Metadata-Version: 2.1
Name: lumiere
Version: 0.0.4
Summary: Paper - Pytorch
Home-page: https://github.com/kyegomez/LUMIERE
License: MIT
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering
Author: Kye Gomez
Author-email: kye@apac.ai
Requires-Python: >=3.6,<4.0
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: einops
Requires-Dist: swarms
Requires-Dist: torch
Requires-Dist: zetascale
Project-URL: Documentation, https://github.com/kyegomez/LUMIERE
Project-URL: Repository, https://github.com/kyegomez/LUMIERE
Description-Content-Type: text/markdown

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# Lumiere 
Implementation of the text to video model LUMIERE from the paper: "A Space-Time Diffusion Model for Video Generation" by Google Research. I will mostly be implementing the modules from the diagram a and b in figure 4

## Install
`pip install lumiere`


## Usage
```python
import torch
from lumiere.model import AttentionBasedInflationBlock

# B, T, H, W, D
x = torch.randn(1, 4, 224, 224, 512)

# Model
model = AttentionBasedInflationBlock(dim=512, heads=4, dropout=0.1)

# Forward pass
out = model(x)

# print
print(out.shape)  # Expected shape: [1, 4, 224, 224, 3]

```


# License
MIT

