Flux.1 Redux AI Image Generator

Image Variation and Restyling with Flux.1 Redux

Flux Redux

Upload images and describe variant images

Upload an image or provide a URL to the image.

Image file is required

Random Seed, useful for generating the same style image

Flux Redux Result

Your generated variant images will appear here

Result Time30s - 1min

Variant images will appear here

Flux Redux Example

Example image 1

Original Image 1

Example image 1

Flux Redux Result 1

Example image 2

Original Image 2

Example image 2

Flux Redux Result 2

Flux Redux FAQ

Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.

What is Flux Redux?

Flux Redux is an adapter for Flux.1 base models that enables image variation generation and restyling. It can reproduce input images with slight variations and allows combining images with text prompts to create new variations.

How does Flux Redux work?

Given an input image, Flux Redux can reproduce the image with variations while maintaining key characteristics. It can also be used for image restyling by combining input images with text prompts to guide the variation generation.

What are the key features of Flux Redux?

Key features include image variation generation, image restyling via prompts, and integration with Flux1.1 Pro Ultra for creating high-quality 4-megapixel outputs with flexible aspect ratios.

What makes Flux Redux unique?

Flux Redux achieves state-of-the-art performance in image variation tasks while maintaining high fidelity to input images. It naturally integrates into complex workflows and supports both simple variations and prompt-guided restyling.

How can I access Flux Redux?

Flux Redux is available in two versions: Flux.1 Redux [dev] which is open source under the Flux Dev License, and Flux.1 Redux [pro] which is available through the BFL API with support for Flux1.1 Pro Ultra.

What are the typical use cases for Flux Redux?

Common use cases include generating variations of existing images, restyling images according to text prompts, and creating high-quality image variations while maintaining core composition and characteristics of the original image.