sglang-diffusion-add-model

To integrate a new diffusion model into SGLang, study the reference Diffusers pipeline, match stage contracts and weight names, implement model components, configure the registry, and validate output quality against the reference pipeline.

Porting a Diffusers pipeline to SGLang requires studying source encoders and schedulers, designing modular denoising and decoding stages, wiring model components, registering the model for discovery, and verifying generated outputs are not noise.

Yes, SGLang supports tensor and sequence parallelism for diffusion models, allowing you to reuse existing distributed execution framework components when implementing and validating new text-to-image or video generation pipelines.

No, adding a diffusion model to SGLang requires matching SGLang stage contracts and weight names exactly to ensure proper multimodal generation runtime execution and output validation against the reference Diffusers pipeline.

Your integrated diffusion model outputs noise in SGLang if stage contracts, latent preparation, or conditioning configurations mismatch the reference Diffusers implementation, requiring output-quality validation and comparison to fix.

The best way to validate a newly ported Diffusers pipeline in SGLang is to compare generated outputs directly against the reference pipeline, ensuring the model components, scheduler, and decoding behavior produce identical, non-noise results.