Client Library for Python

Introduction

The client for Python provides a seamless interface to interact with fal.

Installation

First, add the client as a dependency in your project:

pip install fal-client

Features

1. Call an endpoint

Endpoints requests are managed by a queue system. This allows fal to provide a reliable and scalable service.

The resulting handler provides a way to wait for the result to be fully ready.

import fal_client
 
handler = fal_client.submit(
    "fal-ai/flux/dev",
    arguments={
        "prompt": "a cat",
        "seed": 6252023,
        "image_size": "landscape_4_3",
        "num_images": 4
    },
)
 
result = handler.get()
print(result)

2. Queue Management

You can manage the queue using the following methods:

Submit a Request

Submit a request to the queue using the queue.submit method.

import fal_client
 
handler = fal_client.submit(
    "fal-ai/flux/dev",
    arguments={
        "prompt": "a cat",
        "seed": 6252023,
        "image_size": "landscape_4_3",
        "num_images": 4
    },
)
 
request_id = handler.request_id

This is useful when you want to submit a request to the queue and retrieve the result later. You can save the request_id and use it to retrieve the result later.

Check Request Status

Retrieve the status of a specific request in the queue:

status = handler.status(with_logs=True)

Retrieve Request Result

Get the result of a specific request from the queue:

result = handler.get()

3. File Uploads

Some endpoints require files as input. However, since the endpoints run asynchronously, processed by the queue, you will need to provide URLs to the files instead of the actual file content.

Luckily, the client library provides a way to upload files to the server and get a URL to use in the request.

url = fal_client.upload_file("path/to/file")

4. Streaming

Some endpoints support streaming:

import fal_client
 
def stream():
    stream = fal_client.stream(
        "fal-ai/flux/dev",
        arguments={
            "prompt": "a cat",
            "seed": 6252023,
            "image_size": "landscape_4_3",
            "num_images": 4
        },
    )
    for event in stream:
        print(event)
 
 
if __name__ == "__main__":
    stream()

5. Realtime Communication

For the endpoints that support real-time inference via WebSockets, you can use the realtime client that abstracts the WebSocket connection, re-connection, serialization, and provides a simple interface to interact with the endpoint:

6. Run

The endpoints can also be called directly instead of using the queue system.

import fal_client
 
result = fal_client.run(
    "fal-ai/flux/dev",
    arguments={
        "prompt": "a cat",
        "seed": 6252023,
        "image_size": "landscape_4_3",
        "num_images": 4
    },
)
print(result)

Support

If you encounter any issues or have questions, please visit the GitHub repository (opens in a new tab) or join our Discord Community (opens in a new tab).