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--- |
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license: openrail |
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pipeline_tag: text-generation |
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library_name: transformers |
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widget: |
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- text: "a photograph of" |
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example_title: "photo" |
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- text: "a bizarre cg render" |
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example_title: "render" |
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- text: "the spaghetti" |
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example_title: "meal?" |
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- text: "a (detailed+ intricate)+ picture" |
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example_title: "weights" |
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- text: "photograph of various" |
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example_title: "variety" |
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inference: |
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parameters: |
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temperature: 2.6 |
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max_new_tokens: 250 |
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--- |
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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. |
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This is mostly trained on positive prompts, though you may catch some words in [] brackets, which will be treated as negative. |
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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. |
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To use this model, you can import it as a pipeline like so: |
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```py |
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from transformers import pipeline |
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generator = pipeline(model="cactusfriend/nightmare-invokeai-prompts", |
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tokenizer="cactusfriend/nightmare-invokeai-prompts", |
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task="text-generation") |
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``` |
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Here's an example function that'll generate by default 20 prompts, at a temperature of 1.8 which seems good for this model. |
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```py |
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def makePrompts(prompt: str, *, p: float=0.9, |
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k: int = 40, num: int = 20, |
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temp: float = 1.8, mnt: int = 150): |
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outputs = generator(prompt, max_new_tokens=mnt, |
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temperature=temp, do_sample=True, |
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top_p=p, top_k=k, num_return_sequences=num) |
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items = set([i['generated_text'] for i in outputs]) |
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print("-" * 60) |
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print("\n ---\n".join(items)) |
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print("-" * 60) |
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``` |
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Then, you can call it like so: |
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```py |
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makePrompts("a photograph of") |
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# or, to change some defaults: |
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makePrompts("spaghetti all over", temp=1.4, p=0.92, k=45) |
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``` |
