metadata
license: openrail
pipeline_tag: text-generation
library_name: transformers
A model based upon the prompts of all the images in my InvokeAI's output directory, meant to be used with InvokeAI (a Stable Diffusion implementation) to generate new, probably wild nightmare images.
This is mostly trained on positive prompts, though you may catch some words in [] brackets, which will be treated as negative. GPT-Neo is usually quite good at pairing parenthesis, quotation marks, etc - however, don't be too surprised if it generates something that's note quite InvokeAI prompt syntax.
To use this model, you can import it as a pipeline like so:
from transformers import pipeline
generator = pipeline(model="cactusfriend/nightmare-invokeai-prompts",
tokenizer="cactusfriend/nightmare-invokeai-prompts",
task="text-generation")
Here's an example function that'll generate by default 20 prompts, at a temperature of 1.8 which seems good for this model.
def makePrompts(prompt: str, *, p: float=0.9,
k: int = 40, num: int = 20,
temp: float = 1.8, mnt: int = 150):
outputs = generator(prompt, max_new_tokens=mnt,
temperature=temp, do_sample=True,
top_p=p, top_k=k, num_return_sequences=num)
items = set([i['generated_text'] for i in outputs])
print("-" * 60)
print("\n".join(items))
print("-" * 60)
Then, you can call it like so:
makePrompts("a photograph of")
# or, to change some defaults:
makePrompts("spaghetti all over", temp=1.4, p=0.92, k=45)