readme: # 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)
  ```
  