<h2>Overview</h2>
The Realistic Galaxy Simulation via Score-Based Generative Models is an innovative project that utilizes advanced score-based generative techniques to produce incredibly realistic images of galaxies. Incorporating elements from both PyTorch and TensorFlow implementations, this tool represents a significant leap forward in the field of astrophotography and visualization. It's intriguing to explore how this model differentiates between real and generated galaxies, and the potential applications in both research and art.
The software offers a well-structured framework, allowing users to download datasets, preprocess them, and subsequently train models to produce new galaxy images. With a blend of user-friendly commands and robust coding practices, the project caters to both seasoned programmers and curious newcomers looking to dive into the beauty of cosmic imagery.
<h2>Features</h2>
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Realistic Galaxy Generation: Produces stunningly realistic images of galaxies, showcasing the capabilities of modern generative models.
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Simple Download Process: Easily download the PROBES dataset with straightforward command-line instructions for seamless setup.
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Efficient Dataset Handling: Utilizes GNU Parallel for faster downloading of large datasets like the SDSS, optimizing time and resource usage.
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Customizable Training: Offers flexibility in choosing datasets and milestones for training, allowing users to pick up from previous sessions or start afresh.
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Inference Ready: Run inference effortlessly on trained models with configurable batch processing, facilitating extensive image generation.
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Access to Pretrained Models: Users can leverage pretrained models provided in the project, speeding up the learning process and enabling quick results.
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Open Source Software: Distributed under the GNU Affero General Public License, encouraging sharing and collaboration within the community.
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