How to Generate AI Video with Rivya

Video is where people most quickly feel the difference between a demo AI product and a workflow product.

That is because video asks more from the system:

• higher cost
• longer waits
• more parameters
• more sensitivity to source material and setup

Rivya’s video flow is built around that reality.

This page is the decision-layer guide for video work. If you need the stricter workflow reference for how Rivya splits text-to-video, image-guided motion, transformation, and audio-aware output, Video Workflows in Rivya is the paired workflow reference.

What We Verified

This guide was reviewed against Rivya's live video paths and docs on April 17, 2026.

• public video paths reviewed: /video, /ai-models, /video, and current live video model pages
• signed-in continuation path cross-checked in docs: /studio/video/[modelSlug], History, and Task Lifecycle
• related product guides reviewed: Current Live Features in Rivya, Video Workflows in Rivya, References and Uploads in Rivya

Start in the Right Place

The cleanest public entry points are:

• /video if you want to browse the public video pages
• AI Models if you want to compare the catalog first

Those pages are useful for comparison and selection.

Right now, actual generation and saved continuity still depend on sign-in. So the public pages help you choose, while the signed-in product is where execution becomes real.

First, Decide What You Already Have

The first useful question is not "which model is biggest?"

It is whether the run starts from:

• text
• a still image
• existing moving footage

That question narrows the choice much faster than reputation does.

Why Video Goes Wrong Faster

Video workflows break down faster than image workflows when:

• the brief is still unstable but the run is already expensive
• the wrong source type is chosen before the model is chosen
• duration, ratio, or audio options are treated like filler instead of real constraints

Then Pick the Model by Job, Not Hype

Different video models in Rivya are built for different first constraints.

Some are better for:

• broad defaults
• premium finish
• reference-aware motion
• cheap first-run testing
• video-to-video transformation

The right model usually depends on the stage of the project, not only on raw prestige.

If you want the workflow-level version of that choice, Video Workflows in Rivya is the best companion page.

Use the Controls Like They Matter

Video forms in Rivya are model-specific.

Depending on the selected model, you may see controls for:

• duration
• resolution
• aspect ratio
• camera behavior
• audio-related options

Those are not decorative. They change both cost and result shape, so it is worth treating them like part of the creative decision, not just form filler.

Duration is a budget field as much as a creative field.

Write the Prompt Like Direction Notes

A good video prompt usually needs more than a subject.

Useful details often include:

• what the scene is
• how the camera behaves
• the pace or feeling
• whether the shot should feel static or dynamic
• how the output will actually be used

That matters even more when the clip is part of a launch, ad, or presentation workflow.

Expect a Real Task Lifecycle

Video work in Rivya is not instant chat output. It becomes a tracked generation task.

That means the system:

1. validates the request
2. creates the task
3. consumes the required credits
4. sends the job upstream
5. tracks it through WAITING, GENERATING, SUCCESS, or FAILED

That is one of the clearest differences between a lightweight demo and a usable video workflow.

Reuse History Instead of Starting Over

Once a video settles, it does not need to vanish into browser memory.

Rivya pushes completed and failed runs into generation history so you can:

• review the result later
• compare what worked
• reopen the Studio with the same direction
• continue without rebuilding context from scratch

That matters especially in video, because the best next prompt often depends on a specific earlier clip.

Let Notifications Carry the Long Wait

Video tasks often take long enough that you are no longer staring at the same page when the result lands.

That is why notifications matter here. Success, failure, or low-credit problems can stay visible after the moment passes.

The Shortest Practical Path

If you just want the clean version of the flow, use this:

1. compare one or two likely models in AI Models
2. decide whether the run starts from text, an image, or existing footage
3. sign in before the actual execution step
4. use the real controls instead of ignoring them
5. submit the task
6. check history after the run settles
7. use the next iteration from what you learned, not from scratch

That is usually the difference between "I generated a clip once" and "I have a repeatable video workflow."

If You Need The Workflow Reference Next

• If the real job is a broader campaign clip or marketing asset, AI Video Generator for Marketing is the better decision page.
• If you are still choosing the model family itself, go to Best AI Video Generator in 2026.
• If audio is the real requirement, AI Video Generator With Audio is the better next page.
• For the product-side flow, keep Public vs Authenticated Workflows in Rivya, References and Uploads in Rivya, and Video Workflows in Rivya open together.

Prepare The First Tracked Video Task

In Rivya, a video run is a tracked task. Prepare it like one.

Before submitting, decide:

• whether the run starts from text, an image, or existing footage
• why the selected model fits this stage
• duration, aspect ratio, quality, and audio expectations
• what the first clip must prove
• where you will review the settled task
• what should happen if the task succeeds or fails

This makes video generation feel less like a one-off demo and more like a production step with a visible state.

Decide From The Settled Task

Once the task finishes, review the result and the recorded state together.

Check:

• whether the task succeeded, failed, or needs credit review
• whether the first seconds communicate the idea
• whether motion, subject, and audio stayed aligned
• whether History preserves enough context for the next run
• whether the next step is a tighter prompt, a different model, or a different input type

If the clip is useful, continue from the saved result instead of rebuilding the project from memory. If it fails, use the task state and brief to decide what to change first.