Treat Your References Like Function Arguments: A Developer's Mental Model for Video-to-Video AI

Описание и применение

В видео‑к‑видео генерации источник видео становится контрактом, а ссылки — аргументами. Используя функцию‑подобный подход, можно сократить количество попыток и повысить предсказуемость результата.

Задача

Video‑to‑video generation feels unstructured because the source video is treated as a suggestion rather than a specification, leading to noisy prompts and many retries.

Результат

Using the function‑call mental model reduces prompt noise, improves consistency, and lowers the number of costly retries.

Рабочий процесс

• 1. Choose a source video that is a single continuous shot and contains the desired motion and framing.
• 2. Upload reference images, each dedicated to a single attribute (e.g., face, outfit).
• 3. Use positional reference syntax (e.g., @Image1, @Image2) in the prompt to map references to the source.
• 4. Select mode: "motion" to transfer movement or "swap" to replace a specific element.
• 5. Run a low‑resolution draft (480p) to validate identity, framing, and reference placement.
• 6. If satisfied, re‑render at higher resolution (720p/1080p).
• 7. Log each run in a JSON file with source, refs, prompt, mode, resolution, and notes for reproducibility.

Доказательства

The key insight: the source video is not a suggestion, it is the bulk of your specification.

Источник

Keep the prompt about mapping, not motion. Don’t redescribe the dance.

Источник

Draft at low resolution. Run 480p to check identity, framing and that the right reference landed in the right place.

Источник

If you want to poke at this without wiring up any infrastructure, I've been experimenting with Genjutsu, a browser-based video-to-video generator

Источник