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import os
import gradio as gr
from diffusers import DiffusionPipeline
import torch
import requests
from PIL import Image
from io import BytesIO
import concurrent.futures
import threading
# ==============================
# CONFIGURACIÓN BASE CPU
# ==============================
DEVICE = "cpu"
torch.set_grad_enabled(False)
# PARÁMETROS POR DEFECTO AJUSTADOS PARA CPU MÁS RÁPIDO
DEFAULT_STEPS = 15 # Reducido de 20 para más velocidad
DEFAULT_WIDTH = 512 # Reducido de 576 para menos carga
DEFAULT_HEIGHT = 768 # Reducido de 1024 para menos carga (mantiene relación aproximada)
def load_flux(model_id):
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
pipe.to(DEVICE)
pipe.enable_attention_slicing()
return pipe
# Cache de modelos
MODEL_CACHE = {}
# ==============================
# GENERADOR FLUX
# ==============================
def generate_flux(model_name, prompt, steps, guidance, width, height, seed):
if model_name not in MODEL_CACHE:
MODEL_CACHE[model_name] = load_flux(model_name)
pipe = MODEL_CACHE[model_name]
generator = torch.manual_seed(seed) if seed else None
image = pipe(
prompt=prompt,
num_inference_steps=steps,
guidance_scale=guidance,
width=width,
height=height,
generator=generator
).images[0]
out = "/tmp/flux_output.png"
image.save(out)
return out
# ==============================
# GENERADOR SD1.5
# ==============================
def load_sd15():
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float32)
pipe.to(DEVICE)
pipe.enable_attention_slicing()
return pipe
# SD15 load único
def generate_sd(prompt, steps, guidance, width, height, seed):
if "sd15" not in MODEL_CACHE:
MODEL_CACHE["sd15"] = load_sd15()
pipe = MODEL_CACHE["sd15"]
generator = torch.manual_seed(seed) if seed else None
image = pipe(
prompt=prompt,
num_inference_steps=steps,
guidance_scale=guidance,
width=width,
height=height,
generator=generator
).images[0]
out = "/tmp/sd15_output.png"
image.save(out)
return out
# ==============================
# REVE CREATE MODIFICADO (Generación múltiple con threads)
# ==============================
def generate_single_reve_image(prompt, key, model, index, results_list, lock, progress_callback=None):
"""Función auxiliar para generar una sola imagen"""
try:
url = "https://api.reveai.xyz/v1/images"
headers = {"Authorization": f"Bearer {key}"}
data = {"prompt": prompt, "model": model}
resp = requests.post(url, json=data, headers=headers, timeout=30)
if resp.status_code != 200:
print(f"Error en imagen {index+1}: {resp.status_code}")
return
img_url = resp.json().get("image")
if not img_url:
return
img_data = requests.get(img_url, timeout=30).content
img = Image.open(BytesIO(img_data))
out = f"/tmp/reve_{index}_{threading.current_thread().ident}.png"
img.save(out)
with lock:
results_list.append(out)
# Notificar progreso si hay callback
if progress_callback:
progress_callback(index + 1)
except Exception as e:
print(f"Error generando imagen {index+1}: {e}")
def reve_generate_multiple(prompt, key, model, num_images, progress_callback=None):
if not key:
return None
num_images = min(num_images, 8) # Máximo 8 imágenes
results = []
lock = threading.Lock()
# Usamos ThreadPoolExecutor para generar imágenes en paralelo
with concurrent.futures.ThreadPoolExecutor(max_workers=min(num_images, 4)) as executor:
futures = []
for i in range(num_images):
future = executor.submit(
generate_single_reve_image,
prompt, key, model, i, results, lock, progress_callback
)
futures.append(future)
# Esperar a que todas terminen
concurrent.futures.wait(futures)
return results if results else None
# ==============================
# UI COMPLETA
# ==============================
def build_ui():
with gr.Blocks(title="BATUTO-ART MIX", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# 🖼️ **BATUTO-ART MIX**
*Generador de imágenes con FLUX, Stable Diffusion 1.5 y REVE CREATE*
""")
with gr.Tabs():
# ============================
# TAB: FLUX
# ============================
with gr.Tab("FLUX.2 / 1-Schnell"):
with gr.Row():
with gr.Column(scale=1):
flux_prompt = gr.Textbox(
label="Prompt",
lines=3,
placeholder="Describe la imagen que quieres generar con FLUX..."
)
model_select = gr.Dropdown([
"black-forest-labs/FLUX.1-schnell",
"black-forest-labs/FLUX.1-dev",
"black-forest-labs/FLUX.2-dev"
], value="black-forest-labs/FLUX.1-schnell", label="Modelo FLUX")
with gr.Row():
steps = gr.Slider(5, 50, value=DEFAULT_STEPS, step=1, label="Steps")
guidance = gr.Slider(0, 10, value=3, step=0.1, label="Guidance Scale")
with gr.Row():
width = gr.Number(value=DEFAULT_WIDTH, label="Width", precision=0)
height = gr.Number(value=DEFAULT_HEIGHT, label="Height", precision=0)
seed = gr.Number(value=0, label="Seed (0 = aleatorio)", precision=0)
btn_flux = gr.Button("✨ Generar Imagen", variant="primary", size="lg")
with gr.Column(scale=1):
out_flux_img = gr.Image(
label="Resultado FLUX",
height=400,
interactive=False
)
out_flux_file = gr.File(
label="Descargar imagen",
visible=False
)
# Acción del botón FLUX
def generate_flux_wrapper(model_name, prompt, steps, guidance, width, height, seed):
if not prompt.strip():
return None, "❌ Error: Ingresa un prompt válido"
try:
file_path = generate_flux(
model_name, prompt, int(steps), float(guidance),
int(width), int(height), int(seed)
)
return file_path, gr.update(visible=True)
except Exception as e:
return None, f"❌ Error: {str(e)}"
btn_flux.click(
fn=generate_flux_wrapper,
inputs=[model_select, flux_prompt, steps, guidance, width, height, seed],
outputs=[out_flux_file, out_flux_file]
)
# Mostrar imagen automáticamente
out_flux_file.change(
fn=lambda f: Image.open(f) if f else None,
inputs=[out_flux_file],
outputs=[out_flux_img]
)
# ============================
# TAB: SD1.5
# ============================
with gr.Tab("Stable Diffusion 1.5"):
with gr.Row():
with gr.Column(scale=1):
sd_prompt = gr.Textbox(
label="Prompt",
lines=3,
placeholder="Describe la imagen que quieres generar con SD1.5..."
)
with gr.Row():
sd_steps = gr.Slider(5, 50, value=DEFAULT_STEPS, step=1, label="Steps")
sd_guidance = gr.Slider(0, 10, value=3, step=0.1, label="Guidance Scale")
with gr.Row():
sd_width = gr.Number(value=DEFAULT_WIDTH, label="Width", precision=0)
sd_height = gr.Number(value=DEFAULT_HEIGHT, label="Height", precision=0)
sd_seed = gr.Number(value=0, label="Seed (0 = aleatorio)", precision=0)
btn_sd = gr.Button("✨ Generar Imagen", variant="primary", size="lg")
with gr.Column(scale=1):
out_sd_img = gr.Image(
label="Resultado SD1.5",
height=400,
interactive=False
)
out_sd_file = gr.File(
label="Descargar imagen",
visible=False
)
# Acción del botón SD1.5
def generate_sd_wrapper(prompt, steps, guidance, width, height, seed):
if not prompt.strip():
return None, "❌ Error: Ingresa un prompt válido"
try:
file_path = generate_sd(
prompt, int(steps), float(guidance),
int(width), int(height), int(seed)
)
return file_path, gr.update(visible=True)
except Exception as e:
return None, f"❌ Error: {str(e)}"
btn_sd.click(
fn=generate_sd_wrapper,
inputs=[sd_prompt, sd_steps, sd_guidance, sd_width, sd_height, sd_seed],
outputs=[out_sd_file, out_sd_file]
)
out_sd_file.change(
fn=lambda f: Image.open(f) if f else None,
inputs=[out_sd_file],
outputs=[out_sd_img]
)
# ============================
# TAB: REVE CREATE MODIFICADA
# ============================
with gr.Tab("REVE CREATE"):
with gr.Row():
with gr.Column(scale=1):
reve_api = gr.Textbox(
label="API Key REVE",
type="password",
placeholder="Ingresa tu API key de REVE",
info="Necesitas una clave API válida de REVE"
)
reve_prompt = gr.Textbox(
label="Prompt",
lines=3,
placeholder="Describe la imagen que quieres generar..."
)
reve_model = gr.Dropdown([
"reve-1",
"reve-2",
"reve-fast"
], value="reve-fast", label="Modelo REVE")
# Slider para cantidad de imágenes
num_images_slider = gr.Slider(
minimum=1,
maximum=8,
value=4,
step=1,
label="Cantidad de imágenes a generar"
)
with gr.Row():
btn_reve = gr.Button("🚀 Generar Imágenes", variant="primary", size="lg")
btn_clear = gr.Button("🗑️ Limpiar", variant="secondary")
# Indicador de progreso
progress_info = gr.Markdown("Esperando generación...")
with gr.Column(scale=2):
# Galería para mostrar múltiples imágenes
gallery = gr.Gallery(
label="Imágenes generadas",
show_label=True,
columns=4,
rows=2,
height="auto",
object_fit="contain"
)
# Información de resultados
result_info = gr.Markdown("")
# Botón de descarga
with gr.Row():
btn_download_all = gr.Button(
"📥 Descargar todas las imágenes",
size="lg",
variant="secondary"
)
download_output = gr.File(
label="Archivos descargables",
file_count="multiple",
visible=False
)
# Estado para almacenar las rutas de archivos
last_files = gr.State([])
# Función para generar múltiples imágenes con progreso
def generate_and_update_gallery(prompt, key, model, num_images, progress=gr.Progress()):
if not key:
return [], [], "❌ Error: Ingresa una API key válida", progress_info.update(value="")
if not prompt.strip():
return [], [], "❌ Error: Ingresa un prompt válido", progress_info.update(value="")
progress_info.update(value="⏳ Iniciando generación...")
# Función de callback para progreso
def update_progress(current):
progress_info.update(value=f"⏳ Generando imagen {current}/{num_images}...")
files = reve_generate_multiple(prompt, key, model, num_images, update_progress)
if files:
# Crear lista de imágenes para la galería
images = [(file,) for file in files]
info_text = f"✅ Generadas {len(files)} de {num_images} imágenes"
progress_info.update(value="✅ Generación completada")
return images, files, info_text
else:
progress_info.update(value="❌ Error en la generación")
return [], [], "❌ Error: No se pudieron generar imágenes. Verifica tu API key y conexión."
# Acción del botón de generación
btn_reve.click(
fn=generate_and_update_gallery,
inputs=[reve_prompt, reve_api, reve_model, num_images_slider],
outputs=[gallery, last_files, result_info]
)
# Acción del botón de descarga
def prepare_download(files):
if files:
return gr.update(value=files, visible=True)
return gr.update(value=[], visible=False)
btn_download_all.click(
fn=prepare_download,
inputs=[last_files],
outputs=[download_output]
)
# Acción del botón de limpiar
def clear_gallery():
return [], [], "✅ Galería limpiada", gr.update(value="Listo para nueva generación")
btn_clear.click(
fn=clear_gallery,
outputs=[gallery, last_files, result_info, progress_info]
).then(
fn=lambda: gr.update(visible=False),
outputs=[download_output]
)
return demo
# ==============================
# EJECUCIÓN PRINCIPAL
# ==============================
if __name__ == "__main__":
# Configuración para HuggingFace Spaces
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--share", action="store_true", help="Crear enlace público")
parser.add_argument("--server-name", type=str, default="0.0.0.0", help="Dirección del servidor")
parser.add_argument("--server-port", type=int, default=7860, help="Puerto del servidor")
args = parser.parse_args()
print("=" * 50)
print("🚀 Iniciando BATUTO-ART MIX")
print("=" * 50)
print(f"📱 Dispositivo: {DEVICE}")
print(f"⚡ Steps por defecto: {DEFAULT_STEPS}")
print(f"📐 Resolución por defecto: {DEFAULT_WIDTH}x{DEFAULT_HEIGHT}")
print("=" * 50)
demo = build_ui()
demo.launch(
share=args.share,
server_name=args.server_name,
server_port=args.server_port,
show_error=True
) |