Enhance and modify images using specialized AI tools. Kelly AI provides professional-grade image upscaling and automatic background removal capabilities.
Upscale images
Increase image resolution and quality using the upscale() method.
Prepare your image
Convert your image to base64:import base64
from kellyapi import KellyAPI
# Read and encode the image
with open("low_res.png", "rb") as f:
image_bytes = f.read()
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
kelly = KellyAPI(api_key="your_api_key")
Upscale
upscaled_image = await kelly.upscale(image_data=image_base64)
Save the result
with open("high_res.png", "wb") as f:
f.write(upscaled_image)
Parameters
The source image encoded as a base64 string.
Remove backgrounds
Automatically remove image backgrounds using the removebg() method.
Prepare your image
Convert your image to base64:import base64
from kellyapi import KellyAPI
# Read and encode the image
with open("photo.jpg", "rb") as f:
image_bytes = f.read()
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
kelly = KellyAPI(api_key="your_api_key")
Remove background
no_bg_image = await kelly.removebg(image_data=image_base64)
Save the result
with open("no_background.png", "wb") as f:
f.write(no_bg_image)
Parameters
The source image encoded as a base64 string.
Complete example
import asyncio
import base64
from kellyapi import KellyAPI
async def process_image():
kelly = KellyAPI(api_key="your_api_key")
# Load the original image
with open("product.jpg", "rb") as f:
image_bytes = f.read()
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
# Remove background
print("Removing background...")
no_bg = await kelly.removebg(image_data=image_base64)
with open("product_no_bg.png", "wb") as f:
f.write(no_bg)
# Upscale the result
print("Upscaling image...")
no_bg_base64 = base64.b64encode(no_bg).decode("utf-8")
upscaled = await kelly.upscale(image_data=no_bg_base64)
with open("product_final.png", "wb") as f:
f.write(upscaled)
print("Image processing complete!")
# Run the async function
asyncio.run(process_image())
Use cases
Product photography
Create professional product images:
async def prepare_product_image():
kelly = KellyAPI(api_key="your_api_key")
# Load product photo
with open("product_raw.jpg", "rb") as f:
image_bytes = f.read()
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
# Remove background for clean look
clean_image = await kelly.removebg(image_data=image_base64)
# Upscale for high-quality display
clean_base64 = base64.b64encode(clean_image).decode("utf-8")
final_image = await kelly.upscale(image_data=clean_base64)
with open("product_ecommerce.png", "wb") as f:
f.write(final_image)
Portrait enhancement
Enhance portrait photos:
async def enhance_portrait():
kelly = KellyAPI(api_key="your_api_key")
with open("portrait.jpg", "rb") as f:
image_bytes = f.read()
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
# Upscale for higher quality
enhanced = await kelly.upscale(image_data=image_base64)
with open("portrait_hd.png", "wb") as f:
f.write(enhanced)
Social media content
Prepare images for social media:
async def social_media_image():
kelly = KellyAPI(api_key="your_api_key")
with open("person.jpg", "rb") as f:
image_bytes = f.read()
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
# Remove background for overlay on custom backgrounds
no_bg = await kelly.removebg(image_data=image_base64)
with open("person_cutout.png", "wb") as f:
f.write(no_bg)
Print preparation
Prepare images for high-resolution printing:
async def prepare_for_print():
kelly = KellyAPI(api_key="your_api_key")
with open("photo.jpg", "rb") as f:
image_bytes = f.read()
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
# Upscale to print quality
print_ready = await kelly.upscale(image_data=image_base64)
with open("photo_print.png", "wb") as f:
f.write(print_ready)
async def extract_logo():
kelly = KellyAPI(api_key="your_api_key")
with open("logo_with_bg.png", "rb") as f:
image_bytes = f.read()
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
# Remove background to get transparent logo
logo = await kelly.removebg(image_data=image_base64)
with open("logo_transparent.png", "wb") as f:
f.write(logo)
Batch processing
Process multiple images efficiently:
import asyncio
import base64
from pathlib import Path
from kellyapi import KellyAPI
async def batch_remove_backgrounds():
kelly = KellyAPI(api_key="your_api_key")
# Get all images in directory
image_files = list(Path("originals").glob("*.jpg"))
for image_file in image_files:
print(f"Processing {image_file.name}...")
# Read and encode
with open(image_file, "rb") as f:
image_bytes = f.read()
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
# Remove background
no_bg = await kelly.removebg(image_data=image_base64)
# Save result
output_path = Path("processed") / image_file.with_suffix(".png").name
output_path.parent.mkdir(exist_ok=True)
with open(output_path, "wb") as f:
f.write(no_bg)
print(f"Saved to {output_path}")
asyncio.run(batch_remove_backgrounds())
Both upscale() and removebg() methods require base64-encoded images as input and return raw image data as bytes.
When removing backgrounds, images with clear subject separation from the background produce the best results. For upscaling, the AI enhances details and sharpness while increasing resolution.