Fal Provider

Fal AI provides a generative media platform for developers with lightning-fast inference capabilities. Their platform offers optimized performance for running diffusion models, with speeds up to 4x faster than alternatives.

Setup

The Fal provider is available via the @ai-sdk/fal module. You can install it with

pnpm

npm

yarn

pnpm add @ai-sdk/fal

Provider Instance

You can import the default provider instance fal from @ai-sdk/fal:

import { fal } from '@ai-sdk/fal';

If you need a customized setup, you can import createFal and create a provider instance with your settings:

import { createFal } from '@ai-sdk/fal';

const fal = createFal({
  apiKey: 'your-api-key', // optional, defaults to FAL_API_KEY environment variable, falling back to FAL_KEY
  baseURL: 'custom-url', // optional
  headers: {
    /* custom headers */
  }, // optional
});

You can use the following optional settings to customize the Fal provider instance:

baseURL string

Use a different URL prefix for API calls, e.g. to use proxy servers. The default prefix is https://fal.run.
apiKey string

API key that is being sent using the Authorization header. It defaults to the FAL_API_KEY environment variable, falling back to FAL_KEY.
headers Record<string,string>

Custom headers to include in the requests.
fetch (input: RequestInfo, init?: RequestInit) => Promise<Response>

Custom fetch implementation. You can use it as a middleware to intercept requests, or to provide a custom fetch implementation for e.g. testing.

Image Models

You can create Fal image models using the .image() factory method. For more on image generation with the AI SDK see generateImage().

Basic Usage

import { fal } from '@ai-sdk/fal';
import { experimental_generateImage as generateImage } from 'ai';
import fs from 'fs';

const { image } = await generateImage({
  model: fal.image('fal-ai/fast-sdxl'),
  prompt: 'A serene mountain landscape at sunset',
});

const filename = `image-${Date.now()}.png`;
fs.writeFileSync(filename, image.uint8Array);
console.log(`Image saved to ${filename}`);

Model Capabilities

Fal offers many models optimized for different use cases. Here are a few popular examples. For a full list of models, see the Fal AI documentation.

Model	Description
`fal-ai/fast-sdxl`	High-speed SDXL model optimized for quick inference with up to 4x faster speeds
`fal-ai/flux-lora`	Super fast endpoint for the FLUX.1 [dev] model with LoRA support, enabling rapid and high-quality image generation using pre-trained LoRA adaptations.
`fal-ai/flux-pro/v1.1-ultra`	Professional-grade image generation with up to 2K resolution and enhanced photorealism
`fal-ai/ideogram/v2`	Specialized for high-quality posters and logos with exceptional typography handling
`fal-ai/recraft-v3`	SOTA in image generation with vector art and brand style capabilities
`fal-ai/stable-diffusion-3.5-large`	Advanced MMDiT model with improved typography and complex prompt understanding
`fal-ai/hyper-sdxl`	Performance-optimized SDXL variant with enhanced creative capabilities

Fal models support the following aspect ratios:

1:1 (square HD)
16:9 (landscape)
9:16 (portrait)
4:3 (landscape)
3:4 (portrait)
16:10 (1280x800)
10:16 (800x1280)
21:9 (2560x1080)
9:21 (1080x2560)

Key features of Fal models include:

Up to 4x faster inference speeds compared to alternatives
Optimized by the Fal Inference Engine™
Support for real-time infrastructure
Cost-effective scaling with pay-per-use pricing
LoRA training capabilities for model personalization

Advanced Features

Fal's platform offers several advanced capabilities:

Private Model Inference: Run your own diffusion transformer models with up to 50% faster inference
LoRA Training: Train and personalize models in under 5 minutes
Real-time Infrastructure: Enable new user experiences with fast inference times
Scalable Architecture: Scale to thousands of GPUs when needed

For more details about Fal's capabilities and features, visit the Fal AI documentation.