143 lines
5.0 KiB
Python
143 lines
5.0 KiB
Python
import os
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import torch
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import re
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from pathlib import Path
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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class PromptExpanderConfig:
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"""Configuration settings for PromptExpander"""
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MODEL_NAME = "roborovski/superprompt-v1"
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SYSTEM_PROMPT = "Expand the following prompt to add more detail:"
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DEFAULT_SEED = 1
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# Model settings
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DEFAULT_MAX_TOKENS = 512
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DEFAULT_REP_PENALTY = 1.2
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class PromptExpanderNode:
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"""Node for generating expanded prompts using T5 model"""
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def __init__(self):
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self.model_home_dir = Path.home() / ".models"
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self.model_dir = self.model_home_dir / PromptExpanderConfig.MODEL_NAME
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self.tokenizer = None
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self.model = None
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"prompt": ("STRING", {"default": "Enter prompt here"}),
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"max_new_tokens": ("INT", {
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"default": PromptExpanderConfig.DEFAULT_MAX_TOKENS,
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"min": 1,
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"max": 2048
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}),
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"repetition_penalty": ("FLOAT", {
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"default": PromptExpanderConfig.DEFAULT_REP_PENALTY,
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"min": 0.0,
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"max": 2.0,
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"step": 0.1
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}),
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"remove_incomplete_sentences": ("BOOLEAN", {"default": True}),
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("expanded_prompt",)
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FUNCTION = "expand_prompt"
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CATEGORY = "text"
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@staticmethod
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def remove_incomplete_sentence(paragraph: str) -> str:
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"""Remove incomplete sentences from the end of the text."""
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return re.sub(r'((?:\[^.!?\](?!\[.!?\]))\*+\[^.!?\\s\]\[^.!?\]\*$)', '', paragraph.rstrip())
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def _download_models(self):
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"""Download the model and tokenizer files."""
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try:
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self.tokenizer = T5Tokenizer.from_pretrained(
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PromptExpanderConfig.MODEL_NAME
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)
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self.model = T5ForConditionalGeneration.from_pretrained(
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PromptExpanderConfig.MODEL_NAME,
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torch_dtype=torch.float16
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)
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self.model_dir.mkdir(parents=True, exist_ok=True)
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self.tokenizer.save_pretrained(self.model_dir)
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self.model.save_pretrained(self.model_dir)
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print(f"Downloaded SuperPrompt-v1 model files to {self.model_dir}")
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except Exception as e:
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raise RuntimeError(f"Failed to download models: {str(e)}")
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def _load_models(self):
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"""Load the model and tokenizer from local storage or download if needed."""
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if not self.model_dir.exists():
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self._download_models()
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else:
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print("Model files found. Skipping download.")
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try:
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self.tokenizer = T5Tokenizer.from_pretrained(self.model_dir)
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self.model = T5ForConditionalGeneration.from_pretrained(
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self.model_dir,
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torch_dtype=torch.float16
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)
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print("SuperPrompt-v1 model loaded successfully.")
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except Exception as e:
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raise RuntimeError(f"Failed to load models: {str(e)}")
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def expand_prompt(self, prompt: str, max_new_tokens: int,
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repetition_penalty: float, remove_incomplete_sentences: bool) -> tuple:
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"""
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Generate expanded text from the input prompt.
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Args:
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prompt: Input text to expand
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max_new_tokens: Maximum number of new tokens to generate
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repetition_penalty: Penalty for repetition in generated text
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remove_incomplete_sentences: Whether to remove incomplete sentences
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Returns:
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Tuple containing the generated text
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"""
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if self.tokenizer is None or self.model is None:
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self._load_models()
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# Set up generation
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torch.manual_seed(PromptExpanderConfig.DEFAULT_SEED)
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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# Prepare input
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input_text = f"{PromptExpanderConfig.SYSTEM_PROMPT}{prompt}"
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input_ids = self.tokenizer(input_text, return_tensors="pt").input_ids.to(device)
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# Move model to appropriate device
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if torch.cuda.is_available():
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self.model.to('cuda')
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# Generate text
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outputs = self.model.generate(
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input_ids,
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max_new_tokens=max_new_tokens,
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repetition_penalty=repetition_penalty,
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do_sample=True
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)
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# Process output
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dirty_text = self.tokenizer.decode(outputs[0])
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text = dirty_text.replace("<pad>", "").replace("</s>", "").strip()
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if remove_incomplete_sentences:
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text = self.remove_incomplete_sentence(text)
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return (text,)
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# For testing
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if __name__ == "__main__":
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m = PromptExpanderNode()
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print(m.expand_prompt("a beautiful girl", 512, 1.2, True))
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