add-new-diffusion-model

To add a new diffusion model to SGLang, you must design the pipeline style, implement model components like DiT and VAE, define configurations, and register the model while following SGLang's stage-based architecture. Use reference implementations as a starting point.

Hybrid Monolithic and Modular Composition are two pipeline development styles for model integration in SGLang. The choice between them determines how tightly the model components are coupled during deployment, affecting both implementation flexibility and inference efficiency.

Yes, integrating a custom diffusion model into SGLang requires implementing core components like DiT, VAE, and encoders. You must adapt these components to fit SGLang's architecture and ensure proper configuration setup for successful hybrid inference.

Common pitfalls when adding diffusion models to SGLang include failing to adhere to the stage-based architecture and improperly registering the model. Following the provided checklists and studying reference implementations helps avoid these integration errors.

Yes, SGLang supports deep learning pipeline development for novel diffusion models. It guides you through adapting model components, defining configurations, and ensuring seamless operation within the SGLang ecosystem for hybrid inference.