Quantization made by Richard Erkhov.
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
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:
