Fal AI

Fal AI provides fast, scalable access to state-of-the-art image generation models including FLUX, Stable Diffusion, Imagen, and more.

Overview

Property	Details
Description	Fal AI offers optimized infrastructure for running image generation models at scale with low latency.
Provider Route on LiteLLM	`fal_ai/`
Provider Doc	Fal AI Documentation ↗
Supported Operations	`/images/generations`

Setup

API Key

import os

# Set your Fal AI API key
os.environ["FAL_AI_API_KEY"] = "your-fal-api-key"

Get your API key from fal.ai.

Supported Models

Model Name	Description	Documentation
`fal_ai/fal-ai/flux-pro/v1.1-ultra`	FLUX Pro v1.1 Ultra - High-quality image generation	Docs ↗
`fal_ai/fal-ai/imagen4/preview`	Google's Imagen 4 - Highest quality model	Docs ↗
`fal_ai/fal-ai/recraft/v3/text-to-image`	Recraft v3 - Multiple style options	Docs ↗
`fal_ai/fal-ai/stable-diffusion-v35-medium`	Stable Diffusion v3.5 Medium	Docs ↗
`fal_ai/bria/text-to-image/3.2`	Bria 3.2 - Commercial-grade generation	Docs ↗

Image Generation

Usage - LiteLLM Python SDK

Basic Usage
Imagen 4
Recraft v3
Async Usage
Advanced Parameters

Basic Image Generation
import litellm
import os

# Set your API key
os.environ["FAL_AI_API_KEY"] = "your-fal-api-key"

# Generate an image
response = litellm.image_generation(
    model="fal_ai/fal-ai/flux-pro/v1.1-ultra",
    prompt="A serene mountain landscape at sunset with vibrant colors"
)

print(response.data[0].url)

Google Imagen 4 Generation
import litellm
import os

os.environ["FAL_AI_API_KEY"] = "your-fal-api-key"

# Generate with Imagen 4
response = litellm.image_generation(
    model="fal_ai/fal-ai/imagen4/preview",
    prompt="A vintage 1960s kitchen with flour package on countertop",
    aspect_ratio="16:9",
    num_images=1
)

print(response.data[0].url)

Recraft v3 with Style
import litellm
import os

os.environ["FAL_AI_API_KEY"] = "your-fal-api-key"

# Generate with specific style
response = litellm.image_generation(
    model="fal_ai/fal-ai/recraft/v3/text-to-image",
    prompt="A red panda eating bamboo",
    style="realistic_image",
    image_size="landscape_4_3"
)

print(response.data[0].url)

Async Image Generation
import litellm
import asyncio
import os

async def generate_image():
    os.environ["FAL_AI_API_KEY"] = "your-fal-api-key"
    
    response = await litellm.aimage_generation(
        model="fal_ai/fal-ai/stable-diffusion-v35-medium",
        prompt="A cyberpunk cityscape with neon lights",
        guidance_scale=7.5,
        num_inference_steps=50
    )
    
    print(response.data[0].url)
    return response

asyncio.run(generate_image())

Advanced FLUX Pro Generation
import litellm
import os

os.environ["FAL_AI_API_KEY"] = "your-fal-api-key"

# Generate with advanced parameters
response = litellm.image_generation(
    model="fal_ai/fal-ai/flux-pro/v1.1-ultra",
    prompt="A majestic dragon soaring over mountains",
    n=2,
    size="1792x1024",  # Maps to aspect_ratio="16:9"
    seed=42,
    safety_tolerance="2",
    enhance_prompt=True
)

for image in response.data:
    print(f"Generated image: {image.url}")

Usage - LiteLLM Proxy Server

1. Configure your config.yaml

Fal AI Image Generation Configuration
model_list:
  - model_name: flux-ultra
    litellm_params:
      model: fal_ai/fal-ai/flux-pro/v1.1-ultra
      api_key: os.environ/FAL_AI_API_KEY
    model_info:
      mode: image_generation
  
  - model_name: imagen4
    litellm_params:
      model: fal_ai/fal-ai/imagen4/preview
      api_key: os.environ/FAL_AI_API_KEY
    model_info:
      mode: image_generation
  
  - model_name: stable-diffusion
    litellm_params:
      model: fal_ai/fal-ai/stable-diffusion-v35-medium
      api_key: os.environ/FAL_AI_API_KEY
    model_info:
      mode: image_generation

general_settings:
  master_key: sk-1234

2. Start LiteLLM Proxy Server

Start Proxy Server
litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

3. Make requests

OpenAI SDK
LiteLLM SDK
cURL

Generate via Proxy - OpenAI SDK
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:4000",
    api_key="sk-1234"
)

response = client.images.generate(
    model="flux-ultra",
    prompt="A beautiful sunset over the ocean",
    n=1,
    size="1024x1024"
)

print(response.data[0].url)

Generate via Proxy - LiteLLM SDK
import litellm

response = litellm.image_generation(
    model="litellm_proxy/imagen4",
    prompt="A cozy coffee shop interior",
    api_base="http://localhost:4000",
    api_key="sk-1234"
)

print(response.data[0].url)

Generate via Proxy - cURL
curl --location 'http://localhost:4000/v1/images/generations' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer sk-1234' \
--data '{
    "model": "stable-diffusion",
    "prompt": "A serene Japanese garden with cherry blossoms",
    "n": 1,
    "size": "1024x1024"
}'

Using Model-Specific Parameters

LiteLLM forwards any additional parameters directly to the Fal AI API. You can pass model-specific parameters in your request and they will be sent to Fal AI.

Pass Model-Specific Parameters
import litellm

# Any parameters beyond the standard ones are forwarded to Fal AI
response = litellm.image_generation(
    model="fal_ai/fal-ai/flux-pro/v1.1-ultra",
    prompt="A beautiful sunset",
    # Model-specific Fal AI parameters
    aspect_ratio="16:9",
    safety_tolerance="2",
    enhance_prompt=True,
    seed=42
)

For the complete list of parameters supported by each model, see:

Supported Parameters

Standard OpenAI-compatible parameters that work across all models:

Parameter	Type	Description	Default
`prompt`	string	Text description of desired image	Required
`model`	string	Fal AI model to use	Required
`n`	integer	Number of images to generate (1-4)	`1`
`size`	string	Image dimensions (maps to model-specific format)	Model default
`api_key`	string	Your Fal AI API key	Environment variable

Getting Started

Sign up at fal.ai
Get your API key from your account settings
Set FAL_AI_API_KEY environment variable
Choose a model from the Fal AI model gallery
Start generating images with LiteLLM

Overview​

Setup​

API Key​

Supported Models​

Image Generation​

Usage - LiteLLM Python SDK​

Usage - LiteLLM Proxy Server​

1. Configure your config.yaml​

2. Start LiteLLM Proxy Server​

3. Make requests​

Using Model-Specific Parameters​

Supported Parameters​

Getting Started​

Additional Resources​