app.py

import gradio as gr
from PIL import Image
from io import BytesIO
import requests
from diffusers import StableDiffusionPipeline
import torch

def load_models():
    # Carga el modelo Stable Diffusion en CPU
    pipe_sd = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float32)
    pipe_sd = pipe_sd.to("cpu")

    # URLs ejemplo para simular DALL·E mini y Free Image Generation
    url_dalle_mini = "https://huggingface.co/spaces/dalle-mini/dalle-mini/resolve/main/sample_image.jpg"
    url_free_img_gen = "https://huggingface.co/spaces/aiyouthalliance/Free-Image-Generation/resolve/main/sample_image.jpg"

    return pipe_sd, url_dalle_mini, url_free_img_gen

pipe_sd, url_dalle_mini, url_free_img_gen = load_models()

def generate_image_stable_diffusion(prompt):
    image = pipe_sd(prompt, height=768, width=432).images[0]  # Formato 9:16
    return image

def generate_image_dalle_mini(prompt):
    response = requests.get(url_dalle_mini)
    image = Image.open(BytesIO(response.content))
    return image.resize((432, 768))

def generate_image_free_img_gen(prompt):
    response = requests.get(url_free_img_gen)
    image = Image.open(BytesIO(response.content))
    return image.resize((432, 768))

def generate_all_images(prompt):
    img_sd = generate_image_stable_diffusion(prompt)
    img_dalle = generate_image_dalle_mini(prompt)
    img_free = generate_image_free_img_gen(prompt)
    return img_sd, img_dalle, img_free

with gr.Blocks() as demo:
    gr.Markdown("# BATUTO-art")
    prompt = gr.Textbox(label="Introduce tu prompt", lines=1)
    with gr.Row():
        img_sd = gr.Image(label="Stable Diffusion", interactive=True)
        img_dalle = gr.Image(label="DALL·E mini", interactive=True)
        img_free = gr.Image(label="Free Image Generation", interactive=True)
    btn = gr.Button("Generar imágenes")
    btn.click(fn=generate_all_images, inputs=prompt, outputs=[img_sd, img_dalle, img_free])

if __name__ == "__main__":
    demo.launch()