LaMa vs Stable Diffusion: Which Inpainting Model Is Better?
When an AI "removes" an object from a photo, an inpainting model fills the hole it left. Two very different models dominate this space: LaMa (Large Mask Inpainting) and Stable Diffusion-based inpainting. They solve the same problem with opposite philosophies - and the right choice depends on your goal.
LaMa: reconstruct what was there
LaMa is a specialized image inpainting architecture built around repeated context attention and Fourier convolutions. Its entire purpose is to reconstruct plausible image structure and texture behind a mask. It does not invent new content - it continues what surrounds the hole.
- Strengths: extremely fast (1-2 seconds), deterministic, excellent at continuing textures like sky, walls, grass and skin
- Weaknesses: cannot hallucinate complex new objects; if you removed a person standing in front of a shelf, LaMa continues the shelf - it does not restock it
- Best for: object removal, photo cleanup, watermark and text removal
Stable Diffusion: generate something plausible
Stable Diffusion inpainting uses a diffusion model conditioned on the surrounding image. It can generate entirely new content in the masked area, guided by a text prompt.
- Strengths: can fill complex holes with novel content (replace a product with flowers, rebuild a crowd)
- Weaknesses: slow (10-30+ seconds), non-deterministic (different result every run), can alter the whole image's feel, requires prompt engineering, higher compute cost
- Best for: creative edits, replacing content, generating backgrounds from scratch
Head-to-head for object removal
| LaMa | Stable Diffusion inpainting | |
|---|---|---|
| Speed | 1-2 seconds | 10-30 seconds |
| Consistency | Deterministic | Random per run |
| Texture continuation | Excellent | Good, sometimes stylistic drift |
| Cost per image | Very low | 10-50x higher |
| Inventing new content | No | Yes |
What HappyHorse uses - and why
HappyHorse runs on LaMa (Large Mask Inpainting, by SAIC, Apache 2.0 licensed). For the job our users actually do - "make this object disappear and put the background back" - LaMa's speed and deterministic texture reconstruction win decisively. You get the same clean result every time, in 1-2 seconds, at a fraction of the compute cost.
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