Promptist

🚀 Promptist: Reinforcement Learning for Automatic Prompt Optimization

Promptist uses reinforcement learning to automatically optimize prompts, leveraging language models as an interface to transform user input into model - preferred prompts.

🚀 Quick Start

🔍 News

• [Demo Release] Dec, 2022: Demo at HuggingFace Space
• [Model Release] Dec, 2022: link
• [Paper Release] Dec, 2022: Optimizing Prompts for Text - to - Image Generation

• 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.

📦 Installation

Load Pretrained Model for Stable Diffusion v1.4

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

⚠️ Important 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.

💻 Usage Examples

Basic Usage

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)