HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image
  Inpainting with Diffusion Models
Authors: name: Hayk Manukyan, name: Andranik Sargsyan, name: Barsegh Atanyan, name: Zhangyang Wang, name: Shant Navasardyan, name: Humphrey Shi

Recent progress in text-guided image inpainting, based on the unprecedented
success of text-to-image diffusion models, has led to exceptionally realistic
and visually plausible results. However, there is still significant potential
for improvement in current text-to-image inpainting models, particularly in
better aligning the inpainted area with user prompts and performing
high-resolution inpainting. Therefore, in this paper we introduce HD-Painter, a
completely training-free approach that accurately follows to prompts and
coherently scales to high-resolution image inpainting. To this end, we design
the Prompt-Aware Introverted Attention (PAIntA) layer enhancing self-attention
scores by prompt information and resulting in better text alignment
generations. To further improve the prompt coherence we introduce the
Reweighting Attention Score Guidance (RASG) mechanism seamlessly integrating a
post-hoc sampling strategy into general form of DDIM to prevent
out-of-distribution latent shifts. Moreover, HD-Painter allows extension to
larger scales by introducing a specialized super-resolution technique
customized for inpainting, enabling the completion of missing regions in images
of up to 2K resolution. Our experiments demonstrate that HD-Painter surpasses
existing state-of-the-art approaches qualitatively and quantitatively,
achieving an impressive generation accuracy improvement of 61.4% vs 51.9%. We
will make the codes publicly available at:
https://github.com/Picsart-AI-Research/HD-Painter