Diffusion model for galaxy generation
Project description
Contains 4 generative diffusion models ScoreNet32 and ScoreNet64 for both the HSC and ZTF surveys. These are used to return the gradients of an arbitrary image with respect to a prior distribution of individual artifact free galaxy models.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
littlegrad-0.0.2.tar.gz
(3.1 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file littlegrad-0.0.2.tar.gz.
File metadata
- Download URL: littlegrad-0.0.2.tar.gz
- Upload date:
- Size: 3.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
73c98a14021b0332127acb39cdf8f7d6bc33549711141af3e9466b2847192753
|
|
| MD5 |
365d90e77f20b9a66162b68591826836
|
|
| BLAKE2b-256 |
8cee854ec94a9fc1f069fb01a799ac3a36b9d228e32248a98317ff7483df09a3
|
File details
Details for the file littlegrad-0.0.2-py3-none-any.whl.
File metadata
- Download URL: littlegrad-0.0.2-py3-none-any.whl
- Upload date:
- Size: 3.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
326dcd9bf271af8b5d106f3c464b4d7292ff632a5756e294bdbabea36d511945
|
|
| MD5 |
6105fce456a28b8f03ff4a1898f70593
|
|
| BLAKE2b-256 |
06ca474c9c4bdc3bc40335a6f272177768b5b21b20ac2be6d1f86aba28aedcfd
|
