
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 not 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)
```
{"license": "openrail", "library_name": "transformers", "pipeline_tag": "text-generation", "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"}, {"text": "photograph of various", "example_title": "variety"}], "inference": {"parameters": {"temperature": 2.6, "max_new_tokens": 250}}}
transformers pytorch safetensors gpt_neo text-generation license:openrail autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #license-openrail #autotrain_compatible #endpoints_compatible #has_space #region-us 


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/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 not quite InvokeAI prompt syntax.

To use this model, you can import it as a pipeline like so:


Here's an example function that'll generate by default 20 prompts, at a temperature of 1.8 which seems good for this model.


Then, you can call it like so:

TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #license-openrail #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #license-openrail #autotrain_compatible #endpoints_compatible #has_space #region-us 
