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Pasd Magnify


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The pasd-magnify model is designed for academic and non-commercial use. It uses Pixel-Aware Stable Diffusion for realistic image super-resolution and personalized stylization. It takes an input image (URL given in the input schema) and a prompt to guide the image processing. In addition, it allows users to specify unwanted characteristics for optimal output. The model also allows for customization of denoising steps, guidance scale, upsample scale, and conditioning scale. It then produces a high-quality, stylized output image, delivering the result as a URL.

Use cases

The PASD-Magnify AI model, created for academic and non-commercial use, is designed to enable realistic image super-resolution and personalized stylization. This means it works by taking lower-resolution or degraded images and upgrades them to higher quality, often mimicking the style and detail of a specific prompt provided to it. This realistic super-resolution and personalized stylization can be applied in various contexts. For instance, it can be used in the enhancement and restoration of old or degraded photographs, producing a high-quality, stylized version of the original. Its application also extends to video games, where it can be used to super-resolve gaming images and produce high-resolution gaming environments. Furthermore, the technology could be implemented in digital art platforms for creating highly detailed and stylized images from lower-res originals. Its denoise feature can also be essential in cleaning up noisy, unclear images in other sectors โ€“ such as surveillance imagery, medical imaging, or satellite pictures. The model's capability for personal stylization could also be deployed in fashion or interior design, allowing users to visualize different styles based on specific prompts.


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Summary of this model and related resources.

Model NamePasd Magnify

(Academic and Non-commercial use only) Pixel-Aware Stable Diffusion for Rea...

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Paper LinkView on Arxiv


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