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Generative media

CAN I VIBECODE

Leonardo AI?

NOT REALLYA prototype of the interface is possible, but the durable product depends on difficult systems beyond the UI. Build a safer adjacent workflow instead.

BUILD FIRST

  1. A non-sensitive or read-only version of the core experience
  2. A manual review step wherever correctness or trust matters
  3. A narrow companion tool that removes a real piece of work

LEAVE FOR LATER

  1. Regulated decisions, payments, or irreversible actions
  2. Claims of correctness, compliance, or uptime that the MVP cannot support
  3. Sensitive integrations until security, support, and auditability are ready

RESEARCH NOTES

What this tool actually does.

Checked 2026-09-06

Leonardo.Ai is a generative media platform for AI image and video creation, accessible via web interface and API. It offers text-to-image, image-to-image, image-to-video, upscaling, and background removal across curated model presets (e.g., Lucid Origin, Phoenix, FLUX variants). Users pay for 'fast tokens' that are deducted per generation; the free tier grants 150 fast tokens daily, while paid Essential ($12/mo), Premium, and Ultimate ($60/mo) plans scale token allowances, concurrent jobs (2–6), and queue limits. Users can fine-tune custom models by uploading 10–20 images to replicate a style/theme/subject. 'Blueprints' provide prebuilt workflows for mood boards, bulk resizing, and product mockups. The API supports pay-as-you-go and custom plans, with a visual interface that exports runnable API code with every parameter preconfigured, plus analytics, usage metering, and cost tracking.

SOURCES

RESEARCHED BUILD PROMPT

Give this to your coding agent.

Build 'Pixelforge', a web application inspired by Leonardo.Ai's workflow but with original branding and UI. Core workflow: (1) user selects a base model from a curated list; (2) enters a prompt with optional negative prompt, aspect ratio, guidance scale, and seed; (3) generates an image/video, consuming 'fast tokens' from their balance; (4) optionally sends output through post-processing like upscaling or background removal; (5) trains a custom fine-tuned model by uploading 10–20 sample images; (6) uses 'blueprints' — prebuilt templates for mood boards, resizing, mockups; (7) API mode copies runnable Python/cURL code for the last configuration. User-facing requirements: authentication, token wallet and usage meter, model picker with thumbnails, generation gallery, queue status, fine-tuning form, blueprint gallery, API snippet export. Technical requirements: React or Next.js frontend; PostgreSQL backend (users, generations, tokens, models); job queue (Redis/Bull) for async generation; integrate an external image/video API like Replicate or Stability if credentials exist, otherwise mock generation; race-condition-safe token deduction; model presets stored as data rows; REST endpoints mirroring UI actions. Implementation approach: SSR pages with API routes, polling or WebSocket for job status, S3-compatible storage. Do not copy Leonardo.Ai's brand, logo, layout, colors, or copy. Out of scope: real proprietary model training (mock training status), real video generation (mock if no backend), billing/subscription logic, admin dashboards. Deliver a README with setup steps, env variables, and important limitations (e.g., simulated token accounting if no external API is configured).
Visit the official product page