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README.md

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+---
+license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- prompt-generator
+- arxiv:2210.14140
+widget:
+- text: "amazing"
+- text: "a photo of"
+- text: "a sci-fi"
+- text: "a portrait of"
+- text: "a person standing"
+- text: "a boy watching"
+datasets:
+- FredZhang7/stable-diffusion-prompts-2.47M
+- poloclub/diffusiondb
+- Gustavosta/Stable-Diffusion-Prompts
+- bartman081523/stable-diffusion-discord-prompts
+---
+# Fast GPT2 PromptGen
+
+<style>
+.container {
+  padding-left: 20px;
+  border-left: 5px solid gray;
+}
+</style>
+
+<div class="container">
+  <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>
+</div>
+
+
+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.
+
+Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM.
+
+Major improvements from v1 are:
+- 25% more variations
+- faster and more fluent prompt generation
+- cleaned training data
+  * removed prompts that generate images with nsfw scores > 0.5
+  * removed duplicates, including prompts that differ by capitalization and punctuations
+  * removed punctuations at random places
+  * removed prompts shorter than 15 characters
+
+
+## Live WebUI Demo
+See the Prompt Generator tab of [Paint Journey Demo](https://huggingface.co/spaces/FredZhang7/paint-journey-demo).
+
+
+## Contrastive Search
+
+```bash
+pip install --upgrade transformers
+```
+
+```python
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion-v2')
+
+prompt = r'a cat sitting'     # the beginning of the prompt
+temperature = 0.9             # a higher temperature will produce more diverse results, but with a higher risk of less coherent text
+top_k = 8                     # the number of tokens to sample from at each step
+max_length = 80               # the maximum number of tokens for the output of the model
+repitition_penalty = 1.2      # the penalty value for each repetition of a token
+num_return_sequences=5        # the number of results to generate
+
+# generate the result with contrastive search
+input_ids = tokenizer(prompt, return_tensors='pt').input_ids
+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)
+
+print('\nInput:\n' + 100 * '-')
+print('\033[96m' + prompt + '\033[0m')
+print('\nOutput:\n' + 100 * '-')
+for i in range(len(output)):
+    print('\033[92m' + tokenizer.decode(output[i], skip_special_tokens=True) + '\033[0m\n')
+```
+
+No comma style:
+![constrastive search](./constrastive_search.png)
+
+
+To bring back the commas, assign output without `penalty_alpha` and `no_repeat_ngram_size`:
+```python
+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)
+```
+
+![constrastive search](./contrastive_comma_style.png)

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