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from transformers import pipeline, set_seed
import gradio as grad, random, re
gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
with open("ideas.txt", "r") as f:
line = f.readlines()
def generate(starting_text):
for count in range(4):
seed = random.randint(100, 1000000)
set_seed(seed)
if starting_text == "":
starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
print(starting_text)
response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4)
response_list = []
for x in response:
resp = x['generated_text'].strip()
if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
response_list.append(resp+'\n')
response_end = "\n".join(response_list)
response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
response_end = response_end.replace("<", "").replace(">", "")
if response_end != "":
return response_end
if count == 4:
return response_end
txt = grad.Textbox(lines=1, label="Initial Text", placeholder="English Text here")
out = grad.Textbox(lines=4, label="Generated Prompts")
examples = []
for x in range(8):
examples.append(line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize())
title = "Stable Diffusion Prompt Generator"
description = 'This is a demo of the model series: "MagicPrompt", in this case, aimed at: Stable Diffusion. To use it, simply submit your text or click on one of the examples.<b><br><br>To learn more about the model, go to the link: https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion<br>'
article = "<div><center><img src='https://visitor-badge.glitch.me/badge?page_id=magicprompt_Stable Diffusion' alt='visitor badge'></center></div>"
grad.Interface(fn=generate,
inputs=txt,
outputs=out,
examples=examples,
title=title,
description=description,
article=article,
allow_flagging='never',
cache_examples=False,
theme="default").launch(enable_queue=True, debug=True)
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