Update app.py

app.py

@@ -1,6 +1,6 @@
 import torch
 from controlnet_aux import CannyDetector
-from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
+from diffusers import FluxControlPipeline
 from diffusers.utils import load_image
 import gradio as gr
 import random
@@ -9,69 +9,41 @@ import os
 # Instalar dependencias en Hugging Face Spaces
 os.system("pip install -U diffusers controlnet_aux mediapipe")
 
+# Máximo valor para seeds y tamaño de imagen
 MAX_SEED = 2**32 - 1
 MAX_IMAGE_SIZE = 1024
 
-# Inicializar el modelo ControlNet
-controlnet = ControlNetModel.from_pretrained(
-    "black-forest-labs/FLUX.1-Canny-dev", torch_dtype=torch.float32
-)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-Canny-dev", controlnet=controlnet, torch_dtype=torch.float32
+# Inicialización del pipeline Flux Canny Dev
+pipe = FluxControlPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Canny-dev", torch_dtype=torch.float16
 ).to("cuda" if torch.cuda.is_available() else "cpu")
 
 # Procesador Canny
 processor = CannyDetector()
 
+@spaces.GPU
 def generate_image(image, prompt, seed, random_seed, low_thresh, high_thresh, guidance_scale, num_inference_steps):
+    # Controlar el seed para reproducibilidad
     if random_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.manual_seed(seed)
-
-    # Procesar la imagen con CannyDetector
+    
+    # Aplicar CannyDetector a la imagen de control
     control_image = processor(
         image, low_threshold=low_thresh, high_threshold=high_thresh, 
         detect_resolution=MAX_IMAGE_SIZE, image_resolution=MAX_IMAGE_SIZE
     )
-
-    # Generar la imagen
+    
+    # Generar imagen con el modelo Flux
     result = pipe(
         prompt=prompt,
         control_image=control_image,
-        height=image.height,
-        width=image.width,
-        num_inference_steps=num_inference_steps,
         guidance_scale=guidance_scale,
         generator=generator
     ).images[0]
-
+    
     return result, seed
 
-# Interfaz Gradio
+# Interfaz de usuario Gradio
 with gr.Blocks() as demo:
     gr.Markdown("# Generador de Imágenes con FLUX.1 Canny-dev 🚀")
-    
-    with gr.Row():
-        input_image = gr.Image(type="pil", label="Sube tu imagen de control")
-        output_image = gr.Image(label="Imagen Generada")
-    
-    with gr.Row():
-        prompt = gr.Textbox(label="Prompt", placeholder="Describe la imagen que deseas generar...")
-        generate_button = gr.Button("Generar Imagen")
-
-    with gr.Accordion("Configuración Avanzada", open=False):
-        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-        random_seed = gr.Checkbox(label="Randomizar Seed", value=True)
-        low_thresh = gr.Slider(label="Umbral Bajo Canny", minimum=0, maximum=255, step=1, value=50)
-        high_thresh = gr.Slider(label="Umbral Alto Canny", minimum=0, maximum=255, step=1, value=200)
-        guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=30.0, step=0.1, value=7.5)
-        num_inference_steps = gr.Slider(label="Pasos de Inferencia", minimum=1, maximum=50, step=1, value=20)
-
-    generate_button.click(
-        generate_image,
-        inputs=[input_image, prompt, seed, random_seed, low_thresh, high_thresh, guidance_scale, num_inference_steps],
-        outputs=[output_image, seed]
-    )
-
-if __name__ == "__main__":
-    demo.launch()