fal.ai

What is fal.ai?

fal.ai is a generative media platform designed for developers, offering a wide range of AI models for image, video, and audio generation. It provides developers with the easiest and most cost-effective way to integrate generative AI into their applications.

Key Features:

• Extensive Model Gallery: Access over 600 production-ready image, video, audio, and 3D models.
• Serverless GPUs: Run inference at lightning speed with fal's globally distributed serverless engine. No GPU configuration or autoscaling setup required.
• Unified API and SDKs: Use a simple API and SDKs to call hundreds of open models or your own LoRAs in minutes.
• Dedicated Clusters: Spin up dedicated compute to fine-tune, train, or run custom models with guaranteed performance.
• Fastest Inference Engine: fal Inference Engine™ is up to 10x faster.

How to use fal.ai?

1. Explore Models: Choose from a rich library of models for image, video, voice, and code generation.
2. Call API: Access the models using a simple API. No fine-tuning or setup needed.
3. Deploy Models: Deploy private or fine-tuned models with one click.
4. Utilize Serverless GPUs: Accelerate your workloads with fal Inference Engine.

Why choose fal.ai?

• Speed: Fastest inference engine for diffusion models.
• Scalability: Scale from prototype to 100M+ daily inference calls.
• Ease of Use: Unified API and SDKs for easy integration.
• Flexibility: Deploy private or fine-tuned models with one click.
• Enterprise-Grade: SOC 2 compliant and ready for enterprise procurement processes.

Where can I use fal.ai?

fal.ai is used by developers and leading companies to power AI features in various applications, including:

• Image and Video Search: Used by Perplexity to scale generative media efforts.
• Text-to-Speech Infrastructure: Used by PlayAI to provide near-instant voice responses.
• Image and Video Generation Bots: Used by Quora to power Poe's official bots.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/fast-sdxl", {
  input: {
    prompt: "photo of a cat wearing a kimono"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});