Improving Product Image Quality with AI: Practical Stable Diffusion Usage

If you've run an online store before, you probably know this. Product photo quality directly impacts sales.

According to statistics, over 75% of consumers make purchase decisions based on product photos. But what's the reality? Poor lighting, messy backgrounds, low resolution photos... Especially for small sellers who can't afford professional studio shoots.

I remember struggling with photos when selling second-hand items. Even if the product was in great condition, poor photos meant fewer responses.

After reading Sajid Khan's article, I thought "Oh, AI can do this too?" so I translated it. These are practical methods for improving product images with Stable Diffusion.

What You Can Do with Stable Diffusion

Here are the areas where SD can help with product images.

1. Image Upscaling (Increasing Resolution)

This converts low-resolution product photos to high resolution. It's not just stretching the image - AI generates new details.

When is this useful?

• Old low-quality product photos
• Casually taken phone photos
• Images where only thumbnails remain

Python Code Example

from diffusers import StableDiffusionUpscalePipeline
from PIL import Image

# Load upscaling model
upscale_pipe = StableDiffusionUpscalePipeline.from_pretrained(
    "stabilityai/stable-diffusion-x4-upscaler"
).to("cuda")

# Load low resolution image
low_res_img = Image.open("low_res_product.jpg").convert("RGB")

# Execute upscale
upscaled_img = upscale_pipe(
    prompt="A sharp, detailed product photo of a laptop",
    image=low_res_img
).images[0]

upscaled_img.save("upscaled_product.jpg")

Tip: Adding product type and desired feel to the prompt gives better results. Keywords like "sharp", "detailed", "professional" are effective.

2. Background Removal and Replacement

Platforms like Amazon, Coupang, and Smart Store recommend or require white background product images. SD's inpainting feature can automatically replace backgrounds.

How It Works

1. Mask the product image (mark the background area in white)
2. SD inpainting generates new content for the masked area
3. Replace with desired background

Python Code Example

from diffusers import StableDiffusionInpaintPipeline
from PIL import Image

# Load inpainting model
inpaint_pipe = StableDiffusionInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting"
).to("cuda")

# Load product image and mask
product_img = Image.open("product_with_background.jpg")
mask = Image.open("background_mask.jpg")  # White = area to replace

# Replace with clean background
clean_img = inpaint_pipe(
    prompt="A professional white background",
    image=product_img,
    mask_image=mask
).images[0]

clean_img.save("clean_product.jpg")

Various Background Options

Just changing the prompt lets you create different backgrounds.

3. Lifestyle Mockup Generation

These are images showing products in actual usage environments. For example, a smartwatch on a wrist, furniture placed in a living room, etc.

Originally you'd need to hire models, rent locations, and shoot, but AI can generate this.

Python Code Example

from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5"
).to("cuda")

# Generate lifestyle mockup
mockup = pipe(
    prompt="A smartwatch worn by a stylish young man in a modern office, natural lighting, professional photo",
    guidance_scale=7.5
).images[0]

mockup.save("lifestyle_smartwatch.jpg")

Category-Specific Examples

Note: Rather than generating from scratch, it's more accurate to keep the actual product photo and only change the background. You can use the Image-to-Image feature for this.

4. Damaged Image Restoration (Inpainting)

You can restore product images when parts are damaged or there are unwanted elements.

Use Cases

• When images have watermarks
• When parts of the product are obscured
• When there are unwanted objects in the background
• Damaged parts of old images

How It Works

Designate the area you want to restore as a mask, and AI understands the surrounding context to fill it in naturally.

# Restore damaged parts
restored_img = inpaint_pipe(
    prompt="Product continuation, seamless, professional photo",
    image=damaged_product_img,
    mask_image=damage_mask
).images[0]

Deploying as an API Service (For Developers)

If you need to process large volumes of product images, deploying as an API is efficient. Here's an example using FastAPI.

from fastapi import FastAPI, File, UploadFile
from PIL import Image
import io

app = FastAPI()

@app.post("/enhance-image/")
async def enhance_image(file: UploadFile = File(...)):
    # Read image
    image_bytes = await file.read()
    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
    
    # Enhance image with SD
    enhanced = upscale_pipe(
        prompt="A sharp, high-quality product image",
        image=image
    ).images[0]
    
    enhanced.save("enhanced_image.jpg")
    return {"message": "Image enhanced successfully"}

Run:

uvicorn app:app --host 0.0.0.0 --port 8000

This enables automatic image enhancement through API calls from your shopping mall system.

Real Business Impact

Cost Savings

• Reduced professional photography costs
• Automated processing for bulk product images
• Saved Photoshop editing manpower/time

Increased Sales

• Improved conversion rates with high-quality images
• Consistent brand image maintenance
• Platform image guideline compliance

Time Savings

• Faster processing compared to manual editing
• Reduced new product registration time
• Easy bulk updates for seasonal products

Limitations and Cautions

Honestly, AI isn't a cure-all.

Accuracy Issues

• Complex product details may be distorted
• Colors may differ slightly
• Text/logos may appear strange

Solutions

• Final review required: Human verification of AI results
• Backup originals: Always keep original images
• Prompt tuning: Develop optimized prompts for each product category

Legal Considerations

• Consumer confusion if generated images differ from actual products
• Be especially careful with products where color or texture is important

Cloud Deployment Options

If you don't have a local GPU or need large-scale processing, you can use cloud services.

For small-scale testing, Hugging Face Spaces or Replicate have lower barriers to entry.

Summary: Recommendations by Use Case

How to Get Started

For Non-Developers

1. Clipdrop (stability.ai service) - Background removal and upscaling directly on the web
2. Canva AI - AI features included in simple image editing
3. Remove.bg - Specialized in background removal

For Developers

1. Install Hugging Face Diffusers library
2. Run models locally or in the cloud
3. Build pipeline according to your business logic

AI can really help those struggling with product image quality. Of course, it can't completely replace professional studio photography, but from a cost-benefit perspective, it's a worthwhile option.

It especially shines when you have many products or need frequent updates. Once you build the pipeline, you can keep using it.