AI Prompt Assistant

FrameLoom Prompt Assistant

Chat with AI to craft the perfect video generation prompts. Get expert suggestions, refine your ideas, and explore curated prompt examples below.

How can I help you today?

Ask me anything about video generation prompts. I can help you write, refine, or translate your ideas into effective prompts.

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Prompt Examples

Ocean Sunset Timelapse

A breathtaking timelapse of a golden sunset over the Pacific Ocean, waves gently crashing on a rocky shoreline, warm amber and pink hues reflecting on the water surface, cinematic 4K quality, smooth camera slowly panning right

NatureTimelapseCinematic

Cyberpunk City Night

A neon-lit cyberpunk city street at night, rain-soaked pavement reflecting holographic advertisements, flying cars passing overhead, a lone figure walking with an umbrella, Blade Runner aesthetic, moody blue and purple lighting

Sci-FiUrbanAtmospheric

Coffee Pour in Slow Motion

Extreme slow-motion close-up of espresso being poured into a ceramic cup, rich crema forming on the surface, steam rising elegantly, warm studio lighting with a soft bokeh background, product advertisement style

ProductSlow MotionClose-up

Astronaut on Mars

An astronaut walking across the rust-red Martian landscape, dust swirling around their boots, Earth visible as a tiny blue dot in the orange sky, dramatic long shadows from the low sun, NASA-style documentary cinematography, wide establishing shot

Sci-FiSpaceDocumentary

Prompt Writing Tips

1.Be specific — describe lighting, camera angle, mood, and style
2.Set the motion — specify camera movement (pan, zoom, tracking shot)
3.Reference styles — mention film directors, genres, or visual aesthetics
4.Include details — textures, colors, atmosphere, and time of day matter
5.Keep it focused — one clear scene per prompt works best

Prompt Guides

Prompt guides and workflow notes for FrameLoom Studio

Everything that used to live on the separate guides pages is now collected below the chat experience, so you can refine prompts and read the supporting guidance in one place.

How to Write Wan 2.7 Video Prompts

Learn a practical prompt-writing process for Wan 2.7 — cleaner camera direction, stronger scene anchors, and visual storytelling that holds up across the aggregated models.

Step-by-step workflow

1. Define the visual goal

Start with the subject, action, and setting so the model knows what the shot is trying to show before style details are layered on.

2. Add camera and motion cues

Translate your idea into framing, movement, and pacing language only where it improves the shot rather than making it noisier.

3. Refine style and atmosphere

Use mood, lighting, and texture language to shape the result, then remove any conflicting directions that blur the scene.

What separates a good prompt from a generic one

A generic prompt leaves the model too much room to invent details that may not fit your goal. A better prompt makes the visual target obvious early, then adds style, motion, and emotional texture in a deliberate order.

How to structure a Wan 2.7 scene prompt

Think in layers. Start with who or what is in frame. Then describe the action. After that, shape the camera behavior, visual style, and emotional tone.

Subject and scene anchor

Lead with the main subject and location so the frame has a stable center of gravity.

Action and pacing

Describe what changes inside the shot, not just what exists in the shot.

Camera and mood language

Use cinematic wording to shape how the viewer should feel, but keep it tied to the visual event.

FAQ

Should I write prompts like prose or like instructions?

The best prompts usually sit between the two. They should read naturally, but still give clear visual instructions about subject, action, camera, and mood.

Why do text-only prompts often feel generic?

They usually skip specificity. When the subject, action, and scene objective are vague, the model fills the gap with average-looking choices.

Related resources

Wan 2.7 Text to Video

Use this guide alongside the dedicated text-to-video landing page.

Prompt Assistant

Refine rough prompt drafts through chat-assisted ideation.

How to Use Wan 2.7 Image Prompts

Learn how to write stronger image prompts inside the Wan 2.7 aggregator with clearer composition, style control, and better use of references.

Step-by-step workflow

1. Describe the core subject clearly

Name the subject, environment, and intended style in the opening phrase so the model gets a reliable frame for the image.

2. Add composition and lighting direction

Use composition cues, camera distance, and lighting language to guide how the image should feel rather than only what it contains.

3. Use references when the visual target is specific

Reference images help lock the composition or mood when your target is more exact than a text-only prompt can easily convey.

How image prompts differ from video prompts

Image prompts need more pressure on composition and still-frame detail. You are not describing a sequence of action, but rather a single visual outcome that should feel intentional and complete.

Prompt elements that matter most for image quality

The strongest image prompts balance clarity with restraint. Too few details can feel generic. Too many details can create noisy or contradictory results.

FAQ

Should I use style words at the beginning of an image prompt?

Usually it is better to start with the actual subject and scene, then layer style language once the visual anchor is clear.

When are reference images most useful?

They are most useful when you care about composition, pose, mood, or visual direction that is hard to describe precisely with text alone.

Related resources

Wan 2.7 Image Generator

Explore the image-first landing page and generator workflow.

Wan 2.7 Image to Video

Use strong still prompts as the basis for motion-led workflows.

Text to Video vs Image to Video

Compare text-to-video and image-to-video workflows so you can choose the right Wan 2.7 process for your next project.

Step-by-step workflow

1. Decide whether you already have a visual anchor

If you already have a still image or product render, image-to-video often gives better continuity than text-only prompting.

2. Choose based on creative uncertainty

If the concept is still abstract and you need ideation, text-to-video is often the better starting point.

3. Match the workflow to the deliverable

Pick the method that best fits the asset you already have and the amount of control you need over composition or motion.

When text-to-video is the better choice

Text-to-video works best when you are still writing the scene into existence. It is ideal for rough ideation, storyboard drafting, and translating scripts or marketing copy into visual moments.

When image-to-video is the better choice

Image-to-video is stronger when you already know what the frame should look like. It tends to preserve brand direction and composition more consistently because the model starts from a real visual anchor.

FAQ

Which workflow gives more visual control?

Image-to-video usually offers more visual continuity when you already have a strong still reference. Text-to-video gives more freedom when the scene is still being invented.

Which workflow is better for prompt writing practice?

Text-to-video is the better environment for improving prompt-writing skill because the written description has to carry more of the scene.

Related resources

Wan 2.7 Text to Video

Explore the text-first workflow in detail.

Wan 2.7 Image to Video

Explore the reference-led workflow in detail.

How to Make Product Demo Videos with AI

Learn how to turn product images, scripts, and features into clear demo videos using the Wan 2.7 aggregator workflow.

Step-by-step workflow

1. Map the product story

Decide what the viewer needs to understand first, second, and third so the demo follows a clear sequence rather than a random feature list.

2. Collect the right inputs

Use screenshots, product renders, campaign stills, and short text scenes to give the model enough material for accurate visual direction.

3. Convert features into visual moments

Write prompts and transitions around actions, user benefits, and interface focus instead of only listing technical capabilities.

Why product demos benefit from AI workflows

Traditional product demos can be slow to storyboard and expensive to revise. AI workflows help teams test angles quickly, especially when the story depends on product screens, concept renders, or feature mockups.

What makes a product demo prompt effective

The prompt should describe what part of the product the viewer is noticing, what changes on screen, and what emotional tone the demo should communicate. Clarity beats hype in this context.

FAQ

Should a product demo start with text-to-video or image-to-video?

If you already have product visuals, image-to-video often creates a smoother starting point. If you are still shaping the narrative, text-to-video can help you plan the story first.

What assets help product demo generation the most?

Screenshots, polished product stills, UI mockups, and concise scene prompts are usually the most useful assets.

Related resources

Marketing Videos Use Case

See how product demo logic extends into broader campaign work.

Wan 2.7 Video Generator

Explore the multi-modal workflow for demo production.

GPT Image 2 Guide: Use Cases, Prompting Tips, and Workflow Ideas

A practical GPT Image 2 guide covering what OpenAI's newest image model does well, how to prompt it for better results, and when to use it instead of older or specialist image models.

Step-by-step workflow

1. Define the asset before the aesthetic

Start by naming the actual deliverable such as poster, brochure, title card, character sheet, or concept frame. GPT Image 2 tends to respond better when the prompt reads like a production brief instead of a pile of style adjectives.

2. Write in layers: subject, composition, typography, finish

Anchor the subject and camera first, then describe layout, readable text zones, color direction, and finishing details. This keeps the prompt organized and makes revision requests much easier.

3. Switch from generation to editing once the direction is close

Do not keep regenerating from scratch after the concept is 80 percent right. Move into an edit mindset and describe exactly what should change, what should stay, and what must remain recognizable.

What GPT Image 2 is and why it is getting attention

As of April 21, 2026, OpenAI lists the snapshot `gpt-image-2-2026-04-21` and describes GPT Image 2 as its state-of-the-art image generation model for fast, high-quality generation and editing. That matters because it positions GPT Image 2 as the current reference point for teams building image workflows in the OpenAI ecosystem.

OpenAI's launch examples point at the categories the company wants people to trust the model with: realistic notebook pages, multilingual editorial posters, manga-style pages, brochure layouts, and stylized art. The signal is clear. The pitch is not only prettier pictures. It is usable pictures with layout discipline and better instruction following.

Where GPT Image 2 is strongest

GPT Image 2 is especially compelling when the output has to look usable rather than merely impressive. It is a strong fit for creative-ops teams, founders, marketers, and video producers who care about readable composition, controllable edits, and fast movement from rough idea to presentable asset.

Typed and layout-heavy creative

Posters, cover art, notebook visuals, brochure-like assets, and social ads all punish weak layout discipline. GPT Image 2 stands out because OpenAI is explicitly showcasing categories where text placement, hierarchy, and design clarity matter.

Image-led iteration

The model becomes more useful once a team stops thinking only in terms of fresh generations. When you already have a promising draft or a reference frame, the edit workflow is often where real production speed shows up.

Style range without losing usability

OpenAI's examples span realism, editorial portraiture, manga, and more stylized visual directions. That range matters because modern teams rarely use one image model for a single aesthetic. They use it across ads, product education, thumbnails, and creative exploration.

How to prompt GPT Image 2 for better output

The highest-leverage change is to stop writing prompts like a bag of aesthetic adjectives. GPT Image 2 tends to work better when the prompt behaves like a short creative brief. Name the asset, describe the subject, define the composition, and specify what must be readable or preserved.

If the prompt includes text or layout expectations, say that directly instead of hoping the model will infer them from mood language alone. Clarity about purpose usually beats decorative wording.

How GPT Image 2 compares with older and specialist models

Compared with DALL-E 3, GPT Image 2 is the more current OpenAI choice and the more relevant one for teams designing a fresh workflow today. Compared with FLUX.2, GPT Image 2 feels strongest when you want one current OpenAI-native model for both creation and editing without building a more model-family-specific process.

If your workload depends on heavy multi-reference compositing, FLUX.2 has a clearer official story there. If you mainly want a current OpenAI image stack that appears to care deeply about typography-heavy assets, editorial composition, and polished brand visuals, GPT Image 2 is the one to study first.

Why this matters for video-first teams

Even on an AI video site, strong image models matter upstream. They help teams build moodboards, thumbnail directions, poster frames, title cards, ad stills, and first-frame references that later become video inputs.

That makes GPT Image 2 relevant not as a side hobby, but as a practical pre-production tool for teams that care about visual direction before motion ever starts.

FAQ

Is GPT Image 2 the current OpenAI image model?

As of April 21, 2026, OpenAI's current model page lists the GPT Image 2 snapshot `gpt-image-2-2026-04-21` and positions it as the company's state-of-the-art image model for fast generation and editing.

Is GPT Image 2 better than DALL-E 3?

For most fresh evaluations, yes. OpenAI now frames DALL-E 3 as a previous-generation image model, while GPT Image 2 is the current image model to benchmark first.

When should I choose FLUX.2 instead of GPT Image 2?

FLUX.2 is the stronger first stop when your workload depends on multi-reference editing, reference blending, or very explicit vendor-level control over how multiple inputs are combined.

Why does GPT Image 2 matter on a video-first site?

Because image models often sit upstream of video production. Teams use them for poster art, title cards, moodboards, thumbnails, and first-frame references before they generate motion.

Related resources

GPT Image 2 vs FLUX.2

Use the decision page when you are choosing a production image stack.

How to Use Wan 2.7 Image Prompts

See how image-prompt structure translates into video-first creative workflows.

Showcase Library

Browse public visuals and generated examples that can feed into broader creative direction.

GPT Image 2 vs FLUX.2: Which AI Image Model Is Better for Production?

A practical comparison of GPT Image 2 and FLUX.2 across editing, typography, multi-reference workflows, photoreal direction, and real production use cases.

Step-by-step workflow

1. Map your dominant workload

Decide whether you mostly produce typed marketing assets, cinematic key visuals, product composites, or reference-heavy edits. The answer tells you more than a generic beauty contest ever will.

2. Ask whether multi-reference work is core or occasional

If multiple reference images are central to the workflow every week, FLUX.2 deserves serious weight. If they are occasional, GPT Image 2 may still be the simpler standard.

3. Choose the stack that lowers team friction

The best model is the one your team can brief, iterate, review, and ship with repeatedly. Operational fit matters just as much as isolated output quality.

Quick answer

If you want the shortest recommendation, choose GPT Image 2 when you want one current OpenAI-native model that can handle both generation and editing without a complicated decision tree. Choose FLUX.2 when reference-heavy editing, compositing, and model-family-level control are central to your workflow.

That means neither model is the universal winner. They optimize for different types of certainty. GPT Image 2 optimizes for a cleaner default. FLUX.2 optimizes for deeper specialist control.

Where GPT Image 2 wins

GPT Image 2 is strongest when the creative team wants one current OpenAI image model that feels modern, capable, and straightforward to operationalize. It is especially compelling for marketing creatives, editorial assets, and visual direction work that benefits from strong instruction following without introducing too many product-level choices.

Typed and editorial assets

OpenAI's own examples keep returning to posters, notebook pages, brochure-like layouts, and other composition-heavy deliverables. That makes GPT Image 2 especially interesting for teams creating usable brand assets rather than only dramatic art pieces.

Simple stack for generation plus edits

A simpler product story matters in practice. Teams move faster when they can align on one default model, then spend their energy on briefing and review instead of internal model debates.

Better fit for OpenAI-heavy teams

If the rest of your product workflow already depends on OpenAI tools, GPT Image 2 reduces context switching and lowers the number of moving parts your team has to manage.

Where FLUX.2 wins

FLUX.2 becomes more compelling as soon as reference handling stops being a niche need and becomes a core part of the pipeline. Product marketers, fashion teams, ecommerce studios, and interior-composite workflows often care more about reference blending, color control, and controlled edits than about having the simplest default answer.

Multi-reference editing

Black Forest Labs explicitly emphasizes multi-reference input and edit-centric control in the FLUX.2 family. That matters when your brief depends on several products, poses, colorways, or brand references landing in one coherent image.

More workflow knobs for larger teams

Premium, pro, flex, and other variants can be a burden for a small team, but for a larger production organization they can also be a feature. Different jobs can justify different quality, speed, or cost tradeoffs.

Photoreal product and setup work

When the goal is a polished product composite or a tightly controlled photoreal scenario, specialist reference handling often matters more than having the simplest model menu.

What about FLUX.1 Kontext and DALL-E 3?

Readers still search for FLUX.1 Kontext and DALL-E 3 because both names became shorthand for earlier phases of the image-model market. They still matter as comparison anchors, but neither is the cleanest starting point for a new evaluation in 2026.

Kontext remains useful for understanding why the FLUX ecosystem is associated with edit-centric workflows. DALL-E 3 remains useful because many buyers still want to know whether OpenAI has truly moved beyond its older image model generation. In both cases, the more current answers are FLUX.2 and GPT Image 2.

Which model should a marketing team choose?

If the team mainly makes campaign posters, brochure-style visuals, title cards, social creatives, and concept frames, GPT Image 2 is the stronger first choice. The official OpenAI positioning and launch examples both suggest a serious focus on usable, layout-aware assets.

If the team often combines product shots, multiple references, controlled recolors, and more complex edit scenarios, FLUX.2 deserves the edge. It is built for a more reference-intensive creative process.

Which model should a video team choose for upstream assets?

Video teams should ask a narrower question: which model helps us make better references before motion starts? GPT Image 2 is often the easier recommendation for title cards, poster directions, moodboards, and storyboard frames because it fits a broad creative brief without much workflow ceremony.

FLUX.2 is better when those upstream assets need heavier compositing or several source references blended with more control. In other words, GPT Image 2 is the simpler visual-planning engine, while FLUX.2 is the stronger specialist when input complexity becomes the job.

FAQ

Is GPT Image 2 basically a DALL-E 3 replacement?

For most fresh evaluations, yes. OpenAI now frames DALL-E 3 as a previous-generation image model, while GPT Image 2 is the more current model to benchmark first.

Is FLUX.2 better for image editing?

It often will be when the workflow depends on multiple references, controlled composites, or more explicit vendor-level control over inputs. That is where FLUX.2 has the clearest edge.

Should I still evaluate FLUX.1 Kontext?

Only if your team already prototyped there or your buyers keep asking about it. For a fresh 2026 evaluation, FLUX.2 is the more strategic benchmark in the Black Forest Labs ecosystem.

Which model is better for marketing creatives?

GPT Image 2 is usually the stronger first choice for posters, brand visuals, editorial layouts, and other assets where usable composition and clearer text handling matter.