91 lines
3.4 KiB
Markdown
91 lines
3.4 KiB
Markdown
---
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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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