SD-$π$XL: Generating Low-Resolution Quantized Imagery via Score Distillation

• URL: http://arxiv.org/abs/2410.06236v1
• Date: Tue, 8 Oct 2024 17:48:01 GMT
• Title: SD-$π$XL: Generating Low-Resolution Quantized Imagery via Score Distillation
• Authors: Alexandre Binninger, Olga Sorkine-Hornung,
• Abstract summary: Low-resolution quantized imagery, such as pixel art, is seeing a revival in modern applications. We introduce SD-$pi$XL, an approach for producing quantized images that employs score distillation sampling in conjunction with a differentiable image generator. We show that our method is the ability to transform input images into low-resolution, quantized versions while retaining their key semantic features.
• Score: 64.40561867379627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-resolution quantized imagery, such as pixel art, is seeing a revival in modern applications ranging from video game graphics to digital design and fabrication, where creativity is often bound by a limited palette of elemental units. Despite their growing popularity, the automated generation of quantized images from raw inputs remains a significant challenge, often necessitating intensive manual input. We introduce SD-$\pi$XL, an approach for producing quantized images that employs score distillation sampling in conjunction with a differentiable image generator. Our method enables users to input a prompt and optionally an image for spatial conditioning, set any desired output size $H \times W$, and choose a palette of $n$ colors or elements. Each color corresponds to a distinct class for our generator, which operates on an $H \times W \times n$ tensor. We adopt a softmax approach, computing a convex sum of elements, thus rendering the process differentiable and amenable to backpropagation. We show that employing Gumbel-softmax reparameterization allows for crisp pixel art effects. Unique to our method is the ability to transform input images into low-resolution, quantized versions while retaining their key semantic features. Our experiments validate SD-$\pi$XL's performance in creating visually pleasing and faithful representations, consistently outperforming the current state-of-the-art. Furthermore, we showcase SD-$\pi$XL's practical utility in fabrication through its applications in interlocking brick mosaic, beading and embroidery design.

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