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What is FLUX 3 Image?
FLUX 3 Image is an AI image generation and editing model developed by Black Forest Labs for creating, composing, and modifying images with granular control over visual elements. The model supports standard text-to-image generation with prompt following and an understanding of image composition. Users who need more precise layouts can define subjects and objects with bounding boxes before generating an image. These layouts use a consistent 0-to-1000 coordinate grid across different aspect ratios, allowing individual elements to be positioned within specified regions of the canvas. FLUX 3 Image can also make multiple changes within an existing image while locking other elements so they remain unchanged. This enables workflows such as recoloring specific objects, modifying individual subjects, or making localized adjustments without unnecessarily altering the surrounding composition. The model supports up to 10 reference images that can be brought together into a newly composed scene using reference tokens and placement instructions. FLUX 3 Image can render images natively at 2K and 4K resolutions, with the source demonstrating an output of 5456 × 3072 pixels and 16.8 megapixels. Its pixel-perfect editing functionality is designed to modify a particular region of an image while preserving the original content elsewhere. Black Forest Labs also positions the model for agentic workflows, with native layout and composition understanding that allows an AI agent to create structured images from text prompts and planned element placements. For organizations that require self-hosted image generation, FLUX 3 Image is available through a commercial weights license that supports fine-tuning and deployment on private infrastructure.
What is DragGAN?
Crafting visual content that meets user specifications often requires a significant level of adaptability and accuracy when it comes to controlling the pose, shape, expression, and layout of the generated components. Conventional approaches increase the manageability of generative adversarial networks (GANs) by depending on labeled training datasets or existing 3D models, which often lack the required flexibility, precision, and responsiveness. In this study, we investigate a robust yet somewhat overlooked method for manipulating GANs that enables users to "drag" specific points within an image to precisely reach target locations through interactive participation, as demonstrated in Fig.1. Our proposed method, DragGAN, consists of two main elements: a feature-based motion supervision system that directs the handle point to the desired position, and an innovative point tracking technique that leverages the distinguishing features of GANs to continuously monitor the locations of the handle points. By using DragGAN, individuals have the ability to adjust images with remarkable accuracy in guiding pixel movements, which leads to a more intuitive and user-focused design experience. This innovative approach not only broadens creative opportunities but also allows users to more effectively realize their intended visual results. Ultimately, DragGAN represents a significant advancement in the field of interactive image manipulation, fostering a new era of creativity and precision in visual content creation.
Media
No images available
Integrations Supported
API Availability
Has API
API Availability
Pricing Information
Pricing not provided
Pricing Information
Free
Open source
Free Version
Supported Platforms
SaaS
Supported Platforms
Windows
Mac
Linux
Customer Service / Support
Web-Based Support
Customer Service / Support
Not specified
Training Options
Documentation Hub
Training Options
Documentation Hub
Company Facts
Organization Name
Black Forest Labs
Date Founded
2024
Company Location
Germany
Company Website
bfl.ai/models/flux-3-image
Company Facts
Organization Name
DragGAN
Date Founded
2023
Company Website
vcai.mpi-inf.mpg.de/projects/DragGAN/
Categories and Features
AI Image Models
Not specified
Categories and Features
AI Photo Editors
Not specified
AI Tools
Not specified