FLUX.1 Dev

Table of Contents

Model Overview

Meet FLUX.1 [dev], a game-changing AI model that can generate images from text descriptions. But what makes it so special?

FLUX.1 [dev] is a powerful tool for generating images from text descriptions. It’s a 12 billion parameter rectified flow transformer that can create high-quality images with impressive accuracy.

Tasks and rank

Image-to-Image Translation 5th

Image Manipulation 2nd

Image Generation 7th

Image Editing 2nd

Learn More About the Ranking System

Capabilities

What can it do?

• Generate images from text: Give FLUX.1 [dev] a text description, and it will create an image that matches what you described.
• Competitive prompt following: FLUX.1 [dev] is good at understanding what you want it to do, and it can follow your prompts accurately.
• Efficient performance: Thanks to guidance distillation, FLUX.1 [dev] is more efficient than other models, making it a great choice for developers and creatives.

How does it compare to other models?

• State-of-the-art performance: FLUX.1 [dev] is second only to ==FLUX.1 [pro]== in terms of output quality.
• Open weights: Unlike some other models, FLUX.1 [dev] has open weights, which means that developers and researchers can use it to drive new scientific research and create innovative workflows.

Performance

FLUX.1 [dev] is a powerful AI model that generates images from text descriptions with remarkable speed and accuracy. But how does it perform in various tasks?

Speed

How fast can FLUX.1 [dev] generate images? With its 12 billion parameters, it can process text prompts quickly and efficiently. In fact, it’s capable of generating high-quality images in a matter of seconds.

Accuracy

But speed is not everything. How accurate is FLUX.1 [dev] in generating images that match the text prompts? The answer is: very accurate. FLUX.1 [dev] has been trained using guidance distillation, which makes it more efficient and accurate in generating images that match the text prompts.

Efficiency

FLUX.1 [dev] is not only fast and accurate, but also efficient. Its open weights allow developers and artists to build on top of the model and develop innovative workflows. This means that FLUX.1 [dev] can be used for a wide range of applications, from personal projects to commercial use.

How to use FLUX.1 [dev]

API Endpoints

You can access FLUX.1 [dev] through API endpoints on bfl.ml, replicate.com, fal.ai, and mystic.ai.

Comfy UI

FLUX.1 [dev] is also available in Comfy UI for local inference with a node-based workflow.

Diffusers

You can use FLUX.1 [dev] with the diffusers Python library by installing or upgrading diffusers and following the example code provided.

Limitations

While FLUX.1 [dev] is a powerful model, it’s not perfect. Here are some key things to keep in mind:

1. Lack of Factual Information

FLUX.1 [dev] is not designed to provide factual information. It’s a statistical model that generates images based on patterns and associations in the data it was trained on.

2. Societal Biases

As a statistical model, FLUX.1 [dev] may amplify existing societal biases. This means that the images it generates may reflect and even reinforce stereotypes or prejudices present in the data it was trained on.

3. Prompt Following Challenges

The model may struggle to generate output that matches the prompts, especially if the prompts are unclear or ambiguous.

4. Out-of-Scope Use

There are certain use cases that are not allowed with FLUX.1 [dev]. These include using the model to harm or exploit minors, generating or disseminating false information, and creating non-consensual nudity or illegal pornographic content.

Alternatives

Grounded SAM 13900 Visual task model

Stable Diffusion V1 4 6520 Text-to-image model

Vit Base Patch16 224 In21k 220 Image classification

CLIP ViT B 32 Laion2B S34B B79K 104 Zero-shot image classifier

Format

FLUX.1 [dev] is a powerful AI model that generates images from text descriptions. Let’s dive into its format and how to work with it.

Architecture

FLUX.1 [dev] is a rectified flow transformer with 12 billion parameters. This architecture allows it to produce high-quality images from text prompts.

Supported Data Formats

This model accepts text descriptions as input and generates images as output. The input text should be a prompt that describes the image you want to generate.

Input Requirements

When working with FLUX.1 [dev], you’ll need to provide a text prompt that describes the image you want to generate. For example:

prompt = "A cat holding a sign that says hello world"

You can also specify additional parameters, such as the height and width of the output image:

height=1024
width=1024

Output Requirements

The model generates images as output. You can save the generated image to a file using the following code:

image.save("flux-dev.png")

Special Requirements

To use FLUX.1 [dev] with the diffusers library, you’ll need to install or upgrade the library using pip:

pip install -U diffusers

Then, you can use the FluxPipeline class to run the model:

import torch
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)

Note that you may need to adjust the torch_dtype parameter depending on your hardware.

Examples

Generate an image of a futuristic cityscape with sleek skyscrapers and flying cars Image generated: A futuristic cityscape with sleek skyscrapers and flying cars, with a bright blue sky and a sense of bustling activity.

Create an image of a fantasy creature, a dragon with iridescent scales and wings Image generated: A fantasy creature, a dragon with iridescent scales and wings, set against a backdrop of rolling hills and misty mountains.

Produce an image of a still life, with a vase, flowers, and a book on a wooden table Image generated: A still life, with a vase, flowers, and a book on a wooden table, with a warm and cozy lighting and a sense of serenity.

Example Use Case

Here’s an example of how to use FLUX.1 [dev] to generate an image from a text prompt:

prompt = "A cat holding a sign that says hello world"
image = pipe(prompt, height=1024, width=1024, guidance_scale=3.5, num_inference_steps=50, max_sequence_length=512, generator=torch.Generator("cpu").manual_seed(0)).images[0]
image.save("flux-dev.png")

This code generates an image from the prompt and saves it to a file named “flux-dev.png”.