Aspect Ratios: Which Resolution Should You Use?
SDXL was trained on specific resolution buckets. Use them and images pop. Ignore them and you get mush. Full cheat sheet inside.
July 5, 2026 ยท 4 min read

Why buckets matter
Stable Diffusion XL was trained on a small set of specific resolutions. Generate at 1024ร1024 or 1344ร768 and you'll get sharp, well-composed images. Generate at 1000ร750 and you'll get soft, weirdly proportioned mush โ because the model never saw a training image at that size.
The SDXL cheat sheet
| Ratio | Resolution | Use for |
|---|---|---|
| 1:1 | 1024 ร 1024 | Social posts, avatars, gallery grids |
| 4:3 | 1152 ร 896 | Blog covers, standard photos |
| 3:4 | 896 ร 1152 | Portrait, book covers, magazine pages |
| 16:9 | 1344 ร 768 | YouTube thumbnails, hero banners, desktop wallpapers |
| 9:16 | 768 ร 1344 | TikTok, Reels, Instagram stories, phone wallpapers |
| 21:9 | 1536 ร 640 | Ultra-wide banners, cinematic |
Imagoat's Generate page uses these exact numbers behind the scenes โ pick "16:9" and you get 1344ร768, not some in-between size.
Upscaling later
If you need larger โ a 4K wallpaper or a print-ready poster โ generate at the correct bucket first, then run the result through an upscaler. Upscaling a well-composed 1024 image is dramatically better than generating at 2048 directly.
Portrait tip
For faces specifically, generate in 3:4 at 896ร1152. It's the ratio the model saw most human portraits at during training, and the difference in facial coherence is huge.
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