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Aura S R v2
falIntroduction
AuraSR-V2 is a GAN-based super-resolution model designed for upscaling generated images. It is a variation of the GigaGAN architecture, specifically tailored for image-conditioned upscaling. The model is implemented in PyTorch and is available through the Hugging Face model hub.
Architecture
AuraSR-V2 leverages a GAN (Generative Adversarial Network) framework to enhance image resolution. It is based on the unofficial lucidrains/gigagan-pytorch
repository and adapts the GigaGAN architecture for its super-resolution tasks. The model is optimized for image-to-image tasks and supports various tags including art and super-resolution.
Training
The model card does not provide specific training details for AuraSR-V2. However, it indicates that the implementation follows the strategies outlined in the GigaGAN paper, focusing on image-conditioned upscaling.
Guide: Running Locally
To run AuraSR-V2 locally, follow these steps:
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Install the Aura-SR Package:
$ pip install aura-sr
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Load the Pre-trained Model:
from aura_sr import AuraSR aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2")
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Load and Upscale an Image:
import requests from io import BytesIO from PIL import Image def load_image_from_url(url): response = requests.get(url) image_data = BytesIO(response.content) return Image.open(image_data) image = load_image_from_url("https://mingukkang.github.io/GigaGAN/static/images/iguana_output.jpg").resize((256, 256)) upscaled_image = aura_sr.upscale_4x_overlapped(image)
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Hardware Recommendation:
- For optimal performance, it is recommended to use a cloud GPU service such as AWS EC2 with GPU instances, Google Cloud Platform, or Azure.
License
AuraSR-V2 is licensed under the Apache 2.0 License. This allows for both personal and commercial use, distribution, and modification under the terms of the license.