Update app.py

app.py

@@ -12,14 +12,19 @@ from gradio_client import Client, handle_file
 from huggingface_hub import login
 from gradio_imageslider import ImageSlider
 
+
 MAX_SEED = np.iinfo(np.int32).max
 HF_TOKEN = os.environ.get("HF_TOKEN")
 HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")
 
+
 def enable_lora(lora_add, basemodel):
+    """Habilita o deshabilita LoRA según la opción seleccionada"""
     return basemodel if not lora_add else lora_add
 
+
 async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
+    """Genera una imagen utilizando el modelo seleccionado"""
     try:
         if seed == -1:
             seed = random.randint(0, MAX_SEED)
@@ -29,19 +34,23 @@ async def generate_image(prompt, model, lora_word, width, height, scales, steps,
         image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
         return image, seed
     except Exception as e:
-        print(f"Error generating image: {e}")
+        print(f"Error generando imagen: {e}")
         return None, None
 
+
 def get_upscale_finegrain(prompt, img_path, upscale_factor):
+    """Escala una imagen utilizando FineGrain"""
     try:
         client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER)
         result = client.predict(input_image=handle_file(img_path), prompt=prompt, negative_prompt="", seed=42, upscale_factor=upscale_factor, controlnet_scale=0.6, controlnet_decay=1, condition_scale=6, tile_width=112, tile_height=144, denoise_strength=0.35, num_inference_steps=18, solver="DDIM", api_name="/process")
         return result[1]
     except Exception as e:
-        print(f"Error upscale image: {e}")
+        print(f"Error escalando imagen: {e}")
         return None
 
+
 async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
+    """Función principal que genera y escala la imagen"""
     model = enable_lora(lora_model, basemodel) if process_lora else basemodel
     image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
     if image is None:
@@ -52,16 +61,22 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
     
     if process_upscale:
         upscale_image_path = get_upscale_finegrain(prompt, image_path, upscale_factor)
-        upscale_image = Image.open(upscale_image_path)  
-        upscale_image.save("upscale_image.jpg", format="JPEG")  
-        return [image_path, "upscale_image.jpg"]
+        if upscale_image_path is not None:
+            upscale_image = Image.open(upscale_image_path)
+            upscale_image.save("upscale_image.jpg", format="JPEG")
+            return [image_path, "upscale_image.jpg"]
+        else:
+            print("Error: La ruta de la imagen escalada es None")
+            return [image_path, image_path]
     else:
         return [image_path, image_path]
 
+
 css = """
 #col-container{ margin: 0 auto; max-width: 1024px;}
 """
 
+
 with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
     with gr.Column(elem_id="col-container"):
         with gr.Row():