LLaDA-Image Editor

LLaDA-Image Editor — 지시 안내 편집

Treat LLaDA-Image as an online image editor: upload a reference, write the instruction, and update style or content without a separate editing backbone.

이 사이트는 LLaDA-Image용 독립 서드파티 브라우저 워크스페이스이며 inclusionAI와 무관합니다. 공식 코드와 가중치는 GitHub, Hugging Face, ModelScope에 있습니다.

Edit an existing image with instructions

Upload a photo or previous generation, then describe what should change. LLaDA-Image keeps the subject recognizable while you update background, style, clothing, or on-image text — without a separate editing model.

LLaDA-Image instruction-guided image editing example
Public showcase strip highlighting instruction-guided editing with content preservation.

Edit workflow on this site

Use the workspace upload path when you already have a source image.

Upload a reference

Start from a photo, product shot, or previous generation that should stay recognizable.

Describe the change

Write a concrete instruction: change background, restyle clothing, fix text, or adjust lighting — keep what must stay.

Compare Base and Turbo

Turbo helps you explore edit directions quickly; Base is better when fidelity matters more than latency.

When this editor path fits

Instruction editing is useful when regenerating from scratch would break identity, layout, or product details. LLaDA-Image’s unified checkpoint is meant for that workflow — verify details against the official paper and repo.

  • One model family for generate and edit intents.
  • Useful for posters and bilingual layout tweaks after a first pass.
  • Independent workspace with clear non-affiliation disclaimer.

Editor FAQ

Is LLaDA-Image only a generator?

No. The open family covers text-to-image and instruction-guided editing. This editor page is for the latter intent.

What size constraints matter?

Follow public Diffusers guidance: editing paths typically expect dimensions divisible by 32. The workspace helps you stay in practical ranges.

Can Turbo edit as well?

Yes. Turbo keeps gen + edit intent with fewer sampling steps for faster loops.

Is this an official editor product from inclusionAI?

No. Official weights and code are on GitHub/Hugging Face. This site only provides an independent online UI.

Edit a reference now

Upload an image in the workspace and describe the change. Keep GitHub open for official sampling tips.