# Adding a Diffusion Model 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:

Diffusion Flow

# 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**. ### 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 ``` + 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 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. ## 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). ## 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.