README.md

---
license: openrail
pipeline_tag: text-generation
library_name: transformers
widget:
- text: "a photograph of"
  example_title: "photo"
- text: "a bizarre cg render"
  example_title: "render"
- text: "the spaghetti"
  example_title: "meal?"
- text: "a (detailed+ intricate)+ picture"
  example_title: "weights"
inference:
  parameters:
    temperature: 2.4
    max_new_tokens: 200
---

A model based upon the prompts of all the images in my InvokeAI's output directory, meant to be used with [InvokeAI](https://github.com/invoke-ai/InvokeAI) (a Stable Diffusion implementation/UI) 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:
```py
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.
```py
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 ---\n".join(items))
    print("-" * 60)
```

Then, you can call it like so:
```py
makePrompts("a photograph of")
# or, to change some defaults:
makePrompts("spaghetti all over", temp=1.4, p=0.92, k=45)
```