82 lines
3.0 KiB
Markdown
82 lines
3.0 KiB
Markdown
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---
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license: creativeml-openrail-m
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language:
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- en
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widget:
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- text: 1girl, fate
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- text: 1boy, league of
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- text: 1girl, genshin
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- text: 1boy, national basketball association
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- text: 1girl, spy x
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- text: 1girl, absurdres
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tags:
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- stable-diffusion
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- anime
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- anything-v4
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- art
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- arxiv:2210.14140
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datasets:
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- FredZhang7/anime-prompts-180K
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---
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## Fast Anime PromptGen
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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](https://safebooru.donmai.us/posts/random.json), but only accepted unique prompts with **up_score ≥ 8** and without any [blacklisted tags](./blacklist.txt).
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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.
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Here's the complete [prompt preprocessing algorithm](./preprocess.py).
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## Text-to-image Examples
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Prefix *1girl* | [Generated *1girl* prompts](./anime_girl_settings.txt) | Model *Anything V4*
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Prefix *1boy* | [Generated *1boy* prompts](./anime_boy_settings.txt) | Model *Anything V4*
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## Contrastive Search
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```
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pip install --upgrade transformers
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```
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```python
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import torch
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from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline
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tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
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tokenizer.add_special_tokens({'pad_token': '[PAD]'})
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model = GPT2LMHeadModel.from_pretrained('FredZhang7/anime-anything-promptgen-v2')
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prompt = r'1girl, genshin'
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# generate text using fine-tuned model
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nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)
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# generate 10 samples using contrastive search
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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)
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print('\nInput:\n' + 100 * '-')
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print('\033[96m' + prompt + '\033[0m')
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print('\nOutput:\n' + 100 * '-')
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for i in range(len(outs)):
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# remove trailing commas and double spaces
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outs[i] = str(outs[i]['generated_text']).replace(' ', '').rstrip(',')
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print('\033[92m' + '\n\n'.join(outs) + '\033[0m\n')
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```
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Output Example:
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Please see [Fast GPT PromptGen](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2) for more info on the pipeline parameters.
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## Awesome Tips
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- 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.
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I also use `happy smirk`, `happy smile`, `laughing closed eyes`, etc. to make the characters more lively and expressive.
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- Adding `absurdres`, instead of `highres` and `masterpiece`, to a prompt can drastically increase the sharpness and resolution of a generated image.
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## Danbooru
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[Link to the Danbooru version](https://huggingface.co/FredZhang7/danbooru-tag-generator)
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