Z-Image-Turbo-Quantized
lightx2v's Z-Image-Turbo-Quantized is a image-generation model. Z-Image-Turbo-Quantized is an open-weights image model.
by lightx2v
Best for
Ways to use Z-Image-Turbo-Quantized in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your lightx2v API key. osFoundry discovers Z-Image-Turbo-Quantized automatically — assign it to a Maestro role (router, direct, orchestrator, or fallback) in the Pipeline tab and it is live in every chat. Your key, your provider account — no token markup.
Deploy a dedicated endpoint
Z-Image-Turbo-Quantized is open-weights — run it locally for free, or deploy a dedicated GPU endpoint in your workspace for reserved capacity with no rate limits.
Use it in a Room App
Room Apps declare AI features in their manifest, then call them with invokeAI:
import { invokeAI } from '@osfoundry/app-sdk'
// 'summarize' is an AI feature declared in your app manifest.
const result = await invokeAI('summarize', userText)
Call it from your own apps
Once a model is wired into your workspace you can host it as an API and reach it from your own services, scripts, or CI — outside osFoundry.
Z-Image-Turbo-Quantized vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Z-Image-Turbo-Quantized
Is Z-Image-Turbo-Quantized free to use?
Z-Image-Turbo-Quantized is free to run locally on your own hardware. Hosted access through osFoundry is metered (input Free (local), output Free (local)). You can switch between local and hosted at any time.
Can I use Z-Image-Turbo-Quantized commercially?
Commercial use is allowed with conditions. Licence terms not specified — verify the upstream model card before commercial use. Check upstream documentation.
Can I run Z-Image-Turbo-Quantized locally?
Yes. Z-Image-Turbo-Quantized is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Z-Image-Turbo-Quantized best at?
Z-Image-Turbo-Quantized is well-suited to text to image.
How do I use Z-Image-Turbo-Quantized in osFoundry?
Paste your lightx2v API key in the key dialog (or deploy the open weights for self-hostable models), assign Z-Image-Turbo-Quantized to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by lightx2v on January 28, 2026. Source: https://huggingface.co/lightx2v/Z-Image-Turbo-Quantized