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--- |
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license: creativeml-openrail-m |
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tags: |
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- stable-diffusion |
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- prompt-generator |
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- arxiv:2210.14140 |
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widget: |
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- text: "amazing" |
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- text: "a photo of" |
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- text: "a sci-fi" |
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- text: "a portrait of" |
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- text: "a person standing" |
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- text: "a boy watching" |
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datasets: |
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- FredZhang7/stable-diffusion-prompts-2.47M |
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- poloclub/diffusiondb |
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- Gustavosta/Stable-Diffusion-Prompts |
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- bartman081523/stable-diffusion-discord-prompts |
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--- |
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# Fast GPT2 PromptGen |
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<style> |
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.container { |
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padding-left: 20px; |
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border-left: 5px solid gray; |
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} |
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</style> |
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<div class="container"> |
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<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> |
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</div> |
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This model was trained on 2,470,000 descriptive stable diffusion prompts on the [FredZhang7/distilgpt2-stable-diffusion](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion) checkpoint for another 4,270,000 steps. |
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Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM. |
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Major improvements from v1 are: |
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- 25% more variations |
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- faster and more fluent prompt generation |
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- cleaned training data |
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* removed prompts that generate images with nsfw scores > 0.5 |
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* removed duplicates, including prompts that differ by capitalization and punctuations |
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* removed punctuations at random places |
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* removed prompts shorter than 15 characters |
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## Live WebUI Demo |
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See the Prompt Generator tab of [Paint Journey Demo](https://huggingface.co/spaces/FredZhang7/paint-journey-demo). |
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## Contrastive Search |
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```bash |
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pip install --upgrade transformers |
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``` |
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```python |
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from transformers import GPT2Tokenizer, GPT2LMHeadModel |
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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/distilgpt2-stable-diffusion-v2') |
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prompt = r'a cat sitting' # the beginning of the prompt |
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temperature = 0.9 # a higher temperature will produce more diverse results, but with a higher risk of less coherent text |
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top_k = 8 # the number of tokens to sample from at each step |
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max_length = 80 # the maximum number of tokens for the output of the model |
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repitition_penalty = 1.2 # the penalty value for each repetition of a token |
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num_return_sequences=5 # the number of results to generate |
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# generate the result with contrastive search |
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input_ids = tokenizer(prompt, return_tensors='pt').input_ids |
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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) |
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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(output)): |
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print('\033[92m' + tokenizer.decode(output[i], skip_special_tokens=True) + '\033[0m\n') |
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``` |
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No comma style: |
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To bring back the commas, assign output without `penalty_alpha` and `no_repeat_ngram_size`: |
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```python |
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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) |
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``` |
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