fal.ai
Overview of 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?
- Explore Models: Choose from a rich library of models for image, video, voice, and code generation.
- Call API: Access the models using a simple API. No fine-tuning or setup needed.
- Deploy Models: Deploy private or fine-tuned models with one click.
- 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);
}
},
});
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