Original model description:

license: creativeml-openrail-m tags: - stable-diffusion - prompt-generator - arxiv:2210.14140 widget: - text: "amazing" - text: "a photo of" - text: "a sci-fi" - text: "a portrait of" - text: "a person standing" - text: "a boy watching" datasets: - FredZhang7/stable-diffusion-prompts-2.47M - poloclub/diffusiondb - Gustavosta/Stable-Diffusion-Prompts - bartman081523/stable-diffusion-discord-prompts

Fast GPT2 PromptGen

Fast Anime PromptGen generates descriptive safebooru and danbooru tags for anime text-to-image models.

This model was trained on 2,470,000 descriptive stable diffusion prompts on the FredZhang7/distilgpt2-stable-diffusion checkpoint for another 4,270,000 steps.

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

Major improvements from v1 are:

25% more variations
faster and more fluent prompt generation
cleaned training data
- removed prompts that generate images with nsfw scores > 0.5
- removed duplicates, including prompts that differ by capitalization and punctuations
- removed punctuations at random places
- removed prompts shorter than 15 characters

Live WebUI Demo

See the Prompt Generator tab of Paint Journey Demo.

Contrastive Search

pip install --upgrade transformers

from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion-v2')

prompt = r'a cat sitting'     # the beginning of the prompt
temperature = 0.9             # a higher temperature will produce more diverse results, but with a higher risk of less coherent text
top_k = 8                     # the number of tokens to sample from at each step
max_length = 80               # the maximum number of tokens for the output of the model
repitition_penalty = 1.2      # the penalty value for each repetition of a token
num_return_sequences=5        # the number of results to generate

# generate the result with contrastive search
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
output = model.generate(input_ids, do_sample=True, temperature=temperature, top_k=top_k, max_length=max_length, num_return_sequences=num_return_sequences, repetition_penalty=repitition_penalty, penalty_alpha=0.6, no_repeat_ngram_size=1, early_stopping=True)

print('\nInput:\n' + 100 * '-')
print('\033[96m' + prompt + '\033[0m')
print('\nOutput:\n' + 100 * '-')
for i in range(len(output)):
    print('\033[92m' + tokenizer.decode(output[i], skip_special_tokens=True) + '\033[0m\n')

No comma style:

To bring back the commas, assign output without penalty_alpha and no_repeat_ngram_size:

output = model.generate(input_ids, do_sample=True, temperature=temperature, top_k=top_k, max_length=max_length, num_return_sequences=num_return_sequences, repetition_penalty=repitition_penalty, early_stopping=True)