Does this skill support Python development?

Yes, it provides complete setup and usage patterns for the fal-client Python library, including both synchronous and asynchronous API calls.

How do I choose between fal.subscribe and fal.run?

Use fal.subscribe for most tasks as it handles queue polling automatically; use fal.run only for fast endpoints that complete in under 30 seconds.

What parameters are available for FLUX models?

The skill provides detailed interface definitions for FLUX, including prompt requirements, image_size presets, guidance_scale, and inference steps.

How do I handle file uploads for image-to-image tasks?

The skill includes documentation for fal.storage methods to upload local File objects or remote URLs directly to the fal.media CDN for processing.

Can I implement webhooks with this reference?

Yes, it provides the REST and SDK patterns for configuring webhook URLs to receive asynchronous model results via POST requests.

fal.ai API Reference

Name: fal.ai API Reference
Author: JosiahSiegel

byJosiahSiegel

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Data Science & ML

Provides comprehensive documentation and implementation patterns for integrating fal.ai's generative AI models via JavaScript, Python, and REST APIs.

About

The fal-api-reference skill is a specialized technical guide designed to streamline the integration of fal.ai services into modern applications. It offers developers deep insights into the @fal-ai/client and fal-client libraries, covering essential execution methods such as queue-based subscriptions, synchronous runs, and real-time streaming. By providing standardized code patterns for authentication, webhook configuration, and media storage, this skill ensures that developers can implement robust, scalable generative AI features for image, video, and audio processing while adhering to best practices for error handling and rate limiting.

Key Features

7 GitHub stars
Implementation guides for real-time WebSocket and SSE streaming connections
Standardized file upload patterns for the fal.media CDN storage system
Detailed JavaScript/TypeScript and Python client library configuration
Comprehensive queue management workflows including status polling and result retrieval
Model-specific parameter references for FLUX, Whisper, and video generation

Use Cases

Automating large-scale media processing pipelines with webhook-driven results
Developing real-time interactive media tools using low-latency WebSockets
Building production-grade generative AI applications with asynchronous job processing

About

Key Features

7 GitHub stars
Implementation guides for real-time WebSocket and SSE streaming connections
Standardized file upload patterns for the fal.media CDN storage system
Detailed JavaScript/TypeScript and Python client library configuration
Comprehensive queue management workflows including status polling and result retrieval
Model-specific parameter references for FLUX, Whisper, and video generation

Use Cases

Automating large-scale media processing pipelines with webhook-driven results
Developing real-time interactive media tools using low-latency WebSockets
Building production-grade generative AI applications with asynchronous job processing