AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers

Key points

• Generating images beyond the training resolution of Diffusion Transformers (DiTs) remains challenging, as the number of tokens grows rapidly with resolution, full attention becomes expensive, and outputs often suffer from structural distortions and degraded detail.1
• We observe that attention's effective receptive field narrows during denoising, from global in early steps that form structure to local in later steps that refine details.1
• At the moment only the flux1-krea-dev code has been release.2
• Qwen-Image, Ideogram 4, and Flux2 "coming soon".2

Sources (3)

• [1]AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers
  Hugging Face Daily Papers · Oct 12, 12:00 AM

  We propose AReA, a training-free attention mechanism whose receptive field evolves from coarse to fine, following the denoising progression.

  Generating images beyond the training resolution of Diffusion Transformers (DiTs) remains challenging, as the number of tokens grows rapidly with resolution, full attention becomes expensive, and outputs often suffer from structural distortions and degraded detail.

• [2]AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers (for Flux1/2, Qwen-Image, Ideogram 4)
  r/StableDiffusion (top, day) · Oct 14, 07:37 PM

  At the moment only the flux1-krea-dev code has been release.

  Qwen-Image, Ideogram 4, and Flux2 "coming soon".

• [3]AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 06:01 PM · same content

Extractive summary: sentences quoted from the sources.