| Name | Version | Summary | date |
| graymatter_swissknife |
1.2.3 |
Gray Matter Swiss Knife : Generalized Exchange Model estimators for diffusion MRI |
2024-04-19 20:08:54 |
| diffusion |
6.10.3 |
Python SDK for Diffusion. |
2024-04-09 08:26:16 |
| designer2 |
2.0.7 |
designerV2 |
2024-03-27 18:42:19 |
| souJpg-diffusers |
0.27.2 |
State-of-the-art diffusion in PyTorch and JAX. |
2024-03-25 00:20:06 |
| diffusers |
0.27.2 |
State-of-the-art diffusion in PyTorch and JAX. |
2024-03-20 01:54:25 |
| torchheat |
0.1.0 |
Diffusion based distances in PyTorch |
2024-03-07 03:12:38 |
| paramdiffusion |
0.0.8 |
A package for deriving continuous score-based data solutions |
2024-03-05 16:28:53 |
| galaxygrad |
0.1.5 |
Diffusion model for galaxy generation |
2024-02-20 21:44:35 |
| diffusion-core |
0.0.65 |
Python SDK for Diffusion - CBOR serialisation. |
2024-02-15 16:20:58 |
| freqdiff |
0.1.0 |
Time series diffusion in the frequency domain. |
2024-02-09 10:41:14 |
| live-illustrate |
0.2.0 |
Live-ish illustration for your role-playing campaign |
2024-01-27 08:30:07 |
| hercai |
3.1.0 |
A powerful Python Package for interacting with the Herc.ai API. |
2024-01-18 22:12:37 |
| streamdiffusion |
0.1.1 |
real-time interactive image generation pipeline |
2023-12-31 05:42:49 |
| ForestDiffusion |
1.0.5 |
Generating and Imputing Tabular Data via Diffusion and Flow XGBoost Models |
2023-12-15 18:16:24 |
| diffusersv |
0.25.dev0 |
State-of-the-art diffusion in PyTorch and JAX. |
2023-12-15 02:18:36 |
| andi-datasets |
2.1.2 |
Generate, manage and analyze anomalous diffusion trajectories. |
2023-12-11 17:30:04 |
| diffusers-api |
0.0.1 |
diffusers-api: A Python API for Diffusion Models |
2023-11-10 23:00:54 |
| hngd |
1.0.1 |
Implementation of the Hydride Nucleation-Growth-Dissolution (HNGD) model. Simulates hydrogen behaviour in a 1D geometry using an explicit finite difference scheme (Euler method). |
2023-09-18 19:55:40 |
| ccsd |
0.3.3 |
CCSD (Combinatorial Complex Score-based Diffusion) is a sophisticated score-based diffusion model designed to generate Combinatorial Complexes using Stochastic Differential Equations. This cutting-edge approach enables the generation of complex objects with higher-order structures and relations, thereby enhancing our ability to learn underlying distributions and produce more realistic objects. |
2023-09-18 09:25:22 |
| hf-image-uploader |
0.0.2 |
State-of-the-art diffusion in PyTorch and JAX. |
2023-09-05 13:19:41 |