Flux Pro V1.1 API

V1.1
Redux

Endpoint: POST https://fal.run/fal-ai/flux-pro/v1.1 Endpoint ID: fal-ai/flux-pro/v1.1

Try it in the Playground

Run this model interactively with your own prompts.

Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/flux-pro/v1.1",
    arguments={
        "prompt": "Extreme close-up of a single tiger eye, direct frontal view. Detailed iris and pupil. Sharp focus on eye texture and color. Natural lighting to capture authentic eye shine and depth. The word \"FLUX\" is painted over it in big, white brush strokes with visible texture."
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/flux-pro/v1.1", {
  input: {
      prompt: "Extreme close-up of a single tiger eye, direct frontal view. Detailed iris and pupil. Sharp focus on eye texture and color. Natural lighting to capture authentic eye shine and depth. The word \"FLUX\" is painted over it in big, white brush strokes with visible texture."
    },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/fal-ai/flux-pro/v1.1 \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "Extreme close-up of a single tiger eye, direct frontal view. Detailed iris and pupil. Sharp focus on eye texture and color. Natural lighting to capture authentic eye shine and depth. The word \"FLUX\" is painted over it in big, white brush strokes with visible texture."
}'

Examples

A close-up artistic photograph of fresh blueberries on a white surface. The surface has a marbled, fluid texture with swirls of white and various shades of blue, creating an abstract, paint-like effect. The blueberries are scattered across the image, some sitting on the white areas and others nestle…

Generated image: A close-up artistic photograph of fresh blueberries on a white surface. The surf

A visually stunning image of the Turritopsis dohrnii jellyfish floating gracefully in clear water. Include a close-up of its unique, translucent body, showcasing the regeneration process. The background should have a serene underwater environment with soft lighting.

Generated image: A visually stunning image of the Turritopsis dohrnii jellyfish floating graceful

A cartoon character walking in autumn nature holding a phone and working on his phone In autumn graphic style

Generated image: A cartoon character walking in autumn nature holding a phone and working on his

Input Schema

string

required

The prompt to generate an image from.

ImageSize | Enum

default:"landscape_4_3"

The size of the generated image. Default value: landscape_4_3Possible values: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9

integer

The same seed and the same prompt given to the same version of the model will output the same image every time.

boolean

default:"false"

If True, the media will be returned as a data URI and the output data won’t be available in the request history.

integer

default:"1"

The number of images to generate. Default value: 1Range: 1 to 4

OutputFormatEnum

default:"jpeg"

The format of the generated image. Default value: "jpeg"Possible values: jpeg, png

SafetyToleranceEnum

default:"2"

The safety tolerance level for the generated image. 1 being the most strict and 5 being the most permissive. Default value: "2"Possible values: 1, 2, 3, 4, 5, 6

boolean

default:"false"

Whether to enhance the prompt for better results.

Output Schema

list<registry__image__fast_sdxl__models__Image>

required

The generated image files info.

Timings

required

integer

required

Seed of the generated Image. It will be the same value of the one passed in the input or the randomly generated that was used in case none was passed.

list<boolean>

required

Whether the generated images contain NSFW concepts.

string

required

The prompt used for generating the image.

Input Example

{
  "prompt": "Extreme close-up of a single tiger eye, direct frontal view. Detailed iris and pupil. Sharp focus on eye texture and color. Natural lighting to capture authentic eye shine and depth. The word \"FLUX\" is painted over it in big, white brush strokes with visible texture.",
  "image_size": "landscape_4_3",
  "sync_mode": false,
  "num_images": 1,
  "output_format": "jpeg",
  "safety_tolerance": "2",
  "enhance_prompt": false
}

Output Example

{
  "images": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ],
  "prompt": ""
}

Endpoint: POST https://fal.run/fal-ai/flux-pro/v1.1/redux Endpoint ID: fal-ai/flux-pro/v1.1/redux

Try it in the Playground

Run this model interactively with your own prompts.

Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/flux-pro/v1.1/redux",
    arguments={
        "image_url": "https://fal.media/files/kangaroo/acQvq-Kmo2lajkgvcEHdv.png"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/flux-pro/v1.1/redux", {
  input: {
      image_url: "https://fal.media/files/kangaroo/acQvq-Kmo2lajkgvcEHdv.png"
    },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/fal-ai/flux-pro/v1.1/redux \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://fal.media/files/kangaroo/acQvq-Kmo2lajkgvcEHdv.png"
}'

Input Schema

string

default:""

The prompt to generate an image from. Default value: ""

ImageSize | Enum

default:"landscape_4_3"

The size of the generated image. Default value: landscape_4_3Possible values: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9

integer

default:"28"

The number of inference steps to perform. Default value: 28Range: 1 to 50

integer

The same seed and the same prompt given to the same version of the model will output the same image every time.

float

default:"3.5"

The CFG (Classifier Free Guidance) scale is a measure of how close you want the model to stick to your prompt when looking for a related image to show you. Default value: 3.5Range: 1 to 20

boolean

default:"false"

If True, the media will be returned as a data URI and the output data won’t be available in the request history.

integer

default:"1"

The number of images to generate. Default value: 1Range: 1 to 4

OutputFormatEnum

default:"jpeg"

The format of the generated image. Default value: "jpeg"Possible values: jpeg, png

SafetyToleranceEnum

default:"2"

The safety tolerance level for the generated image. 1 being the most strict and 5 being the most permissive. Default value: "2"Possible values: 1, 2, 3, 4, 5, 6

boolean

default:"false"

Whether to enhance the prompt for better results.

string

required

The image URL to generate an image from. Needs to match the dimensions of the mask.

Output Schema

list<registry__image__fast_sdxl__models__Image>

required

The generated image files info.

Timings

required

integer

required

Seed of the generated Image. It will be the same value of the one passed in the input or the randomly generated that was used in case none was passed.

list<boolean>

required

Whether the generated images contain NSFW concepts.

string

required

The prompt used for generating the image.

Input Example

{
  "prompt": "",
  "image_size": "landscape_4_3",
  "num_inference_steps": 28,
  "guidance_scale": 3.5,
  "sync_mode": false,
  "num_images": 1,
  "output_format": "jpeg",
  "safety_tolerance": "2",
  "enhance_prompt": false,
  "image_url": "https://fal.media/files/kangaroo/acQvq-Kmo2lajkgvcEHdv.png"
}

Output Example

{
  "images": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ],
  "prompt": ""
}

FLUX1.1 [pro] - Professional Text-to-Image Generation

Transform your text descriptions into stunning, high-quality images with FLUX1.1 [pro], an enhanced version of FLUX.1 [pro] with improved image generation capabilities, delivering superior composition, detail, and artistic fidelity compared to its predecessor.

Overview

FLUX1.1 [pro] generates professional-grade images from text descriptions with enhanced performance and speed. Built on a 12 billion parameter flow transformer architecture, it delivers exceptional quality while maintaining rapid generation speeds and superior prompt adherence.

Key Capabilities

Enhanced image generation with superior composition and detail
Improved prompt adherence and output diversity
Six times faster than the original FLUX.1 [pro]
Highest Elo score on Artificial Analysis image arena
Commercial-ready image quality with artistic fidelity
Professional-grade performance optimized for production use

Getting Started

Getting up and running with FLUX1.1 [pro] takes just a few minutes. Here’s how to begin:

Get your API key at https://fal.ai/login
Install the client library for your preferred language
Make your first API call

JavaScript/TypeScript Installation:

npm install --save @fal-ai/client

Python Installation:

pip install fal-client

Simple Integration Example

Here’s a basic example of generating an image using TypeScript:

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

const result = await fal.subscribe("fal-ai/flux-pro/v1.1", {
  input: {
    prompt: "A serene mountain landscape at sunset, photorealistic style"
  }
});

Advanced Features

FLUX1.1 [pro] offers sophisticated controls for professional use cases:

Enhanced Performance

Six times faster generation than its predecessor while maintaining superior image quality and better prompt adherence.

Superior Composition

Delivers improved artistic fidelity with enhanced detail rendering and composition quality compared to previous versions.

Prompt Adherence

Excels at accurately interpreting and generating images based on complex prompts and specific requirements, making it highly versatile for various creative needs.

Output Diversity

Increased variety and creativity in generated outputs while maintaining consistency and quality.

Best Practices

Maximize your results with these proven approaches:

Image Quality

Use detailed, specific prompts that describe both content and style. The enhanced prompt adherence means more precise descriptions yield better results.

Performance Optimization

Take advantage of the improved speed while maintaining high quality output. The model balances efficiency with superior visual fidelity.

Production Implementation

Implement proper error handling and retry logic for robust production deployments. The model’s enhanced reliability makes it ideal for enterprise use.

Technical Specifications

Model Architecture

12 billion parameter flow transformer
Enhanced version of FLUX.1 [pro]
Hybrid architecture with multimodal and parallel diffusion transformer blocks
Flow matching methodology with rotary positional embeddings

Performance Metrics

Speed: 6x faster than original FLUX.1 [pro]
Quality: Superior composition, detail, and artistic fidelity
Benchmark: Highest overall Elo score on Artificial Analysis arena
Commercial usage: Approved for professional and commercial use

Codename “Blueberry”

FLUX1.1 [pro] was tested under the codename “blueberry” and achieved the highest overall Elo score among all image models on the Artificial Analysis image arena, surpassing competing models in visual quality and prompt accuracy.

API Reference

The FLUX1.1 [pro] API accepts standard text-to-image parameters:

interface FluxProParameters {
  prompt: string;              // Your detailed text description
  width?: number;             // Output image width
  height?: number;            // Output image height
  num_inference_steps?: number; // Generation steps
  guidance_scale?: number;     // Prompt adherence strength
  seed?: number;              // For reproducible results
}

Pricing and Usage

Your request will cost $0.04 per megapixel. Images are billed by rounding up to the nearest megapixel. Our transparent, usage-based pricing offers excellent value:

Competitive pricing: $0.04 per megapixel
Commercial usage rights included
No hidden fees or minimum commitments
Enterprise-grade reliability with cost-effective scaling

View detailed pricing or contact sales for enterprise solutions.

Support and Resources

Additional resources to help you succeed:

Documentation

Complete API documentation available at https://docs.fal.ai/

Community

Join our active developer community for support and inspiration

Enterprise Support

Dedicated support channels for enterprise customers Ready to transform your text into stunning images with the most advanced FLUX model available? Sign up now at https://fal.ai and start creating with FLUX1.1 [pro]. Get Started with FLUX1.1 Pro

FLUX1.1 [pro] ultra — Image Generation
FLUX.1 [pro] Fill — Image Generation
FLUX1.1 [pro] Redux — Image Generation
FLUX1.1 [pro] ultra Redux — Image Generation
FLUX1.1 [pro] ultra Fine-tuned — Image Generation
FLUX.1 [pro] Fill Fine-tuned — Image Generation
FLUX1.1 [pro] — Image Generation

Limitations

image_size restricted to: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9
num_images range: 1 to 4
output_format restricted to: jpeg, png
safety_tolerance restricted to: 1, 2, 3, 4, 5, 6
num_inference_steps range: 1 to 50
guidance_scale range: 1 to 20

Try it in the Playground

​Quick Start

​Examples

​Input Schema

​Output Schema

​Input Example

​Output Example

Try it in the Playground

​Quick Start

​Input Schema

​Output Schema

​Input Example

​Output Example

​FLUX1.1 [pro] - Professional Text-to-Image Generation

​Overview

​Key Capabilities

​Getting Started

​JavaScript/TypeScript Installation:

​Python Installation:

​Simple Integration Example

​Advanced Features

​Enhanced Performance

​Superior Composition

​Prompt Adherence

​Output Diversity

​Best Practices

​Image Quality

​Performance Optimization

​Production Implementation

​Technical Specifications

​Model Architecture

​Performance Metrics

​Codename “Blueberry”

​API Reference

​Pricing and Usage

​Support and Resources

​Documentation

​Community

​Enterprise Support

​Related

​Limitations

Quick Start

Examples

Input Schema

Output Schema

Input Example

Output Example

Quick Start

Input Schema

Output Schema

Input Example

Output Example

FLUX1.1 [pro] - Professional Text-to-Image Generation

Overview

Key Capabilities

Getting Started

JavaScript/TypeScript Installation:

Python Installation:

Simple Integration Example

Advanced Features

Enhanced Performance

Superior Composition

Prompt Adherence

Output Diversity

Best Practices

Image Quality

Performance Optimization

Production Implementation

Technical Specifications

Model Architecture

Performance Metrics

Codename “Blueberry”

API Reference

Pricing and Usage

Support and Resources

Documentation

Community

Enterprise Support

Related

Limitations