𝗜𝗺𝗮𝗴𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗔𝗜-𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗖𝗼𝗻𝘁𝗲𝗻𝘁
AI images create a unique performance problem. You do not know the image size, format, or content until the generation finishes. You still need to prevent slow loading and layout shifts.
I used these steps when building a free AI image generator.
Standard optimization assumes you know the image ahead of time. You can calculate dimensions and optimize at build time. Generated images break this rule. The image does not exist until a user requests it.
How to handle delivery:
- Raw AI output is usually PNG. PNG files are too large, often between 800KB and 1.2MB.
- Convert images to WebP. WebP files are 25% to 35% smaller than PNG at the same quality.
- Use 85 quality for WebP. AI images have natural texture noise. Higher settings add file size without improving visual quality.
How to prevent layout shifts:
Users pick an aspect ratio before they click generate. Use this to your advantage.
- Pre-size the container based on the chosen ratio (1:1, 16:9, etc.).
- This keeps the container at the correct size before the image arrives.
- It eliminates cumulative layout shift (CLS).
How to improve loading speed:
The generated image is usually the Largest Contentful Paint (LCP) element.
- Mark the image as a priority in your code.
- Use a preconnect link for your CDN. This handles DNS and TLS handshakes early.
- Use a skeleton loader that occupies the exact space the final image will fill.
Caching strategy:
- Do not cache at the inference layer. Every request should feel fresh.
- Cache at the CDN layer for delivery speed.
- Each image has a unique URL, so aggressive caching is safe.
Results of these changes:
- Average size: 900KB to 200KB.
- LCP: 4.1s to 1.9s.
- CLS: Eliminated.
Summary of actions:
- Convert to WebP at 85 quality.
- Pre-size containers using the selected aspect ratio.
- Set the primary image to priority loading.
- Cache heavily at the CDN level.
- Use lazy loading only for secondary content below the fold.
Optional learning community: https://t.me/GyaanSetuAi