What Is Luma Boards and Agents? How to Build AI Storyboards with Thinking Image Models

Luma’s Canvas for AI-Powered Visual Storytelling

Luma AI built its name on video generation with Dream Machine. Now the company has layered in something different: Boards and Agents, an agentic canvas that combines their Uni1 image model with an autonomous workflow for building AI storyboards, character sheets, and video pre-production sequences.

The core idea is straightforward. Instead of generating images one at a time and assembling them manually — downloading files, organizing folders, losing track of which version was which — you work on a shared canvas where an AI agent handles sequencing, consistency, and iteration. The result is a faster, more structured path from a scene description to a finished storyboard.

This article covers what Luma Boards and Agents actually does, how the Uni1 thinking image model approaches generation differently from standard tools, and how to build a practical storyboard workflow using the platform.

What Is Luma Boards?

Luma Boards is an infinite canvas inside the Luma AI platform. It’s similar in concept to tools like Figma or Miro — a shared workspace where visual elements live together — but built specifically for AI image and video generation.

On a Board, you can generate images with text prompts or reference images, organize frames into sequences, use the Agents feature to automatically populate sections of the board, and iterate on specific frames while the rest stays intact.

The canvas approach solves a genuine problem with AI image generation: the workflow is naturally fragmented. You prompt, you get an image, you download it, you move on. Connecting those images into a coherent visual narrative is entirely on you. Boards keeps everything in one place and lets you build relationships between frames — so frame 5 can reference what frame 1 established.

The Canvas vs. Single-Image Generation

Standard AI image tools — Midjourney, DALL-E, most Stable Diffusion interfaces — operate on a per-image basis. You get an image, maybe a set of variations, and then you’re done. Assembling those images into a coherent storyboard is manual work that happens outside the tool.

Luma Boards treats the sequence as the unit of work. When you set up a storyboard, you’re telling the system that these frames belong together — they share a character, an environment, a visual language. The agent uses that established context when generating each new frame.

This is a different mental model than “generate image, next image, next image.” It’s closer to how a director or storyboard artist actually works: thinking in sequences, not isolated shots.

What Is Uni1? How Thinking Image Models Work

Uni1 is Luma’s image generation model. The “thinking” label refers to its architecture: rather than mapping a prompt directly to pixels in a single pass, Uni1 applies a reasoning step first.

What “Thinking” Means for Image Generation

In large language models, thinking or chain-of-thought reasoning means the model works through intermediate steps before producing a final answer. Image models have adapted a version of this approach.

Before Uni1 generates an image, it processes the semantic content of the prompt (what objects, characters, and environments are involved), the relationships between elements (how things are positioned spatially, how light falls, what the implied camera angle means for composition), and context from other images already established in the session or board.

This reasoning pass produces better compositional decisions, more consistent character rendering across frames, and more coherent scene logic than a system that converts a text string to pixels without intermediate processing.

Why Thinking Matters for Storyboarding

Storyboarding requires more than aesthetically pleasing images. Each frame has to show the right character at the right emotional beat, match the lighting and environment of adjacent frames, communicate a camera angle that makes sense in sequence, and maintain consistent design details — clothing, props, facial structure — across every shot.

Standard image models struggle here because they have no real continuity between generations. Each image is essentially independent. Uni1’s reasoning step pulls in contextual anchors from earlier frames, which gives the model a way to make generation decisions that account for what already exists on the board.

For anyone who’s spent time manually trying to maintain character consistency across a Midjourney or DALL-E session, the difference in practice is significant.

How Luma Agents Work

The Agents in Luma Boards and Agents are AI agents that work autonomously on your canvas. You provide a task — “create an eight-frame storyboard for this opening sequence” — and the agent plans the frames, makes structural decisions, and executes the generation, drawing on your reference material and the existing state of the board.

What an Agent Can Do

A Luma Agent can generate a sequence from a brief (you provide a scene description, the agent breaks it into frames and decides on camera angles), maintain visual consistency by tracking character designs and environment references across the board, iterate on feedback (direction like “make frames 3 and 4 use a tighter close-up” gets applied while keeping the rest intact), and generate character sheets by producing multiple reference views — front, side, three-quarter, expressions — from a single character description.

Agent vs. Manual Generation

You can still generate images manually on a Board — that remains available. Agents are most useful when you need to produce a large number of frames quickly, or when you want the system to handle structural decisions (breaking a script into shots, deciding shot types) rather than making those calls yourself.

The practical split: manual generation is better for precise art direction where you want full control over every frame. Agents are better for speed and for handling the connective tissue between shots — the decisions that are time-consuming to make individually but are actually fairly systematic.

How to Build an AI Storyboard with Luma Boards

Here’s a practical walkthrough of building a storyboard from scratch using Luma Boards and Agents.

Step 1: Set Up Your Board

Create a new Board in the Luma AI platform. Give it a name that reflects the project — a film sequence, an ad concept, a game cutscene. This is just establishing the workspace; no generation happens yet.

Step 2: Define Your Style References

Before generating anything, upload or create style reference images. These might be concept art from your project, stills from a film with a visual language you want to match, or character sketches and mood boards.

Reference images anchor the agent’s visual decisions. The more specific your references, the more consistent your output will be. Vague references produce vague results.

Step 3: Create Your Character Sheet

If your storyboard features recurring characters, generate character sheets before starting the sequence. Prompts should include the character’s physical description (height, build, age, distinguishing features), clothing and props, and art style — whether that’s cinematic realism, graphic novel style, or something else.

Ask the agent to generate multiple views: front-facing, three-quarter, profile, and a set of expression variations. Pin these to a dedicated reference section of your board. Everything generated afterward will treat these as the canonical visual definition of the character.

Step 4: Write Your Scene Brief

Draft a short scene brief — a description of what happens in the sequence you want to storyboard. This doesn’t need to be a formatted script. A paragraph describing the action, setting, and emotional tone is enough.

Example:

“A detective enters an abandoned warehouse at night. She sweeps the room with a flashlight, finds a clue on the floor, and hears a sound from above. She draws her weapon and looks up.”

Clear, specific briefs produce better-structured storyboards than vague direction.

Step 5: Run the Agent

Give the agent your scene brief, your character sheet reference, and your style references. Instruct it to break the scene into frames and specify the number you want (six to eight is typical for a short sequence), any specific shot types (wide establishing shot, medium, close-up), and any particular moments that need their own frame.

The agent generates the frames in sequence, applying your references and reasoning about consistency across shots.

Step 6: Review and Iterate

Go through the generated storyboard. For each frame you can accept it, regenerate with modified instructions, or edit it manually using inpainting. Specific feedback works better than general feedback — “frame 4 should show her hand reaching down toward the clue, not her face” is actionable. “Make it better” is not.

Step 7: Export or Connect to Video

Once the storyboard is complete, export the frames as image files or feed them into Luma’s Dream Machine to animate specific shots into video clips.

Building Character Sheets for Consistent AI Generation

Character sheets are one of the most practically useful outputs from Luma Boards and Agents, particularly for animation, game development, and any project involving recurring characters across multiple scenes.

What a Complete Character Sheet Includes

A useful character sheet goes beyond a single reference image. It should include multiple angle views (front, back, side, three-quarter), an expression range (neutral, happy, angry, scared, determined — whatever the character’s emotional vocabulary requires), action poses relevant to the story, and close-up detail shots of face, hands, or key props.

The more complete the character sheet, the more material the agent has to draw on when generating new scenes.

Using Character Sheets Across Multiple Sequences

Once a character sheet is established on your Board, you can reference it in all subsequent generation requests — whether on the same Board or imported into a new one. The agent uses the sheet to maintain consistency even as the character appears in different lighting conditions, environments, and poses.

This is where Uni1’s reasoning capabilities matter most practically. The model isn’t copying pixels from the character sheet; it’s reasoning about what this specific character looks like in a new context — different emotion, different light source, different camera angle — and generating accordingly.

For teams working on a series of episodes or a multi-scene production, this changes the economics of character consistency significantly. What previously required either a dedicated artist or extensive manual prompt engineering becomes a reusable reference artifact.

From Storyboard to Video: Connecting Boards to Dream Machine

Luma’s video generation model, Dream Machine, integrates with the Boards workflow. Once you have a storyboard frame you’re satisfied with, you can use it as a starting image for Dream Machine to animate.

The Basic Flow

Select a storyboard frame on your Board. Pass it to Dream Machine with a motion prompt describing the camera or character movement — “the camera slowly pushes in,” “the detective reaches down to pick up the evidence.” Review the generated video clip. Repeat for each frame that needs animation.

This produces a rough animatic: a video version of your storyboard that demonstrates timing, motion, and camera work before you commit to final production.

Pre-Production Value

For independent filmmakers, animators, and game developers, this workflow replaces a step that previously required either significant budget or significant time. Hiring storyboard artists and animators for pre-production is expensive. Doing it manually in traditional tools is slow.

The output isn’t finished animation — it’s a production planning tool. But it gives directors and producers something concrete to review, share with teams, and iterate on before anyone commits to final production resources.

Where MindStudio Fits in AI Media Workflows

Luma Boards and Agents is a purpose-built tool for visual storytelling within Luma’s own platform. If you’re building AI media workflows that extend beyond a single tool — combining image generation, video production, automated asset delivery, and integration with business processes — that’s where MindStudio’s AI Media Workbench comes in.

MindStudio gives you access to all the major image and video generation models in one place — FLUX, Veo, Sora, and others — without managing separate accounts or API keys. You can chain media generation steps into automated workflows: generate a character sheet, upscale the output, remove the background, and push the final assets to a shared Notion database or Google Drive, all in one pipeline.

Where Luma Boards focuses on the creative canvas, MindStudio handles the operational layer: routing outputs between tools, integrating with business systems, and running workflows on a schedule or triggered by an event.

If you’re a creative team that needs to automate the repetitive work surrounding image production — resizing, formatting, distributing, logging generated assets — building an AI agent in MindStudio that wraps around your generation steps can cut hours of manual work per project. The platform includes 24+ built-in media tools (upscaling, face swap, background removal, subtitle generation) alongside access to the same image and video models you’d use in standalone tools.

You can get started free at mindstudio.ai.

Frequently Asked Questions

What is Uni1, Luma’s thinking image model?

Uni1 is Luma AI’s image generation model. The “thinking” designation refers to its reasoning step — before generating an image, Uni1 processes semantic content, compositional relationships, and contextual information from previous images in the same session or board. This intermediate reasoning produces better consistency across related frames and stronger handling of complex, multi-element prompts compared to standard single-pass image generation.

What is a thinking image model, and how does it differ from standard image generation?

Standard image generation maps a text prompt to pixels in a single pass. Thinking image models apply an intermediate reasoning step — similar to chain-of-thought reasoning in language models — where the model evaluates compositional relationships, contextual signals, and semantic structure before committing to output. The practical benefits include stronger cross-frame consistency, better handling of complex scene logic, and more reliable character rendering across multiple generations.

Is Luma Boards free to use?

Luma AI offers a free tier with limited generation credits. Full access to Boards, Agents, and the Uni1 model is available on paid plans. Credit allocations and pricing change periodically, so checking Luma’s current pricing page before committing is worth doing.

How does Luma Boards maintain character consistency across storyboard frames?

Luma Boards uses character sheets pinned to the board as persistent visual references. When generating new frames, the agent draws on these references and uses Uni1’s reasoning capabilities to apply consistent character design — same face, clothing, proportions — across different poses, lighting conditions, and camera angles. The consistency isn’t perfect in every case, but it’s substantially better than trying to maintain character appearance through prompt engineering alone.

Can I export storyboards from Luma Boards?

Yes. Individual frames and complete storyboard sequences can be exported as image files. Frames can also be passed directly to Dream Machine for video animation, or used in external production tools like Premiere Pro, After Effects, or dedicated storyboard software.

How is Luma Boards different from other AI storyboard tools?

Most AI storyboard tools are either standard image generators with a board-style layout or tools that require manually managing consistency between frames. Luma Boards differentiates through the Agents component — an AI that actively manages the storyboard structure — and the Uni1 thinking model, which reasons about consistency across frames rather than treating each generation as independent. The direct integration with Dream Machine for video animation is also a meaningful practical advantage for video pre-production workflows.

Key Takeaways

Luma Boards is an agentic canvas for building visual sequences — storyboards, character sheets, and shot lists — with built-in continuity management across frames.
Uni1 applies a reasoning step before generating, which produces better character consistency, stronger compositional logic, and more coherent scene relationships than single-pass image models.
Luma Agents handle structural decisions: breaking a brief into frames, applying visual references, and iterating based on specific feedback — so you can direct rather than manually sequence.
The Dream Machine integration lets you animate storyboard frames into rough video clips, giving you a complete pre-production pipeline from scene description to animatic.
For AI media workflows that go beyond a single tool, MindStudio’s AI Media Workbench provides access to multiple image and video models in one place, along with the ability to chain generation into automated pipelines connected to your existing tools.

If you’re working on video pre-production, animation, game development, or any project that benefits from consistent AI-generated visuals across a sequence, Luma Boards and Agents is a significant step forward from manual per-image generation. And if you want to automate what happens before or after those images are created, start with MindStudio for free.