# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("saltacc/RandomPrompt-v1")
model = AutoModelForCausalLM.from_pretrained("saltacc/RandomPrompt-v1", device_map="auto")Quick Links
RandomPrompt-v1
A fine tuned GPT-neo 125M
The purpose of this model is to autocomplete or generate danbooru-like prompts for generating images in Stable Diffusion derivatives that use danbooru tags for text conditioning.
Usage
THE HOSTED INTERFACE DOES NOT WORK, USE THE HUGGINGFACE SPACE
Autocompletion
Type in a few tags, and it will generate a completion of the prompt
Generation
Type in nothing, and it will generate a prompt
Training
Trained on 400k tags from danbooru posts for 600k steps, or around 0.25 epochs
https://wandb.ai/saltacc/RandomPrompt/runs/2v2arf0u?workspace=user-saltacc
I plan on doing further runs on better hardware to try to get more accurate prompt completion
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="saltacc/RandomPrompt-v1")