Instructions to use pearsonkyle/ArtPrompter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pearsonkyle/ArtPrompter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pearsonkyle/ArtPrompter")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pearsonkyle/ArtPrompter") model = AutoModelForCausalLM.from_pretrained("pearsonkyle/ArtPrompter") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use pearsonkyle/ArtPrompter with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pearsonkyle/ArtPrompter" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pearsonkyle/ArtPrompter", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pearsonkyle/ArtPrompter
- SGLang
How to use pearsonkyle/ArtPrompter with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "pearsonkyle/ArtPrompter" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pearsonkyle/ArtPrompter", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "pearsonkyle/ArtPrompter" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pearsonkyle/ArtPrompter", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pearsonkyle/ArtPrompter with Docker Model Runner:
docker model run hf.co/pearsonkyle/ArtPrompter
ArtPrompter
A 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.
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 or a discord bot that can interact with MidJourney.
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
- Downloads last month
- 16


Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "pearsonkyle/ArtPrompter"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pearsonkyle/ArtPrompter", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'