How I Built an AI Birthday Photo Generator with Cloudflare Workers, Gemini 2.5 Flash, and FLUX.2 Pro

TL;DR: Upload a selfie → Gemini analyzes the photo and writes 3 birthday scene prompts → FLUX.2 Pro generates the images → Cloudflare R2 stores them. The whole backend runs on Cloudflare Workers with zero servers.

I recently launched

The Stack

Frontend: Next.js 15 (App Router) → static export → Cloudflare Pages

Backend:  Cloudflare Workers (TypeScript)

Database: Cloudflare D1 (SQLite)

Storage:  Cloudflare R2

Cache:    Cloudflare KV (sessions)

Queue:    Cloudflare Queues

AI:       Gemini 2.5 Flash (via Replicate) + BFL FLUX.2 Pro

Payment:  PayPal

Auth:     Google OAuth

The entire backend runs on a single Cloudflare Worker. No EC2, no containers, no ops headaches.

The Core Pipeline

The generation flow looks like this:

User uploads photo

      ↓

Worker validates + deducts credits + enqueues job

      ↓

Queue consumer picks it up (runs up to 15 min)

      ↓

Step 1: Gemini 2.5 Flash analyzes the photo → outputs 3 scene prompts (JSON)

      ↓

Step 2: Submit 3 FLUX.2 Pro jobs sequentially → each fires a webhook when done

      ↓

Webhook handler saves images to R2, finalizes task

      ↓

User polls /api/task/:id → gets results

The key insight: use Cloudflare Queues to break the CPU/time limit of a single Worker request. Workers have a 30-second wall clock limit on HTTP requests, but Queue consumers can run for up to 15 minutes — enough time for Gemini + 3 FLUX calls.

Step 1: Gemini Analyzes the Photo

The hardest part wasn't the image generation. It was writing a prompt good enough to make Gemini output exactly what FLUX needs.

I use Gemini 2.5 Flash via Replicate's API. The system prompt is ~800 words and instructs Gemini to:

1. Count people in the foreground (ignore background bystanders)


2. Describe each person's face features in detail (for face preservation in the generated image)


3. Design 3 completely different birthday scene themes


4. Output a structured JSON with start_prompt, end_prompt, and 3 scenes
   

The JSON structure separates shared prompt parts from scene-specific content:

{

  "people_count": 1,

  "start_prompt": "This is the same person from the reference photo. Preserve their exact face shape...",

  "end_prompt": "Shot on Canon EOS R5, 85mm f/1.4, photorealistic, 8K ultra HD",

  "scenes": [

    { "name": "Golden_Gala", "prompt": "Glamorous gold ballroom with 40 metallic gold balloons..." },

    { "name": "Tropical_Paradise", "prompt": "Vibrant beach party setting with palm trees..." },

    { "name": "Enchanted_Garden", "prompt": "Magical outdoor garden with floral arches..." }

  ]

}

When submitting to FLUX, I assemble the full prompt as:

const fullPrompt = [analysis.start_prompt, scene.prompt, analysis.end_prompt]

  .filter(Boolean)

  .join(' ');

This makes it easy to tune the shared "face preservation" instructions without touching each scene prompt.

Step 2: FLUX.2 Pro Image Generation

I use — you get 10 free credits on signup (no credit card required).

Happy to answer any questions about the architecture in the comments!