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from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler | |
from transformers import pipeline | |
import torch | |
from IPython.display import clear_output | |
import gradio as gr | |
model_name = "stabilityai/stable-diffusion-2" | |
scheduler = EulerDiscreteScheduler.from_pretrained(model_name, subfolder="scheduler") | |
pipe = StableDiffusionPipeline.from_pretrained(model_name, scheduler=scheduler, revision="fp16", torch_dtype=torch.float16) | |
# Movemos el pipeline a GPU para tener una inferencia más rápida. | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
pipe = pipe.to(device) | |
pipe.enable_attention_slicing() | |
# Cargamos el modelo traductor y creamos el pipeline para traducir al ingles | |
spanish_model_name = "Helsinki-NLP/opus-mt-es-en" | |
translator_es_en = pipeline("translation", model=spanish_model_name) | |
def predict(text): | |
prompt_es = text | |
english_text = translator_es_en(prompt_es) | |
prompt_en = english_text[0]['translation_text'] | |
image = pipe(prompt_en).images[0] | |
return image | |
description = """ | |
<h2 style="text-align:center">Programa para generar imágenes a partir de texto con Stable Diffusion 2</h2> | |
<h2 style="text-align:center">Prompt en español!!</h2> | |
""" | |
gr.Interface(fn=predict, | |
title="Texto a Imagen en Español", | |
inputs= gr.Textbox("", max_lines = 2, label = "Inserte su texto aqui"), | |
outputs = "image", | |
description = description).launch(debug=True) |