Diffusion model for galaxy generation
Project description
Contains two jax functions ScoreNet32 and ScoreNet64. These are used to return the gradients of an arbitrary image with respect to a prior distribution of individual artifact free galaxy models. Current functions include ScoreNetXX(image) returns gradients as stated. generateSample(samples=n, hi_res=bool, seed=XXXX) will generate an array of n galaxy samples which can be plotted with imshow.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
galaxygrad-0.0.8.tar.gz
(19.4 MB
view details)
Built Distribution
File details
Details for the file galaxygrad-0.0.8.tar.gz.
File metadata
- Download URL: galaxygrad-0.0.8.tar.gz
- Upload date:
- Size: 19.4 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | cdeceb3bda904ec140e930cdddf5ec7853d888a559a060df1d6defa011404b32 |
|
| MD5 | 5578186f6cf1c87fae08c6a9ff543156 |
|
| BLAKE2b-256 | ed23d35715cb5245aec2399d206ecf1ff94fddde310185e8e32f64cc65e06927 |
File details
Details for the file galaxygrad-0.0.8-py3-none-any.whl.
File metadata
- Download URL: galaxygrad-0.0.8-py3-none-any.whl
- Upload date:
- Size: 19.4 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | a6a311cd2b3a65cc4dee91540b5ce69c1054b44081a98f96bb7629df8e80c0ad |
|
| MD5 | 78b7c1d1922413e1c9bf840c5c2a994a |
|
| BLAKE2b-256 | 895c23b809c4ed4146a46e8421fdbfc1506841eaf8a761b5d564eb2266cd1846 |
