Uni-1 by Luma AI

Uni-1 Image Intelligence.

A multimodal model for image reasoning, editing, and generation.

Public free trial opened on March 23, 2026

0.51RISEBench Score
2048pxOutput Resolution
46.2ODinW-13 mAP
8Reference Images
Built-In Generator

Uni-1 Image Generator

Generate and edit images directly on the homepage.

Use the main CTA above for the official Uni-1 demo on Luma.

Seedance 2.0

Le tue immagini generate appariranno qui

Showcase

Uni-1 Outputs From Real Prompts.

A hand-picked gallery of Uni-1 image results, compressed for fast loading and placed directly under the homepage generator.

What Is Uni-1

Uni-1 —

Unified Intelligence for Images.

Uni-1 is Luma AI's first Unified Intelligence model and the opening release in its broader multimodal roadmap. It launched alongside Luma Agents on March 5, 2026, then opened public free trial access on March 23, 2026, positioning itself as a reasoning model that can generate pixels instead of a conventional image-only generator.

It is built for designers, marketers, storytellers, and enterprise creative teams who need more than style transfer. Uni-1 shines when prompts contain multiple constraints, ambiguous subject relationships, heavy editing, reference-driven consistency, or infographic-like structure that ordinary diffusion pipelines often miss.

One Model, One Weight Set

Uni-1 runs understanding and generation inside a single autoregressive architecture, without handing work from a language model to a separate image model.

Think-and-Generate Workflow

Its text and image tokens live in one interleaved sequence, so the model reasons through intent while it is actively generating the image.

Reference-Heavy Production Control

From sketch refinement to identity-consistent character work, Uni-1 supports one to eight references for higher control across real production workflows.

A Roadmap Beyond Images

Luma frames Uni-1 as the first step toward a unified model family that expands into video, audio, speech agents, and interactive world simulation.

How It Works

Uni-1 in

Three Reasoning Steps.

From intent to pixels, Uni-1 plans before it renders.

01
01

Describe the Goal

Write a natural-language brief, upload a sketch, or attach one to eight reference images. Uni-1 interprets role relationships, layout constraints, style cues, and ambiguous instructions inside one shared text-image sequence instead of splitting understanding and drawing into separate systems.

Use explicit relationships like 'horse rides astronaut' or 'foreground sharp, background soft' to let Uni-1 resolve scene logic precisely.

02
02

Let Uni-1 Reason

Before and during generation, Uni-1 decomposes the request, plans composition, checks physical plausibility, and autoregressively emits image tokens. That unified loop is why it performs better on spatial, causal, and logical constraints than diffusion-first pipelines.

Uni-1 is strongest when the prompt includes multiple constraints that would normally confuse standard text-to-image tools.

03
03

Iterate with Context

Refine the output across multiple turns without restating every detail. Uni-1 keeps context, applies new edits or references, and preserves identity, framing, and structure more reliably across follow-up generations.

Use follow-up edits for pose changes, age progression, infographic cleanup, or reference blending instead of regenerating from scratch.

Uni-1 Features

Why Uni-1 Feels

Different from Diffusion Models.

Uni-1 wins when image generation requires understanding, not just style transfer.

Unified Understanding and Generation

Uni-1 uses a single decoder-only autoregressive transformer for both comprehension and pixel generation. It avoids the classic intent gap where one model interprets the prompt and another model tries to draw it.

One architecture. One reasoning loop. One output.

Structured Internal Reasoning

Luma describes Uni-1 as a multimodal reasoning model that can generate pixels. It can break instructions into subproblems, plan composition, and self-correct while rendering the image.

It reasons before the image is finished.

Multi-Reference Control

Uni-1 accepts one to eight reference images for character consistency, pose transfer, UV workflows, and sketch-to-polish generation. That makes it useful for real production pipelines instead of only one-off prompt experiments.

Up to 8 references in a single workflow.

State-of-the-Art Visual Reasoning

On RISEBench, Uni-1 reached 0.51 overall, ahead of Nano Banana 2, Nano Banana Pro, and GPT Image 1.5. Its spatial reasoning score of 0.58 and logic score of 0.32 show a clear edge on tasks that require actual thinking.

Built for spatial, causal, and logical edits.

Professional 2K Output

Uni-1 currently targets 2048px output for production-ready image generation and editing. That resolution, combined with strong reasoning and reference control, makes it practical for marketing assets, storyboards, and polished design work.

2K output built for real creative workflows.

Infographics and Chinese Text

Community feedback highlights unusually strong Chinese rendering, meme literacy, and infographic quality. Uni-1 also supports more than 76 artistic and cultural styles, from manga to polished commercial design.

Better text. Better culture-aware outputs.

Enterprise-Grade Creative Compression

Luma positions Uni-1 inside its Agents platform for ad localization, storyboarding, and large-scale asset production. Early enterprise stories describe campaigns compressed from long timelines to much faster delivery windows.

From boutique teams to global campaign ops.

Roadmap to Video, Audio, and Agents

Uni-1 is the first step in Luma's broader Unified Intelligence roadmap. The same architecture direction is meant to extend into video, audio, speech agents, and interactive world simulation.

Not just an image model. A platform direction.

Use Cases

Uni-1 for Reasoning-Heavy Creative Work.

Where ordinary image generators break on ambiguity, Uni-1 keeps going.

Storyboards

Shot Planning with Real Scene Logic

Generate storyboard frames, beat boards, and continuity explorations that preserve camera direction, role relationships, and lighting logic. Uni-1 is especially useful when a scene contains multiple subjects and precise foreground/background instructions.

Social Content

Memes, Infographics, and Culture-Native Visuals

Create visuals that mix text, iconography, internet culture, and strong composition. Uni-1's cultural literacy and stronger text rendering make it a better fit for modern social posts than purely aesthetic models.

Advertising

Localized Campaign Production

Use multi-reference workflows to preserve brand subjects, products, and layouts while generating many language or market variants. Uni-1 helps creative teams turn a master concept into a full campaign system faster.

Product & Design

Concepts, Mockups, and Visual Exploration

Move from sketches or rough comps to polished visuals with consistent structure. Product teams can test packaging, fashion, props, or spatial layouts without rewriting the entire prompt every time.

Character Systems

Identity-Consistent Character Iteration

With a single reference image, Uni-1 can keep a character stable across age progression, camera changes, and environmental shifts. That makes it useful for comics, fashion lookbooks, and branded mascots.

Education & Research

Explain Complex Ideas Visually

Generate diagrams, storyboards, and explanatory graphics that require accurate spatial organization and readable labeling. Uni-1 is well suited to teaching materials, scientific explainers, and visual knowledge work.

Community Signal

Why Early Users Keep Talking About Uni-1.

It feels like idea-to-result instead of prompt-to-image. The model resolves ambiguous instructions the way a collaborator would.

XC
X Creator
Visual AI Thread

We stopped simplifying prompts for the model. Uni-1 handles nested relationships, role swaps, and composition notes without collapsing the scene.

AD
Agency Designer
Creative Workflow Test

Reference control is the real unlock. Sketches, photos, and character anchors survive editing much better than in diffusion-heavy tools.

MD
Motion Director
Preproduction Team

For infographics and Chinese text, Uni-1 is the first model I would reach for. It feels cultured, not just polished.

PD
Content Strategist
Education Publisher

At 2K, Uni-1 was more reliable than the tools we were benchmarking on difficult prompts and multi-reference tasks.

SC
Growth Marketer
AI Creative Ops

You can see the architecture difference. It behaves more like an agent that evaluates and fixes, not a model blindly denoising pixels.

XR
Research Observer
X Community Review

Explore

Uni-1

See why March 2026 became a turning point for image generation. Test the public demo on Luma or use the FAQ below to compare benchmarks, workflows, and access.

Official public free trial opened on March 23, 2026. API access is rolling out through Luma's waitlist and enterprise channels.

Official Luma demoPublic free trialAPI waitlist availableEnterprise rollout underway
Uni-1 FAQ

Uni-1 —

Frequently Asked Questions.

Uni-1 is Luma AI's first Unified Intelligence multimodal model for image understanding and image generation. It launched with Luma Agents on March 5, 2026 and opened public free trial access on March 23, 2026.

Diffusion systems usually start from noise and rely on separate prompt-understanding components. Uni-1 uses a single decoder-only autoregressive transformer that reasons and generates inside one loop, so it can plan composition, resolve ambiguity, and self-correct while producing the image.

Uni-1 scored 0.51 overall on RISEBench, including 0.58 on spatial reasoning and 0.32 on logic reasoning. On ODinW-13, the full model reached 46.2 mAP, essentially tied with Gemini 3 Pro at 46.3 and ahead of Qwen3-VL-Thinking at 43.2.

Uni-1 is currently positioned around 2048px output for image generation and editing. Luma frames that as its main production resolution today, with broader multimodal expansion planned beyond images.

Uni-1 supports one to eight reference images in a single workflow. That range is designed for character consistency, style transfer, pose guidance, UV tasks, and sketch-to-finish editing.

Yes. Early community feedback consistently highlights strong Chinese text rendering, good infographic quality, and better handling of culture-specific formats like memes and manga. Some users still note room to improve on non-Latin text edge cases and very high-resolution speed.

Uni-1 leads on reasoning-heavy benchmarks and offers stronger reference and editing control than many competing tools. Nano Banana can still be faster on some lower-resolution tasks, while Midjourney v8 may retain an edge on pure aesthetic polish rather than structured problem solving.

You can try Uni-1 on Luma's official web experience at lumalabs.ai/uni-1. API access is rolling out through a waitlist, and enterprise teams can access it through Luma Agents.

Still have questions? Contact us