How to Storyboard Realistic AI Videos with Google Flow
Shows AI filmmakers how to use Google Flow's chat agent for end-to-end pre-production, character consistency sheets, and shot continuity bridging without custom frame export tools.
What this lesson covers
Youri van Hofwegen demonstrates how to plan and generate a coherent 40-second AI short film using Google Flow's assistant interface. The workflow covers testing Omni Flash 1.1 for realism, creating multi-panel character consistency sheets with Nano Banana Pro, and chaining sequential shots via manual screenshot references.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 00:49 ↗
Enabling 'Confirm before generating' prevents unintentional credit spend when orchestrating 10-second Omni Flash 1.1 video generations.
- 03:46 ↗
Structuring scenes with close-up framing and minimal motion maximizes Omni Flash 1.1's realistic skin and hair rendering while avoiding motion distortion.
- 05:19 ↗
Generating a three-panel character sheet (headless front, back, and neutral portrait) on a flat gray background ensures consistent identity without locking in scene-specific lighting.
- 07:11 ↗
Because Google Flow lacks a native frame export tool, taking a manual screenshot of the final frame and passing it as a reference image into the next scene maintains cut continuity.
Workflow outlined in the video
- Set Google Flow workspace defaults to 'Confirm before generating', Nano Banana Pro for images, and Omni Flash 1.1 for 16:9 10-second video.
- Establish baseline realism style prompts in the assistant chat so aesthetic parameters carry over automatically across future generations.
- Prompt the assistant to break down the rough story idea into a sequential multi-shot storyboard before initiating any video renders.
- Generate multi-panel character sheets and environment reference images using Nano Banana Pro with flat lighting and neutral backgrounds.
- Render shots sequentially, screenshotting the exact last frame of each completed video to feed into the assistant as an image reference for the next scene.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
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How to interpret the numbers
16,258 views captured 3 October 2026. ReelStack recommendation score: 56/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 65% performance: 1.02× views vs 16 other channel videos observed at a similar age. View-evidence factor 94% (views / (views + 1,000))
- 25% freshness: 97/100 with a 14-day half-life
- Momentum pending: collecting daily snapshots; its 20% weight goes to observed performance, not free points
- 10% engagement: 5/100; likes + 4× comments, smoothed with a 500-view neutral prior