diff --git a/nodes/upscaler_node.py b/nodes/upscaler_node.py
new file mode 100644
index 0000000..64fa050
--- /dev/null
+++ b/nodes/upscaler_node.py
@@ -0,0 +1,139 @@
+import os
+import configparser
+import tempfile
+import requests
+from PIL import Image
+import io
+import numpy as np
+import torch
+from fal_client import submit, upload_file
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.dirname(current_dir)
+config_path = os.path.join(parent_dir, "config.ini")
+
+config = configparser.ConfigParser()
+config.read(config_path)
+
+try:
+    fal_key = config['API']['FAL_KEY']
+    os.environ["FAL_KEY"] = fal_key
+except KeyError:
+    print("Error: FAL_KEY not found in config.ini")
+
+def upload_image(image):
+    try:
+        if isinstance(image, torch.Tensor):
+            image_np = image.cpu().numpy()
+        else:
+            image_np = np.array(image)
+
+        if image_np.ndim == 4:
+            image_np = image_np.squeeze(0)
+        if image_np.ndim == 2:
+            image_np = np.stack([image_np] * 3, axis=-1)
+        elif image_np.shape[0] == 3:
+            image_np = np.transpose(image_np, (1, 2, 0))
+
+        if image_np.dtype == np.float32 or image_np.dtype == np.float64:
+            image_np = (image_np * 255).astype(np.uint8)
+
+        pil_image = Image.fromarray(image_np)
+
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
+            pil_image.save(temp_file, format="PNG")
+            temp_file_path = temp_file.name
+
+        image_url = upload_file(temp_file_path)
+        return image_url
+    except Exception as e:
+        print(f"Error uploading image: {str(e)}")
+        return None
+    finally:
+        if 'temp_file_path' in locals():
+            os.unlink(temp_file_path)
+
+class UpscalerNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "upscale_factor": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 4.0, "step": 0.5}),
+                "negative_prompt": ("STRING", {"default": "(worst quality, low quality, normal quality:2)", "multiline": True}),
+                "creativity": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.05}),
+                "resemblance": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 1.0, "step": 0.05}),
+                "guidance_scale": ("FLOAT", {"default": 4.0, "min": 1.0, "max": 20.0, "step": 0.5}),
+                "num_inference_steps": ("INT", {"default": 18, "min": 1, "max": 100}),
+                "enable_safety_checker": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "seed": ("INT", {"default": -1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "generate_upscaled_image"
+    CATEGORY = "FAL/Image"
+
+    def generate_upscaled_image(self, image, upscale_factor, negative_prompt, creativity, resemblance, guidance_scale, num_inference_steps, enable_safety_checker, seed=-1):
+        image_url = upload_image(image)
+        if not image_url:
+            print("Failed to upload image for upscaling.")
+            return self.create_blank_image()
+
+        arguments = {
+            "image_url": image_url,
+            "prompt": "masterpiece, best quality, highres",
+            "upscale_factor": upscale_factor,
+            "negative_prompt": negative_prompt,
+            "creativity": creativity,
+            "resemblance": resemblance,
+            "guidance_scale": guidance_scale,
+            "num_inference_steps": num_inference_steps,
+            "enable_safety_checker": enable_safety_checker
+        }
+
+        if seed != -1:
+            arguments["seed"] = seed
+
+        try:
+            handler = submit("fal-ai/clarity-upscaler", arguments=arguments)
+            result = handler.get()
+            return self.process_result(result)
+        except Exception as e:
+            print(f"Error generating upscaled image: {str(e)}")
+            return self.create_blank_image()
+
+    def process_result(self, result):
+        try:
+            img_url = result["image"]["url"]
+            img_response = requests.get(img_url)
+            img = Image.open(io.BytesIO(img_response.content))
+            img_array = np.array(img).astype(np.float32) / 255.0
+
+            # Stack the images along a new first dimension
+            stacked_images = np.stack([img_array], axis=0)
+            
+            # Convert to PyTorch tensor
+            img_tensor = torch.from_numpy(stacked_images)
+            return (img_tensor,)
+        except Exception as e:
+            print(f"Error processing result: {str(e)}")
+            return self.create_blank_image()
+
+    def create_blank_image(self):   
+        blank_img = Image.new('RGB', (512, 512), color='black')
+        img_array = np.array(blank_img).astype(np.float32) / 255.0
+        img_tensor = torch.from_numpy(img_array)[None,]
+        return (img_tensor,)
+
+# Node class mappings
+NODE_CLASS_MAPPINGS = {
+    "Upscaler_fal": UpscalerNode,
+}
+
+# Node display name mappings
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Upscaler_fal": "Clarity Upscaler (fal)",
+}
\ No newline at end of file