Spaces:
Running
Running
File size: 2,539 Bytes
5542365 1b9d7ec 704ee93 5542365 b0b9920 5542365 bcac695 5542365 bcac695 5542365 bcac695 00ed1ab bcac695 5542365 b0b9920 5506862 bdaeeba 5542365 bdaeeba 5542365 df7b7be bdaeeba 5542365 00ed1ab 5542365 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
---
title: Dalle Mini
emoji: 🎨
colorFrom: red
colorTo: blue
sdk: gradio
app_file: app/app_gradio_ngrok.py
pinned: false
---
# DALL-E Mini
_Generate images from a text prompt_
TODO: add some cool example
## Create my own images with the demo → Coming soon
## How does it work?
Refer to [our report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA?accessToken=2ua7j8ebc810fuxyv49wbipmq3fb2e78yq3rvs5dy4wew07wwm2csdo8zcuyr14e).
## Development
This section is for the adventurous people wanting to look into the code.
### Dependencies Installation
The root folder and associated `requirements.txt` is only for the app.
You will find necessary requirements in each sub-section.
You should create a new python virtual environment and install the project dependencies inside the virtual env. You need to use the `-f` (`--find-links`) option for `pip` to be able to find the appropriate `libtpu` required for the TPU hardware.
Adapt the installation to your own hardware and follow library installation instructions.
```
$ pip install -r requirements.txt -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
```
If you use `conda`, you can create the virtual env and install everything using: `conda env update -f environments.yaml`
### Training of VQGAN
The VQGAN was trained using [taming-transformers](https://github.com/CompVis/taming-transformers).
We recommend using the latest version available.
### Conversion of VQGAN to JAX
Use [patil-suraj/vqgan-jax](https://github.com/patil-suraj/vqgan-jax).
### Training of Seq2Seq
Refer to `seq2seq` folder (some parameters may have been hardcoded for convenience when training on our TPU VM).
You can also adjust the [sweep configuration file](https://docs.wandb.ai/guides/sweeps) if you need to perform a hyperparameter search.
### Inference
Refer to the demo notebooks.
TODO: add links
## Authors
- [Boris Dayma](https://github.com/borisdayma)
- [Suraj Patil](https://github.com/patil-suraj)
- [Pedro Cuenca](https://github.com/pcuenca)
- [Khalid Saifullah](https://github.com/khalidsaifullaah)
- [Tanishq Abraham](https://github.com/tmabraham)
- [Phúc Lê Khắc](https://github.com/lkhphuc)
- [Luke Melas](https://github.com/lukemelas)
- [Ritobrata Ghosh](https://github.com/ghosh-r)
## Acknowledgements
- 🤗 Hugging Face for organizing [the FLAX/JAX community week](https://github.com/huggingface/transformers/tree/master/examples/research_projects/jax-projects)
- Google Cloud team for providing access to TPU's
|