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3DIS-FLUX - A multi-instance generation framework jointly developed by Zhejiang University and Harvard University

3DIS-FLUX is a deep learning-based multi-instance generation framework that achieves high-quality image generation through decoupled instance synthesis. Combining the depth-driven scene construction of the 3DIS framework and the diffusion transformer architecture of the FLUX model, it is implemented in two stages...

What is 3DIS-FLUX?

3DIS-FLUX is a deep learning-based multi-instance generation framework that achieves high-quality image generation by decoupling instance synthesis. Combining the depth-driven scene construction of the 3DIS framework and the diffusion transformer architecture of the FLUX model, it operates in two stages: first, it generates a scene depth map, and then it performs detail rendering based on the FLUX model. Through attention mechanism control, it ensures that the image token for each instance only focuses on the corresponding text token, achieving accurate instance rendering. 3DIS-FLUX requires no additional training on the pre-trained model, retaining its powerful generation capabilities while significantly outperforming traditional methods in terms of instance success rate and image quality.

Main functions of 3DIS-FLUX

  • Deep-driven scene building3DIS-FLUX divides multi-instance generation into two stages. First, it generates a scene depth map by laying out a depth model, which is used for accurate instance positioning and scene layout.
  • Detail rendering and attribute controlIn the second phase, 3DIS-FLUX uses the FLUX.1-Depth-dev model for detailed rendering. By introducing a detailed renderer, it manipulates the attention mask in FLUX's joint attention mechanism based on layout information, ensuring that fine-grained attributes of each instance (such as color, shape, etc.) are rendered accurately.
  • No additional training requiredThe framework only requires adapter training during the scene building phase, and does not require additional training of the pre-trained model during the detail rendering phase, which significantly reduces resource consumption.
  • Performance and quality improvementExperiments show that 3DIS-FLUX outperforms traditional methods, such as the 3DIS framework based on SD2 and SDXL, and current state-of-the-art adapter methods, in terms of instance success rate and image quality.
  • Flexibility and compatibility3DIS-FLUX is compatible with a variety of pre-trained models and can be seamlessly integrated into existing generative AI systems.

3DIS-FLUX Technical Principles

  • Two-stage generation process
    • Scene building phaseThis stage uses a layout-to-depth model to generate a scene depth map based on the layout information provided by the user. This requires adapter training to ensure that the scene layout matches the user-defined instance positions.
    • Detail rendering stageThis stage generates high-quality RGB images based on the depth map using the FLUX.1-Depth-dev model. No additional training is required in this stage; the pre-trained FLUX model is used directly for rendering.
  • Integration of FLUX modelsFLUX is an advanced model based on Diffusion Transformer (DiT) that offers powerful text control and high-quality image generation. During the detail rendering stage, 3DIS-FLUX aligns image and text embeddings through the FLUX model's Joint Attention mechanism and ensures that each instance's image token focuses only on its corresponding text token through an Attention Mask.
  • Optimization of attention mechanismTo further improve the rendering accuracy of fine-grained attributes of instances, 3DIS-FLUX introduces a Detail Renderer, which ensures the rendering accuracy of each instance by manipulating the attention mask in the joint attention mechanism of the FLUX model.

3DIS-FLUX project address

Application scenarios of 3DIS-FLUX

  • E-commerce design and poster generation3DIS-FLUX can be used to generate high-quality e-commerce product images and poster designs. By defining the layout and properties of multiple instances, users can quickly generate scene images containing multiple products, meeting the high requirements for product display and visual effects in e-commerce design.
  • Creative Design and Artistic CreationIt supports creative design and artistic creation, generating multi-instance images with detailed attributes, such as natural landscapes, cityscapes, and portraits. Users can generate complex scenes that meet their creative needs through text descriptions and layout definitions.
  • Virtual scene construction3DIS-FLUX can be used to build virtual scenes, such as game backgrounds and virtual exhibitions. By combining depth maps and detail rendering, it can generate multi-instance images with a sense of space and realism, meeting the precise requirements for object layout and detail in virtual scenes.
  • Advertising and Marketing Content GenerationIn the advertising and marketing field, 3DIS-FLUX can quickly generate visual content containing multiple elements, such as advertising posters and promotional images.
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