GrounDiT: Grounding Diffusion Transformers via Noisy Patch
  Transplantation
Authors: name: Phillip Y. Lee, name: Taehoon Yoon, name: Minhyuk Sung

We introduce a novel training-free spatial grounding technique for
text-to-image generation using Diffusion Transformers (DiT). Spatial grounding
with bounding boxes has gained attention for its simplicity and versatility,
allowing for enhanced user control in image generation. However, prior
training-free approaches often rely on updating the noisy image during the
reverse diffusion process via backpropagation from custom loss functions, which
frequently struggle to provide precise control over individual bounding boxes.
In this work, we leverage the flexibility of the Transformer architecture,
demonstrating that DiT can generate noisy patches corresponding to each
bounding box, fully encoding the target object and allowing for fine-grained
control over each region. Our approach builds on an intriguing property of DiT,
which we refer to as semantic sharing. Due to semantic sharing, when a smaller
patch is jointly denoised alongside a generatable-size image, the two become
"semantic clones". Each patch is denoised in its own branch of the generation
process and then transplanted into the corresponding region of the original
noisy image at each timestep, resulting in robust spatial grounding for each
bounding box. In our experiments on the HRS and DrawBench benchmarks, we
achieve state-of-the-art performance compared to previous training-free spatial
grounding approaches.