# Promptist: reinforcement learning for automatic prompt optimization

## News
- [Demo Release] Dec, 2022: [Demo at HuggingFace Space](https://aka.ms/promptist-demo)
- [Model Release] Dec, 2022: [link](#load-pretrained-model-for-stable-diffusion-v14)
- [Paper Release] Dec, 2022: [Optimizing Prompts for Text-to-Image Generation](https://aka.ms/promptist-paper)

> - Language models serve as a prompt interface that optimizes user input into model-preferred prompts.

> - Learn a language model for automatic prompt optimization via reinforcement learning.

![image](https://user-images.githubusercontent.com/1070872/207856962-02f08d92-f2bf-441a-b1c3-efff1a4b6187.png)


## Load Pretrained Model for [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4)

You can try the online demo at [https://huggingface.co/spaces/microsoft/Promptist](https://huggingface.co/spaces/microsoft/Promptist).

`[Note]` the online demo at HuggingFace Space is using CPU, so slow generation speed would be expected. Please load the model locally with GPUs for faster generation.

```python
import gradio as grad
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

def load_prompter():
  prompter_model = AutoModelForCausalLM.from_pretrained("microsoft/Promptist")
  tokenizer = AutoTokenizer.from_pretrained("gpt2")
  tokenizer.pad_token = tokenizer.eos_token
  tokenizer.padding_side = "left"
  return prompter_model, tokenizer

prompter_model, prompter_tokenizer = load_prompter()

def generate(plain_text):
    input_ids = prompter_tokenizer(plain_text.strip()+" Rephrase:", return_tensors="pt").input_ids
    eos_id = prompter_tokenizer.eos_token_id
    outputs = prompter_model.generate(input_ids, do_sample=False, max_new_tokens=75, num_beams=8, num_return_sequences=8, eos_token_id=eos_id, pad_token_id=eos_id, length_penalty=-1.0)
    output_texts = prompter_tokenizer.batch_decode(outputs, skip_special_tokens=True)
    res = output_texts[0].replace(plain_text+" Rephrase:", "").strip()
    return res

txt = grad.Textbox(lines=1, label="Initial Text", placeholder="Input Prompt")
out = grad.Textbox(lines=1, label="Optimized Prompt")
examples = ["A rabbit is wearing a space suit", "Several railroad tracks with one train passing by", "The roof is wet from the rain", "Cats dancing in a space club"]

grad.Interface(fn=generate,
               inputs=txt,
               outputs=out,
               title="Promptist Demo",
               description="Promptist is a prompt interface for Stable Diffusion v1-4 (https://huggingface.co/CompVis/stable-diffusion-v1-4) that optimizes user input into model-preferred prompts.",
               examples=examples,
               allow_flagging='never',
               cache_examples=False,
               theme="default").launch(enable_queue=True, debug=True)
```