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-0.0.7.tar.gz
(19.4 MB
view details)
Built Distribution
File details
Details for the file galaxygrad-0.0.7.tar.gz.
File metadata
- Download URL: galaxygrad-0.0.7.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 | ac1ca7264e4daaee8b251cf970a7dc8fa0dc7a3ae17421568b470f0d0090cb3e |
|
| MD5 | 5f47bea9529d0a6adf2848807e6e7735 |
|
| BLAKE2b-256 | caec80a647826b7c0192a738a5864a1067630b9b0465c1351258dffbb9702c65 |
File details
Details for the file galaxygrad-0.0.7-py3-none-any.whl.
File metadata
- Download URL: galaxygrad-0.0.7-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 | cfe0035d4dd33f5ff616e8716015de07a289b89c0d0717dd9cb92e3973a2d50b |
|
| MD5 | f69bd5e7a29dc9446b229aab76de0c4e |
|
| BLAKE2b-256 | 3c2dcf93636de47fe518892ea537c210b8747d9b0db71d33ab6023b7d64c0ffb |
