semvec/pypi-packages huggingface dataset:
libcasm-xtal 2.3.0
CASM crystallography
Requires Python: <3.15,>=3.10

<img alt="Shows the CASM logo" src="https://raw.githubusercontent.com/prisms-center/CASMcode_global/main/python/doc/_static/logo.svg" width="600" />

#### libcasm-xtal

The libcasm-xtal package is the CASM crystallography module. This includes:

- Data structures for representing lattices, crystal structures, and
  degrees of freedom (DoF).
- Methods for enumerating superlattices, making superstructures, finding primitive and reduced cells, and finding symmetry operations.


#### Install

    pip install libcasm-xtal


#### Usage

See the [libcasm docs](https://prisms-center.github.io/CASMcode_pydocs/libcasm/overview/latest/).


#### About CASM

The libcasm-xtal package is part of the [CASM](https://prisms-center.github.io/CASMcode_docs/) open source software package, which is designed to perform first-principles statistical mechanical studies of multi-component crystalline solids.

CASM is developed by the Van der Ven group, originally at the University of Michigan and currently at the University of California Santa Barbara.

For more information, see the [CASM homepage](https://prisms-center.github.io/CASMcode_docs/).


#### License

GNU Lesser General Public License (LGPL). Please see the file LICENSE for details.
skrample 0.5.1
Composable sampling functions for diffusion models
Requires Python: >=3.12

# Skrample 0.5.1
Composable sampling functions for diffusion models

## Status
Mostly complete for common models, superseding all diffusers features in [quickdif](https://github.com/Beinsezii/quickdif.git)

### Quickstart
Fastest way to jump in is [examples](./examples/). The classes and functions themselves have docstrings and type hints, so it's recommended to make liberal use of your IDE or python `help()`

### Feature Flags
 - `beta-schedule` -> `scipy` : For the `Beta()` schedule modifier
 - `brownian-noise` -> `torchsde` : For the `Brownian()` noise generator
 - `cdf-schedule` -> `scipy` : For the `SigmoidCDF()` schedule
 - `diffusers-wrapper` -> `torch` : For the `diffusers` integration module
 - `pytorch` -> `torch` : For the `pytorch` module
   - `pytorch.noise` : Custom generators
 - `all` : All of the above
 - `dev` : For running `tests/`

### Samplers
- Euler
- DPM
  - 1st order, 2nd order, 3rd order
  - SDE
- Adams/IPNDM
- UniP & UniPC
  - N order, limited to 9 for stability
  - Custom solver via other SkrampleSampler types
- SPC
  - Basic fully customizable midpoint corrector

### Schedules
- Linear
- Scaled
  - `uniform` flag, AKA `"trailing"` in diffusers
- SigmaCDF
- ZSNR

### Schedule modifiers
- Karras
- Exponential
- FlowShift
- Beta
- Hyper

### Predictors
- Epsilon
- Velocity / vpred
- Flow

### Noise generators
- Random
- Brownian
- Offset
- Pyramid

## Integrations
### Diffusers
- [X] Compatibility for pipelines
  - [X] SD1
  - [X] SDXL
  - [X] SD3
  - [X] Flux
- [X] Import from config
  - [X] Sampler
  - [X] Schedule
  - [X] Predictor
- [X] Manage state
  - [X] Steps
  - [X] Higher order
  - [X] Generators
  - [X] Config as presented

## Implementations
### quickdif
My diffusers cli [quickdif](https://github.com/Beinsezii/quickdif) has full support for all major Skrample features, allowing extremely fine-grained customization.