Create app.py

app.py

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+import gradio as gr
+import torch
+from controlnet_aux import CannyDetector
+from diffusers import FluxControlPipeline
+from diffusers.utils import load_image
+import random
+import os
+from huggingface_hub import login
+
+# Autenticarse en HuggingFace
+login(os.getenv("HUGGINGFACEHUB_TOKEN"))
+
+# Configuración del modelo
+model_id = "black-forest-labs/FLUX.1-Canny-dev"
+pipe = FluxControlPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16).to("cuda")
+
+# Procesador Canny para detección de bordes
+processor = CannyDetector()
+
+MAX_SEED = 2**32 - 1
+MAX_IMAGE_SIZE = 1024
+
+def generate_image(image, prompt, seed, random_seed, low_thresh, high_thresh, guidance_scale, num_inference_steps):
+    # Control de Seed
+    if random_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.manual_seed(seed)
+    
+    # Cargar imagen y aplicar Canny
+    control_image = processor(image, low_threshold=low_thresh, high_threshold=high_thresh, 
+                              detect_resolution=MAX_IMAGE_SIZE, image_resolution=MAX_IMAGE_SIZE)
+    
+    # Generar imagen con Diffusers
+    generated_image = pipe(
+        prompt=prompt,
+        control_image=control_image,
+        height=MAX_IMAGE_SIZE,
+        width=MAX_IMAGE_SIZE,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator=generator,
+    ).images[0]
+    
+    return generated_image, seed
+
+# Interfaz Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("# Generador de Imágenes con FLUX.1 Canny 🚀")
+    
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Sube tu imagen de control")
+        result_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=[image_input, prompt, seed, random_seed, low_thresh, high_thresh, guidance_scale, num_inference_steps],
+        outputs=[result_image, seed]
+    )
+
+if __name__ == "__main__":
+    demo.launch()