How do you generate YouTube and Instagram cover art from actual video frames using AI?

Export the strongest frames from your finished video, upload them to an image model with a design brief, and generate covers in each platform's native format. In one documented episodic production, an AI agent completed 11 of 11 YouTube and Instagram cover assets using Nano Banana Pro, incorporating actual frames from the episode.

Start by pulling the frames, not by prompting from scratch — cover art built from actual video frames matches what viewers will see, while fully generated imagery risks a cover that doesn't resemble the content. invideo is an agentic video creation tool with the current image models — Nano Banana Pro, Nano Banana 2, Recraft, GPT-Image-2 — available inside it, so the same project that produced your video can produce its covers.

1. Export the best frames from your cut. Scrub the finished video and grab the frames that carry its identity: character close-ups, the key action beat, the most graphic composition. These become the base assets for every cover.

2. Upload the frames to an image model with a design brief. Give the model the frames plus instructions on layout, title text, and mood. Nano Banana Pro is the documented model for this job: in one episodic production, the invideo agent completed 11 of 11 marketing assets — YouTube and Instagram covers — with Nano Banana Pro, incorporating actual frames from the episode, as part of 920 total agent tasks for that single episode. Running the generation through the invideo agent means the model already holds your characters, palette, and style from the production context, so covers stay on-brand without re-describing anything.

3. Generate per-platform variants, not crops. Request each cover in its platform-native format — widescreen for the YouTube thumbnail, square or vertical for Instagram — as separate generations, so the composition is designed for each frame shape instead of awkwardly cropped from one master. Keep faces and title text away from the edges where platform UI elements sit.

4. Run a text-fix pass. AI image models garble rendered text and logos on first pass; Nano Banana 2 is documented as the fix step for text rendering and graphic/logo accuracy where Nano Banana Pro output comes back garbled. Regenerate only the text and logo elements rather than the whole composition.

5. Generate several options per platform and select. Image generation costs little, especially in invideo, so ask for multiple concepts per cover and pick the strongest — the same options-first habit real directors use. If you want alternative renders, Recraft produces photoreal faces with skin-level imperfections and GPT-Image-2 handles alternative looks; the invideo agent routes the request to whichever model fits, so you never need a separate thumbnail tool.

Nano Banana Pro, it has insane prompt adherence. Something about these images felt extremely stock photo-y to me.

— a filmmaker documenting a multi-agent AI production workflow