sgl-project/sglang

[Feature] Detailed Break down of time spend on Launching SGLang Diffusion

Open

#19,087 opened on Feb 20, 2026

View on GitHub
 (10 comments) (0 reactions) (1 assignee)Python (28,442 stars) (6,216 forks)auto 404
good first issue

Description

Checklist

Motivation

Diffusion and LLM have huge differences in compute characteristics. We want to have a detailed optimization of the launch time spent on SGLang Diffusion.

In this sense, to optimize the launch time, we should have a detailed breakdown of what is actually taking time when we launch our models. Please use Qwen-Image as an example, and try to break down the time spent. Then let's see whether we shall spend our time on optimize the launching time.

Related resources

No response

Contributor guide

Tech stack
python
Domain
backendmachine learningperformance
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
Python
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60
Research direction
Profile the launch time of SGLang Diffusion using Qwen Image as an example. Break down the time into components such as model loading, weight initialization, compilation, and any other initialization steps. Identify bottlenecks and report findings.