RichardErkhov/FredZhang7_-_distilgpt2-stable-diffusion-gguf

Quantization made by Richard Erkhov.

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distilgpt2-stable-diffusion - GGUF

• —Model creator: https://huggingface.co/FredZhang7/
• —Original model: https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion/

Original model description: --- license: creativeml-openrail-m tags:

• —stable-diffusion
• —prompt-generator
• —distilgpt2 datasets:
• —FredZhang7/krea-ai-prompts
• —Gustavosta/Stable-Diffusion-Prompts
• —bartman081523/stable-diffusion-discord-prompts widget:
• —text: "amazing"
• —text: "a photo of"
• —text: "a sci-fi"
• —text: "a portrait of"
• —text: "a person standing"
• —text: "a boy watching" ---

DistilGPT2 Stable Diffusion Model Card

<a href="https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2"> <font size="4"> <bold> Version 2 is here! </bold> </font> </a>

DistilGPT2 Stable Diffusion is a text generation model used to generate creative and coherent prompts for text-to-image models, given any text. This model was finetuned on 2.03 million descriptive stable diffusion prompts from Stable Diffusion discord, Lexica.art, and (my hand-picked) Krea.ai. I filtered the hand-picked prompts based on the output results from Stable Diffusion v1.4.

Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM.

PyTorch

pip install --upgrade transformers

from transformers import GPT2Tokenizer, GPT2LMHeadModel

# load the pretrained tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
tokenizer.max_len = 512

# load the fine-tuned model
model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion')

# generate text using fine-tuned model
from transformers import pipeline
nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)
ins = "a beautiful city"

# generate 10 samples
outs = nlp(ins, max_length=80, num_return_sequences=10)

# print the 10 samples
for i in range(len(outs)):
    outs[i] = str(outs[i]['generated_text']).replace('  ', '')
print('\033[96m' + ins + '\033[0m')
print('\033[93m' + '\n\n'.join(outs) + '\033[0m')

Example Output: [image]