fal: generative macromodeling API for developers of rich media classes

General Introduction

fal is an online AI inference platform that helps users build real-time AI applications with high-quality generative media models, including images, video, and audio. No cold start required, pay-as-you-go. fal provides a variety of pre-trained generative models such as Stable Diffusion XL, Stable Diffusion with LoRAs, Optimized Latent Consistency (SDv1.5), etc., which allow users to use simple text descriptions and doodle sketches to quickly generate images.

fal also supports users to upload customized models or use shared models with fine-grained control and automatic capacity expansion and contraction. fal supports multiple machine types and specifications, such as GPU-A100, GPU-A10G, GPU-T4, etc., which can satisfy different performance and cost requirements. fal has detailed documentation and examples, which can help users to get started and use it quickly.

Powered by its proprietary fal inference engine, the platform is capable of running diffusion models up to 4x faster than other alternatives, enabling new real-time AI experiences. fal.ai, founded in 2021 and headquartered in San Francisco, is dedicated to lowering the barriers to creative expression by optimizing the speed and efficiency of inference.

Function List

• Efficient inference engine: Provides the world's fastest diffusion model inference engine, with inference speeds up to 400%.
• Multiple generation models: Supports a variety of pre-trained generative models such as Stable Diffusion 3.5 and FLUX.1.
• LoRA training: Provides the industry's best LoRA training tools, with the ability to personalize or train a new style in less than 5 minutes.
• API Integration: A variety of client-side libraries such as JavaScript, Python, and Swift are available for easy integration by developers.
• real time inference: Supports real-time generation of media inference for real-time creative tools and camera input.
• Cost optimization: Pay-per-use to ensure cost-effective calculations.

Using Help

Installation and Integration

1. register an account: Visit fal.ai and sign up for a developer account.
2. Getting the API key: After logging in, generate and obtain your API key on the "API Key" page.
3. Installing client libraries::
   • JavaScript::

     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);
     }
     },
     });
     

   • Python::

     from fal import Client
     client = Client(api_key="YOUR_API_KEY")
     result = client.subscribe("fal-ai/fast-sdxl", input={"prompt": "photo of a cat wearing a kimono"})
     print(result)
     

   • Swift::

     import FalAI
     let client = FalClient(apiKey: "YOUR_API_KEY")
     client.subscribe(model: "fal-ai/fast-sdxl", input: ["prompt": "photo of a cat wearing a kimono"]) { result in
     print(result)
     }
     

Using Generative Models

1. Select Model: Select the appropriate model for your project from fal.ai's model library, such as Stable Diffusion 3.5 or FLUX.1.
2. Configuration parameters: Configure model parameters, such as the number of inference steps, input image size, etc., according to project requirements.
3. running inference: Use API calls to run inference and get generated media content.
4. Optimization and Adjustment: Based on the generated results, adjust the parameters or select a different model for optimization.

LoRA training

1. Upload data: Prepare the training data and upload it to the fal.ai platform.
2. Selecting Training Models: Select a suitable LoRA training model such as FLUX.1.
3. Configuring Training Parameters: Set training parameters such as learning rate, number of training steps, etc.
4. Start training: Start the training process and the platform will complete the training and generate a new style model in a short period of time.
5. Applying the new model: Inference using newly trained models to generate personalized media content.

All models are divided into debugging interface and API two parts, you can use in the debugging interface no problem in the call API:

fal optional model