### **You**

Yep — you can slot NanoBanana (image) + Veo (video) into a fully headless “artifact compiler” pipeline cleanly. The key is to treat them as generators inside a deterministic build system, not as the editing surface.
Below are practical pipelines that work.

1) Slide deck → images (NanoBanana) → video (Veo) → final edit (FFmpeg)
Step A — Extract a “scene plan” from slides
For each slide:

pull: title, bullets, speaker notes, any embedded images

LLM produces a SceneSpec:

prompt\_img (for keyframe)

prompt\_vid (for motion + camera + audio)

duration (e.g., 6–8s if using Veo clip length)

styleTokens (brand/look)

sfx / dialogue / musicMood

Step B — Generate keyframes with NanoBanana
Call NanoBanana to generate one (or more) images per slide:

16:9 or 9:16

1K/2K/4K depending on your needs (their docs mention these tiers). NanoBanana API+1

This gives you deterministic “first frames” for each segment (even if the model isn’t perfectly deterministic, your pipeline is reproducible via manifests + caching).
Step C — Turn keyframes into clips with Veo
Use image-to-video:

Veo 3.1 supports image-based direction (up to three reference images), and can generate video with native audio. Google AI for Developers+1

Veo 2 on Vertex is “stable line,” does text→video and image→video, 5–8 seconds, 720p, with quota limits like 10 RPM/project. Google Cloud Documentation

Step D — Stitch + polish headlessly with FFmpeg
Once you have N clips:

concatenate

normalize audio / mix background music

add captions

add transitions, lower-thirds, logo bug, etc.

Why FFmpeg anyway?

Veo gives you generated content; FFmpeg gives you deterministic finishing (branding, export matrix, QC). Veo itself is explicitly watermarked and stored only briefly server-side, so you need to download and archive outputs quickly. Google AI for Developers

2) “Animated slide” pipeline (when you want slide-like fidelity)
If your goal is “video that looks like the actual slides” (not fully cinematic scenes):

Render slides to PNG frames deterministically (PPTX/PDF → PNG).

Generate motion overlays:

pan/zoom

highlight boxes

bullet reveals (mask wipe)

Add narration (TTS) + music ducking.

Export MP4.

This avoids generative video entirely for the “core deck,” and only uses Veo for b-roll cutaways (optional).

3) Storyboard → NanoBanana frame set → Veo “Frames to Video” / extensions
If you want stronger control than pure prompt:

Generate:

frame\_0 (NanoBanana)

frame\_end (NanoBanana, same characters, changed pose/scene)

Feed first/last frames to Veo (Veo 3.1 supports frame-specific generation: first/last frames). Google AI for Developers

Then use Veo video extension to extend scenes. Google AI for Developers

This tends to produce more coherent motion arcs than “text only.”

4) Editing / “remix” loops (headless)
You need two layers:
A) Generative “edit”

Re-run NanoBanana for revised keyframes (e.g., “same character, remove logo, add whiteboard”)

Re-run Veo with new reference images / prompts

B) Deterministic “edit”

Use FFmpeg for:

trimming

cropping

speed ramps

audio ducking

captions / burn-ins

overlays

This is how you get reliable iteration without needing Clipchamp/iMovie.

5) Production gotchas you must design for
Veo operational constraints

Output retention: the Gemini API doc states generated videos are stored for 2 days server-side; you must download within that window. Google AI for Developers

Latency can be significant (seconds to minutes). Google AI for Developers

Veo outputs are watermarked using SynthID. Google AI for Developers

Quotas/rate limits exist (e.g., Veo 2 on Vertex shows 10 RPM/project, 4 videos/request). Google Cloud Documentation

NanoBanana reliability note
There are multiple “NanoBanana” branded sites floating around; I can’t vouch that any one is “official Google.” Treat it as a third-party model provider unless you’re directly using Gemini image generation yourself. (Their docs describe an API that looks “Gemini-ish,” but branding on the open web is messy.) NanoBanana API+1

6) Minimal “compiler contract” (what your pipeline should store)
For each scene/clip, store a manifest:

\`\`\`
JSON{  "sceneId": "slide-07",  "inputs": {    "slideTextHash": "...",    "brandTokensHash": "...",    "refImages": ["s3://.../keyframe.png"]  },  "generation": {    "nanobanana": {"prompt": "...", "aspect": "16:9", "size": "2K"},    "veo": {"model": "veo-3.1", "prompt": "...", "refs": ["keyframe.png"]}  },  "outputs": {    "keyframe": "s3://.../keyframe.png",    "clip": "s3://.../clip.mp4"  }}
\`\`\`

This is what makes the system “headless + reproducible.”

If you tell me which Veo surface you’re targeting (Gemini API Veo 3.1 vs Vertex Veo 2), I’ll give you a concrete DAG (jobs + retries + caching rules) and the exact “scene compiler” stages you want for slide-derived videos.

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### **ChatGPT**

3.1

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