feat(db): mesh data model — meshes, members, invites, audit log
- pgSchema "mesh" with 4 tables isolating the peer mesh domain - Enums: visibility, transport, tier, role - audit_log is metadata-only (E2E encryption enforced at broker/client) - Cascade on mesh delete, soft-delete via archivedAt/revokedAt Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Alejandro Gutiérrez
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---
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title: Image Generation
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description: Learn how to generate images using AI models within the TurboStarter AI demo application.
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url: /ai/docs/image
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---
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# Image Generation
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The [Image Generation](https://ai.turbostarter.dev/image) demo application allows users to create unique visuals from textual descriptions using various AI models. It provides a simple interface to input prompts, select models, and view generated images.
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<AIAppShowcase id="image" />
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## Features
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Explore the capabilities of the AI-powered image generation tool:
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<Cards>
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<Card title="Prompt-based generation">
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Create images simply by describing what you want to see in text.
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</Card>
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<Card title="Multi-model support">
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Choose from different AI image generation models offered by various
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providers.
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</Card>
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<Card title="Aspect ratio control">
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Select the desired aspect ratio for your generated images (e.g. square,
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landscape, portrait).
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</Card>
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<Card title="Batch generation">
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Create multiple design variations from a single prompt simultaneously,
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accelerating your creative workflow.
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</Card>
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<Card title="Generation history">
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Access and reference your complete generation history, including all prompts
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and resulting images for continued iteration.
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</Card>
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</Cards>
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## Setup
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To implement image generation in your application, you'll need to configure the necessary backend services.
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<Cards>
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<Card href="/ai/docs/database" title="Database" description="Configure a PostgreSQL database to store generation history and image metadata." />
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<Card href="/ai/docs/storage" title="Storage" description="Set up S3-compatible storage to securely manage generated image assets." />
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</Cards>
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You'll also need API keys for your preferred AI models. Follow the detailed setup instructions in the provider documentation linked below.
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## AI models
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The Image Generation app leverages the AI SDK to support various models capable of creating images from text. Configure the providers for the models you wish to use:
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<Cards>
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<Card href="/ai/docs/openai" title="OpenAI" description="Implement DALL·E models for exceptional image quality and creative fidelity." icon={<OpenAI />} />
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<Card href="/ai/docs/replicate" title="Replicate" description="Access a diverse ecosystem of open-source models including Stable Diffusion variants." icon={<Replicate />} />
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</Cards>
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For detailed implementation guidance, refer to the [AI SDK documentation](https://sdk.vercel.ai/docs/ai-sdk-core/image-generation) covering the `generateImage` function and supported providers.
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## Data persistence
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Details about image generation requests and the resulting images are stored to maintain user history.
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<Card href="/ai/docs/database" title="Database" description="Learn more about database services in TurboStarter AI." />
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Data is organized within a dedicated PostgreSQL schema named `image`:
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* `generations`: captures detailed information about each generation request, including the `prompt`, selected `model`, `aspectRatio`, requested image `count`, `userId`, and precise timestamps.
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* `images`: stores complete metadata for each generated image, linked to its parent `generation` record via `generationId` and maintaining the `url` reference to the stored image file.
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<Card href="/ai/docs/storage" title="Storage" description="Learn more about cloud storage services in TurboStarter AI." />
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The generated image files are securely stored in [cloud storage](/ai/docs/storage) (S3-compatible). Each image's location is tracked via the `url` field in the `images` table for reliable retrieval.
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## Structure
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The Image Generation feature is architected across the monorepo for optimal code organization and reusability.
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### Core
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The `@turbostarter/ai` package (`packages/ai`) contains the essential logic under `modules/image`:
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* Comprehensive types, validation schemas (for prompts, aspect ratios, etc.), and constants
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* Core API logic for processing image generation requests and interfacing with AI models
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* Database operations for recording generation details and image metadata
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* Utilities for uploading generated images to cloud storage
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### API
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The `packages/api` package defines the backend API endpoints using Hono:
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* `src/modules/ai/image/image.router.ts`: implements Hono RPC routes for image generation, handles input validation, applies necessary middleware (authentication, credit management), and invokes the core logic from `@turbostarter/ai`.
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### Web
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The Next.js application (`apps/web`) delivers an intuitive user interface:
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* `src/app/[locale]/(apps)/image/**`: contains the Next.js App Router pages and layouts for the image generation experience
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* `src/components/image/**`: houses reusable React components tailored to the image generation UI (prompt input, model selector, image gallery, etc.)
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### Mobile
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The Expo/React Native application (`apps/mobile`) provides a native mobile experience:
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* `src/app/image/**`: defines the screens for the mobile image generation interface
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* `src/components/image/**`: contains React Native components optimized for mobile interaction
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* **API integration**: utilizes the same Hono RPC client (`packages/api`) as the web app for consistent backend communication
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This architecture ensures perfect consistency across platforms while enabling tailored UI implementations optimized for each environment.
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