文件列表:

LICENSE (1067, 2023-05-25)
conditonal_diffusion (0, 2023-05-25)
conditonal_diffusion\SR_inference.py (2175, 2023-05-25)
conditonal_diffusion\SR_inference_genSR.py (2249, 2023-05-25)
conditonal_diffusion\conditioned_diffusion.py (15039, 2023-05-25)
conditonal_diffusion\psf_conditioned_diffusion.py (16901, 2023-05-25)
dataset (0, 2023-05-25)
dataset\cralwer.py (1630, 2023-05-25)
glow-pytorch (0, 2023-05-25)
glow-pytorch\LICENSE (1071, 2023-05-25)
glow-pytorch\checkpoint (0, 2023-05-25)
glow-pytorch\model.py (11209, 2023-05-25)
glow-pytorch\sample (0, 2023-05-25)
glow-pytorch\train.py (5959, 2023-05-25)
nsf (0, 2023-05-25)
nsf\LICENSE.md (1118, 2023-05-25)
nsf\data (0, 2023-05-25)
nsf\data\__init__.py (536, 2023-05-25)
nsf\data\base.py (7091, 2023-05-25)
nsf\data\bsds300.py (1395, 2023-05-25)
nsf\data\caltech101.py (0, 2023-05-25)
nsf\data\celeba.py (3139, 2023-05-25)
nsf\data\cifar10.py (817, 2023-05-25)
nsf\data\download.py (1151, 2023-05-25)
nsf\data\frey.py (0, 2023-05-25)
nsf\data\gas.py (2709, 2023-05-25)
nsf\data\hepmass.py (3671, 2023-05-25)
nsf\data\imagenet.py (3170, 2023-05-25)
nsf\data\miniboone.py (2926, 2023-05-25)
nsf\data\omniglot.py (1620, 2023-05-25)
nsf\data\plane.py (9151, 2023-05-25)
... ...

# galgen This repository contains the implementation of various generative models for generating synthetic galaxy images as part of our project. The models implemented include Variational Autoencoders (VAE), Unconditional Diffusion, Conditional Diffusion, nsf and Glow. ## Model Implementations The implementations of the models used in this project are based on the following repositories: ### Variational Autoencoder (VAE) - Original implementation: [PyTorch-VAE](https://github.com/AntixK/PyTorch-VAE) by [AntixK](https://github.com/AntixK) ### Unconditional Diffusion - Original implementation: [diffusers](https://github.com/huggingface/diffusers) by [Hugging Face](https://github.com/huggingface) ### Conditional Diffusion - Original implementation: [Conditional_Diffusion_MNIST](https://github.com/TeaPearce/Conditional_Diffusion_MNIST) by [TeaPearce](https://github.com/TeaPearce) ### Glow (Normalizing Flow) - Original implementation: [glow-pytorch](https://github.com/rosinality/glow-pytorch) by [rosinality](https://github.com/rosinality) ### Neural Spline Flows (Normalizing Flow) - Original implementation: [nsf](https://github.com/bayesiains/nsf) by [bayesiains](https://github.com/bayesiains) ## Results Sample galaxy images generated by each model, along with quantitative results, can be found in our corresponding report. ## Acknowledgements We would like to express our gratitude to the authors of the original implementations for providing the foundation for our project. Their work has been instrumental in helping us explore and evaluate various generative models for synthetic galaxy image generation.

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