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Model: FredZhang7/anime-anything-promptgen-v2
Source: Original Platform

README.md

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+---
+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](https://safebooru.donmai.us/posts/random.json), but only accepted unique prompts with **up_score ≥ 8** and without any [blacklisted tags](./blacklist.txt). 
+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](./preprocess.py).
+
+
+## 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-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:
+
+![](./contrastive_search.png)
+
+Please see [Fast GPT PromptGen](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2) 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](https://huggingface.co/FredZhang7/danbooru-tag-generator)