README.md

---
license: mit
tags:
- generated_from_trainer
model-index:
- name: ArtPrompter
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# [ArtPrompter](https://pearsonkyle.github.io/Art-Prompter/)

A [gpt2](https://huggingface.co/gpt2) powered predictive algorithm for making descriptive text prompts for A.I. image generators (e.g. MidJourney, Stable Diffusion, ArtBot, etc). The model was trained on a custom dataset containing 666K unique prompts from MidJourney. Simply start a prompt and let the algorithm suggest ways to finish it.

![](https://huggingface.co/pearsonkyle/ArtPrompter/resolve/main/starry_night.gif)

[![Art Prompter Basic](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1HQOtD2LENTeXEaxHUfIhDKUaPIGd6oTR?usp=sharing)



```python
from transformers import pipeline

prompter = pipeline('text-generation',model='pearsonkyle/ArtPrompter', tokenizer='gpt2')

texts = prompter('A portal to a galaxy, view with', max_length=30, num_return_sequences=5)

for i in range(5):
    print(texts[i]['generated_text']+'\n')
```

## Intended uses & limitations

Build sick prompts and lots of them.. use it to [make animations](https://colab.research.google.com/drive/1Ooe7c87xGMa9oG5BDrFVzYqJLvnoKcyZ?usp=sharing) or a discord bot that can interact with MidJourney.

[![](https://pearsonkyle.github.io/Art-Prompter/images/discord_bot.png)](https://discord.gg/3S8Taqa2Xy)


## Examples

- *The entire universe is a simulation,a confessional with a smiling guy fawkes mask, symmetrical, inviting,hyper realistic* 

- *a pug disguised as a teacher. Setting is a class room*

- *I wish I had an angel For one moment of love I wish I had your angel Your Virgin Mary undone Im in love with my desire Burning angelwings to dust*

- *The heart of a galaxy, surrounded by stars, magnetic fields, big bang, cinestill 800T,black background, hyper detail, 8k, black*


## Training procedure

~30 hours of finetune on RTX3070 with 666K unique prompts

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1
- Tokenizers 0.13.2