| ## DALL-E Mini - Generate image from text | |
| ## Tentative Strategy of training (proposed by Luke and Suraj) | |
| ### Data: | |
| * [Conceptual 12M](https://github.com/google-research-datasets/conceptual-12m) Dataset (already loaded and preprocessed in TPU VM by Luke). | |
| * [YFCC100M Subset](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md) | |
| * [Coneptual Captions 3M](https://github.com/google-research-datasets/conceptual-captions) | |
| ### Architecture: | |
| * Use the Taming Transformers VQ-GAN (with 16384 tokens) | |
| * Use a seq2seq (language encoder --> image decoder) model with a pretrained non-autoregressive encoder (e.g. BERT) and an autoregressive decoder (like GPT). | |
| ### Remaining Architecture Questions: | |
| * Whether to freeze the text encoder? | |
| * Whether to finetune the VQ-GAN? | |
| * Which text encoder to use (e.g. BERT, RoBERTa, etc.)? | |
| * Hyperparameter choices for the decoder (e.g. positional embedding, initialization, etc.) | |
| ## TODO | |
| * experiment with flax/jax and setup of the TPU instance that we should get shortly | |
| * work on dataset loading - [see suggested datasets](https://discuss.huggingface.co/t/dall-e-mini-version/7324/4) | |
| * Optionally create the OpenAI YFCC100M subset (see [this post](https://discuss.huggingface.co/t/dall-e-mini-version/7324/30?u=boris)) | |
| * work on text/image encoding | |
| * concatenate inputs (not sure if we need fixed length for text or use a special token separating text & image) | |
| * adapt training script | |
| * create inference function | |
| * integrate CLIP for better results (only if we have the time) | |
| * work on a demo (streamlit or colab or maybe just HF widget) | |
| * document (set up repo on model hub per instructions, start on README writeup…) | |
| * help with coordinating activities & progress | |
| ## Dependencies Installation | |
| 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: | |
| ``` | |
| $ 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` | |