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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 | |
# Just use 1 beam and get 1 output, this is much, much, much faster than 8 beams and 8 outputs and we're only using the first. | |
outputs = prompter_model.generate(input_ids, do_sample=False, max_new_tokens=75, eos_token_id=eos_id, pad_token_id=eos_id, length_penalty=-1.0) | |
# Use [input_ids.shape[-1]:] because the decoded tokenised version of plain_text may have a different number of characters to the original | |
res = tokenizer.decode(outputs[0][input_ids.shape[-1]:]) | |
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. The online demo at Hugging Face Spaces is using CPU, so slow generation speed would be expected. Please load the model locally with GPUs for faster generation.", | |
examples=examples, | |
allow_flagging='never', | |
cache_examples=False, | |
theme="default").launch(enable_queue=True, debug=True) |