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# app.py - Space Gradio para SDXL (listo para pegar)
import os
import torch
from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler
import gradio as gr
from PIL import Image
# --- CONFIG ---
MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0" # base SDXL
REFINER_ID = "stabilityai/stable-diffusion-xl-refiner-1.0" # refiner (opcional)
USE_REFINER = True
# Cargar token si está en variable de entorno HF_ACCESS_TOKEN (recomendado en Settings -> Secrets)
HF_TOKEN = os.environ.get("HF_ACCESS_TOKEN", None)
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Device:", device)
# Carga del pipeline (ajusta torch_dtype según device)
torch_dtype = torch.float16 if device == "cuda" else torch.float32
pipe = StableDiffusionXLPipeline.from_pretrained(
MODEL_ID,
torch_dtype=torch_dtype,
use_safetensors=True,
revision="fp16" if device == "cuda" else None,
use_auth_token=HF_TOKEN
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
if device == "cuda":
pipe.enable_xformers_memory_efficient_attention()
pipe.to("cuda")
else:
pipe.to("cpu")
# Cargar refiner si se desea (mejora detalles)
refiner = None
if USE_REFINER:
try:
refiner = StableDiffusionXLPipeline.from_pretrained(
REFINER_ID,
torch_dtype=torch_dtype,
use_safetensors=True,
use_auth_token=HF_TOKEN
)
if device == "cuda":
refiner.enable_xformers_memory_efficient_attention()
refiner.to("cuda")
else:
refiner.to("cpu")
except Exception as e:
print("No se pudo cargar refiner:", e)
refiner = None
# Función principal de generación
def generate(prompt, negative_prompt, steps, guidance_scale, width, height, seed):
generator = torch.Generator(device=device)
if seed is not None and seed != "":
try:
seed = int(seed)
generator = torch.Generator(device=device).manual_seed(seed)
except:
seed = None
# Ajustes
height = int(height)
width = int(width)
with torch.autocast(device_type="cuda" if device=="cuda" else "cpu"):
output = pipe(
prompt,
negative_prompt=negative_prompt if negative_prompt else None,
num_inference_steps=int(steps),
guidance_scale=float(guidance_scale),
generator=generator,
height=height,
width=width
)
image = output.images[0]
# Refinamiento opcional
if refiner is not None:
with torch.autocast(device_type="cuda" if device=="cuda" else "cpu"):
refined = refiner(
prompt,
image=image,
num_inference_steps=10,
guidance_scale=float(guidance_scale),
generator=generator
)
image = refined.images[0]
return image
# Interfaz Gradio
with gr.Blocks() as demo:
gr.Markdown("# SDXL — Generador fotorrealista")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt (describe la escena fotorrealista)", lines=4, placeholder="Portrait of a young woman... cinematic lighting, 85mm lens, photorealistic")
negative = gr.Textbox(label="Negative prompt (evitar)", lines=2, placeholder="lowres, deformed, cartoon, watermark")
steps = gr.Slider(minimum=10, maximum=60, step=1, value=28, label="Steps")
scale = gr.Slider(minimum=1.0, maximum=12.0, step=0.5, value=7.5, label="Guidance scale (fidelity)")
width = gr.Dropdown([512, 640, 768, 1024], value=1024, label="Width")
height = gr.Dropdown([512, 640, 768, 1024], value=1024, label="Height")
seed = gr.Textbox(label="Seed (opcional)", placeholder="123456")
btn = gr.Button("Generar")
with gr.Column():
gallery = gr.Image(label="Resultado", type="pil")
btn.click(fn=generate, inputs=[prompt, negative, steps, scale, width, height, seed], outputs=[gallery])
if __name__ == "__main__":
demo.launch() |