RichardErkhov/FredZhang7_-_distilgpt2-stable-diffusion-v2-4bits

Quantization made by Richard Erkhov.

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distilgpt2-stable-diffusion-v2 - bnb 4bits

• —Model creator: https://huggingface.co/FredZhang7/
• —Original model: https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2/

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

<style> .container { padding-left: 20px; border-left: 5px solid gray; } </style>

<div class="container"> <p><strong><a href="https://huggingface.co/FredZhang7/anime-anything-promptgen-v2">Fast Anime PromptGen</a></strong> generates descriptive safebooru and danbooru tags for anime text-to-image models.</p> </div>

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: [image]

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)

[image]