Text-to-Video vs Image-to-Video: Which Should You Use?

Most AI video tools offer two starting points: text-to-video (T2V) and image-to-video (I2V). They sound similar but solve different problems. Choosing the right one saves credits and gets you a usable result faster.

Text-to-video (T2V)

You describe a scene in words and the model invents everything - subject, setting, motion, lighting. It is the fastest way to explore ideas and great when you have no source image and want the model to surprise you.

Trade-off: less control over the exact look. The same prompt can produce very different frames, so you iterate on wording.

Image-to-video (I2V)

You give the model a starting image (a product shot, a character, a generated still) and it animates it. This is the right choice when the look matters - brand assets, a specific face, or a composition you already nailed in an image generator.

Trade-off: you need a good source image first. A common, reliable workflow is to generate the perfect still, then animate it.

When to use which

• Exploring concepts with no assets yet -> text-to-video.
• You have a product photo or character to keep consistent -> image-to-video.
• You want a precise composition -> generate the image first, then image-to-video.
• Reference-driven, multi-image scenes -> use a model that accepts multiple reference images (e.g. Seedance 2.0).

Models and where to run them

Leading video models include Seedance 2.0, Gemini Omni, and Kling 3.0, each with their own strengths in motion, fidelity, and duration. Karya supports both text-to-video and image-to-video across these models from one canvas, and shows the credit cost before you generate so there are no surprises.