ModelsLab Provider for Vercel AI SDK: generateImage() Setup Guide (2026)

Why Add ModelsLab to Vercel AI SDK?

Vercel's AI SDK ships with image providers for DALL-E and a handful of Imagen/Stability models. That's useful if you want one or two models. It's not enough if you're building a product where model variety matters — character consistency, photorealism, anime style, product photography, inpainting.

ModelsLab gives you 50,000+ models under a single API key. FLUX.1 Schnell for speed. SDXL Turbo for style variety. Fluxgram for portrait consistency. And pricing that starts at $0.002 per image — versus $0.04+ from OpenAI's DALL-E 3.

The AI SDK's generateImage() function works with any provider that implements the ImageModelV1 interface. This post shows you how to build that adapter, drop it into a Next.js app, and start generating images through ModelsLab from your existing AI SDK code.

Prerequisites

• Next.js 14+ app with the App Router
• AI SDK installed: npm install ai
• A ModelsLab API key — get one free at modelslab.com

Step 1: Build the ModelsLab Provider Adapter

The AI SDK exposes an ImageModelV1 interface from @ai-sdk/provider. Any class that satisfies this interface can be passed to generateImage().

Create lib/modelslab.ts in your project:

// lib/modelslab.ts
import type { ImageModelV1, ImageModelV1CallOptions } from '@ai-sdk/provider'
/**
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,[object Object],
,[object Object],
,[object Object],
,[object Object],
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A few things worth noting about this adapter:

• specificationVersion: 'v1' — required by the AI SDK provider contract.
• maxImagesPerCall: 4 — ModelsLab's realtime endpoint supports up to 4 samples per request. The AI SDK batches automatically when you request more with n.
• Image fetching — ModelsLab returns CDN URLs, not base64. The adapter fetches each URL and converts it so the AI SDK gets the consistent { base64, uint8Array } format.
• Error handling — checks both HTTP status and the status: 'error' JSON response that ModelsLab uses for API-level errors.

Step 2: Basic Usage With generateImage()

Once you have the adapter, using it is identical to any other AI SDK image provider:

import { generateImage } from 'ai'
import { createModelsLab } from '@/lib/modelslab'
import fs from 'node:fs'
const modelslab = createModelsLab(process.env.MODELSLAB_API_KEY!),[object Object],
,[object Object],
,[object Object],

main()

Generate multiple images in one call:

const { images } = await generateImage({
model: modelslab.image('sdxl'),
prompt: 'Product photography of a sleek wireless keyboard on a white background',
size: '1024x1024',
n: 4, // generates 4 variants
})
images.forEach((img, i) => {
fs.writeFileSync(product-${i}.png, img.uint8Array)
})

Step 3: Next.js API Route for Client-Side Image Generation

The typical pattern is a server-side API route that accepts a prompt from your frontend and returns the generated image as base64 data. This keeps your API key off the client.

// app/api/generate-image/route.ts
import { generateImage } from 'ai'
import { createModelsLab } from '@/lib/modelslab'
import { NextRequest, NextResponse } from 'next/server'
const modelslab = createModelsLab(process.env.MODELSLAB_API_KEY!),[object Object],
,[object Object],
,[object Object],
,[object Object],
,[object Object],
,[object Object],
,[object Object],

} catch (error) {
console.error('ModelsLab generateImage error:', error)
return NextResponse.json(
{ error: 'image generation failed' },
{ status: 500 }
)
}
}

Client-side usage from a React component:

'use client'
import { useState } from 'react'
export function ImageGenerator() {
const [prompt, setPrompt] = useState('')
const [src, setSrc] = useState(null)
const [loading, setLoading] = useState(false),[object Object],
,[object Object],

return (
setPrompt(e.target.value)} />
{loading ? 'Generating...' : 'Generate'}
{src && ,[object Object],}
)
}

Model Selection Guide

ModelsLab's catalog has 50,000+ models. Here are the ones worth reaching for first:

• flux-1-schnell — fastest option, 4-step generation, good for prototyping. Use when latency matters more than quality.
• flux-1-dev — higher quality FLUX.1 variant, 28-step generation. Use for production image generation where you need detail.
• fluxgram-v1-0 — character-consistent generation. Use when you're generating the same person across multiple images (product demos, character design).
• sdxl — SDXL base, reliable for general-purpose generation. Large community of fine-tuned variants if you need a specific style.
• realistic-vision-v6 — photorealistic output. Use for product photography, lifestyle images, anything that needs to look like a real photo.

You can browse the full catalog at modelslab.com/models and swap any model slug into the modelId parameter above.

Environment Variables

Add to your .env.local:

MODELSLAB_API_KEY=your_api_key_here

Get your key from the ModelsLab dashboard. Plans start at $21/month.

Switching Providers Without Changing Code

One of the core benefits of the AI SDK's provider abstraction is that swapping providers is a one-line change. If you later want to compare ModelsLab output against DALL-E 3 or Stability AI:

// Compare providers without touching your application logic
const provider = useModelsLab
? modelslab.image('flux-1-dev')
: openai.image('dall-e-3')
const { image } = await generateImage({
model: provider,
prompt,
size: '1024x1024',
})

Same generateImage() call. Different providers. This is the abstraction that makes multi-provider architectures practical in production.

What's Next

This adapter covers the straightforward text-to-image path. ModelsLab also supports:

• Image-to-image via /api/v6/image_to_image — pass an init image and strength parameter
• Inpainting — mask a region and regenerate it
• ControlNet — pose-guided and depth-guided generation
• LoRA fine-tunes — load custom LoRA weights for brand-specific styles

Each of these maps to a different endpoint and can be wrapped the same way as the adapter above. The providerOptions parameter in generateImage() is the right place to pass model-specific parameters like init_image or mask_image once you extend the adapter to support them.

Start with generateImage() and a single model. Get it generating. Then expand from there as your use case grows.

Get your ModelsLab API key →