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ANSWER

How does text-to-video AI work?

Short answer

A text-to-video model is trained on huge numbers of video clips paired with descriptions, and learns to turn random noise, step by step, into frames that match a prompt. It generates a short clip of a few seconds at a time, which is why films are built from many clips.

The short technical version

  1. Encoding. Video is compressed into a smaller representation of space and time, often split into patches or tokens, so the model can work on it efficiently.
  2. Conditioning. The prompt (and any reference images, audio or video) is turned into numbers the model attends to while generating.
  3. Denoising. Starting from noise, the model repeatedly predicts a slightly cleaner version of the clip until it is a coherent video that matches the conditioning.
  4. Decoding. The result is decoded back into frames, and on some models into a synchronized audio track.

Models differ in their training data, size, and what they condition on. Some generate audio together with video; some accept reference images, audio or video to steer the result.

ByteDance describes Seedance 2.0 as a unified multimodal audio-video joint generation architecture that supports text, image, audio and video inputs, with images, audio and video usable as references. Source: ByteDance Seed, Seedance 2.0 (seed.bytedance.com/en/seedance2_0), checked 2026-10-18

Why clips are short

Generating every frame of a clip at once is expensive and gets less coherent as length grows, so models cap clip length. Arc's Seedance 2.0 clips are 4–15 seconds. Price follows the same logic: Seedance is billed by video tokens, which grow with resolution and length, so a 1080p clip costs more than a 720p one and a 10-second clip twice a 5-second one (in Arc, 376 versus 751 credits at 720p).

Why characters drift

Each generation starts from fresh noise. If a character is only described in words, the model reinterprets the description every time, so faces and clothes change between clips. Reference images give the model the same visual evidence each time, which is why production tools build films from references rather than prompts alone.

What this means in practice

  • Write prompts as camera descriptions: what is visible, who does what, the light.
  • Expect variation between takes of the same prompt; budget for retakes.
  • Models sometimes add things the prompt implies: we have seen invented voice-overs on near-empty shots and colour words land on skin.
  • Text inside the frame, hands doing fine work and crowds doing different things are still weak spots.

Where Katechon Arc fits

Arc wraps text-to-video in a production workflow (script, references, storyboards) so the model's tendency to reinterpret is constrained shot by shot.

Arc does not train or fine-tune models and offers only Seedance 2.0 models; for research or custom models, work with model providers directly.

Related answers

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