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Original Paper: https://arxiv.org/abs/2312.14091
By: Hayk Manukyan, Andranik Sargsyan, Barsegh Atanyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi
Abstract:
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:
Summary Notes
Enhancing Text-Guided Image Inpainting with HD-Painter
In the evolving field of AI, text-guided image inpainting combines natural language and visual creativity to regenerate parts of an image based on textual descriptions.
Despite the progress, aligning images with text and producing high-resolution outputs remain significant challenges. HD-Painter offers a promising approach to overcome these issues, improving prompt alignment and supporting high-resolution image generation without the need for additional training.
Simplifying Text-Guided Image Inpainting
Text-guided image inpainting has advanced with diffusion models, but often struggles with prompt alignment and high-resolution creation.
HD-Painter aims to address these issues by introducing two innovative components and a specialized super-resolution technique, making it possible to generate images up to 2K resolution that closely align with textual prompts.
Key Features of HD-Painter
- Prompt-Aware Introverted Attention (PAIntA): This component enhances the self-attention mechanism in diffusion models, making the content more relevant to the text prompt by minimizing the influence of non-prompt-related information.
- Reweighting Attention Score Guidance (RASG): RASG helps the image generation stay true to the text prompt by adjusting the diffusion process, ensuring both alignment and natural image statistics are maintained.
- Inpainting-Specific Super-Resolution: Unlike traditional techniques, this approach improves the resolution of inpainted areas by incorporating high-frequency details from the original image, ensuring a seamless and detailed result.
Performance and Results
HD-Painter shines when compared to current state-of-the-art methods, excelling in prompt alignment and high-quality image generation.
Evaluations using CLIP score, aesthetic score, and user feedback highlight its effectiveness.
Conclusion
HD-Painter significantly advances text-guided image inpainting by solving key issues of prompt alignment and high-resolution image generation. It offers a new tool for AI engineers, enhancing the potential for creative and practical AI applications.
With components like PAIntA and RASG, HD-Painter can produce images that are both high-quality and true to textual descriptions, marking a notable innovation in the AI field.
For a closer look at HD-Painter and its capabilities, the implementation is publicly available, signaling a step forward in text-guided image inpainting technology.
HD-Painter represents the forward-thinking achievements of AI engineers, showcasing the potential to merge visual and textual creativity through AI, paving the way for future advancements.
How Athina AI can help
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