https://github.com/lucidrains/genie2-pytorch
Implementation of a framework for Genie2 in Pytorch
https://github.com/lucidrains/genie2-pytorch
artificial-intelligence attention deep-learning transformers world-models
Last synced: over 1 year ago
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Implementation of a framework for Genie2 in Pytorch
- Host: GitHub
- URL: https://github.com/lucidrains/genie2-pytorch
- Owner: lucidrains
- License: mit
- Created: 2024-08-28T16:40:40.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-07T16:36:03.000Z (over 1 year ago)
- Last Synced: 2025-04-12T23:38:07.470Z (over 1 year ago)
- Topics: artificial-intelligence, attention, deep-learning, transformers, world-models
- Language: Python
- Homepage:
- Size: 1.33 MB
- Stars: 145
- Watchers: 17
- Forks: 12
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Genie2 - Pytorch (wip)
Implementation of a framework for Genie2 in Pytorch
## Install
```bash
$ pip install genie2-pytorch
```
## Usage
```python
import torch
from genie2_pytorch import Genie2
genie = Genie2(
dim = 512,
depth = 12,
dim_latent = 768,
num_actions = 256,
latent_channel_first = True,
is_video_enc_dec = True
)
video = torch.randn(2, 768, 3, 2, 2)
actions = torch.randint(0, 256, (2, 3))
loss, breakdown = genie(video, actions = actions)
loss.backward()
generated_video = genie.generate(video[:, :, 0], num_frames = 16)
assert generated_video.shape == (2, 768, 16 + 1, 2, 2)
```
Interactive
```python
import torch
from genie2_pytorch import Genie2
genie = Genie2(
dim = 512,
depth = 12,
dim_latent = 768,
num_actions = 256,
latent_channel_first = True,
is_video_enc_dec = True
)
video = torch.randn(1, 768, 3, 2, 2)
actions = torch.randint(0, 256, (1, 3))
loss, breakdown = genie(video, actions = actions)
loss.backward()
generated_video, actions = genie.generate(
video[:, :, 0],
num_frames = 16,
interactive = True,
init_action = 0
)
# you will be prompted to enter the next action id(s) at every next time frame of the video
assert generated_video.shape == (1, 768, 16 + 1, 2, 2)
```
## Citations
```bibtex
@inproceedings{Valevski2024DiffusionMA,
title = {Diffusion Models Are Real-Time Game Engines},
author = {Dani Valevski and Yaniv Leviathan and Moab Arar and Shlomi Fruchter},
year = {2024},
url = {https://api.semanticscholar.org/CorpusID:271962839}
}
```
```bibtex
@article{Ding2024DiffusionWM,
title = {Diffusion World Model},
author = {Zihan Ding and Amy Zhang and Yuandong Tian and Qinqing Zheng},
journal = {ArXiv},
year = {2024},
volume = {abs/2402.03570},
url = {https://api.semanticscholar.org/CorpusID:267499902}
}
```
```bibtex
@inproceedings{Sadat2024EliminatingOA,
title = {Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models},
author = {Seyedmorteza Sadat and Otmar Hilliges and Romann M. Weber},
year = {2024},
url = {https://api.semanticscholar.org/CorpusID:273098845}
}
```
```bibtex
@misc{ParkerHolder2024,
author = {Jack Parker-Holder, Philip Ball, Jake Bruce, Vibhavari Dasagi, Kristian Holsheimer, Christos Kaplanis, Alexandre Moufarek, Guy Scully, Jeremy Shar, Jimmy Shi, Stephen Spencer, Jessica Yung, Michael Dennis, Sultan Kenjeyev, Shangbang Long, Vlad Mnih, Harris Chan, Maxime Gazeau, Bonnie Li, Fabio Pardo, Luyu Wang, Lei Zhang, Frederic Besse, Tim Harley, Anna Mitenkova, Jane Wang, Jeff Clune, Demis Hassabis, Raia Hadsell, Adrian Bolton, Satinder Singh, Tim Rocktäschel},
url = {https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/}
}
```
```bibtex
@inproceedings{Nguyen2024TurningUT,
title = {Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs},
author = {Minh Nguyen and Andrew Baker and Clement Neo and Allen Roush and Andreas Kirsch and Ravid Shwartz-Ziv},
year = {2024},
url = {https://api.semanticscholar.org/CorpusID:270870613}
}
```
```bibtex
@article{Lee2022AutoregressiveIG,
title = {Autoregressive Image Generation using Residual Quantization},
author = {Doyup Lee and Chiheon Kim and Saehoon Kim and Minsu Cho and Wook-Shin Han},
journal = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
pages = {11513-11522},
url = {https://api.semanticscholar.org/CorpusID:247244535}
}
```