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	######################
	# Datos importados
	######################

	#Datos importados de: https://huggingface.co/runwayml/stable-diffusion-v1-5

	######################
	# Import libraries
	######################

	import gradio as gr

	# ######################
	# Variables globales
	# ######################

	import os
	USUARIO = os.getenv("USUARIO")

	#model_id = "helenai/runwayml-stable-diffusion-v1-5-ov-fp32"
	model_id = "runwayml/stable-diffusion-v1-5" #-> original

	# ######################
	# Funciones auxiliares
	# ######################

	if False:
	def pipe_callback(step: int, timestep: int, latents: torch.FloatTensor):

	with st.container():

	st.write(f'Vamos por la iteración {step}')
	st.progress(step*2) #bar_progress debe empezar con 0 y terminar en 100
	st.write(f'Quedan {timestep/100:.0f} segundos')

	# ######################
	# Modelo
	# ######################

	def ia_imagenes(modelo, prompt, prompt_negativo, uploaded_file, my_strength, my_guidance_scale):

	if modelo == "Texto":

	from diffusers import StableDiffusionPipeline
	import torch

	pipe = StableDiffusionPipeline.from_pretrained(
	model_id,
	#revision="fp16" if torch.cuda.is_available() else "fp32",
	torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
	requires_safety_checker = False
	).to("cpu")


	image_pipe = pipe(prompt, negative_prompt=prompt_negativo, width=728, height=728) #otras variables: guidance_scale=guidance_scale, num_inference_steps=steps, callback = pipe_callback

	imagen = image_pipe.images[0]

	return imagen


	elif modelo == "Imagen":

	from diffusers import StableDiffusionImg2ImgPipeline
	from PIL import Image
	import torch

	uploaded_file = Image.fromarray(uploaded_file)

	pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
	model_id,
	#revision="fp16" if torch.cuda.is_available() else "fp32",
	torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
	requires_safety_checker = False
	).to("cpu")

	my_strength = 0.8 if my_strength == 0 or my_strength == None else my_strength
	my_guidance_scale = 7.5 if my_guidance_scale == 0 or my_guidance_scale == None else my_guidance_scale

	imagen = pipe(prompt, image=uploaded_file, negative_prompt=prompt_negativo, strength = my_strength, guidance_scale = my_guidance_scale).images[0] #, strength=0.8, guidance_scale=7.5

	return imagen

	else:
	raise gr.Error("Te has olvidado de marcar una opción")


	demo = gr.Interface(
	fn = ia_imagenes,
	inputs = [
	gr.Radio(["Texto", "Imagen"],value = "Texto"),
	"text",
	"text",
	"image",
	"number",
	"number"

	],
	outputs = "image",
	title="Creación de Imagenes 🖼️",
	)


	demo.launch(show_error = True, auth=(USUARIO, USUARIO), share=False)



	'''
	FALTA:
	-BOTON PARA DESCARGAR IMAGEN
	-TIEMPO QUE TARDA.
	'''