semvec/pypi-packages huggingface dataset:
torchpt 0.0.3
Unified model state dict and architecture checkpoints utils.
License: MIT

# Checkpointing
Unified model state dict and architecture checkpoints utils.


Traditional model loading requires 
1. Model architecture
2. model state dict.

Normally we only save the model state dict in the checkpoint file, this forces us to
have the model architecture during inference. We can either load the model architecture
by instaling the package which contains the architecture dependency or we can copy the architecture file
to current repo. This creates additional dependencies for the production code.

One way to solve this problem is to save the model architecture to the checkpoint file itself and load both
the model architecture and state dict from the same checkpoint file. This solves few development issues:
1. If model architecture is changed in the architecture repo we wont have to use some old commit of the repo. 
For any architecture change and there is no need to map checkpoints to architecture version. 
2. Each checkpoint is self sufficient and independent.

Checkpointing add following utils:
1. Given a model architecture and state dict create a checkpointing checkpoint.
2. Given a checkpointing checkpoint return a the model with state dict loaded.
revalorise 0.0.3
Who is Revalorise
License: MIT
Keywords: Revalorise,Introduction

The package is the introduction of my online-self
=================================================

PyPi: https://pypi.org/project/Revalorise/

Hello! This package is the introduction of my online-self named
(Revalorise)

How to install
~~~~~~~~~~~~~~

open CMD / Terminal

.. code:: python

   pip install revalorise

How to use this package
~~~~~~~~~~~~~~~~~~~~~~~

-  open IDLE and type..

.. code:: python

   from revalorise import RevaloriseJr

   rev = RevaloriseJr() #Declare class name
   rev.show_name() #Show name
   rev.show_youtube() #Show youtube link
   rev.show_github() #Show Github link
   rev.about_me() #Show my bio
   rev.show_art() #Show ASCII Art

Developed by : Revalorise FB: https://www.facebook.com/

YouTube: https://www.youtube.com/ # The package is the introduction of
my online-self

PyPi: https://pypi.org/project/Revalorise/

Hello! This package is the introduction of my online-self named
(Revalorise)

.. _how-to-install-1:

How to install
~~~~~~~~~~~~~~

open CMD / Terminal

.. code:: python

   pip install revalorise

.. _how-to-use-this-package-1:

How to use this package
~~~~~~~~~~~~~~~~~~~~~~~

-  open IDLE and type..

.. code:: python

   from revalorise import RevaloriseJr

   rev = RevaloriseJr() #Declare class name
   rev.show_name() #Show name
   rev.show_youtube() #Show youtube link
   rev.show_github() #Show Github link
   rev.about_me() #Show my bio
   rev.show_art() #Show ASCII Art

Developed by : Revalorise FB: https://www.facebook.com/

YouTube: https://www.youtube.com/
diffusion-sde 0.1.1
Use score-based generative models to generate new images.
License: MIT
Requires Python: >=3.7
Keywords: generative modelling,SDE,diffusion models

# Diffusion SDE - A score-based generative modelling with SDEs package

Synthesize new images using the score-based generative models.

# Installation

Currently, `diffusion_sde` supports release of Python 3.7 onwards.

To install the current release:

```shell
$ pip install -U diffusion_sde
```

# Getting Started 

Start by instantiating a dataset class with a path where the custom dataset is located

```python
from diffusion_sde import datasets

# Specify the path of the custom dataset in the dataset class
ds = datasets(path_to_dataset)
```

Then, instantiate the `diffSDE` class to train the model and generate samples and pass the dataset using `.set_loaders()` method

```python
from diffusion_sde import diffSDE

# Instantiate the diffSDE class
cls_diff = diffSDE()

# Set the dataloaders by passing the dataset instantiation as above
cls_diff.set_loaders(dataset=ds)
```

Begin the model training using the `.train()` method and select the desired number of epochs for training.

```python
# Train the model
cls_diff.train(n_iters)
```

Generate the samples from the trained model with the `.generate_samples()` method and specify the desired number of steps for the sampler. We suggest setting the value of `n_steps` in the range of $\sim1500$-$2000$ steps to produce high-quality samples

```python
# Generate samples from the trained model
cls_diff.generate_samples(n_steps)
```