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FLUX Tools - A suite of model tools from Black Forest Labs

FLUX Tools is a suite of modeling tools developed by Black Forest Labs that enhances the controllability and operability of the basic text-to-image model FLUX.1. FLUX Tools includes FLUX.1 Fill (image repair and expansion), FLUX.1 D...

What are FLUX Tools?

FLUX Tools is a suite of modeling tools developed by Black Forest Labs that enhances the controllability and operability of the basic text-to-image model FLUX.1. FLUX Tools includes FLUX.1 Fill (image inpainting and expansion), FLUX.1 Depth (depth map-based structure guidance), FLUX.1 Canny (Canny edge-based structure guidance), and FLUX.1 Redux (image blending and reconstruction). Based on provided text descriptions and masks, the tools allow users to precisely edit and expand real and generated images, enhancing image transformation and reconstruction capabilities. FLUX Tools is released as an open-access model in the FLUX.1 [dev] model family and is used in the BFL API as a complement to FLUX.1 [pro].

Main functions of FLUX Tools

  • FLUX.1 FillUsed for image inpainting and outpainting. Edits and expands real and generated images based on text descriptions and binary masks. Supports seamless editing, allowing changes to blend naturally into the image.
  • FLUX.1 DepthStructure guidance is provided based on depth maps extracted from the input image and text cues. Precise control is maintained during the image transformation process, especially when retexturing images.
  • FLUX.1 CannyStructure guidance is provided based on Canny edges and text cues extracted from the input image. Suitable for precise detail adjustments.
  • FLUX.1 ReduxAdapter: Supports mixing and recreating input images and text prompts. Generates new versions of images with slight changes while retaining the core elements of the original image. Supports image reconstruction and style transfer based on prompts.

Technical principles of FLUX Tools

  • Deep learning and neural networksDeep learning models, especially convolutional neural networks (CNNs), are used to process and generate images. During training, these models learn the relationship between image features and textual descriptions using large amounts of data.
  • Structure guidanceThis method extracts structural information from images based on depth maps or Canny edges. Combined with text prompts, it guides the model to perform image editing and transformation while preserving the original structure.
  • Text-to-image conversionBased on Natural Language Processing (NLP) technology, text descriptions are converted into guiding signals for image editing. By combining image content and text descriptions, images that conform to the descriptions are generated.
  • Model optimization and inference efficiencyOptimize the model to improve inference efficiency and enable rapid response in practical applications. Use model compression techniques (such as LoRA) to simplify the development process and improve model accessibility.
  • Hybridization and ReconstructionBased on adapter technology, it blends input images and text prompts to generate images with new features. It supports image reconstruction and style transfer, providing more diverse image outputs.

FLUX Tools project address

Application scenarios of FLUX Tools

  • Digital art creation:Artists and designers create new digital artworks by quickly generating or modifying images based on text prompts, realizing their creative ideas.
  • Game development:In game design, it enables the rapid generation or editing of images of game environments, characters, and props, improving development efficiency.
  • Film and video production:In film post-production, it involves repairing damaged parts of old films or creating special effects to enhance visual appeal.
  • Advertising and Marketing:Marketers can quickly generate compelling ad images or customize visual content to meet client needs.
  • Education and training:In the field of education, creating teaching materials, such as simulated images of historical events, helps students better understand the learning content.
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