Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import numpy as np
 import random
-import spaces
 import torch
 from diffusers import DiffusionPipeline
 from accelerate import init_empty_weights, load_checkpoint_and_dispatch
@@ -17,8 +16,11 @@ def load_model():
     global pipe
     if pipe is None:
         with init_empty_weights():
-            pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype)
-        # Cargar el modelo en la RAM y despachar los pesos a la GPU
+            pipe = DiffusionPipeline.from_pretrained(
+                "black-forest-labs/FLUX.1-schnell", 
+                torch_dtype=dtype
+            )
+        # Despachar los pesos a la GPU (sin "named_parameters")
         pipe = load_checkpoint_and_dispatch(
             pipe, 
             "black-forest-labs/FLUX.1-schnell", 
@@ -40,24 +42,24 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_in
     images = []
     for _ in range(num_images):
         image = pipe(
-                prompt = prompt, 
-                width = width,
-                height = height,
-                num_inference_steps = num_inference_steps, 
-                generator = generator,
+                prompt=prompt, 
+                width=width,
+                height=height,
+                num_inference_steps=num_inference_steps, 
+                generator=generator,
                 guidance_scale=0.0
         ).images[0]
         images.append(image)
     
     return images, seed
- 
+
 examples = [
     "a tiny astronaut hatching from an egg on the moon",
     "a cat holding a sign that says hello world",
     "an anime illustration of a wiener schnitzel",
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
@@ -149,4 +151,5 @@ with gr.Blocks(css=css) as demo:
         outputs = [results, seed]
     )
 
-demo.launch()
+# Crear un enlace público con share=True
+demo.launch(share=True)