[Doc] Adding diffusion model

docs/.nav.yml

@@ -41,6 +41,7 @@ nav:
   - Model Implementation:
     - contributing/model/README.md
     - contributing/model/adding_multi_stage_model.md
+    - contributing/model/adding_diffusion_model.md
   - CI: contributing/ci
   - Tests: contributing/tests
   - Design Documents:

docs/contributing/model/README.md

@@ -4,7 +4,7 @@ This section provides comprehensive guidance on how to add a new model to vLLM-O
 
 ## Documentation
 
-- **[Adding a New Model Guide](adding_multi_stage_model.md)**: Complete step-by-step guide using Qwen3-Omni as an example
+- **[Adding a New Omni Model Guide](adding_multi_stage_model.md)**: Complete step-by-step guide using Qwen3-Omni as an example
 
 The guide covers:
 - Directory structure and organization
@@ -15,6 +15,15 @@ The guide covers:
 - Stage input processors
 - Testing strategies
 
+- **[Adding a New Diffusion Model Guide](adding_diffusion_model.md)**: Complete step-by-step guide using Qwen/Qwen-Image-Edit as an example
+The guide covers:
+- Overview
+- Directory Structure
+- Step-by-Step Implementation
+- Testing
+
+
+
 ## Quick Start
 
-For a quick reference, see the [Adding a New Model Guide](adding_multi_stage_model.md) which walks through the complete implementation of Qwen3-Omni, a multi-stage omni-modality model.
+For a quick reference, see the [Adding a New Omni Model Guide](adding_multi_stage_model.md) and [Adding a New Diffusion Model Guide](adding_diffusion_model.md).

docs/contributing/model/adding_diffusion_model.md

@@ -0,0 +1,147 @@
+# Adding a New Diffusion Model to vLLM-Omni
+This guide walks through the process of adding a new Diffusion model to vLLM-Omni, using Qwen/Qwen-Image-Edit as a comprehensive example.
+
+# Table of Contents
+1. [Overview](#overview)
+2. [Directory Structure](#directory-structure)
+3. [Step-by-Step Implementation](#step-by-step-implementation)
+4. [Testing](#testing)
+5. [Adding a Model Recipe](#adding-a-model-recipe)
+
+
+# Overview
+When add a new diffusion model into vLLM-Omni, additional adaptation work is required due to the following reasons:
+
++ New model must follow the framework’s parameter passing mechanisms and inference flow.
+
++ Replacing the model’s default implementations with optimized modules, which is necessary to achieve the better performance.
+
+The diffusion execution flow as follow:
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/vllm-omni-diffusion-flow.png">
+    <img alt="Diffusion Flow" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/vllm-omni-diffusion-flow.png" width=55%>
+  </picture>
+</p>
+
+
+# Directory Structure
+File Structure for Adding a New Diffusion Model
+
+```
+vllm_omni/
+└── examples/
+    └──offline_inference
+        └── example script                # reuse existing if possible (e.g., image_edit.py)
+    └──online_serving
+        └── example script
+└── diffusion/
+    └── registry.py                       # Registry work
+    ├── request.py                        # Request Info
+    └── models/your_model_name/           # Model directory (e.g., qwen_image)
+        └── pipeline_xxx.py               # Model implementation (e.g., pipeline_qwen_image_edit.py)
+```
+
+# Step-by-step-implementation
+## Step 1: Model Implementation
+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.
+
+### 1.2 Pre-Processing and Post-Processing Extraction
+Extract the pre-processing and post-processing logic from the pipeline class to follow vLLM-Omni’s execution flow. For Qwen-Image-Edit:
+```python
+def get_qwen_image_edit_pre_process_func(
+    od_config: OmniDiffusionConfig,
+):
+    """
+    Define a pre-processing function that resizes input images and
+    pre-process for subsequent inference.
+    """
+```
+
+```python
+def get_qwen_image_edit_post_process_func(
+    od_config: OmniDiffusionConfig,
+):
+    """
+    Defines a post-processing function that post-process images.
+    """
+```
+
+### 1.3 Define the forward function
+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 if req.prompt is not None else prompt
+```
++ pre/post-processing are handled by the framework elsewhere, so skip them.
+
+## 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
+prompt: str | list[str] | None = None
+negative_prompt: str | list[str] | None = None
+...
+```
+
+## Step 3: Registry
++ registry diffusion model in registry.py
+```python
+_DIFFUSION_MODELS = {
+    # arch:(mod_folder, mod_relname, cls_name)
+    ...
+    "QwenImageEditPipeline": (
+        "qwen_image",
+        "pipeline_qwen_image_edit",
+        "QwenImageEditPipeline",
+    ),
+    ...
+}
+```
++ registry pre-process get function
+```python
+_DIFFUSION_PRE_PROCESS_FUNCS = {
+    # arch: pre_process_func
+    ...
+    "QwenImageEditPipeline": "get_qwen_image_edit_pre_process_func",
+    ...
+}
+```
+
++ registry post-process get function
+```python
+_DIFFUSION_POST_PROCESS_FUNCS = {
+    # arch: post_process_func
+    ...
+    "QwenImageEditPipeline": "get_qwen_image_edit_post_process_func",
+    ...
+}
+```
+
+## Step 4: Add an Example Script
+For each newly integrated model, we need to provide examples script under the examples/ to demonstrate how to initialize the pipeline with Omni, pass in user inputs, and generate outputs.
+Key point for writing the example:
+
++ Use the Omni entrypoint to load the model and construct the pipeline.
+
++ Show how to format user inputs and pass them via omni.generate(...).
+
++ Demonstrate the common runtime arguments, such as:
+
+    + model path or model name
+
+    + input image(s) or prompt text
+
+    + key diffusion parameters (e.g., inference steps, guidance scale)
+
+    + optional acceleration backends (e.g., Cache-DiT, TeaCache)
+
++ Save or display the generated results so users can validate the integration.
+
+# Testing
+For comprehensive testing guidelines, please refer to the [Test File Structure and Style Guide](../tests/tests_style.md).
+
+
+## Adding a Model Recipe
+After implementing and testing your model, please add a model recipe to the [vllm-project/recipes](https://github.com/vllm-project/recipes) repository. This helps other users understand how to use your model with vLLM-Omni.