RichardErkhov/FredZhang7_-_anime-anything-promptgen-v2-4bits

Quantization made by Richard Erkhov.

anime-anything-promptgen-v2 - bnb 4bits

Model creator: https://huggingface.co/FredZhang7/
Original model: https://huggingface.co/FredZhang7/anime-anything-promptgen-v2/

Original model description:

license: creativeml-openrail-m language: - en widget: - text: 1girl, fate - text: 1boy, league of - text: 1girl, genshin - text: 1boy, national basketball association - text: 1girl, spy x - text: 1girl, absurdres tags: - stable-diffusion - anime - anything-v4 - art - arxiv:2210.14140 datasets: - FredZhang7/anime-prompts-180K

Fast Anime PromptGen

This model was trained on a dataset of 80,000 safe anime prompts for 3 epochs. I fetched the prompts from the Safebooru API endpoint, but only accepted unique prompts with up_score ≥ 8 and without any blacklisted tags. I didn't release the V1 model because it often generated gibberish prompts. After trying all means to correct that behavior, I eventually figured that the cause of the gibberish prompts is not from the pipeline params, model structure or training duration, but rather from the random usernames in the training data. Here's the complete prompt preprocessing algorithm.

Text-to-image Examples

Prefix 1girl | Generated 1girl prompts | Model Anything V4

Prefix 1boy | Generated 1boy prompts | Model Anything V4

Contrastive Search

pip install --upgrade transformers

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model = GPT2LMHeadModel.from_pretrained('FredZhang7/anime-anything-promptgen-v2')

prompt = r'1girl, genshin'

# generate text using fine-tuned model
nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)

# generate 10 samples using contrastive search
outs = nlp(prompt, max_length=76, num_return_sequences=10, do_sample=True, repetition_penalty=1.2, temperature=0.7, top_k=4, early_stopping=True)

print('\nInput:\n' + 100 * '-')
print('\033[96m' + prompt + '\033[0m')
print('\nOutput:\n' + 100 * '-')
for i in range(len(outs)):
    # remove trailing commas and double spaces
    outs[i] = str(outs[i]['generated_text']).replace('  ', '').rstrip(',')
print('\033[92m' + '\n\n'.join(outs) + '\033[0m\n')

Output Example:

Please see Fast GPT PromptGen for more info on the pipeline parameters.

Awesome Tips

If you feel like a generated anime character doesn't show emotions, try emoticons like ;o, :o, ;p, :d, :p, and ;d in the prompt. I also use happy smirk, happy smile, laughing closed eyes, etc. to make the characters more lively and expressive.
Adding absurdres, instead of highres and masterpiece, to a prompt can drastically increase the sharpness and resolution of a generated image.

Danbooru

Link to the Danbooru version