README.md

---
license: creativeml-openrail-m
language:
- en
widget:
- text: 1girl, fate
- text: 1boy, league of legends
- text: 1girl, genshin impact
- 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

The main model (`pytorch_model.bin`) was trained on a dataset of **80,000** anime tags and for 3 epochs. I fetched the tags from the [Safebooru API endpoint](https://safebooru.donmai.us/posts/random.json), but only accepted the ones with **up_score ≥ 8** and without any [blacklisted tags](./blacklist.txt). 
I didn't release the V1 model because it only 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 model or training duration, but rather from the random usernames in the training data. 
Here's the complete [prompt preprocessing algorithm](./preprocess.py).


Todo:
- upload Danbooru model

## Text-to-image Examples

Prefix *1girl* | [Generated *1girl* prompts](./anime_girl_settings.txt) | Model *Anything V4*

![](./anime_girls.png)

Prefix *1boy*  | [Generated *1boy* prompts](./anime_boy_settings.txt) | Model *Anything V4*

![](./anime_boys.png)

## Contrastive Search
```
pip install --upgrade transformers
```
```python
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')

prompt = r'1girl, genshin'

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

# generate 10 samples using greedy search
outs = nlp(prompt, max_length=76, num_return_sequences=10, do_sample=True, repetition_penalty=1.1, 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:

![](./contrastive_search.png)

Please see [Fast GPT PromptGen](https://huggingface.co./FredZhang7/distilgpt2-stable-diffusion-v2) for more info on the pipeline parameters.


## 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 often 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 tends to increase the sharpness of a generated image.