3DIS-FLUX: simple and efficient multi-instance generation with DiT
  rendering
Authors: name: Dewei Zhou, name: Ji Xie, name: Zongxin Yang, name: Yi Yang

The growing demand for controllable outputs in text-to-image generation has
driven significant advancements in multi-instance generation (MIG), enabling
users to define both instance layouts and attributes. Currently, the
state-of-the-art methods in MIG are primarily adapter-based. However, these
methods necessitate retraining a new adapter each time a more advanced model is
released, resulting in significant resource consumption. A methodology named
Depth-Driven Decoupled Instance Synthesis (3DIS) has been introduced, which
decouples MIG into two distinct phases: 1) depth-based scene construction and
2) detail rendering with widely pre-trained depth control models. The 3DIS
method requires adapter training solely during the scene construction phase,
while enabling various models to perform training-free detail rendering.
Initially, 3DIS focused on rendering techniques utilizing U-Net architectures
such as SD1.5, SD2, and SDXL, without exploring the potential of recent
DiT-based models like FLUX. In this paper, we present 3DIS-FLUX, an extension
of the 3DIS framework that integrates the FLUX model for enhanced rendering
capabilities. Specifically, we employ the FLUX.1-Depth-dev model for depth map
controlled image generation and introduce a detail renderer that manipulates
the Attention Mask in FLUX's Joint Attention mechanism based on layout
information. This approach allows for the precise rendering of fine-grained
attributes of each instance. Our experimental results indicate that 3DIS-FLUX,
leveraging the FLUX model, outperforms the original 3DIS method, which utilized
SD2 and SDXL, and surpasses current state-of-the-art adapter-based methods in
terms of both performance and image quality. Project Page:
https://limuloo.github.io/3DIS/.