkeypatch.setattr(manager, "_load_adapter", _fake_load)
-    monkeypatch.setattr(manager, "_replace_layers_with_lora", lambda _peft: None)
-    monkeypatch.setattr(manager, "_activate_adapter", lambda _adapter_id: None)
-
-    req1 = _dummy_lora_request(1)
-    req2 = _dummy_lora_request(2)
-    req3 = _dummy_lora_request(3)
-
-    manager.set_active_adapter(req1, lora_scale=1.0)
-    manager.set_active_adapter(req2, lora_scale=1.0)
-
-    # Touch adapter 1 so adapter 2 becomes LRU.
-    manager.set_active_adapter(req1, lora_scale=1.0)
-
-    manager.set_active_adapter(req3, lora_scale=1.0)
-
-    assert set(manager.list_adapters()) == {1, 3}
-
-
-def test_lora_manager_does_not_evict_pinned_adapter(monkeypatch):
-    manager = DiffusionLoRAManager(
-        pipeline=torch.nn.Module(),
-        device=torch.device("cpu"),
-        dtype=torch.bfloat16,
-        max_cached_adapters=2,
-    )
-
-    def _fake_load(_req: LoRARequest):
-        lora_model = type("LM", (), {"id": _req.lora_int_id})()
-        peft_helper = type("PH", (), {})()
-        return lora_model, peft_helper
-
-    monkeypatch.setattr(manager, "_load_adapter", _fake_load)
-    monkeypatch.setattr(manager, "_replace_layers_with_lora", lambda _peft: None)
-    monkeypatch.setattr(manager, "_activate_adapter", lambda _adapter_id: None)
-
-    manager.set_active_adapter(_dummy_lora_request(1), lora_scale=1.0)
-    assert manager.pin_adapter(1)
-
-    manager.set_active_adapter(_dummy_lora_request(2), lora_scale=1.0)
-    manager.set_active_adapter(_dummy_lora_request(3), lora_scale=1.0)
-
-    assert set(manager.list_adapters()) == {1, 3}
-
-
-def test_lora_manager_warns_when_all_adapters_pinned(monkeypatch):
-    manager = DiffusionLoRAManager(
-        pipeline=torch.nn.Module(),
-        device=torch.device("cpu"),
-        dtype=torch.bfloat16,
-        max_cached_adapters=2,
-    )
-
-    def _fake_load(_req: LoRARequest):
-        lora_model = type("LM", (), {"id": _req.lora_int_id})()
-        peft_helper = type("PH", (), {})()
-        return lora_model, peft_helper
-
-    monkeypatch.setattr(manager, "_load_adapter", _fake_load)
-    monkeypatch.setattr(manager, "_replace_layers_with_lora", lambda _peft: None)
-    monkeypatch.setattr(manager, "_activate_adapter", lambda _adapter_id: None)
-
-    manager.set_active_adapter(_dummy_lora_request(1), lora_scale=1.0)
-    manager.set_active_adapter(_dummy_lora_request(2), lora_scale=1.0)
-
-    assert manager.pin_adapter(1)
-    assert manager.pin_adapter(2)
-
-    manager.max_cached_adapters = 1
-    manager._evict_if_needed()
-
-    assert set(manager.list_adapters()) == {1, 2}

tests/distributed/omni_connectors/test_kv_flow.py

@@ -1,12 +1,10 @@
 import unittest
-from types import SimpleNamespace
 from unittest.mock import MagicMock, patch
 
 import torch
 
 from vllm_omni.diffusion.models.bagel.pipeline_bagel import BagelPipeline
 from vllm_omni.diffusion.request import OmniDiffusionRequest
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.worker.gpu_ar_model_runner import GPUARModelRunner
 
 
@@ -111,12 +109,11 @@ class TestKVFlow(unittest.TestCase):
         transfer_data.metadata = {"kv_lens": [self.seq_len], "ropes": [0]}
 
         # 2. Setup Request with Injected Data
-        sp = OmniDiffusionSamplingParams(
-            past_key_values=SimpleNamespace(**layer_blocks),
-            kv_metadata=transfer_data.metadata,
-        )
+        req = OmniDiffusionRequest(prompt="test")
+        from types import SimpleNamespace
 
-        req = OmniDiffusionRequest(["test"], sp)
+        req.past_key_values = SimpleNamespace(**layer_blocks)
+        req.kv_metadata = transfer_data.metadata
 
         # 3. Setup Pipeline
         pipeline = MockBagelPipeline()
@@ -147,7 +144,7 @@ class TestKVFlow(unittest.TestCase):
         current_cache = RealNaiveCache(self.num_layers)
 
         # --- Logic from Source Code ---
-        injected_kv = req.sampling_params.past_key_values
+        injected_kv = req.past_key_values
         if isinstance(current_cache, RealNaiveCache) and hasattr(injected_kv, "key_cache"):
             # Assuming injected_kv is SimpleNamespace or object with list attrs
             for layer_idx in range(len(injected_kv.key_cache)):
@@ -179,11 +176,9 @@ class TestKVFlow(unittest.TestCase):
         data_dict = runner_test_result.to_dict()
 
         # 3. Receiver (Request Setup)
-        sp = OmniDiffusionSamplingParams(
-            past_key_values=data_dict["layer_blocks"],
-            kv_metadata=data_dict["metadata"],
-        )
-        req = OmniDiffusionRequest(["integration_test"], sp)  # noqa: F841
+        req = OmniDiffusionRequest(prompt="integration_test")
+        req.past_key_values = data_dict["layer_blocks"]
+        req.kv_metadata = data_dict["metadata"]
 
         # 4. Receiver (Injection Simulation)
         # Use the logic verification again

tests/e2e/offline_inference/conftest.py

@@ -7,12 +7,10 @@ Pytest configuration and fixtures for vllm-omni tests.
 from typing import Any
 
 import pytest
-from vllm import TextPrompt
 from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
+from vllm.sampling_params import SamplingParams
 
-from tests.conftest import _run_post_test_cleanup
 from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniSamplingParams
 from vllm_omni.outputs import OmniRequestOutput
 
 PromptAudioInput = list[tuple[Any, int]] | tuple[Any, int] | None
@@ -65,7 +63,7 @@ class OmniRunner:
             **kwargs,
         )
 
-    def get_default_sampling_params_list(self) -> list[OmniSamplingParams]:
+    def get_default_sampling_params_list(self) -> list[SamplingParams]:
         """
         Get a list of default sampling parameters for all stages.
 
@@ -83,7 +81,7 @@ class OmniRunner:
         videos: PromptVideoInput = None,
         mm_processor_kwargs: dict[str, Any] | None = None,
         modalities: list[str] | None = None,
-    ) -> list[TextPrompt]:
+    ) -> list[dict[str, Any]]:
         """
         Construct Omni input format from prompts and multimodal data.
 
@@ -176,7 +174,7 @@ class OmniRunner:
                 f"<|im_start|>assistant\n"
             )
 
-            input_dict: TextPrompt = {"prompt": full_prompt}
+            input_dict: dict[str, Any] = {"prompt": full_prompt}
             if multi_modal_data:
                 input_dict["multi_modal_data"] = multi_modal_data
             if modalities:
@@ -190,8 +188,8 @@ class OmniRunner:
 
     def generate(
         self,
-        prompts: list[TextPrompt],
-        sampling_params_list: list[OmniSamplingParams] | None = None,
+        prompts: list[dict[str, Any]],
+        sampling_params_list: list[SamplingParams] | None = None,
     ) -> list[OmniRequestOutput]:
         """
         Generate outputs for the given prompts.
@@ -213,7 +211,7 @@ class OmniRunner:
     def generate_multimodal(
         self,
         prompts: list[str] | str,
-        sampling_params_list: list[OmniSamplingParams] | None = None,
+        sampling_params_list: list[SamplingParams] | None = None,
         system_prompt: str | None = None,
         audios: PromptAudioInput = None,
         images: PromptImageInput = None,
@@ -250,7 +248,7 @@ class OmniRunner:
     def generate_audio(
         self,
         prompts: list[str] | str,
-        sampling_params_list: list[OmniSamplingParams] | None = None,
+        sampling_params_list: list[SamplingParams] | None = None,
         system_prompt: str | None = None,
         audios: PromptAudioInput = None,
         mm_processor_kwargs: dict[str, Any] | None = None,
@@ -277,7 +275,7 @@ class OmniRunner:
     def generate_video(
         self,
         prompts: list[str] | str,
-        sampling_params_list: list[OmniSamplingParams] | None = None,
+        sampling_params_list: list[SamplingParams] | None = None,
         system_prompt: str | None = None,
         videos: PromptVideoInput = None,
         mm_processor_kwargs: dict[str, Any] | None = None,
@@ -304,7 +302,7 @@ class OmniRunner:
     def generate_image(
         self,
         prompts: list[str] | str,
-        sampling_params_list: list[OmniSamplingParams] | None = None,
+        sampling_params_list: list[SamplingParams] | None = None,
         system_prompt: str | None = None,
         images: PromptImageInput = None,
         mm_processor_kwargs: dict[str, Any] | None = None,
@@ -337,7 +335,6 @@ class OmniRunner:
         self.close()
         del self.omni
         cleanup_dist_env_and_memory()
-        _run_post_test_cleanup()
 
     def close(self):
         """Close and cleanup the Omni instance."""

tests/e2e/offline_inference/test_cache_dit.py

@@ -15,8 +15,6 @@ from pathlib import Path
 import pytest
 import torch
 
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
 # ruff: noqa: E402
 REPO_ROOT = Path(__file__).resolve().parents[2]
 if str(REPO_ROOT) not in sys.path:
@@ -42,51 +40,42 @@ def test_cache_dit(model_name: str):
         "residual_diff_threshold": 0.24,
         "max_continuous_cached_steps": 3,
     }
-    m = None
-    try:
-        m = Omni(
-            model=model_name,
-            cache_backend="cache_dit",
-            cache_config=cache_config,
-        )
 
-        # Use minimal settings for fast testing
-        height = 256
-        width = 256
-        num_inference_steps = 4  # Minimal steps for fast test
+    m = Omni(
+        model=model_name,
+        cache_backend="cache_dit",
+        cache_config=cache_config,
+    )
 
-        outputs = m.generate(
-            "a photo of a cat sitting on a laptop keyboard",
-            OmniDiffusionSamplingParams(
-                height=height,
-                width=width,
-                num_inference_steps=num_inference_steps,
-                guidance_scale=0.0,
-                generator=torch.Generator("cuda").manual_seed(42),
-                num_outputs_per_prompt=1,  # Single output for speed
-            ),
-        )
-        # Extract images from request_output[0]['images']
-        first_output = outputs[0]
-        assert first_output.final_output_type == "image"
-        if not hasattr(first_output, "request_output") or not first_output.request_output:
-            raise ValueError("No request_output found in OmniRequestOutput")
+    # Use minimal settings for fast testing
+    height = 256
+    width = 256
+    num_inference_steps = 4  # Minimal steps for fast test
 
-        req_out = first_output.request_output[0]
-        if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
-            raise ValueError("Invalid request_output structure or missing 'images' key")
+    outputs = m.generate(
+        "a photo of a cat sitting on a laptop keyboard",
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=0.0,
+        generator=torch.Generator("cuda").manual_seed(42),
+        num_outputs_per_prompt=1,  # Single output for speed
+    )
+    # Extract images from request_output[0]['images']
+    first_output = outputs[0]
+    assert first_output.final_output_type == "image"
+    if not hasattr(first_output, "request_output") or not first_output.request_output:
+        raise ValueError("No request_output found in OmniRequestOutput")
 
-        images = req_out.images
+    req_out = first_output.request_output[0]
+    if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
+        raise ValueError("Invalid request_output structure or missing 'images' key")
 
-        # Verify generation succeeded
-        assert images is not None
-        assert len(images) == 1
-        # Check image size
-        assert images[0].width == width
-        assert images[0].height == height
-    except Exception as e:
-        print(f"Test failed with error: {e}")
-        raise
-    finally:
-        if m is not None and hasattr(m, "close"):
-            m.close()
+    images = req_out.images
+
+    # Verify generation succeeded
+    assert images is not None
+    assert len(images) == 1
+    # Check image size
+    assert images[0].width == width
+    assert images[0].height == height

tests/e2e/offline_inference/test_diffusion_cpu_offload.py

@@ -3,10 +3,8 @@ from pathlib import Path
 
 import pytest
 import torch
-from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
 
 from tests.utils import GPUMemoryMonitor
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.utils.platform_utils import is_npu, is_rocm
 
 # ruff: noqa: E402
@@ -19,41 +17,34 @@ from vllm_omni import Omni
 models = ["riverclouds/qwen_image_random"]
 
 
-def inference(model_name: str, offload: bool = True):
-    torch.cuda.empty_cache()
-    device_index = torch.cuda.current_device()
-    monitor = GPUMemoryMonitor(device_index=device_index, interval=0.02)
-    monitor.start()
-    m = Omni(model=model_name, enable_cpu_offload=offload)
-    torch.cuda.reset_peak_memory_stats(device=device_index)
-    height = 256
-    width = 256
+@pytest.mark.skipif(is_npu() or is_rocm(), reason="Hardware not supported")
+@pytest.mark.parametrize("model_name", models)
+def test_cpu_offload_diffusion_model(model_name: str):
+    def inference(offload: bool = True):
+        torch.cuda.empty_cache()
+        device_index = torch.cuda.current_device()
+        monitor = GPUMemoryMonitor(device_index=device_index, interval=0.02)
+        monitor.start()
+        m = Omni(model=model_name, enable_cpu_offload=offload)
+        torch.cuda.reset_peak_memory_stats(device=device_index)
+        height = 256
+        width = 256
 
-    m.generate(
-        "a photo of a cat sitting on a laptop keyboard",
-        OmniDiffusionSamplingParams(
+        m.generate(
+            "a photo of a cat sitting on a laptop keyboard",
             height=height,
             width=width,
             num_inference_steps=9,
             guidance_scale=0.0,
             generator=torch.Generator("cuda").manual_seed(42),
-        ),
-    )
-    peak = monitor.peak_used_mb
-    monitor.stop()
+        )
+        peak = monitor.peak_used_mb
+        monitor.stop()
 
-    return peak
+        return peak
 
-
-@pytest.mark.skipif(is_npu() or is_rocm(), reason="Hardware not supported")
-@pytest.mark.parametrize("model_name", models)
-def test_cpu_offload_diffusion_model(model_name: str):
-    try:
-        no_offload_peak_memory = inference(model_name, offload=False)
-        cleanup_dist_env_and_memory()
-        offload_peak_memory = inference(model_name, offload=True)
-    except Exception:
-        pytest.fail("Inference failed")
+    offload_peak_memory = inference(offload=True)
+    no_offload_peak_memory = inference(offload=False)
     print(f"Offload peak memory: {offload_peak_memory} MB")
     print(f"No offload peak memory: {no_offload_peak_memory} MB")
     assert offload_peak_memory + 2500 < no_offload_peak_memory, (

tests/e2e/offline_inference/test_diffusion_lora.py

@@ -1,143 +0,0 @@
-import json
-import os
-import sys
-from pathlib import Path
-
-import pytest
-import torch
-from safetensors.torch import save_file
-
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-from vllm_omni.outputs import OmniRequestOutput
-from vllm_omni.utils.platform_utils import is_npu
-
-# ruff: noqa: E402
-REPO_ROOT = Path(__file__).resolve().parents[2]
-if str(REPO_ROOT) not in sys.path:
-    sys.path.insert(0, str(REPO_ROOT))
-
-from vllm_omni import Omni
-
-os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "1"
-
-
-# This test is specific to Z-Image LoRA behavior. Keep it focused on a single
-# model to reduce runtime and avoid extra downloads.
-models = ["Tongyi-MAI/Z-Image-Turbo"]
-
-# NPU still can't run Tongyi-MAI/Z-Image-Turbo properly.
-if is_npu():
-    pytest.skip("Tongyi-MAI/Z-Image-Turbo is not supported on NPU yet.", allow_module_level=True)
-
-
-@pytest.mark.parametrize("model_name", models)
-def test_diffusion_model(model_name: str, tmp_path: Path):
-    def _extract_images(outputs: list[OmniRequestOutput]):
-        if not outputs:
-            raise ValueError("Empty outputs from Omni.generate()")
-        first_output = outputs[0]
-        assert first_output.final_output_type == "image"
-        if not hasattr(first_output, "request_output") or not first_output.request_output:
-            raise ValueError("No request_output found in OmniRequestOutput")
-
-        req_out = first_output.request_output[0]
-        if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
-            raise ValueError("Invalid request_output structure or missing 'images' key")
-        return req_out.images
-
-    def _write_zimage_lora(adapter_dir: Path) -> str:
-        adapter_dir.mkdir(parents=True, exist_ok=True)
-
-        # Z-Image transformer uses dim=3840 by default (see ZImageTransformer2DModel).
-        dim = 3840
-        module_name = "transformer.layers.0.attention.to_qkv"
-        rank = 1
-        lora_a = torch.zeros((rank, dim), dtype=torch.float32)
-        lora_a[0, 0] = 1.0
-
-        # QKVParallelLinear packs (Q, K, V). With tp=1 and n_kv_heads==n_heads in Z-Image,
-        # each slice is `dim`, so total out dim is `3 * dim`.
-        lora_b = torch.zeros((3 * dim, rank), dtype=torch.float32)
-        # Apply a visible delta to the Q slice only to keep the perturbation bounded.
-        lora_b[:dim, 0] = 0.1
-
-        save_file(
-            {
-                f"base_model.model.{module_name}.lora_A.weight": lora_a,
-                f"base_model.model.{module_name}.lora_B.weight": lora_b,
-            },
-            str(adapter_dir / "adapter_model.safetensors"),
-        )
-        (adapter_dir / "adapter_config.json").write_text(
-            json.dumps(
-                {
-                    "r": rank,
-                    "lora_alpha": rank,
-                    "target_modules": [module_name],
-                }
-            ),
-            encoding="utf-8",
-        )
-        return str(adapter_dir)
-
-    m = Omni(model=model_name)
-    try:
-        # high resolution may cause OOM on L4
-        height = 256
-        width = 256
-        prompt = "a photo of a cat sitting on a laptop keyboard"
-
-        outputs = m.generate(
-            prompt,
-            OmniDiffusionSamplingParams(
-                height=height,
-                width=width,
-                num_inference_steps=2,
-                guidance_scale=0.0,
-                generator=torch.Generator("cuda").manual_seed(42),
-                num_outputs_per_prompt=1,
-            ),
-        )
-        images = _extract_images(outputs)
-
-        assert len(images) == 1
-        # check image size
-        assert images[0].width == width
-        assert images[0].height == height
-
-        # Real LoRA E2E: generate again with a real on-disk PEFT adapter and
-        # verify that output changes.
-        if model_name == "Tongyi-MAI/Z-Image-Turbo":
-            from vllm_omni.lora.request import LoRARequest
-            from vllm_omni.lora.utils import stable_lora_int_id
-
-            lora_dir = _write_zimage_lora(tmp_path / "zimage_lora")
-            lora_request = LoRARequest(
-                lora_name="test",
-                lora_int_id=stable_lora_int_id(lora_dir),
-                lora_path=lora_dir,
-            )
-            outputs_lora = m.generate(
-                prompt,
-                OmniDiffusionSamplingParams(
-                    height=height,
-                    width=width,
-                    num_inference_steps=2,
-                    guidance_scale=0.0,
-                    generator=torch.Generator("cuda").manual_seed(42),
-                    num_outputs_per_prompt=1,
-                    lora_request=lora_request,
-                    lora_scale=2.0,
-                ),
-            )
-            images_lora = _extract_images(outputs_lora)
-            assert len(images_lora) == 1
-            assert images_lora[0].width == width
-            assert images_lora[0].height == height
-
-            import numpy as np
-
-            diff = np.abs(np.array(images[0], dtype=np.int16) - np.array(images_lora[0], dtype=np.int16)).mean()
-            assert diff > 0.0
-    finally:
-        m.close()

tests/e2e/offline_inference/test_ovis_image.py

@@ -25,7 +25,6 @@ from vllm_omni.diffusion.data import OmniDiffusionConfig, TransformerConfig
 from vllm_omni.diffusion.distributed.utils import get_local_device
 from vllm_omni.diffusion.models.ovis_image.pipeline_ovis_image import OvisImagePipeline
 from vllm_omni.diffusion.request import OmniDiffusionRequest
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 
 @pytest.fixture
@@ -163,13 +162,11 @@ def test_basic_generation(ovis_pipeline):
     """Test the forward pass logic."""
     # Setup request
     req = OmniDiffusionRequest(
-        prompts=["A photo of a cat"],
-        sampling_params=OmniDiffusionSamplingParams(
-            height=256,
-            width=256,
-            num_inference_steps=2,
-            guidance_scale=1.0,
-        ),
+        prompt="A photo of a cat",
+        height=256,
+        width=256,
+        num_inference_steps=2,
+        guidance_scale=1.0,
     )
 
     output = ovis_pipeline(req)
@@ -187,18 +184,12 @@ def test_basic_generation(ovis_pipeline):
 def test_guidance_scale(ovis_pipeline):
     """Test that classifier-free guidance path is taken when scale > 1.0."""
     req = OmniDiffusionRequest(
-        prompts=[
-            {
-                "prompt": "A photo of a cat",
-                "negative_prompt": "bad quality",
-            }
-        ],
-        sampling_params=OmniDiffusionSamplingParams(
-            height=256,
-            width=256,
-            num_inference_steps=1,
-            guidance_scale=2.0,  # Trigger CFG
-        ),
+        prompt="A photo of a cat",
+        negative_prompt="bad quality",
+        height=256,
+        width=256,
+        num_inference_steps=1,
+        guidance_scale=2.0,  # Trigger CFG
     )
 
     ovis_pipeline(req)
@@ -209,11 +200,9 @@ def test_resolution_check(ovis_pipeline):
     """Test resolution divisible validation logic if present."""
     # Pass odd resolution
     req = OmniDiffusionRequest(
-        prompts=["test"],
-        sampling_params=OmniDiffusionSamplingParams(
-            height=250,  # Not divisible by 16 (8*2)
-            width=250,
-        ),
+        prompt="test",
+        height=250,  # Not divisible by 16 (8*2)
+        width=250,
     )
 
     # Should warn but proceed (as per code I read earlier) or resize?

tests/e2e/offline_inference/test_qwen3_omni.py

@@ -7,8 +7,6 @@ E2E offline tests for Omni model with video input and audio output.
 import os
 
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "0"
-
 from pathlib import Path
 
 import pytest

tests/e2e/offline_inference/test_sequence_parallel.py

@@ -21,8 +21,6 @@ import torch
 import torch.distributed as dist
 from PIL import Image
 
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
 # ruff: noqa: E402
 REPO_ROOT = Path(__file__).resolve().parents[3]
 if str(REPO_ROOT) not in sys.path:
@@ -83,14 +81,12 @@ def _run_baseline(model_name: str, dtype: torch.dtype, attn_backend: str, height
     try:
         outputs = baseline.generate(
             PROMPT,
-            OmniDiffusionSamplingParams(
-                height=height,
-                width=width,
-                num_inference_steps=4,
-                guidance_scale=0.0,
-                generator=torch.Generator(get_device_name()).manual_seed(seed),
-                num_outputs_per_prompt=1,
-            ),
+            height=height,
+            width=width,
+            num_inference_steps=4,
+            guidance_scale=0.0,
+            generator=torch.Generator(get_device_name()).manual_seed(seed),
+            num_outputs_per_prompt=1,
         )
         return outputs[0].request_output[0].images
     finally:
@@ -120,14 +116,12 @@ def _run_sp(
     try:
         outputs = sp.generate(
             PROMPT,
-            OmniDiffusionSamplingParams(
-                height=height,
-                width=width,
-                num_inference_steps=4,
-                guidance_scale=0.0,
-                generator=torch.Generator(get_device_name()).manual_seed(seed),
-                num_outputs_per_prompt=1,
-            ),
+            height=height,
+            width=width,
+            num_inference_steps=4,
+            guidance_scale=0.0,
+            generator=torch.Generator(get_device_name()).manual_seed(seed),
+            num_outputs_per_prompt=1,
         )
         return outputs[0].request_output[0].images
     finally:

tests/e2e/offline_inference/test_stable_audio_model.py

@@ -5,7 +5,6 @@ import numpy as np
 import pytest
 import torch
 
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.outputs import OmniRequestOutput
 
 # ruff: noqa: E402
@@ -30,33 +29,30 @@ def test_stable_audio_model(model_name: str):
     sample_rate = 44100  # Stable Audio uses 44100 Hz
 
     outputs = m.generate(
-        prompts={
-            "prompt": "The sound of a dog barking",
-            "negative_prompt": "Low quality.",
+        "The sound of a dog barking",
+        negative_prompt="Low quality.",
+        num_inference_steps=4,  # Minimal steps for speed
+        guidance_scale=7.0,
+        generator=torch.Generator("cuda").manual_seed(42),
+        num_outputs_per_prompt=1,
+        extra={
+            "audio_start_in_s": audio_start_in_s,
+            "audio_end_in_s": audio_end_in_s,
         },
-        sampling_params_list=OmniDiffusionSamplingParams(
-            num_inference_steps=4,  # Minimal steps for speed
-            guidance_scale=7.0,
-            generator=torch.Generator("cuda").manual_seed(42),
-            num_outputs_per_prompt=1,
-            extra_args={
-                "audio_start_in_s": audio_start_in_s,
-                "audio_end_in_s": audio_end_in_s,
-            },
-        ),
     )
 
     # Extract audio from OmniRequestOutput
     assert outputs is not None
     first_output = outputs[0]
-    assert first_output.final_output_type == "image"
+    assert first_output.final_output_type == "image"  # Generic output type
     assert hasattr(first_output, "request_output") and first_output.request_output
 
     req_out = first_output.request_output[0]
     assert isinstance(req_out, OmniRequestOutput)
-    assert req_out.final_output_type == "audio"
-    assert hasattr(req_out, "multimodal_output") and req_out.multimodal_output
-    audio = req_out.multimodal_output.get("audio")
+    assert hasattr(req_out, "images") and len(req_out.images) >= 1
+
+    # For stable audio, the "images" field contains audio numpy arrays
+    audio = req_out.images[0]
     assert isinstance(audio, np.ndarray)
     # audio shape: (batch, channels, samples)
     # For stable-audio-open-1.0: sample_rate=44100, so 2 seconds = 88200 samples

tests/e2e/offline_inference/test_t2i_model.py

@@ -5,7 +5,6 @@ from pathlib import Path
 import pytest
 import torch
 
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.utils.platform_utils import is_npu, is_rocm
 
@@ -35,43 +34,33 @@ elif is_rocm():
 
 @pytest.mark.parametrize("model_name", models)
 def test_diffusion_model(model_name: str):
-    m = None
-    try:
-        m = Omni(model=model_name)
-        # high resolution may cause OOM on L4
-        height = 256
-        width = 256
-        outputs = m.generate(
-            "a photo of a cat sitting on a laptop keyboard",
-            OmniDiffusionSamplingParams(
-                height=height,
-                width=width,
-                num_inference_steps=2,
-                guidance_scale=0.0,
-                generator=torch.Generator("cuda").manual_seed(42),
-                num_outputs_per_prompt=2,
-            ),
-        )
-        # Extract images from request_output[0]['images']
-        first_output = outputs[0]
-        assert first_output.final_output_type == "image"
-        if not hasattr(first_output, "request_output") or not first_output.request_output:
-            raise ValueError("No request_output found in OmniRequestOutput")
+    m = Omni(model=model_name)
+    # high resolution may cause OOM on L4
+    height = 256
+    width = 256
+    outputs = m.generate(
+        "a photo of a cat sitting on a laptop keyboard",
+        height=height,
+        width=width,
+        num_inference_steps=2,
+        guidance_scale=0.0,
+        generator=torch.Generator("cuda").manual_seed(42),
+        num_outputs_per_prompt=2,
+    )
+    # Extract images from request_output[0]['images']
+    first_output = outputs[0]
+    assert first_output.final_output_type == "image"
+    if not hasattr(first_output, "request_output") or not first_output.request_output:
+        raise ValueError("No request_output found in OmniRequestOutput")
 
-        req_out = first_output.request_output[0]
-        if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
-            raise ValueError("Invalid request_output structure or missing 'images' key")
+    req_out = first_output.request_output[0]
+    if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
+        raise ValueError("Invalid request_output structure or missing 'images' key")
 
-        images = req_out.images
+    images = req_out.images
 
-        assert len(images) == 2
-        # check image size
-        assert images[0].width == width
-        assert images[0].height == height
-        images[0].save("image_output.png")
-    except Exception as e:
-        print(f"Test failed with error: {e}")
-        raise
-    finally:
-        if m is not None and hasattr(m, "close"):
-            m.close()
+    assert len(images) == 2
+    # check image size
+    assert images[0].width == width
+    assert images[0].height == height
+    images[0].save("image_output.png")

tests/e2e/offline_inference/test_t2v_model.py

@@ -5,8 +5,6 @@ from pathlib import Path
 import pytest
 import torch
 
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
 # ruff: noqa: E402
 REPO_ROOT = Path(__file__).resolve().parents[2]
 if str(REPO_ROOT) not in sys.path:
@@ -35,15 +33,13 @@ def test_video_diffusion_model(model_name: str):
     width = 640
     num_frames = 5
     outputs = m.generate(
-        prompts="A cat sitting on a table",
-        sampling_params_list=OmniDiffusionSamplingParams(
-            height=height,
-            width=width,
-            num_frames=num_frames,
-            num_inference_steps=2,
-            guidance_scale=1.0,
-            generator=torch.Generator("cuda").manual_seed(42),
-        ),
+        "A cat sitting on a table",
+        height=height,
+        width=width,
+        num_frames=num_frames,
+        num_inference_steps=2,
+        guidance_scale=1.0,
+        generator=torch.Generator("cuda").manual_seed(42),
     )
     first_output = outputs[0]
     assert first_output.final_output_type == "image"

tests/e2e/offline_inference/test_teacache.py

@@ -15,8 +15,6 @@ from pathlib import Path
 import pytest
 import torch
 
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
 # ruff: noqa: E402
 REPO_ROOT = Path(__file__).resolve().parents[2]
 if str(REPO_ROOT) not in sys.path:
@@ -38,51 +36,42 @@ def test_teacache(model_name: str):
     cache_config = {
         "rel_l1_thresh": 0.2,  # Default threshold
     }
-    m = None
-    try:
-        m = Omni(
-            model=model_name,
-            cache_backend="tea_cache",
-            cache_config=cache_config,
-        )
 
-        # Use minimal settings for fast testing
-        height = 256
-        width = 256
-        num_inference_steps = 4  # Minimal steps for fast test
+    m = Omni(
+        model=model_name,
+        cache_backend="tea_cache",
+        cache_config=cache_config,
+    )
 
-        outputs = m.generate(
-            "a photo of a cat sitting on a laptop keyboard",
-            OmniDiffusionSamplingParams(
-                height=height,
-                width=width,
-                num_inference_steps=num_inference_steps,
-                guidance_scale=0.0,
-                generator=torch.Generator("cuda").manual_seed(42),
-                num_outputs_per_prompt=1,  # Single output for speed
-            ),
-        )
-        # Extract images from request_output[0]['images']
-        first_output = outputs[0]
-        assert first_output.final_output_type == "image"
-        if not hasattr(first_output, "request_output") or not first_output.request_output:
-            raise ValueError("No request_output found in OmniRequestOutput")
+    # Use minimal settings for fast testing
+    height = 256
+    width = 256
+    num_inference_steps = 4  # Minimal steps for fast test
 
-        req_out = first_output.request_output[0]
-        if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
-            raise ValueError("Invalid request_output structure or missing 'images' key")
+    outputs = m.generate(
+        "a photo of a cat sitting on a laptop keyboard",
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=0.0,
+        generator=torch.Generator("cuda").manual_seed(42),
+        num_outputs_per_prompt=1,  # Single output for speed
+    )
+    # Extract images from request_output[0]['images']
+    first_output = outputs[0]
+    assert first_output.final_output_type == "image"
+    if not hasattr(first_output, "request_output") or not first_output.request_output:
+        raise ValueError("No request_output found in OmniRequestOutput")
 
-        images = req_out.images
+    req_out = first_output.request_output[0]
+    if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
+        raise ValueError("Invalid request_output structure or missing 'images' key")
 
-        # Verify generation succeeded
-        assert images is not None
-        assert len(images) == 1
-        # Check image size
-        assert images[0].width == width
-        assert images[0].height == height
-    except Exception as e:
-        print(f"Test failed with error: {e}")
-        raise
-    finally:
-        if m is not None and hasattr(m, "close"):
-            m.close()
+    images = req_out.images
+
+    # Verify generation succeeded
+    assert images is not None
+    assert len(images) == 1
+    # Check image size
+    assert images[0].width == width
+    assert images[0].height == height

tests/e2e/offline_inference/test_zimage_tensor_parallel.py

@@ -12,8 +12,6 @@ import torch
 from PIL import Image
 from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
 
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
 # ruff: noqa: E402
 REPO_ROOT = Path(__file__).resolve().parents[2]
 if str(REPO_ROOT) not in sys.path:
@@ -92,14 +90,12 @@ def _run_zimage_generate(
         num_requests = 4  # 1 warmup + 3 timed
         gen = m.generate(
             [PROMPT] * num_requests,
-            OmniDiffusionSamplingParams(
-                height=height,
-                width=width,
-                num_inference_steps=num_inference_steps,
-                guidance_scale=0.0,
-                seed=seed,
-                num_outputs_per_prompt=1,
-            ),
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=0.0,
+            seed=seed,
+            num_outputs_per_prompt=1,
             py_generator=True,
         )
 

tests/e2e/online_serving/test_async_omni.py

@@ -5,7 +5,6 @@ from contextlib import ExitStack
 from pathlib import Path
 
 import pytest
-from vllm import SamplingParams
 from vllm.inputs import PromptType
 
 from vllm_omni.entrypoints.async_omni import AsyncOmni, ClientRequestState
@@ -26,15 +25,15 @@ async def generate(
 ) -> tuple[int, str]:
     # Ensure generate doesn't complete too fast for cancellation test.
     await asyncio.sleep(0.2)
-    thinker_sampling_params = SamplingParams(
-        temperature=0.4,  # Deterministic
-        top_p=0.9,
-        top_k=1,
-        max_tokens=max_tokens,
-        repetition_penalty=1.05,
-        stop_token_ids=[151645],  # Qwen EOS token <|im_end|>
-        seed=SEED,
-    )
+    thinker_sampling_params = {
+        "temperature": 0.4,  # Deterministic
+        "top_p": 0.9,
+        "top_k": 1,
+        "max_tokens": max_tokens,
+        "repetition_penalty": 1.05,
+        "stop_token_ids": [151645],  # Qwen EOS token <|im_end|>
+        "seed": SEED,
+    }
 
     sampling_params_list = [
         thinker_sampling_params,

tests/e2e/online_serving/test_images_generations_lora.py

@@ -1,259 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-E2E online serving test for /v1/images/generations with per-request LoRA.
-
-This validates:
-- The API server accepts a per-request `lora` object in the Images API payload.
-- LoRA can be switched per request (adapter A -> adapter B -> no LoRA).
-- Output correctness is asserted using a small image slice with tolerance.
-"""
-
-import base64
-import json
-import os
-import signal
-import subprocess
-import sys
-import time
-from io import BytesIO
-from pathlib import Path
-
-import numpy as np
-import pytest
-import requests
-import torch
-from PIL import Image
-from safetensors.torch import save_file
-from vllm.utils.network_utils import get_open_port
-
-from vllm_omni.utils.platform_utils import is_npu
-
-os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-
-MODEL = "Tongyi-MAI/Z-Image-Turbo"
-
-
-PROMPT = "a photo of a cat sitting on a laptop keyboard"
-SIZE = "256x256"
-SEED = 42
-
-
-class OmniServer:
-    """Omniserver for vLLM-Omni tests."""
-
-    def __init__(
-        self,
-        model: str,
-        serve_args: list[str],
-        *,
-        env_dict: dict[str, str] | None = None,
-    ) -> None:
-        self.model = model
-        self.serve_args = serve_args
-        self.env_dict = env_dict
-        self.proc: subprocess.Popen | None = None
-        self.host = "127.0.0.1"
-        self.port = get_open_port()
-
-    def _start_server(self) -> None:
-        env = os.environ.copy()
-        env["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-        if self.env_dict is not None:
-            env.update(self.env_dict)
-
-        cmd = [
-            sys.executable,
-            "-m",
-            "vllm_omni.entrypoints.cli.main",
-            "serve",
-            self.model,
-            "--omni",
-            "--host",
-            self.host,
-            "--port",
-            str(self.port),
-        ] + self.serve_args
-
-        print(f"Launching OmniServer with: {' '.join(cmd)}")
-        self.proc = subprocess.Popen(
-            cmd,
-            env=env,
-            cwd=os.path.dirname(os.path.dirname(os.path.abspath(__file__))),  # vllm-omni root
-            start_new_session=True,
-        )
-
-        # Wait for server to be ready.
-        max_wait = 1200
-        url = f"http://{self.host}:{self.port}/v1/models"
-        start_time = time.time()
-        while time.time() - start_time < max_wait:
-            try:
-                resp = requests.get(url, headers={"Authorization": "Bearer EMPTY"}, timeout=10)
-                if resp.status_code == 200:
-                    print(f"Server ready on {self.host}:{self.port}")
-                    return
-            except Exception:
-                pass
-            time.sleep(2)
-
-        raise RuntimeError(f"Server failed to become ready within {max_wait} seconds")
-
-    def __enter__(self):
-        self._start_server()
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        if self.proc is None:
-            return
-        try:
-            os.killpg(self.proc.pid, signal.SIGTERM)
-        except ProcessLookupError:
-            pass
-        try:
-            self.proc.wait(timeout=30)
-        except subprocess.TimeoutExpired:
-            try:
-                os.killpg(self.proc.pid, signal.SIGKILL)
-            except ProcessLookupError:
-                pass
-            self.proc.wait()
-
-
-@pytest.fixture(scope="module")
-def omni_server():
-    if is_npu():
-        pytest.skip("Tongyi-MAI/Z-Image-Turbo is not supported on NPU yet.")
-    with OmniServer(MODEL, ["--num-gpus", "1"]) as server:
-        yield server
-
-
-def _write_zimage_lora(adapter_dir: Path, *, q_scale: float = 0.0, k_scale: float = 0.0, v_scale: float = 0.0):
-    adapter_dir.mkdir(parents=True, exist_ok=True)
-
-    # Z-Image transformer uses dim=3840 by default.
-    dim = 3840
-    module_name = "transformer.layers.0.attention.to_qkv"
-    rank = 1
-
-    lora_a = torch.zeros((rank, dim), dtype=torch.float32)
-    lora_a[0, 0] = 1.0
-
-    # QKVParallelLinear packs (Q, K, V) => out dim is 3 * dim (tp=1).
-    lora_b = torch.zeros((3 * dim, rank), dtype=torch.float32)
-    if q_scale:
-        lora_b[:dim, 0] = q_scale
-    if k_scale:
-        lora_b[dim : 2 * dim, 0] = k_scale
-    if v_scale:
-        lora_b[2 * dim :, 0] = v_scale
-
-    save_file(
-        {
-            f"base_model.model.{module_name}.lora_A.weight": lora_a,
-            f"base_model.model.{module_name}.lora_B.weight": lora_b,
-        },
-        str(adapter_dir / "adapter_model.safetensors"),
-    )
-    (adapter_dir / "adapter_config.json").write_text(
-        json.dumps(
-            {
-                "r": rank,
-                "lora_alpha": rank,
-                "target_modules": [module_name],
-            }
-        ),
-        encoding="utf-8",
-    )
-
-
-def _post_images(server: OmniServer, payload: dict) -> Image.Image:
-    url = f"http://{server.host}:{server.port}/v1/images/generations"
-    resp = requests.post(url, json=payload, headers={"Authorization": "Bearer EMPTY"}, timeout=900)
-    resp.raise_for_status()
-    data = resp.json()
-    b64 = data["data"][0]["b64_json"]
-    img_bytes = base64.b64decode(b64)
-    img = Image.open(BytesIO(img_bytes))
-    img.load()
-    return img.convert("RGB")
-
-
-def _image_blue_tail_slice(img: Image.Image) -> np.ndarray:
-    arr = np.asarray(img, dtype=np.uint8)
-    assert arr.ndim == 3 and arr.shape[-1] == 3
-    tail = arr[-3:, -3:, -1].astype(np.float32)
-    assert tail.shape == (3, 3)
-    return tail
-
-
-def _assert_slice_close(actual: np.ndarray, expected: np.ndarray, *, label: str) -> None:
-    assert actual.shape == (3, 3)
-    assert expected.shape == (3, 3)
-    diff = np.abs(actual - expected)
-    max_diff = float(diff.max())
-    mean_diff = float(diff.mean())
-    # NOTE: Different attention backends / torch.compile can introduce small
-    # floating-point drift that shows up as a few LSBs in uint8 pixels. Keep
-    # the reset check tolerant but bounded to avoid flaky CI.
-    assert max_diff <= 5.0 and mean_diff <= 3.0, (
-        f"{label} slice mismatch (max={max_diff:.1f}, mean={mean_diff:.1f}): {actual.tolist()}"
-    )
-
-
-def _assert_slice_diff(actual: np.ndarray, baseline: np.ndarray, *, label: str) -> None:
-    assert actual.shape == (3, 3)
-    assert baseline.shape == (3, 3)
-    diff = np.abs(actual - baseline).mean()
-    assert diff > 0.1, f"{label} slice diff too small: {diff} ({actual.tolist()} vs {baseline.tolist()})"
-
-
-def _basic_payload() -> dict:
-    return {
-        "prompt": PROMPT,
-        "n": 1,
-        "size": SIZE,
-        "num_inference_steps": 2,
-        "guidance_scale": 0.0,
-        "seed": SEED,
-    }
-
-
-def test_images_generations_per_request_lora_switching(omni_server: OmniServer, tmp_path: Path) -> None:
-    # Base generation.
-    base_img = _post_images(omni_server, _basic_payload())
-    base_slice = _image_blue_tail_slice(base_img)
-
-    # Adapter A: apply delta to V slice only.
-    lora_a_dir = tmp_path / "zimage_lora_a"
-    _write_zimage_lora(lora_a_dir, v_scale=8.0)
-    payload_a = _basic_payload()
-    payload_a["lora"] = {"name": "a", "path": str(lora_a_dir), "scale": 64.0}
-    img_a = _post_images(omni_server, payload_a)
-    a_slice = _image_blue_tail_slice(img_a)
-    _assert_slice_diff(a_slice, base_slice, label="lora_a_vs_base")
-    a_vs_base = float(np.abs(a_slice - base_slice).mean())
-
-    # Adapter B: apply delta to K slice only (should differ from adapter A).
-    lora_b_dir = tmp_path / "zimage_lora_b"
-    _write_zimage_lora(lora_b_dir, k_scale=4.0)
-    payload_b = _basic_payload()
-    payload_b["lora"] = {"name": "b", "path": str(lora_b_dir), "scale": 64.0}
-    img_b = _post_images(omni_server, payload_b)
-    b_slice = _image_blue_tail_slice(img_b)
-    _assert_slice_diff(b_slice, base_slice, label="lora_b_vs_base")
-    _assert_slice_diff(b_slice, a_slice, label="lora_b_vs_lora_a")
-    b_vs_base = float(np.abs(b_slice - base_slice).mean())
-    b_vs_a = float(np.abs(b_slice - a_slice).mean())
-
-    # Ensure switching back to no-LoRA restores the base output.
-    base_img_2 = _post_images(omni_server, _basic_payload())
-    base_slice_2 = _image_blue_tail_slice(base_img_2)
-    _assert_slice_close(base_slice_2, base_slice, label="base_after_reset")
-    base_reset = float(np.abs(base_slice_2 - base_slice).mean())
-
-    # Ensure LoRA effects are clearly above the baseline drift.
-    min_delta = base_reset + 0.5
-    assert a_vs_base > min_delta, f"lora_a_vs_base drift too small: {a_vs_base} <= {min_delta}"
-    assert b_vs_base > min_delta, f"lora_b_vs_base drift too small: {b_vs_base} <= {min_delta}"
-    assert b_vs_a > min_delta, f"lora_b_vs_lora_a drift too small: {b_vs_a} <= {min_delta}"

tests/e2e/online_serving/test_qwen3_omni.py

@@ -7,86 +7,139 @@ E2E Online tests for Qwen3-Omni model with video input and audio output.
 import os
 
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "0"
 
+import base64
 import concurrent.futures
-import threading
+import ctypes
+import signal
+import socket
+import subprocess
+import sys
 import time
 from pathlib import Path
 
 import openai
 import pytest
+from vllm.assets.video import VideoAsset
+from vllm.utils.network_utils import get_open_port
 
-from tests.conftest import (
-    OmniServer,
-    convert_audio_to_text,
-    cosine_similarity_text,
-    dummy_messages_from_mix_data,
-    generate_synthetic_audio,
-    generate_synthetic_image,
-    generate_synthetic_video,
-    modify_stage_config,
-)
 from vllm_omni.utils import is_rocm
 
 models = ["Qwen/Qwen3-Omni-30B-A3B-Instruct"]
 
-
-def get_default_config():
-    return str(Path(__file__).parent.parent / "stage_configs" / "qwen3_omni_ci.yaml")
-
-
-def get_chunk_config():
-    path = modify_stage_config(
-        get_default_config(),
-        updates={
-            "async_chunk": True,
-            "stage_args": {
-                0: {
-                    "engine_args.custom_process_next_stage_input_func": "vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_async_chunk"
-                },
-                1: {
-                    "engine_args.custom_process_next_stage_input_func": "vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav_async_chunk"
-                },
-            },
-        },
-        deletes={"stage_args": {2: ["custom_process_input_func"]}},
-    )
-    return path
-
-
-CHUNK_CONFIG_PATH = get_chunk_config()
 # CI stage config for 2xH100-80G GPUs or AMD GPU MI325
 if is_rocm():
     # ROCm stage config optimized for MI325 GPU
     stage_configs = [str(Path(__file__).parent / "stage_configs" / "rocm" / "qwen3_omni_ci.yaml")]
 else:
-    stage_configs = [get_default_config(), CHUNK_CONFIG_PATH]
+    stage_configs = [str(Path(__file__).parent / "stage_configs" / "qwen3_omni_ci.yaml")]
 
 # Create parameter combinations for model and stage config
 test_params = [(model, stage_config) for model in models for stage_config in stage_configs]
 
 
-_omni_server_lock = threading.Lock()
+class OmniServer:
+    """Omniserver for vLLM-Omni tests."""
+
+    def __init__(
+        self,
+        model: str,
+        serve_args: list[str],
+        *,
+        env_dict: dict[str, str] | None = None,
+    ) -> None:
+        self.model = model
+        self.serve_args = serve_args
+        self.env_dict = env_dict
+        self.proc: subprocess.Popen | None = None
+        self.host = "127.0.0.1"
+        self.port = get_open_port()
+
+    def _start_server(self) -> None:
+        """Start the vLLM-Omni server subprocess."""
+        env = os.environ.copy()
+        env["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+        if self.env_dict is not None:
+            env.update(self.env_dict)
+
+        cmd = [
+            sys.executable,
+            "-m",
+            "vllm_omni.entrypoints.cli.main",
+            "serve",
+            self.model,
+            "--omni",
+            "--host",
+            self.host,
+            "--port",
+            str(self.port),
+        ] + self.serve_args
+
+        # Helper to ensure child process dies when parent dies
+        libc = ctypes.CDLL("libc.so.6")
+
+        def preexec_fn():
+            # Ensure the child process receives SIGTERM when the parent (this test runner) dies.
+            # This prevents orphaned processes if the test is killed unexpectedly.
+            PR_SET_PDEATHSIG = 1
+            libc.prctl(PR_SET_PDEATHSIG, signal.SIGTERM)
+
+        print(f"Launching OmniServer with: {' '.join(cmd)}")
+        self.proc = subprocess.Popen(
+            cmd,
+            env=env,
+            cwd=os.path.dirname(os.path.dirname(os.path.abspath(__file__))),  # Set working directory to vllm-omni root
+            start_new_session=True,
+            preexec_fn=preexec_fn,
+        )
+
+        # Wait for server to be ready
+        max_wait = 600  # 10 minutes
+        start_time = time.time()
+        while time.time() - start_time < max_wait:
+            try:
+                with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+                    sock.settimeout(1)
+                    result = sock.connect_ex((self.host, self.port))
+                    if result == 0:
+                        print(f"Server ready on {self.host}:{self.port}")
+                        return
+            except Exception:
+                pass
+            time.sleep(2)
+
+        raise RuntimeError(f"Server failed to start within {max_wait} seconds")
+
+    def __enter__(self):
+        self._start_server()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self.proc:
+            try:
+                os.killpg(self.proc.pid, signal.SIGTERM)
+            except ProcessLookupError:
+                pass
+
+            try:
+                self.proc.wait(timeout=30)
+            except subprocess.TimeoutExpired:
+                try:
+                    os.killpg(self.proc.pid, signal.SIGKILL)
+                except ProcessLookupError:
+                    pass
+                self.proc.wait()
 
 
-@pytest.fixture(scope="module")
+@pytest.fixture
 def omni_server(request):
     """Start vLLM-Omni server as a subprocess with actual model weights.
-    Uses session scope so the server starts only once for the entire test session.
+    Uses module scope so the server starts only once for all tests.
     Multi-stage initialization can take 10-20+ minutes.
     """
-    with _omni_server_lock:
-        model, stage_config_path = request.param
-
-        print(f"Starting OmniServer with model: {model}")
-
-        with OmniServer(model, ["--stage-configs-path", stage_config_path, "--stage-init-timeout", "120"]) as server:
-            print("OmniServer started successfully")
-            yield server
-            print("OmniServer stopping...")
-
-        print("OmniServer stopped")
+    model, stage_config_path = request.param
+    with OmniServer(model, ["--stage-configs-path", stage_config_path, "--stage-init-timeout", "90"]) as server:
+        yield server
 
 
 @pytest.fixture
@@ -98,6 +151,15 @@ def client(omni_server):
     )
 
 
+@pytest.fixture(scope="session")
+def base64_encoded_video() -> str:
+    """Base64 encoded video for testing."""
+    video = VideoAsset(name="baby_reading", num_frames=4)
+    with open(video.video_path, "rb") as f:
+        content = f.read()
+        return base64.b64encode(content).decode("utf-8")
+
+
 def get_system_prompt():
     return {
         "role": "system",
@@ -131,55 +193,71 @@ def dummy_messages_from_video_data(
     ]
 
 
-def get_prompt(prompt_type="text_only"):
-    prompts = {
-        "text_only": "What is the capital of China?",
-        "mix": "What is recited in the audio? What is in this image? Describe the video briefly.",
-    }
-    return prompts.get(prompt_type, prompts["text_only"])
-
-
-def get_max_batch_size(size_type="few"):
-    batch_sizes = {"few": 5, "medium": 100, "large": 256}
-    return batch_sizes.get(size_type, 5)
-
-
-@pytest.mark.skipif(is_rocm(), reason="Test skipped on AMD environment due to known output issues")
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
-def test_mix_to_text_audio_001(client: openai.OpenAI, omni_server, request) -> None:
-    """
-    Test multi-modal input processing and text/audio output generation via OpenAI API.
-    Deploy Setting: default yaml
-    Input Modal: text + audio + video + image
-    Output Modal: text + audio
-    Input Setting: stream=True
-    Datasets: single request
-    """
-    # TODO：This skip will be removed when the chunk scenario supports multimodal input.
-    param = request.node.callspec.params.get("omni_server")
+def test_video_to_audio_concurrent(
+    client: openai.OpenAI,
+    omni_server,
+    base64_encoded_video: str,
+) -> None:
+    """Test processing video with multiple concurrent completions, generating audio output via OpenAI API."""
+    # Create data URL for the base64 encoded video
+    video_data_url = f"data:video/mp4;base64,{base64_encoded_video}"
 
-    if param[1] == CHUNK_CONFIG_PATH:
-        pytest.skip("The current chunk scenario does not support multimodal.")
+    messages = dummy_messages_from_video_data(video_data_url)
 
-    # Test single completion
-    e2e_list = list()
-    video_data_url = f"data:video/mp4;base64,{generate_synthetic_video(224, 224, 300)['base64']}"
-    image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(224, 224)['base64']}"
-    audio_data_url = f"data:audio/wav;base64,{generate_synthetic_audio(5, 1)['base64']}"
-    messages = dummy_messages_from_mix_data(
-        system_prompt=get_system_prompt(),
-        video_data_url=video_data_url,
-        image_data_url=image_data_url,
-        audio_data_url=audio_data_url,
-        content_text=get_prompt("mix"),
+    # Test multiple concurrent completions
+    num_concurrent_requests = 5
+
+    with concurrent.futures.ThreadPoolExecutor(max_workers=num_concurrent_requests) as executor:
+        # Submit multiple completion requests concurrently
+        futures = [
+            executor.submit(
+                client.chat.completions.create,
+                model=omni_server.model,
+                messages=messages,
+            )
+            for _ in range(num_concurrent_requests)
+        ]
+
+        # Wait for all requests to complete and collect results
+        chat_completions = [future.result() for future in concurrent.futures.as_completed(futures)]
+
+    # Verify all completions succeeded
+    assert len(chat_completions) == num_concurrent_requests
+
+    for chat_completion in chat_completions:
+        assert len(chat_completion.choices) == 2  # 1 for text output, 1 for audio output
+
+        # Verify text output
+        text_choice = chat_completion.choices[0]
+        assert text_choice.finish_reason == "length"
+
+        # Verify we got a response
+        text_message = text_choice.message
+        assert text_message.content is not None and len(text_message.content) >= 10
+        assert text_message.role == "assistant"
+
+        # Verify audio output
+        audio_choice = chat_completion.choices[1]
+        assert audio_choice.finish_reason == "stop"
+        audio_message = audio_choice.message
+
+        # Check if audio was generated
+        if hasattr(audio_message, "audio") and audio_message.audio:
+            assert audio_message.audio.data is not None
+            assert len(audio_message.audio.data) > 0
+
+    # Test streaming completion
+    chat_completion = client.chat.completions.create(
+        model=omni_server.model,
+        messages=messages,
+        stream=True,
     )
 
-    # Test single completion
-    start_time = time.perf_counter()
-    chat_completion = client.chat.completions.create(model=omni_server.model, messages=messages, stream=True)
-
+    # Collect text and audio data from stream
     text_content = ""
     audio_data = None
+
     for chunk in chat_completion:
         for choice in chunk.choices:
             if hasattr(choice, "delta"):
@@ -190,94 +268,18 @@ def test_mix_to_text_audio_001(client: openai.OpenAI, omni_server, request) -> N
             modality = getattr(chunk, "modality", None)
 
             if modality == "audio" and content:
-                # Audio chunk - content
+                # Audio chunk - decode base64 content
                 if audio_data is None:
-                    audio_data = content
+                    audio_data = base64.b64decode(content)
                 else:
-                    audio_data += content
+                    audio_data += base64.b64decode(content)
             elif modality == "text" and content:
                 # Text chunk - accumulate text content
                 text_content += content if content else ""
 
-    # Verify E2E
-    current_e2e = time.perf_counter() - start_time
-    print(f"the request e2e is: {current_e2e}")
-    # TODO: Verify the E2E latency after confirmation baseline.
-    e2e_list.append(current_e2e)
+    # Verify text output
+    assert text_content is not None and len(text_content) >= 2
 
-    print(f"the avg e2e is: {sum(e2e_list) / len(e2e_list)}")
-    # Verify all completions succeeded
-    assert audio_data is not None, "No audio output is generated"
-
-    # Verify text output success
-    assert text_content is not None and len(text_content) >= 2, "No text output is generated"
-    assert any(
-        keyword in text_content.lower() for keyword in ["square", "quadrate", "sphere", "globe", "circle", "round"]
-    ), "The output does not contain any of the keywords."
-
-    # Verify text output same as audio output
-    audio_content = convert_audio_to_text(audio_data)
-    print(f"text content is: {text_content}")
-    print(f"audio content is: {audio_content}")
-    similarity = cosine_similarity_text(audio_content.lower(), text_content.lower())
-    print(f"similarity is: {similarity}")
-    assert similarity > 0.9, "The audio content is not same as the text"
-
-
-@pytest.mark.skipif(is_rocm(), reason="Test skipped on AMD environment due to known output issues")
-@pytest.mark.parametrize("omni_server", test_params, indirect=True)
-def test_text_to_text_audio_001(client: openai.OpenAI, omni_server) -> None:
-    """
-    Test text input processing and text/audio output generation via OpenAI API.
-    Deploy Setting: default yaml
-    Input Modal: text
-    Output Modal: text + audio
-    Datasets: few requests
-    """
-
-    num_concurrent_requests = get_max_batch_size()
-    messages = dummy_messages_from_mix_data(system_prompt=get_system_prompt(), content_text=get_prompt())
-
-    e2e_list = list()
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_concurrent_requests) as executor:
-        # Submit multiple completion requests concurrently
-        futures = [
-            executor.submit(client.chat.completions.create, model=omni_server.model, messages=messages)
-            for _ in range(num_concurrent_requests)
-        ]
-        start_time = time.perf_counter()
-        # Wait for all requests to complete and collect results
-        chat_completions = list()
-        for future in concurrent.futures.as_completed(futures):
-            chat_completions.append(future.result())
-            # Verify E2E
-            current_e2e = time.perf_counter() - start_time
-            print(f"the request e2e is: {current_e2e}")
-            # TODO: Verify the E2E latency after confirmation baseline.
-            e2e_list.append(current_e2e)
-
-    print(f"the avg e2e is: {sum(e2e_list) / len(e2e_list)}")
-    # Verify all completions succeeded
-    assert len(chat_completions) == num_concurrent_requests, "Not all requests succeeded."
-    for chat_completion in chat_completions:
-        # Verify audio output success
-        audio_data = None
-        text_content = None
-        for choice in chat_completion.choices:
-            if choice.message.audio is not None:
-                audio_message = choice.message
-                audio_data = audio_message.audio.data
-                assert audio_message.audio.expires_at > time.time(), "The generated audio has expired."
-
-            if choice.message.content is not None:
-                # Verify text output success
-                text_content = choice.message.content
-                assert "beijing" in text_content.lower(), "The output do not contain keywords."
-
-        # Verify text output same as audio output
-        audio_content = convert_audio_to_text(audio_data)
-        print(f"text content is: {text_content}")
-        print(f"audio content is: {audio_content}")
-        similarity = cosine_similarity_text(audio_content.lower(), text_content.lower())
-        print(f"similarity is: {similarity}")
-        assert similarity > 0.9, "The audio content is not same as the text"
+    # Verify audio output
+    assert audio_data is not None
+    assert len(audio_data) > 0

tests/e2e/online_serving/test_qwen3_omni_expansion.py

@@ -73,14 +73,12 @@ def get_max_batch_size(size_type="few"):
 def get_deploy_config(deploy_type="TP1"):
     result = {
         "TP1": {
-            "stage_args": {
-                0: {
-                    "engine_args.gpu_memory_utilization": 0.95,
-                    "engine_args.tensor_parallel_size": 1,
-                    "runtime.devices": "0",
-                },
-                2: {"runtime.devices": "1"},
-            }
+            0: {
+                "engine_args.gpu_memory_utilization": 0.95,
+                "engine_args.tensor_parallel_size": 1,
+                "runtime.devices": "0",
+            },
+            2: {"runtime.devices": "1"},
         }
     }
     return result.get(deploy_type, result["TP1"])
@@ -113,6 +111,69 @@ def test_text_to_text_001(test_config: tuple[str, str]) -> None:
         assert "beijing" in text_choice.message.content.lower(), "The output do not contain keywords."
 
 
+@pytest.mark.parametrize("test_config", test_params)
+def test_text_to_text_audio_001(test_config: tuple[str, str]) -> None:
+    """Test processing text, generating text and audio output via OpenAI API."""
+
+    model, stage_config_path = test_config
+    num_concurrent_requests = get_max_batch_size()
+    stage_config_path = modify_stage_config(
+        stage_config_path,
+        {
+            0: {"runtime.max_batch_size": num_concurrent_requests},
+            1: {"runtime.max_batch_size": num_concurrent_requests},
+        },
+    )
+    with OmniServer(model, ["--stage-configs-path", stage_config_path, "--stage-init-timeout", "90"]) as server:
+        messages = dummy_messages_from_mix_data(
+            system_prompt=get_system_prompt(), content_text="What is the capital of China?"
+        )
+
+        # Test single completion
+        api_client = client(server)
+        e2e_list = list()
+        with concurrent.futures.ThreadPoolExecutor(max_workers=num_concurrent_requests) as executor:
+            # Submit multiple completion requests concurrently
+            futures = [
+                executor.submit(api_client.chat.completions.create, model=server.model, messages=messages)
+                for _ in range(num_concurrent_requests)
+            ]
+            start_time = time.perf_counter()
+            # Wait for all requests to complete and collect results
+            chat_completions = list()
+            for future in concurrent.futures.as_completed(futures):
+                chat_completions.append(future.result())
+                # Verify E2E
+                current_e2e = time.perf_counter() - start_time
+                print(f"the request e2e is: {current_e2e}")
+                # TODO: Verify the E2E latency after confirmation baseline.
+                e2e_list.append(current_e2e)
+
+        print(f"the avg e2e is: {sum(e2e_list) / len(e2e_list)}")
+        # Verify all completions succeeded
+        assert len(chat_completions) == num_concurrent_requests, "Not all requests succeeded."
+        for chat_completion in chat_completions:
+            # Verify audio output success
+            audio_message = chat_completion.choices[1].message
+            audio_data = audio_message.audio.data
+            assert audio_data is not None, "No audio output is generated"
+            assert audio_message.audio.expires_at > time.time(), "The generated audio has expired."
+
+            # Verify text output success
+            text_choice = chat_completion.choices[0]
+            text_content = text_choice.message.content
+            assert text_choice.message.content is not None, "No text output is generated"
+            assert "beijing" in text_choice.message.content.lower(), "The output do not contain keywords."
+
+            # Verify text output same as audio output
+            audio_content = convert_audio_to_text(audio_data)
+            print(f"text content is: {text_content}")
+            print(f"audio content is: {audio_content}")
+            assert cosine_similarity_text(audio_content.lower(), text_content.lower()) > 0.9, (
+                "The audio content is not same as the text"
+            )
+
+
 @pytest.mark.parametrize("test_config", test_params)
 def test_audio_to_text_001(test_config: tuple[str, str]) -> None:
     """Test processing text, generating text output via OpenAI API."""
@@ -153,10 +214,8 @@ def test_audio_to_text_audio_001(test_config: tuple[str, str]) -> None:
     stage_config_path = modify_stage_config(
         stage_config_path,
         {
-            "stage_args": {
-                0: {"runtime.max_batch_size": num_concurrent_requests},
-                1: {"runtime.max_batch_size": num_concurrent_requests},
-            }
+            0: {"runtime.max_batch_size": num_concurrent_requests},
+            1: {"runtime.max_batch_size": num_concurrent_requests},
         },
     )
     with OmniServer(model, ["--stage-configs-path", stage_config_path, "--stage-init-timeout", "90"]) as server:
@@ -250,10 +309,8 @@ def test_image_to_text_audio_001(test_config: tuple[str, str]) -> None:
     stage_config_path = modify_stage_config(
         stage_config_path,
         {
-            "stage_args": {
-                0: {"runtime.max_batch_size": num_concurrent_requests},
-                1: {"runtime.max_batch_size": num_concurrent_requests},
-            }
+            0: {"runtime.max_batch_size": num_concurrent_requests},
+            1: {"runtime.max_batch_size": num_concurrent_requests},
         },
     )
     with OmniServer(model, ["--stage-configs-path", stage_config_path, "--stage-init-timeout", "90"]) as server:

tests/e2e/stage_configs/qwen3_omni_ci.yaml

@@ -8,7 +8,7 @@ stage_args:
 - stage_id: 0
   runtime:
     devices: "0,1"
-    max_batch_size: 5
+    max_batch_size: 1
   engine_args:
     model_stage: thinker
     model_arch: Qwen3OmniMoeForConditionalGeneration
@@ -39,7 +39,7 @@ stage_args:
 - stage_id: 1
   runtime:
     devices: "1"
-    max_batch_size: 5
+    max_batch_size: 1
   engine_args:
     model_stage: talker
     model_arch: Qwen3OmniMoeForConditionalGeneration
@@ -79,7 +79,7 @@ stage_args:
     engine_output_type: audio  # Final output: audio waveform
     gpu_memory_utilization: 0.1
     distributed_executor_backend: "mp"
-    max_num_batched_tokens: 100000
+    max_num_batched_tokens: 1000000
     hf_config_name: thinker_config
     async_scheduling: false
   engine_input_source: [1]

tests/entrypoints/openai_api/test_image_server.py

@@ -14,13 +14,11 @@ from unittest.mock import AsyncMock, Mock
 import pytest
 from fastapi.testclient import TestClient
 from PIL import Image
-from vllm import SamplingParams
 
 from vllm_omni.entrypoints.openai.image_api_utils import (
     encode_image_base64,
     parse_size,
 )
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 # Unit Tests
 
@@ -113,7 +111,7 @@ class FakeAsyncOmni:
 
     def __init__(self):
         self.stage_list = ["llm", "diffusion"]
-        self.default_sampling_params_list = [SamplingParams(temperature=0.1), OmniDiffusionSamplingParams()]
+        self.default_sampling_params_list = [{"temperature": 0.1}, {"top_p": 0.9}]
         self.captured_sampling_params_list = None
 
     async def generate(self, prompt, request_id, sampling_params_list):
@@ -126,13 +124,10 @@ class FakeAsyncOmni:
 def mock_async_diffusion():
     """Mock AsyncOmniDiffusion instance that returns fake images"""
     mock = Mock()
-    mock.is_running = True  # For health endpoint
-    mock.check_health = AsyncMock()  # For LLM mode health check
 
     async def generate(**kwargs):
         # Return n PIL images wrapped in result object
-        print("!!!!!!!!!!!!!!!!!!!!! kwargs", kwargs)
-        n = kwargs["sampling_params_list"][0].num_outputs_per_prompt
+        n = kwargs.get("num_outputs_per_prompt", 1)
         images = [Image.new("RGB", (64, 64), color="blue") for _ in range(n)]
         return MockGenerationResult(images)
 
@@ -152,9 +147,8 @@ def test_client(mock_async_diffusion):
 
     # Set up app state with diffusion engine
     app.state.engine_client = mock_async_diffusion
-    app.state.diffusion_engine = mock_async_diffusion  # Also set for health endpoint
     app.state.stage_configs = [{"stage_type": "diffusion"}]
-    app.state.diffusion_model_name = "Qwen/Qwen-Image"  # For models endpoint
+    app.state.served_model_names = "Qwen/Qwen-Image"
 
     return TestClient(app)
 
@@ -175,58 +169,10 @@ def async_omni_test_client():
     return TestClient(app)
 
 
+@pytest.mark.skip(reason="Async API server uses different health check mechanism")
 def test_health_endpoint(test_client):
-    """Test health check endpoint for diffusion mode"""
-    response = test_client.get("/health")
-    assert response.status_code == 200
-    data = response.json()
-    assert data["status"] == "healthy"
-
-
-def test_health_endpoint_no_engine():
-    """Test health check endpoint when no engine is initialized"""
-    from fastapi import FastAPI
-
-    from vllm_omni.entrypoints.openai.api_server import router
-
-    app = FastAPI()
-    app.include_router(router)
-    # Don't set any engine
-
-    client = TestClient(app)
-    response = client.get("/health")
-    assert response.status_code == 503
-    data = response.json()
-    assert data["status"] == "unhealthy"
-
-
-def test_models_endpoint(test_client):
-    """Test /v1/models endpoint for diffusion mode"""
-    response = test_client.get("/v1/models")
-    assert response.status_code == 200
-    data = response.json()
-    assert data["object"] == "list"
-    assert len(data["data"]) == 1
-    assert data["data"][0]["id"] == "Qwen/Qwen-Image"
-    assert data["data"][0]["object"] == "model"
-
-
-def test_models_endpoint_no_engine():
-    """Test /v1/models endpoint when no engine is initialized"""
-    from fastapi import FastAPI
-
-    from vllm_omni.entrypoints.openai.api_server import router
-
-    app = FastAPI()
-    app.include_router(router)
-    # Don't set any engine
-
-    client = TestClient(app)
-    response = client.get("/v1/models")
-    assert response.status_code == 200
-    data = response.json()
-    assert data["object"] == "list"
-    assert len(data["data"]) == 0
+    """Test health check endpoint - skipped for async server"""
+    pass
 
 
 def test_generate_single_image(test_client):
@@ -271,11 +217,11 @@ def test_generate_images_async_omni_sampling_params(async_omni_test_client):
     captured = engine.captured_sampling_params_list
     assert captured is not None
     assert len(captured) == 2
-    assert captured[0].temperature == 0.1
-    assert captured[1].num_outputs_per_prompt == 2
-    assert captured[1].height == 256
-    assert captured[1].width == 256
-    assert captured[1].seed == 7
+    assert captured[0] == {"temperature": 0.1}
+    assert captured[1]["num_outputs_per_prompt"] == 2
+    assert captured[1]["height"] == 256
+    assert captured[1]["width"] == 256
+    assert captured[1]["seed"] == 7
 
 
 def test_generate_multiple_images(test_client):
@@ -508,11 +454,30 @@ def test_parameters_passed_through(test_client, mock_async_diffusion):
 
     # Ensure generate() was called exactly once
     mock_async_diffusion.generate.assert_awaited_once()
-    call_kwargs = mock_async_diffusion.generate.call_args[1]["sampling_params_list"][0]
-    assert call_kwargs.num_inference_steps == 100
-    assert call_kwargs.guidance_scale == 7.5
-    assert call_kwargs.true_cfg_scale == 3.0
-    assert call_kwargs.seed == 42
+    call_kwargs = mock_async_diffusion.generate.call_args[1]
+    assert call_kwargs["num_inference_steps"] == 100
+    assert call_kwargs["guidance_scale"] == 7.5
+    assert call_kwargs["true_cfg_scale"] == 3.0
+    assert call_kwargs["seed"] == 42
+
+
+def test_optional_parameters_omitted(test_client, mock_async_diffusion):
+    """Verify optional parameters not passed when omitted"""
+    response = test_client.post(
+        "/v1/images/generations",
+        json={
+            "prompt": "test",
+            "size": "512x512",
+        },
+    )
+    assert response.status_code == 200
+
+    # Ensure generate() was called exactly once
+    mock_async_diffusion.generate.assert_awaited_once()
+    call_kwargs = mock_async_diffusion.generate.call_args[1]
+    assert "num_inference_steps" not in call_kwargs
+    assert "guidance_scale" not in call_kwargs
+    assert "true_cfg_scale" not in call_kwargs
 
 
 def test_model_field_omitted_works(test_client):

tests/entrypoints/openai_api/test_serving_speech.py

@@ -10,10 +10,8 @@ from fastapi import FastAPI
 from fastapi.testclient import TestClient
 
 from vllm_omni.entrypoints.openai.audio_utils_mixin import AudioMixin
-from vllm_omni.entrypoints.openai.protocol.audio import CreateAudio, OpenAICreateSpeechRequest
-from vllm_omni.entrypoints.openai.serving_speech import (
-    OmniOpenAIServingSpeech,
-)
+from vllm_omni.entrypoints.openai.protocol.audio import CreateAudio
+from vllm_omni.entrypoints.openai.serving_speech import OmniOpenAIServingSpeech
 from vllm_omni.outputs import OmniRequestOutput
 
 logger = logging.getLogger(__name__)
@@ -267,78 +265,3 @@ class TestSpeechAPI:
         audio_obj = call_args[0]
         assert isinstance(audio_obj, CreateAudio)
         assert audio_obj.speed == 2.5
-
-
-class TestTTSMethods:
-    """Unit tests for TTS validation and parameter building."""
-
-    @pytest.fixture
-    def speech_server(self):
-        mock_engine_client = MagicMock()
-        mock_engine_client.errored = False
-        mock_engine_client.stage_list = None
-        mock_models = MagicMock()
-        mock_models.is_base_model.return_value = True
-        return OmniOpenAIServingSpeech(
-            engine_client=mock_engine_client,
-            models=mock_models,
-            request_logger=MagicMock(),
-        )
-
-    def test_is_tts_model(self, speech_server):
-        """Test TTS model detection."""
-        # No stage_list -> False
-        assert speech_server._is_tts_model() is False
-
-        # With qwen3_tts stage -> True
-        mock_stage = MagicMock()
-        mock_stage.model_stage = "qwen3_tts"
-        speech_server.engine_client.stage_list = [mock_stage]
-        assert speech_server._is_tts_model() is True
-
-    def test_build_tts_prompt(self, speech_server):
-        """Test TTS prompt format."""
-        prompt = speech_server._build_tts_prompt("Hello")
-        assert prompt == "<|im_start|>assistant\nHello<|im_end|>\n<|im_start|>assistant\n"
-
-    def test_validate_tts_request_basic(self, speech_server):
-        """Test basic validation cases."""
-        # Empty input
-        req = OpenAICreateSpeechRequest(input="")
-        assert speech_server._validate_tts_request(req) == "Input text cannot be empty"
-
-        # Invalid language
-        req = OpenAICreateSpeechRequest(input="Hello", language="InvalidLang")
-        assert "Invalid language" in speech_server._validate_tts_request(req)
-
-        # Invalid speaker
-        req = OpenAICreateSpeechRequest(input="Hello", voice="Invalid")
-        assert "Invalid speaker" in speech_server._validate_tts_request(req)
-
-        # Valid request
-        req = OpenAICreateSpeechRequest(input="Hello", voice="Vivian")
-        assert speech_server._validate_tts_request(req) is None
-
-    def test_validate_tts_request_task_types(self, speech_server):
-        """Test task-specific validation."""
-        # Base task requires ref_audio
-        req = OpenAICreateSpeechRequest(input="Hello", task_type="Base")
-        assert "ref_audio" in speech_server._validate_tts_request(req)
-
-        # VoiceDesign requires instructions
-        req = OpenAICreateSpeechRequest(input="Hello", task_type="VoiceDesign")
-        assert "instructions" in speech_server._validate_tts_request(req)
-
-        # ref_text only for Base
-        req = OpenAICreateSpeechRequest(input="Hello", ref_text="text")
-        assert "Base task" in speech_server._validate_tts_request(req)
-
-    def test_build_tts_params(self, speech_server):
-        """Test TTS parameter building."""
-        req = OpenAICreateSpeechRequest(input="Hello", voice="Ryan", language="English")
-        params = speech_server._build_tts_params(req)
-
-        assert params["text"] == ["Hello"]
-        assert params["speaker"] == ["Ryan"]
-        assert params["language"] == ["English"]
-        assert params["task_type"] == ["CustomVoice"]

tests/entrypoints/test_omni_llm.py

@@ -5,7 +5,6 @@ from typing import Any
 from unittest.mock import MagicMock
 
 import pytest
-from vllm import SamplingParams
 
 from vllm_omni.entrypoints.stage_utils import SHUTDOWN_TASK
 
@@ -83,9 +82,8 @@ class _FakeStage:
         self.stage_id = getattr(config, "stage_id", 0)
         self.engine_args = config.engine_args
         self.model_stage = getattr(config.engine_args, "model_stage", None)
-        self.stage_type = "llm"
         # set default sampling params
-        self.default_sampling_params = SamplingParams(temperature=1.0)
+        self.default_sampling_params = {"temperature": 1.0}
         # Allow configuring final_output and final_output_type
         self.final_output = config.final_output if hasattr(config, "final_output") else False
         self.final_output_type = getattr(config, "final_output_type", None)
@@ -639,10 +637,8 @@ def test_generate_pipeline_and_final_outputs(monkeypatch, fake_stage_config):
         }
     )
 
-    sampling_params_list = [
-        SamplingParams(temperature=0.7),
-        SamplingParams(temperature=0.8),
-    ]
+    # Use dicts instead of object() for serializable sampling params
+    sampling_params_list = [{"temperature": 0.7}, {"temperature": 0.8}]
     prompts = ["hi"]
     outputs = omni.generate(prompts=prompts, sampling_params_list=sampling_params_list)
 
@@ -726,13 +722,8 @@ def test_generate_no_final_output_returns_empty(monkeypatch, fake_stage_config):
         }
     )
 
-    outputs = omni.generate(
-        prompts=["p"],
-        sampling_params_list=[
-            SamplingParams(temperature=0.7),
-            SamplingParams(temperature=0.8),
-        ],
-    )
+    # Use dicts instead of object() for serializable sampling params
+    outputs = omni.generate(prompts=["p"], sampling_params_list=[{"temperature": 0.7}, {"temperature": 0.8}])
     assert outputs == []
 
 
@@ -931,7 +922,8 @@ def test_generate_handles_error_messages(monkeypatch, fake_stage_config):
     )
 
     # Generate should handle error gracefully (log but continue)
-    sampling_params_list = [SamplingParams(temperature=0.7)]
+    # Use dict instead of object() for serializable sampling params
+    sampling_params_list = [{"temperature": 0.7}]
     outputs = omni.generate(prompts=["hi"], sampling_params_list=sampling_params_list)
     # Should return final output (error was logged but didn't stop processing)
     assert isinstance(outputs, list)

tests/entrypoints/test_omni_new_request_data.py

@@ -13,7 +13,6 @@ def test_omni_new_request_data_copies_payloads():
     }
     request = SimpleNamespace(
         request_id="req-1",
-        external_req_id="ext-1",
         prompt_token_ids=[101, 102],
         mm_features=None,
         sampling_params=None,
@@ -34,7 +33,6 @@ def test_omni_new_request_data_copies_payloads():
 def test_omni_new_request_data_allows_missing_payloads():
     request = SimpleNamespace(
         request_id="req-2",
-        external_req_id="ext-2",
         prompt_token_ids=[201, 202],
         mm_features=None,
         sampling_params=None,

tests/test_outputs.py

@@ -1,171 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Tests for OmniRequestOutput class."""
-
-from unittest.mock import Mock
-
-from PIL import Image
-
-from vllm_omni.outputs import OmniRequestOutput
-
-
-class TestOmniRequestOutput:
-    """Tests for OmniRequestOutput class."""
-
-    def test_from_diffusion(self):
-        """Test creating output from diffusion model."""
-        images = [Image.new("RGB", (64, 64), color="red")]
-        output = OmniRequestOutput.from_diffusion(
-            request_id="test-123",
-            images=images,
-            prompt="a cat",
-            metrics={"steps": 50},
-        )
-        assert output.request_id == "test-123"
-        assert output.images == images
-        assert output.prompt == "a cat"
-        assert output.metrics == {"steps": 50}
-        assert output.is_diffusion_output
-        assert output.num_images == 1
-
-    def test_from_pipeline(self):
-        """Test creating output from pipeline stage."""
-        mock_request_output = Mock()
-        mock_request_output.request_id = "pipeline-123"
-        mock_request_output.prompt_token_ids = [1, 2, 3]
-        mock_request_output.outputs = [Mock()]
-        mock_request_output.encoder_prompt_token_ids = None
-        mock_request_output.prompt_logprobs = None
-        mock_request_output.num_cached_tokens = 10
-        mock_request_output.kv_transfer_params = None
-        mock_request_output.multimodal_output = {"image": Mock()}
-
-        output = OmniRequestOutput.from_pipeline(
-            stage_id=0,
-            final_output_type="text",
-            request_output=mock_request_output,
-        )
-
-        assert output.request_id == "pipeline-123"
-        assert output.stage_id == 0
-        assert output.final_output_type == "text"
-        assert output.is_pipeline_output
-
-    def test_prompt_token_ids_property(self):
-        """Test prompt_token_ids property for streaming compatibility."""
-        mock_request_output = Mock()
-        mock_request_output.prompt_token_ids = [1, 2, 3, 4, 5]
-
-        output = OmniRequestOutput.from_pipeline(
-            stage_id=0,
-            final_output_type="text",
-            request_output=mock_request_output,
-        )
-
-        assert output.prompt_token_ids == [1, 2, 3, 4, 5]
-
-    def test_prompt_token_ids_none_when_no_request_output(self):
-        """Test prompt_token_ids returns None when no request_output."""
-        output = OmniRequestOutput.from_diffusion(
-            request_id="test-123",
-            images=[],
-            prompt="a cat",
-        )
-        assert output.prompt_token_ids is None
-
-    def test_outputs_property(self):
-        """Test outputs property for chat completion compatibility."""
-        mock_output = Mock()
-        mock_request_output = Mock()
-        mock_request_output.outputs = [mock_output]
-
-        output = OmniRequestOutput.from_pipeline(
-            stage_id=0,
-            final_output_type="text",
-            request_output=mock_request_output,
-        )
-
-        assert output.outputs == [mock_output]
-
-    def test_outputs_empty_when_no_request_output(self):
-        """Test outputs returns empty list when no request_output."""
-        output = OmniRequestOutput.from_diffusion(
-            request_id="test-123",
-            images=[],
-            prompt="a cat",
-        )
-        assert output.outputs == []
-
-    def test_encoder_prompt_token_ids_property(self):
-        """Test encoder_prompt_token_ids property."""
-        mock_request_output = Mock()
-        mock_request_output.encoder_prompt_token_ids = [10, 20, 30]
-
-        output = OmniRequestOutput.from_pipeline(
-            stage_id=0,
-            final_output_type="text",
-            request_output=mock_request_output,
-        )
-
-        assert output.encoder_prompt_token_ids == [10, 20, 30]
-
-    def test_num_cached_tokens_property(self):
-        """Test num_cached_tokens property."""
-        mock_request_output = Mock()
-        mock_request_output.num_cached_tokens = 42
-
-        output = OmniRequestOutput.from_pipeline(
-            stage_id=0,
-            final_output_type="text",
-            request_output=mock_request_output,
-        )
-
-        assert output.num_cached_tokens == 42
-
-    def test_multimodal_output_property(self):
-        """Test multimodal_output property."""
-        mock_request_output = Mock()
-        mock_audio = Mock()
-        expected_output = {"audio": mock_audio}
-        mock_request_output.multimodal_output = expected_output
-
-        output = OmniRequestOutput.from_pipeline(
-            stage_id=0,
-            final_output_type="audio",
-            request_output=mock_request_output,
-        )
-
-        assert output.multimodal_output is expected_output
-
-    def test_to_dict_diffusion(self):
-        """Test to_dict for diffusion output."""
-        output = OmniRequestOutput.from_diffusion(
-            request_id="test-123",
-            images=[Image.new("RGB", (64, 64), color="red")],
-            prompt="a cat",
-            metrics={"steps": 50},
-        )
-        result = output.to_dict()
-
-        assert result["request_id"] == "test-123"
-        assert result["finished"] is True
-        assert result["final_output_type"] == "image"
-        assert result["num_images"] == 1
-        assert result["prompt"] == "a cat"
-
-    def test_to_dict_pipeline(self):
-        """Test to_dict for pipeline output."""
-        mock_request_output = Mock()
-        mock_request_output.request_id = "pipeline-123"
-
-        output = OmniRequestOutput.from_pipeline(
-            stage_id=0,
-            final_output_type="text",
-            request_output=mock_request_output,
-        )
-        result = output.to_dict()
-
-        assert result["request_id"] == "pipeline-123"
-        assert result["finished"] is True
-        assert result["final_output_type"] == "text"
-        assert result["stage_id"] == 0

vllm_omni/config/__init__.py

@@ -2,10 +2,8 @@
 Configuration module for vLLM-Omni.
 """
 
-from vllm_omni.config.lora import LoRAConfig
 from vllm_omni.config.model import OmniModelConfig
 
 __all__ = [
     "OmniModelConfig",
-    "LoRAConfig",
 ]

vllm_omni/config/lora.py

@@ -1,8 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# for now, it suffices to use vLLM's implementation directly
-# as this is a user-facing variable, defined here to so that user can directly import LoRAConfig from vllm_omni
-from vllm.config.lora import LoRAConfig
-
-__all__ = ["LoRAConfig"]

vllm_omni/config/model.py

@@ -1,5 +1,4 @@
 import warnings
-from dataclasses import field
 from typing import Any
 
 import torch
@@ -21,7 +20,10 @@ from vllm.transformers_utils.config import (
     get_hf_text_config,
     get_pooling_config,
 )
-from vllm.transformers_utils.gguf_utils import is_gguf, maybe_patch_hf_config_from_gguf
+from vllm.transformers_utils.gguf_utils import (
+    is_gguf,
+    maybe_patch_hf_config_from_gguf,
+)
 from vllm.transformers_utils.utils import maybe_model_redirect
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
@@ -40,7 +42,6 @@ class OmniModelConfig(ModelConfig):
 
     Attributes:
         stage_id: Identifier for the stage in a multi-stage pipeline (default: 0)
-        async_chunk: If set to True, perform async chunk
         model_stage: Stage type identifier, e.g., "thinker" or "talker"
             (default: "thinker")
         model_arch: Model architecture name
@@ -48,8 +49,6 @@ class OmniModelConfig(ModelConfig):
         engine_output_type: Optional output type specification for the engine.
             Used to route outputs to appropriate processors (e.g., "image",
             "audio", "latents"). If None, output type is inferred.
-        stage_connector_config: Stage connector configuration dictionary.
-            Contains "name" (connector name), "extra" (extra connector config).
 
     Example:
         >>> config = OmniModelConfig(
@@ -60,18 +59,10 @@ class OmniModelConfig(ModelConfig):
     """
 
     stage_id: int = 0
-    async_chunk: bool = False
     model_stage: str = "thinker"
     model_arch: str = "Qwen2_5OmniForConditionalGeneration"
     engine_output_type: str | None = None
     hf_config_name: str | None = None
-    custom_process_next_stage_input_func: str | None = None
-    stage_connector_config: dict[str, Any] = field(
-        default_factory=lambda: {
-            "name": "SharedMemoryConnector",
-            "extra": {},
-        }
-    )
     omni_kv_config: dict | None = None
 
     @property

vllm_omni/core/sched/omni_ar_scheduler.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import importlib
 from collections import defaultdict
 from dataclasses import asdict, dataclass
 from time import time
@@ -20,10 +19,6 @@ from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.request import Request, RequestStatus
 from vllm.v1.spec_decode.metrics import SpecDecodingStats
 
-from vllm_omni.distributed.omni_connectors.adapter import get_chunk, put_chunk
-from vllm_omni.distributed.omni_connectors.factory import OmniConnectorFactory
-from vllm_omni.distributed.omni_connectors.utils.config import ConnectorSpec
-
 logger = init_logger(__name__)
 
 
@@ -64,25 +59,6 @@ class OmniARScheduler(VLLMScheduler):
 
         # Track requests that have already triggered prefill transfer to avoid duplicates
         self.transfer_triggered_requests: set[str] = set()
-        model_config = self.vllm_config.model_config
-        self.omni_connector = None
-        if model_config.async_chunk:
-            connector_config = model_config.stage_connector_config
-            connector_specs = ConnectorSpec(
-                name=connector_config.get("name", "SharedMemoryConnector"),
-                extra=connector_config.get("extra", {}),
-            )
-            self.omni_connector = OmniConnectorFactory.create_connector(connector_specs)
-
-            custom_process_next_stage_input_func = getattr(
-                self.vllm_config.model_config, "custom_process_next_stage_input_func", None
-            )
-            if custom_process_next_stage_input_func:
-                module_path, func_name = custom_process_next_stage_input_func.rsplit(".", 1)
-                module = importlib.import_module(module_path)
-                self.custom_process_next_stage_input_func = getattr(module, func_name)
-
-        self.stage_id = getattr(self.vllm_config.model_config, "stage_id", None)
 
     def _get_kv_transfer_criteria(self) -> dict | None:
         # Note: vllm_config is available in Scheduler after super().__init__
@@ -166,7 +142,6 @@ class OmniARScheduler(VLLMScheduler):
                 # Build omni entry preserving all base fields
                 omni_nr = OmniNewRequestData(
                     req_id=nr.req_id,
-                    external_req_id=(getattr(request, "external_req_id", None) if request else None),
                     prompt_token_ids=nr.prompt_token_ids,
                     mm_features=nr.mm_features,
                     sampling_params=nr.sampling_params,
@@ -181,8 +156,6 @@ class OmniARScheduler(VLLMScheduler):
                 new_list.append(omni_nr)
 
             scheduler_output.scheduled_new_reqs = new_list  # type: ignore[assignment]
-            if self.omni_connector is not None:
-                get_chunk(self.omni_connector, scheduler_output)
 
             # Add information about requests needing KV cache transfer
             scheduler_output.finished_requests_needing_kv_transfer = self.get_finished_requests_needing_kv_transfer()
@@ -364,9 +337,6 @@ class OmniARScheduler(VLLMScheduler):
                         num_nans_in_logits=request.num_nans_in_logits,
                     )
                 )
-                if self.omni_connector is not None:
-                    custom_process_next_stage_input_func = self.custom_process_next_stage_input_func
-                    put_chunk(self.omni_connector, pooler_output, request, custom_process_next_stage_input_func)
             else:
                 # Invariant: EngineCore returns no partial prefill outputs.
                 assert not prompt_logprobs_tensors

vllm_omni/core/sched/omni_generation_scheduler.py

@@ -12,27 +12,11 @@ from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutp
 from vllm.v1.request import Request, RequestStatus
 from vllm.v1.spec_decode.metrics import SpecDecodingStats
 
-from vllm_omni.core.sched.output import OmniCachedRequestData, OmniNewRequestData
-from vllm_omni.distributed.omni_connectors.adapter import get_chunk_for_generation
-from vllm_omni.distributed.omni_connectors.factory import OmniConnectorFactory
-from vllm_omni.distributed.omni_connectors.utils.config import ConnectorSpec
+from vllm_omni.core.sched.output import OmniNewRequestData
 from vllm_omni.outputs import OmniModelRunnerOutput
 
 
 class OmniGenerationScheduler(VLLMScheduler):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        model_config = self.vllm_config.model_config
-        self.omni_connector = None
-        if model_config.async_chunk:
-            connector_config = model_config.stage_connector_config
-            connector_specs = ConnectorSpec(
-                name=connector_config.get("name", "SharedMemoryConnector"),
-                extra=connector_config.get("extra", {}),
-            )
-            self.omni_connector = OmniConnectorFactory.create_connector(connector_specs)
-        self.stage_id = getattr(self.vllm_config.model_config, "stage_id", None)
-
     def schedule(self) -> SchedulerOutput:
         """Diffusion fast path:
         - Feed all input tokens of the request at once
@@ -48,54 +32,27 @@ class OmniGenerationScheduler(VLLMScheduler):
 
         req_to_new_blocks: dict[str, KVCacheBlocks] = {}
         num_scheduled_tokens: dict[str, int] = {}
-        scheduled_running_reqs: list[Request] = []
         scheduled_spec_decode_tokens: dict[str, list[int]] = {}
         scheduled_encoder_inputs: dict[str, list[int]] = {}
-        cached_prompt_token_ids: dict[str, list[int]] = {}
 
         # Temporary queue: preserve waiting order, do not disturb non-diffusion requests
         skipped_waiting_requests = create_request_queue(self.policy)
-        req_index = 0
-        while req_index < len(self.running) and token_budget > 0:
-            request = self.running[req_index]
-            if self.omni_connector is not None:
-                get_chunk_for_generation(self.omni_connector, request)
-            num_computed_tokens = request.num_computed_tokens
-            required_tokens = max(len(request.prompt_token_ids) - num_computed_tokens, 1)
-            num_new_tokens = min(required_tokens, token_budget)
-            new_blocks = self.kv_cache_manager.allocate_slots(
-                request,
-                num_new_tokens,
-                num_lookahead_tokens=self.num_lookahead_tokens,
-            )
-            if new_blocks is None:
-                # Allocation failed (e.g., VRAM pressure); stop fast path and
-                # fall back to default scheduling
-                # Put the current request back to the head of the waiting queue
-                # Note: the original queue order is preserved
-                break
-            if self.log_stats:
-                request.record_event(EngineCoreEventType.SCHEDULED, scheduled_timestamp)
-            req_to_new_blocks[request.request_id] = new_blocks
-            num_scheduled_tokens[request.request_id] = num_new_tokens
-            cached_prompt_token_ids[request.request_id] = request.prompt_token_ids
-            token_budget -= num_new_tokens
-            scheduled_running_reqs.append(request)
-            req_index += 1
 
         # Fast path selection and scheduling (treat all as diffusion requests,
         # independent of pooling_params)
         while self.waiting and token_budget > 0 and len(self.running) < self.max_num_running_reqs:
             request = self.waiting.peek_request()
-            if self.omni_connector is not None:
-                get_chunk_for_generation(self.omni_connector, request)
             # Uniformly treat as diffusion. A feature flag can be added later
             # via config or request tag.
 
             # Allocate all input tokens for the request in one shot
             # (allocate 1 placeholder if zero)
-            required_tokens = max(len(request.prompt_token_ids), 1)
-            num_new_tokens = min(required_tokens, token_budget)
+            required_tokens = max(getattr(request, "num_prompt_tokens", 0), 1)
+            if required_tokens > token_budget:
+                # Insufficient budget to process all inputs at once;
+                # stop fast path attempt
+                break
+            num_new_tokens = required_tokens
             new_blocks = self.kv_cache_manager.allocate_slots(
                 request,
                 num_new_tokens,
@@ -111,6 +68,7 @@ class OmniGenerationScheduler(VLLMScheduler):
             # Officially schedule this request
             request = self.waiting.pop_request()
             self.running.append(request)
+            request.status = RequestStatus.RUNNING
             if self.log_stats:
                 request.record_event(EngineCoreEventType.SCHEDULED, scheduled_timestamp)
 
@@ -151,24 +109,13 @@ class OmniGenerationScheduler(VLLMScheduler):
             ]
         # No running/resumed reqs scheduled in our fast path
         cached_reqs_data = self._make_cached_request_data(
-            running_reqs=scheduled_running_reqs,
+            running_reqs=[],
             resumed_reqs=[],
             num_scheduled_tokens=num_scheduled_tokens,
             spec_decode_tokens=scheduled_spec_decode_tokens,
             req_to_new_blocks=req_to_new_blocks,
         )
 
-        cached_reqs_data = OmniCachedRequestData(
-            req_ids=cached_reqs_data.req_ids,
-            resumed_req_ids=cached_reqs_data.resumed_req_ids,
-            new_token_ids=cached_reqs_data.new_token_ids,
-            all_token_ids=cached_reqs_data.all_token_ids,
-            new_block_ids=cached_reqs_data.new_block_ids,
-            num_computed_tokens=cached_reqs_data.num_computed_tokens,
-            num_output_tokens=cached_reqs_data.num_output_tokens,
-            prompt_token_ids=cached_prompt_token_ids,
-        )
-
         total_num_scheduled_tokens = sum(num_scheduled_tokens.values())
         scheduler_output = SchedulerOutput(
             scheduled_new_reqs=new_reqs_data,
@@ -210,7 +157,6 @@ class OmniGenerationScheduler(VLLMScheduler):
                 # Build omni entry preserving all base fields
                 omni_nr = OmniNewRequestData(
                     req_id=nr.req_id,
-                    external_req_id=(getattr(request, "external_req_id", None) if request else None),
                     prompt_token_ids=nr.prompt_token_ids,
                     mm_features=nr.mm_features,
                     sampling_params=nr.sampling_params,
@@ -311,20 +257,21 @@ class OmniGenerationScheduler(VLLMScheduler):
             new_logprobs = None
             new_token_ids = generated_token_ids
             kv_transfer_params = None
+            status_before_stop = request.status
             pooler_output = None
             if pooler_outputs:
                 pooler_output = pooler_outputs[req_index]
 
             # Diffusion request: completes in one step; mark finished and free resources
-            if request.status == RequestStatus.FINISHED_STOPPED or (
-                self.omni_connector is None and request.num_computed_tokens >= request.num_prompt_tokens
-            ):
-                request.status = RequestStatus.FINISHED_STOPPED
-                # Optional: set a stop_reason for front-end clarity
-                # (does not affect protocol)
-                request.stop_reason = request.stop_reason  # or "generation_done"
-                kv_transfer_params = self._free_request(request)
+            request.status = RequestStatus.FINISHED_STOPPED
+            # Optional: set a stop_reason for front-end clarity
+            # (does not affect protocol)
+            request.stop_reason = request.stop_reason  # or "generation_done"
+            kv_transfer_params = self._free_request(request)
+            if status_before_stop == RequestStatus.RUNNING:
                 stopped_running_reqs.add(request)
+            else:
+                stopped_preempted_reqs.add(request)
 
             # Extract sample logprobs if needed.
             if request.sampling_params is not None and request.sampling_params.logprobs is not None and logprobs:

vllm_omni/core/sched/output.py

@@ -1,6 +1,6 @@
 from dataclasses import dataclass
 
-from vllm.v1.core.sched.output import CachedRequestData, NewRequestData
+from vllm.v1.core.sched.output import NewRequestData
 from vllm.v1.request import Request
 
 from vllm_omni.engine import AdditionalInformationPayload, PromptEmbedsPayload
@@ -21,8 +21,6 @@ class OmniNewRequestData(NewRequestData):
 
     # Optional serialized prompt embeddings
     prompt_embeds: PromptEmbedsPayload | None = None
-    # Optional external request ID for tracking
-    external_req_id: str | None = None
     # Optional serialized additional information
     additional_information: AdditionalInformationPayload | None = None
 
@@ -44,7 +42,6 @@ class OmniNewRequestData(NewRequestData):
         """
         return cls(
             req_id=request.request_id,
-            external_req_id=request.external_req_id,
             prompt_token_ids=request.prompt_token_ids,
             mm_features=request.mm_features,
             sampling_params=request.sampling_params,
@@ -56,14 +53,3 @@ class OmniNewRequestData(NewRequestData):
             prefill_token_ids=prefill_token_ids,
             additional_information=request.additional_information,
         )
-
-
-@dataclass
-class OmniCachedRequestData(CachedRequestData):
-    """Cached request data for omni models with embeddings support.
-
-    Args:
-        prompt_token_ids: Mapping from request ID to list of prompt token IDs
-    """
-
-    prompt_token_ids: dict[int, list[int]]

vllm_omni/diffusion/attention/backends/flash_attn.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import torch
+from fa3_fwd_interface import flash_attn_func, flash_attn_varlen_func
 from vllm.logger import init_logger
 
 from vllm_omni.diffusion.attention.backends.abstract import (
@@ -9,13 +10,7 @@ from vllm_omni.diffusion.attention.backends.abstract import (
     AttentionImpl,
     AttentionMetadata,
 )
-from vllm_omni.diffusion.attention.backends.utils.fa import (
-    _pad_input,
-    _unpad_input,
-    _upad_input,
-    flash_attn_func,
-    flash_attn_varlen_func,
-)
+from vllm_omni.diffusion.attention.backends.utils.fa import _pad_input, _unpad_input, _upad_input
 
 logger = init_logger(__name__)
 

vllm_omni/diffusion/attention/backends/utils/fa.py

@@ -15,20 +15,6 @@
 import torch
 import torch.nn.functional as F
 
-from vllm_omni.utils.platform_utils import is_rocm
-
-if is_rocm():
-    from vllm._aiter_ops import is_aiter_found_and_supported
-
-    # Choose to enable this by default on ROCm
-    # Whenever possible as it is the fastest backend
-    if is_aiter_found_and_supported():
-        from aiter import flash_attn_func, flash_attn_varlen_func  # noqa: F401
-    else:
-        raise ImportError("Aiter is not found and supported on currentROCm device.")
-else:
-    from fa3_fwd_interface import flash_attn_func, flash_attn_varlen_func  # noqa: F401
-
 
 def _index_first_axis(tensor, indices):
     """

vllm_omni/diffusion/attention/selector.py

@@ -81,28 +81,6 @@ def get_attn_backend(head_size: int) -> type[AttentionBackend]:
                                "Falling back to TORCH_SDPA backend."""
                 )
                 backend_name = "TORCH_SDPA"
-    elif detect_device_type() == "cuda" and is_rocm():
-        from vllm._aiter_ops import is_aiter_found_and_supported
-
-        compute_capability = torch.cuda.get_device_capability()
-        major, minor = compute_capability
-
-        # Choose to enable this by default on ROCm
-        # Whenever possible as it is the fastest backend
-        # is_aiter_found_and_supported() checks if aiter library is found
-        # and is aiter supported on the current platform
-        # aiter currently only is supported on gfx942 and gfx950
-        # https://github.com/vllm-project/vllm/blob/main/vllm/_aiter_ops.py
-        if is_aiter_found_and_supported() and 90 < major * 10 + minor < 100:
-            if backend_name is None:
-                backend_name = "FLASH_ATTN"
-        else:
-            if backend_name == "FLASH_ATTN":
-                logger.warning(
-                    "Flash Attention requires `aiter` library which is only supported "
-                    "on gfx942 and gfx950. Falling back to TORCH_SDPA backend."
-                )
-                backend_name = "TORCH_SDPA"
 
     if backend_name is not None:
         backend_name_upper = backend_name.upper()

vllm_omni/diffusion/cache/cache_dit_backend.py

@@ -791,7 +791,7 @@ def enable_cache_for_bagel(pipeline: Any, cache_config: Any) -> Callable[[int],
 CUSTOM_DIT_ENABLERS.update(
     {
         "WanPipeline": enable_cache_for_wan22,
-        # "FluxPipeline": enable_cache_for_flux,
+        "FluxPipeline": enable_cache_for_flux,
         "LongCatImagePipeline": enable_cache_for_longcat_image,
         "LongCatImageEditPipeline": enable_cache_for_longcat_image,
         "StableDiffusion3Pipeline": enable_cache_for_sd3,

vllm_omni/diffusion/cache/teacache/backend.py

@@ -15,42 +15,10 @@ from vllm.logger import init_logger
 from vllm_omni.diffusion.cache.base import CacheBackend
 from vllm_omni.diffusion.cache.teacache.config import TeaCacheConfig
 from vllm_omni.diffusion.cache.teacache.hook import TeaCacheHook, apply_teacache_hook
-from vllm_omni.diffusion.data import DiffusionCacheConfig
 
 logger = init_logger(__name__)
 
 
-def enable_bagel_teacache(pipeline: Any, config: DiffusionCacheConfig) -> None:
-    """
-    Enable TeaCache for Bagel model.
-    """
-    teacache_config = TeaCacheConfig(
-        transformer_type="Bagel",
-        rel_l1_thresh=config.rel_l1_thresh,
-        coefficients=config.coefficients,
-    )
-    transformer = pipeline.bagel
-    original_forward_flow = transformer._forward_flow
-
-    import types
-
-    def forward_alias(self, *args, **kwargs):
-        return original_forward_flow(*args, **kwargs)
-
-    transformer.forward = types.MethodType(forward_alias, transformer)
-    apply_teacache_hook(transformer, teacache_config)
-    transformer._forward_flow = transformer.forward
-    pipeline.transformer = transformer
-
-    logger.info(
-        f"TeaCache applied with rel_l1_thresh={teacache_config.rel_l1_thresh}, "
-        f"transformer_class={teacache_config.transformer_type}"
-    )
-
-
-CUSTOM_TEACACHE_ENABLERS = {"BagelPipeline": enable_bagel_teacache}
-
-
 class TeaCacheBackend(CacheBackend):
     """
     TeaCache implementation using hooks.
@@ -83,45 +51,38 @@ class TeaCacheBackend(CacheBackend):
                      - transformer: pipeline.transformer
                      - transformer_type: pipeline.transformer.__class__.__name__
         """
-        # Helper to get pipeline class name
-        pipeline_type = pipeline.__class__.__name__
+        # Extract transformer and transformer_type from pipeline
+        transformer = pipeline.transformer
+        transformer_type = transformer.__class__.__name__
 
-        # Check for pipeline-level custom enablers
-        if pipeline_type in CUSTOM_TEACACHE_ENABLERS:
-            logger.info(f"Using custom TeaCache enabler for model: {pipeline_type}")
-            CUSTOM_TEACACHE_ENABLERS[pipeline_type](pipeline, self.config)
-        else:
-            transformer = pipeline.transformer
-            transformer_type = transformer.__class__.__name__
-
-            # Create TeaCacheConfig from DiffusionCacheConfig with transformer_type
-            # Access parameters via attribute access: config.rel_l1_thresh
-            # rel_l1_thresh already has a default value of 0.2 in DiffusionCacheConfig
-            try:
-                teacache_config = TeaCacheConfig(
-                    transformer_type=transformer_type,
-                    rel_l1_thresh=self.config.rel_l1_thresh,
-                    coefficients=self.config.coefficients,
-                )
-            except Exception as e:
-                logger.error(f"Failed to create TeaCacheConfig: {e}")
-                raise ValueError(
-                    f"Invalid TeaCache configuration: {e}. "
-                    f"Expected keys: rel_l1_thresh, coefficients (optional). "
-                    f"transformer_type is automatically extracted from pipeline.transformer.__class__.__name__."
-                )
-
-            # Apply hook to transformer
-            apply_teacache_hook(transformer, teacache_config)
-
-            logger.info(
-                f"TeaCache applied with rel_l1_thresh={teacache_config.rel_l1_thresh}, "
-                f"transformer_class={teacache_config.transformer_type}"
+        # Create TeaCacheConfig from DiffusionCacheConfig with transformer_type
+        # Access parameters via attribute access: config.rel_l1_thresh
+        # rel_l1_thresh already has a default value of 0.2 in DiffusionCacheConfig
+        try:
+            teacache_config = TeaCacheConfig(
+                transformer_type=transformer_type,
+                rel_l1_thresh=self.config.rel_l1_thresh,
+                coefficients=self.config.coefficients,
             )
+        except Exception as e:
+            logger.error(f"Failed to create TeaCacheConfig: {e}")
+            raise ValueError(
+                f"Invalid TeaCache configuration: {e}. "
+                f"Expected keys: rel_l1_thresh, coefficients (optional). "
+                f"transformer_type is automatically extracted from pipeline.transformer.__class__.__name__."
+            )
+
+        # Apply hook to transformer
+        apply_teacache_hook(transformer, teacache_config)
 
         # Mark as enabled
         self.enabled = True
 
+        logger.info(
+            f"TeaCache applied with rel_l1_thresh={teacache_config.rel_l1_thresh}, "
+            f"transformer_class={teacache_config.transformer_type}"
+        )
+
     def refresh(self, pipeline: Any, num_inference_steps: int, verbose: bool = True) -> None:
         """
         Refresh TeaCache state for new generation.

vllm_omni/diffusion/cache/teacache/config.py

@@ -25,9 +25,6 @@ _MODEL_COEFFICIENTS = {
         3.20000000e00,
         -2.00000000e-02,
     ],
-    # Bagel transformer coefficients
-    # Using Qwen's coefficients as reasonable default given shared architecture
-    "Bagel": [1.33313129e06, -1.68644226e05, 7.95050740e03, -1.63747873e02, 1.26352397e00],
     # Z-Image transformer coefficients
     # Copied from Qwen-Image, need to be tuned specifically for Z-Image in future
     "ZImageTransformer2DModel": [

vllm_omni/diffusion/cache/teacache/extractors.py

@@ -20,8 +20,6 @@ from typing import Any
 import torch
 import torch.nn as nn
 
-from vllm_omni.diffusion.forward_context import get_forward_context
-
 
 @dataclass
 class CacheContext:
@@ -219,37 +217,14 @@ def extract_qwen_context(
         """Execute all Qwen transformer blocks."""
         h = hidden_states
         e = encoder_hidden_states
-        encoder_mask = encoder_hidden_states_mask
-        hidden_states_mask = None  # default
-        if module.parallel_config is not None and module.parallel_config.sequence_parallel_size > 1:
-            ctx = get_forward_context()
-            if ctx.sp_original_seq_len is not None and ctx.sp_padding_size > 0:
-                # Create mask for the full (padded) sequence
-                # valid positions = True, padding positions = False
-                batch_size = hidden_states.shape[0]
-                padded_seq_len = ctx.sp_original_seq_len + ctx.sp_padding_size
-                hidden_states_mask = torch.ones(
-                    batch_size,
-                    padded_seq_len,
-                    dtype=torch.bool,
-                    device=hidden_states.device,
-                )
-                hidden_states_mask[:, ctx.sp_original_seq_len :] = False
-
-        # if mask is all true, set it to None
-        if hidden_states_mask is not None and hidden_states_mask.all():
-            hidden_states_mask = None
-        if encoder_mask is not None and encoder_mask.all():
-            encoder_mask = None
         for block in module.transformer_blocks:
             e, h = block(
                 hidden_states=h,
                 encoder_hidden_states=e,
-                encoder_hidden_states_mask=encoder_mask,
+                encoder_hidden_states_mask=encoder_hidden_states_mask,
                 temb=temb,
                 image_rotary_emb=image_rotary_emb,
                 joint_attention_kwargs=attention_kwargs,
-                hidden_states_mask=hidden_states_mask,
             )
         return (h, e)
 
@@ -279,107 +254,6 @@ def extract_qwen_context(
     )
 
 
-def extract_bagel_context(
-    module: nn.Module,
-    x_t: torch.Tensor,
-    timestep: torch.Tensor | float | int,
-    packed_vae_token_indexes: torch.LongTensor,
-    packed_vae_position_ids: torch.LongTensor,
-    packed_text_ids: torch.LongTensor,
-    packed_text_indexes: torch.LongTensor,
-    packed_indexes: torch.LongTensor,
-    packed_position_ids: torch.LongTensor,
-    packed_seqlens: torch.IntTensor,
-    key_values_lens: torch.IntTensor,
-    past_key_values: Any,
-    packed_key_value_indexes: torch.LongTensor,
-    **kwargs: Any,
-) -> CacheContext:
-    """
-    Extract cache context for Bagel model.
-
-    Args:
-        module: Bagel instance
-        x_t: Latent image input
-        timestep: Current timestep
-        packed_vae_token_indexes: Indexes for VAE tokens in packed sequence
-        packed_vae_position_ids: Position IDs for VAE tokens
-        packed_text_ids: Text token IDs
-        packed_text_indexes: Indexes for text tokens in packed sequence
-        packed_indexes: Global indexes
-        packed_position_ids: Global position IDs
-        packed_seqlens: Sequence lengths
-        key_values_lens: KV cache lengths
-        past_key_values: KV cache
-        packed_key_value_indexes: KV cache indexes
-        **kwargs: Additional keyword arguments
-
-    Returns:
-        CacheContext with all information needed for generic caching
-    """
-
-    # 1. Embed text
-    packed_text_embedding = module.language_model.model.embed_tokens(packed_text_ids)
-    packed_sequence = packed_text_embedding.new_zeros((sum(packed_seqlens), module.hidden_size))
-    packed_sequence[packed_text_indexes] = packed_text_embedding
-
-    # 2. Embed timestep
-    if not isinstance(timestep, torch.Tensor):
-        timestep = torch.tensor([timestep], device=x_t.device)
-    if timestep.dim() == 0:
-        timestep = timestep.unsqueeze(0)
-
-    # 3. Embed image (x_t)
-    packed_pos_embed = module.latent_pos_embed(packed_vae_position_ids)
-    packed_timestep_embeds = module.time_embedder(timestep)
-
-    x_t_emb = module.vae2llm(x_t) + packed_timestep_embeds + packed_pos_embed
-    if x_t_emb.dtype != packed_sequence.dtype:
-        x_t_emb = x_t_emb.to(packed_sequence.dtype)
-
-    packed_sequence[packed_vae_token_indexes] = x_t_emb
-
-    # Use the full packed sequence as modulated input to match hidden_states size
-    modulated_input = packed_sequence
-
-    def run_transformer_blocks():
-        extra_inputs = {}
-        if module.use_moe:
-            extra_inputs = {
-                "mode": "gen",
-                "packed_vae_token_indexes": packed_vae_token_indexes,
-                "packed_text_indexes": packed_text_indexes,
-            }
-
-        output = module.language_model.forward(
-            packed_query_sequence=packed_sequence,
-            query_lens=packed_seqlens,
-            packed_query_position_ids=packed_position_ids,
-            packed_query_indexes=packed_indexes,
-            past_key_values=past_key_values,
-            key_values_lens=key_values_lens,
-            packed_key_value_indexes=packed_key_value_indexes,
-            update_past_key_values=False,
-            is_causal=False,
-            **extra_inputs,
-        )
-        return (output.packed_query_sequence,)
-
-    def postprocess(h):
-        v_t = module.llm2vae(h)
-        v_t = v_t[packed_vae_token_indexes]
-        return v_t
-
-    return CacheContext(
-        modulated_input=modulated_input,
-        hidden_states=packed_sequence,  # Use full packed sequence
-        encoder_hidden_states=None,
-        temb=packed_timestep_embeds,  # Approximate
-        run_transformer_blocks=run_transformer_blocks,
-        postprocess=postprocess,
-    )
-
-
 def extract_zimage_context(
     module: nn.Module,
     x: list[torch.Tensor],
@@ -574,7 +448,6 @@ def extract_zimage_context(
 # on the transformer module and multiple pipelines can share the same transformer.
 EXTRACTOR_REGISTRY: dict[str, Callable] = {
     "QwenImageTransformer2DModel": extract_qwen_context,
-    "Bagel": extract_bagel_context,
     "ZImageTransformer2DModel": extract_zimage_context,
     # Future models:
     # "FluxTransformer2DModel": extract_flux_context,

vllm_omni/diffusion/cache/teacache/hook.py

@@ -122,7 +122,7 @@ class TeaCacheHook(ModelHook):
         #   - cfg_rank 0: positive branch
         #   - cfg_rank > 0: negative branch
         # Without CFG-parallel, branches alternate within a single rank
-        if getattr(module, "do_true_cfg", False):
+        if module.do_true_cfg:
             cfg_parallel_size = get_classifier_free_guidance_world_size()
             if cfg_parallel_size > 1:
                 cfg_rank = get_classifier_free_guidance_rank()

vllm_omni/diffusion/data.py

@@ -276,9 +276,12 @@ class OmniDiffusionConfig:
     # pipeline_config: PipelineConfig = field(default_factory=PipelineConfig, repr=False)
 
     # LoRA parameters
+    # (Wenxuan) prefer to keep it here instead of in pipeline config to not make it complicated.
     lora_path: str | None = None
-    lora_scale: float = 1.0
-    max_cpu_loras: int | None = None
+    lora_nickname: str = "default"  # for swapping adapters in the pipeline
+    # can restrict layers to adapt, e.g. ["q_proj"]
+    # Will adapt only q, k, v, o by default.
+    lora_target_modules: list[str] | None = None
 
     output_type: str = "pil"
 
@@ -443,24 +446,11 @@ class OmniDiffusionConfig:
             # If it's neither dict nor DiffusionCacheConfig, convert to empty config
             self.cache_config = DiffusionCacheConfig()
 
-        if self.max_cpu_loras is None:
-            self.max_cpu_loras = 1
-        elif self.max_cpu_loras < 1:
-            raise ValueError("max_cpu_loras must be >= 1 for diffusion LoRA")
-
     def update_multimodal_support(self) -> None:
         self.supports_multimodal_inputs = self.model_class_name in {"QwenImageEditPlusPipeline"}
 
     @classmethod
     def from_kwargs(cls, **kwargs: Any) -> "OmniDiffusionConfig":
-        # Backwards-compatibility: older callers may use a diffusion-specific
-        # "static_lora_scale" kwarg. Normalize it to the canonical "lora_scale"
-        # before constructing the dataclass to avoid TypeError on unknown fields.
-        if "static_lora_scale" in kwargs:
-            if "lora_scale" not in kwargs:
-                kwargs["lora_scale"] = kwargs["static_lora_scale"]
-            kwargs.pop("static_lora_scale", None)
-
         # Check environment variable as fallback for cache_backend
         # Support both old DIFFUSION_CACHE_ADAPTER and new DIFFUSION_CACHE_BACKEND for backwards compatibility
         if "cache_backend" not in kwargs:

vllm_omni/diffusion/diffusion_engine.py

@@ -17,7 +17,6 @@ from vllm_omni.diffusion.registry import (
     get_diffusion_pre_process_func,
 )
 from vllm_omni.diffusion.request import OmniDiffusionRequest
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniTextPrompt
 from vllm_omni.outputs import OmniRequestOutput
 
 logger = init_logger(__name__)
@@ -30,13 +29,6 @@ def supports_image_input(model_class_name: str) -> bool:
     return bool(getattr(model_cls, "support_image_input", False))
 
 
-def supports_audio_output(model_class_name: str) -> bool:
-    model_cls = DiffusionModelRegistry._try_load_model_cls(model_class_name)
-    if model_cls is None:
-        return False
-    return bool(getattr(model_cls, "support_audio_output", False))
-
-
 class DiffusionEngine:
     """The diffusion engine for vLLM-Omni diffusion models."""
 
@@ -61,118 +53,103 @@ class DiffusionEngine:
             self.close()
             raise e
 
-    def step(self, request: OmniDiffusionRequest) -> list[OmniRequestOutput]:
-        # Apply pre-processing if available
-        if self.pre_process_func is not None:
-            preprocess_start_time = time.time()
-            request = self.pre_process_func(request)
-            preprocess_time = time.time() - preprocess_start_time
-            logger.info(f"Pre-processing completed in {preprocess_time:.4f} seconds")
+    def step(self, requests: list[OmniDiffusionRequest]):
+        try:
+            # Apply pre-processing if available
+            if self.pre_process_func is not None:
+                preprocess_start_time = time.time()
+                requests = self.pre_process_func(requests)
+                preprocess_time = time.time() - preprocess_start_time
+                logger.info(f"Pre-processing completed in {preprocess_time:.4f} seconds")
 
-        output = self.add_req_and_wait_for_response(request)
-        if output.error:
-            raise Exception(f"{output.error}")
-        logger.info("Generation completed successfully.")
+            output = self.add_req_and_wait_for_response(requests)
+            if output.error:
+                raise Exception(f"{output.error}")
+            logger.info("Generation completed successfully.")
 
-        if output.output is None:
-            logger.warning("Output is None, returning empty OmniRequestOutput")
-            return [
-                OmniRequestOutput.from_diffusion(
-                    request_id=request.request_ids[i] if i < len(request.request_ids) else "",
-                    images=[],
-                    prompt=prompt,
-                    metrics={},
-                    latents=None,
-                )
-                for i, prompt in enumerate(request.prompts)
-            ]
-
-        postprocess_start_time = time.time()
-        outputs = self.post_process_func(output.output) if self.post_process_func is not None else output.output
-        postprocess_time = time.time() - postprocess_start_time
-        logger.info(f"Post-processing completed in {postprocess_time:.4f} seconds")
-
-        # Convert to OmniRequestOutput format
-        # Ensure outputs is a list
-        if not isinstance(outputs, list):
-            outputs = [outputs] if outputs is not None else []
-
-        # Handle single request or multiple requests
-        if len(request.prompts) == 1:
-            # Single request: return single OmniRequestOutput
-            prompt = request.prompts[0]
-            request_id = request.request_ids[0] if request.request_ids else ""
-
-            metrics = {}
-            if output.trajectory_timesteps is not None:
-                metrics["trajectory_timesteps"] = output.trajectory_timesteps
-
-            if supports_audio_output(self.od_config.model_class_name):
-                audio_payload = outputs[0] if len(outputs) == 1 else outputs
-                return [
-                    OmniRequestOutput.from_diffusion(
+            if output.output is None:
+                logger.warning("Output is None, returning empty OmniRequestOutput")
+                # Return empty output for the first request
+                if len(requests) > 0:
+                    request = requests[0]
+                    request_id = request.request_id or ""
+                    prompt = request.prompt
+                    if isinstance(prompt, list):
+                        prompt = prompt[0] if prompt else None
+                    return OmniRequestOutput.from_diffusion(
                         request_id=request_id,
                         images=[],
                         prompt=prompt,
-                        metrics=metrics,
-                        latents=output.trajectory_latents,
-                        multimodal_output={"audio": audio_payload},
-                        final_output_type="audio",
-                    ),
-                ]
-            else:
-                return [
-                    OmniRequestOutput.from_diffusion(
-                        request_id=request_id,
-                        images=outputs,
-                        prompt=prompt,
-                        metrics=metrics,
-                        latents=output.trajectory_latents,
-                    ),
-                ]
-        else:
-            # Multiple requests: return list of OmniRequestOutput
-            # Split images based on num_outputs_per_prompt for each request
-            results = []
-            output_idx = 0
+                        metrics={},
+                        latents=None,
+                    )
+                return None
 
-            for i, prompt in enumerate(request.prompts):
-                request_id = request.request_ids[i] if i < len(request.request_ids) else ""
+            postprocess_start_time = time.time()
+            images = self.post_process_func(output.output) if self.post_process_func is not None else output.output
+            postprocess_time = time.time() - postprocess_start_time
+            logger.info(f"Post-processing completed in {postprocess_time:.4f} seconds")
 
-                # Get images for this request
-                num_outputs = request.sampling_params.num_outputs_per_prompt
-                request_outputs = outputs[output_idx : output_idx + num_outputs] if output_idx < len(outputs) else []
-                output_idx += num_outputs
+            # Convert to OmniRequestOutput format
+            # Ensure images is a list
+            if not isinstance(images, list):
+                images = [images] if images is not None else []
+
+            # Handle single request or multiple requests
+            if len(requests) == 1:
+                # Single request: return single OmniRequestOutput
+                request = requests[0]
+                request_id = request.request_id or ""
+                prompt = request.prompt
+                if isinstance(prompt, list):
+                    prompt = prompt[0] if prompt else None
 
                 metrics = {}
                 if output.trajectory_timesteps is not None:
                     metrics["trajectory_timesteps"] = output.trajectory_timesteps
 
-                if supports_audio_output(self.od_config.model_class_name):
-                    audio_payload = request_outputs[0] if len(request_outputs) == 1 else request_outputs
+                return OmniRequestOutput.from_diffusion(
+                    request_id=request_id,
+                    images=images,
+                    prompt=prompt,
+                    metrics=metrics,
+                    latents=output.trajectory_latents,
+                )
+            else:
+                # Multiple requests: return list of OmniRequestOutput
+                # Split images based on num_outputs_per_prompt for each request
+                results = []
+                image_idx = 0
+
+                for request in requests:
+                    request_id = request.request_id or ""
+                    prompt = request.prompt
+                    if isinstance(prompt, list):
+                        prompt = prompt[0] if prompt else None
+
+                    # Get images for this request
+                    num_outputs = request.num_outputs_per_prompt
+                    request_images = images[image_idx : image_idx + num_outputs] if image_idx < len(images) else []
+                    image_idx += num_outputs
+
+                    metrics = {}
+                    if output.trajectory_timesteps is not None:
+                        metrics["trajectory_timesteps"] = output.trajectory_timesteps
+
                     results.append(
                         OmniRequestOutput.from_diffusion(
                             request_id=request_id,
-                            images=[],
-                            prompt=prompt,
-                            metrics=metrics,
-                            latents=output.trajectory_latents,
-                            multimodal_output={"audio": audio_payload},
-                            final_output_type="audio",
-                        )
-                    )
-                else:
-                    results.append(
-                        OmniRequestOutput.from_diffusion(
-                            request_id=request_id,
-                            images=request_outputs,
+                            images=request_images,
                             prompt=prompt,
                             metrics=metrics,
                             latents=output.trajectory_latents,
                         )
                     )
 
-            return results
+                return results
+        except Exception as e:
+            logger.error(f"Generation failed: {e}")
+            return None
 
     @staticmethod
     def make_engine(config: OmniDiffusionConfig) -> "DiffusionEngine":
@@ -186,8 +163,8 @@ class DiffusionEngine:
         """
         return DiffusionEngine(config)
 
-    def add_req_and_wait_for_response(self, request: OmniDiffusionRequest):
-        return self.executor.add_req(request)
+    def add_req_and_wait_for_response(self, requests: list[OmniDiffusionRequest]):
+        return self.executor.add_req(requests)
 
     def start_profile(self, trace_filename: str | None = None) -> None:
         """
@@ -310,6 +287,8 @@ class DiffusionEngine:
 
     def _dummy_run(self):
         """A dummy run to warm up the model."""
+        prompt = "dummy run"
+        # note that num_inference_steps=1 will cause timestep and temb None in the pipeline
         num_inference_steps = 1
         height = 1024
         width = 1024
@@ -319,19 +298,17 @@ class DiffusionEngine:
             dummy_image = PIL.Image.new("RGB", (width, height), color=(0, 0, 0))
         else:
             dummy_image = None
-        prompt: OmniTextPrompt = {"prompt": "dummy run", "multi_modal_data": {"image": dummy_image}}
         req = OmniDiffusionRequest(
-            prompts=[prompt],
-            sampling_params=OmniDiffusionSamplingParams(
-                height=height,
-                width=width,
-                num_inference_steps=num_inference_steps,
-                num_outputs_per_prompt=1,
-            ),
+            prompt=prompt,
+            height=height,
+            width=width,
+            pil_image=dummy_image,
+            num_inference_steps=num_inference_steps,
+            num_outputs_per_prompt=1,
         )
         logger.info("dummy run to warm up the model")
-        request = self.pre_process_func(req) if self.pre_process_func is not None else req
-        self.add_req_and_wait_for_response(request)
+        requests = self.pre_process_func([req]) if self.pre_process_func is not None else [req]
+        self.add_req_and_wait_for_response(requests)
 
     def collective_rpc(
         self,

vllm_omni/diffusion/executor/abstract.py

@@ -3,7 +3,7 @@ from typing import Any
 
 from vllm.utils.import_utils import resolve_obj_by_qualname
 
-from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.data import OmniDiffusionConfig
 from vllm_omni.diffusion.request import OmniDiffusionRequest
 
 
@@ -59,7 +59,7 @@ class DiffusionExecutor(ABC):
         pass
 
     @abstractmethod
-    def add_req(self, requests: OmniDiffusionRequest) -> DiffusionOutput:
+    def add_req(self, requests: list[OmniDiffusionRequest]):
         """Add requests to the execution queue."""
         pass
 

vllm_omni/diffusion/executor/multiproc_executor.py

@@ -6,7 +6,7 @@ from typing import Any
 
 from vllm.logger import init_logger
 
-from vllm_omni.diffusion.data import SHUTDOWN_MESSAGE, DiffusionOutput
+from vllm_omni.diffusion.data import SHUTDOWN_MESSAGE
 from vllm_omni.diffusion.executor.abstract import DiffusionExecutor
 from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.scheduler import Scheduler
@@ -130,8 +130,8 @@ class MultiprocDiffusionExecutor(DiffusionExecutor):
 
         return processes, result_handle
 
-    def add_req(self, request: OmniDiffusionRequest) -> DiffusionOutput:
-        return self.scheduler.add_req(request)
+    def add_req(self, requests: list[OmniDiffusionRequest]):
+        return self.scheduler.add_req(requests)
 
     def collective_rpc(
         self,

vllm_omni/diffusion/lora/__init__.py

@@ -1,6 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from vllm_omni.diffusion.lora.manager import DiffusionLoRAManager
-
-__all__ = ["DiffusionLoRAManager"]

vllm_omni/diffusion/lora/layers/__init__.py

@@ -1,22 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from .base_linear import DiffusionBaseLinearLayerWithLoRA
-from .column_parallel_linear import (
-    DiffusionColumnParallelLinearWithLoRA,
-    DiffusionMergedColumnParallelLinearWithLoRA,
-    DiffusionMergedQKVParallelLinearWithLoRA,
-    DiffusionQKVParallelLinearWithLoRA,
-)
-from .replicated_linear import DiffusionReplicatedLinearWithLoRA
-from .row_parallel_linear import DiffusionRowParallelLinearWithLoRA
-
-__all__ = [
-    "DiffusionBaseLinearLayerWithLoRA",
-    "DiffusionReplicatedLinearWithLoRA",
-    "DiffusionColumnParallelLinearWithLoRA",
-    "DiffusionMergedColumnParallelLinearWithLoRA",
-    "DiffusionRowParallelLinearWithLoRA",
-    "DiffusionQKVParallelLinearWithLoRA",
-    "DiffusionMergedQKVParallelLinearWithLoRA",
-]

vllm_omni/diffusion/lora/layers/base_linear.py

@@ -1,152 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-import torch
-from vllm.lora.layers.base_linear import BaseLinearLayerWithLoRA
-
-
-class DiffusionBaseLinearLayerWithLoRA(BaseLinearLayerWithLoRA):
-    """
-    Diffusion-specific base that overrides apply() to use direct torch matmul
-    instead of punica_wrapper.
-
-    punica_wrapper is used to hold multiple LoRA slots and slices efficiently.
-
-    This matches the semantics of PunicaWrapperGPU.add_lora_linear():
-    - Shrink: buffer = (x @ lora_a.T)
-    - Expand: y += buffer @ lora_b.T
-
-    All other functionality (weight management, TP slicing, forward logic)
-    is inherited from vLLM's BaseLinearLayerWithLoRA.
-    """
-
-    def create_lora_weights(
-        self,
-        max_loras: int,
-        lora_config,
-        model_config=None,
-    ) -> None:
-        super().create_lora_weights(max_loras, lora_config, model_config)
-        # Keep a direct reference for attribute forwarding: `base_layer` is a
-        # registered submodule (stored under `_modules`), so direct access via
-        # `object.__getattribute__` will not find it. We stash a ref in
-        # `__dict__` for robust lookups in `__getattr__`.
-        modules = object.__getattribute__(self, "_modules")
-        base_layer = modules.get("base_layer") or object.__getattribute__(self, "__dict__").get("base_layer")
-        object.__setattr__(self, "_diffusion_base_layer_ref", base_layer)
-        n_slices = getattr(self, "n_slices", 1)
-        self._diffusion_lora_active_slices = (False,) * int(n_slices)
-
-    def reset_lora(self, index: int):
-        super().reset_lora(index)
-        n_slices = getattr(self, "n_slices", 1)
-        self._diffusion_lora_active_slices = (False,) * int(n_slices)
-
-    def set_lora(
-        self,
-        index: int,
-        lora_a: torch.Tensor | list[torch.Tensor | None],
-        lora_b: torch.Tensor | list[torch.Tensor | None],
-    ):
-        super().set_lora(index, lora_a, lora_b)  # type: ignore[arg-type]
-
-        n_slices = getattr(self, "n_slices", 1)
-        if isinstance(lora_a, list) or isinstance(lora_b, list):
-            assert isinstance(lora_a, list)
-            assert isinstance(lora_b, list)
-            active_slices = []
-            for a_i, b_i in zip(lora_a[:n_slices], lora_b[:n_slices]):
-                active_slices.append(a_i is not None and b_i is not None)
-            if len(active_slices) < n_slices:
-                active_slices.extend([False] * (n_slices - len(active_slices)))
-            self._diffusion_lora_active_slices = tuple(active_slices)
-        else:
-            # Single-slice layer.
-            self._diffusion_lora_active_slices = (True,)
-
-    def apply(self, x: torch.Tensor, bias: torch.Tensor | None = None) -> torch.Tensor:
-        """
-        override: Use simple matmul instead of punica_wrapper.add_lora_linear().
-
-        This matches the exact computation in PunicaWrapperGPU.add_lora_linear()
-        for the single-LoRA case. For packed projections (e.g. fused QKV), we
-        apply LoRA per-slice using `output_slices`.
-        """
-        output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
-
-        if not hasattr(self, "lora_a_stacked") or not hasattr(self, "lora_b_stacked"):
-            return output
-        if not self.lora_a_stacked or not self.lora_b_stacked:
-            return output
-        # Fast path: if no LoRA is active for this layer, skip matmuls.
-        active_slices = getattr(self, "_diffusion_lora_active_slices", None)
-        if active_slices is not None and not any(active_slices):
-            return output
-
-        # In fully-sharded LoRA mode, vLLM uses an all-gather between shrink and
-        # expand for ColumnParallelLinear variants. This diffusion path doesn't
-        # implement that communication yet.
-        if getattr(self, "lora_config", None) is not None:
-            if self.lora_config.fully_sharded_loras and self.tp_size > 1:
-                raise NotImplementedError(
-                    "Diffusion LoRA apply() does not support fully_sharded_loras with tensor parallelism yet."
-                )
-
-        original_shape = output.shape
-        x_flat = x.reshape(-1, x.shape[-1])
-        y_flat = output.reshape(-1, output.shape[-1])
-
-        output_slices = getattr(self, "output_slices", None)
-        if output_slices is None:
-            # Fallback: infer slice sizes from the allocated tensors.
-            output_slices = tuple(lora_b.shape[2] for lora_b in self.lora_b_stacked)
-
-        if len(output_slices) != len(self.lora_a_stacked) or len(output_slices) != len(self.lora_b_stacked):
-            raise RuntimeError(
-                "LoRA slice metadata mismatch: "
-                f"output_slices={len(output_slices)}, "
-                f"lora_a_stacked={len(self.lora_a_stacked)}, "
-                f"lora_b_stacked={len(self.lora_b_stacked)}"
-            )
-
-        offset = 0
-        for slice_idx, slice_size in enumerate(output_slices):
-            if active_slices is not None and slice_idx < len(active_slices) and not active_slices[slice_idx]:
-                offset += slice_size
-                continue
-
-            A = self.lora_a_stacked[slice_idx][0, 0, :, :]  # (rank, in_dim)
-            B = self.lora_b_stacked[slice_idx][0, 0, :, :]  # (out_dim, rank)
-
-            if A.numel() == 0 or B.numel() == 0:
-                offset += slice_size
-                continue
-
-            # LoRA shrink & expand as in add_lora_linear():
-            #   buffer = (x @ A.T)
-            #   y += buffer @ B.T
-            delta = (x_flat @ A.t()) @ B.t()
-            y_flat[:, offset : offset + slice_size] = y_flat[:, offset : offset + slice_size] + delta
-            offset += slice_size
-
-        return y_flat.view(original_shape)
-
-    def __getattr__(self, name: str):
-        # The diffusion model implementations may access attributes directly
-        # from linear layers (e.g. QKVParallelLinear.num_heads). vLLM's LoRA
-        # wrappers don't forward these attributes by default, so we delegate
-        # missing attribute lookups to the underlying base_layer.
-        try:
-            return super().__getattr__(name)
-        except AttributeError as exc:
-            base_layer = object.__getattribute__(self, "__dict__").get("_diffusion_base_layer_ref")
-            if base_layer is None:
-                base_layer = object.__getattribute__(self, "_modules").get("base_layer")
-            if base_layer is None:
-                raise exc
-            try:
-                return getattr(base_layer, name)
-            except AttributeError:
-                raise exc

vllm_omni/diffusion/lora/layers/column_parallel_linear.py

@@ -1,61 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-from vllm.lora.layers.column_parallel_linear import (
-    ColumnParallelLinearWithLoRA,
-    MergedColumnParallelLinearWithLoRA,
-    MergedQKVParallelLinearWithLoRA,
-    QKVParallelLinearWithLoRA,
-)
-
-from .base_linear import DiffusionBaseLinearLayerWithLoRA
-
-
-class DiffusionColumnParallelLinearWithLoRA(
-    DiffusionBaseLinearLayerWithLoRA,
-    ColumnParallelLinearWithLoRA,
-):
-    """
-    Diffusion ColumnParallelLinear with LoRA.
-    Prioritize apply() in DiffusionBaseLinearLayerWithLoRA
-    """
-
-    pass
-
-
-class DiffusionMergedColumnParallelLinearWithLoRA(
-    DiffusionBaseLinearLayerWithLoRA,
-    MergedColumnParallelLinearWithLoRA,
-):
-    """
-    Diffusion MergedColumnParallelLinear (gate_up_proj) with LoRA.
-    Prioritize apply() in DiffusionBaseLinearLayerWithLoRA
-    """
-
-    pass
-
-
-class DiffusionQKVParallelLinearWithLoRA(
-    DiffusionBaseLinearLayerWithLoRA,
-    QKVParallelLinearWithLoRA,
-):
-    """
-    Diffusion QKVParallelLinear with single LoRA.
-    Prioritize apply() in DiffusionBaseLinearLayerWithLoRA
-    """
-
-    pass
-
-
-class DiffusionMergedQKVParallelLinearWithLoRA(
-    DiffusionBaseLinearLayerWithLoRA,
-    MergedQKVParallelLinearWithLoRA,
-):
-    """
-    Diffusion MergedQKVParallelLinear (to_qkv) with 3 LoRAs.
-    Prioritize apply() in DiffusionBaseLinearLayerWithLoRA
-    """
-
-    pass

vllm_omni/diffusion/lora/layers/replicated_linear.py

@@ -1,20 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-from vllm.lora.layers.replicated_linear import ReplicatedLinearWithLoRA
-
-from .base_linear import DiffusionBaseLinearLayerWithLoRA
-
-
-class DiffusionReplicatedLinearWithLoRA(
-    DiffusionBaseLinearLayerWithLoRA,
-    ReplicatedLinearWithLoRA,
-):
-    """
-    Diffusion ReplicatedLinear with LoRA.
-    Prioritize apply() in DiffusionBaseLinearLayerWithLoRA
-    """
-
-    pass