  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, we introduce HD-Painter, a training free
approach that accurately follows 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 resulting in better text aligned generations. To further improve
the prompt coherence we introduce the Reweighting Attention Score Guidance
(RASG) mechanism seamlessly integrating a post-hoc sampling strategy into the
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
quantitatively and qualitatively across multiple metrics and a user study. Code
is publicly available at: https://github.com/Picsart-AI-Research/HD-Painter
