Sampler
A sampler is the algorithm that decides how noise is removed at each step of generation. Different samplers reach the final image by different paths, trading off speed, detail and consistency.
A diffusion model predicts how to clean up a noisy image, but it needs a numerical method to actually apply those predictions across many steps. That method is the sampler (also called the sampling method or scheduler). Think of it as the route the generation takes from noise to picture.
Common samplers
- Euler / Euler a: fast and reliable. The "a" (ancestral) variants add a bit of randomness for more variety.
- DPM++ family (e.g. DPM++ 2M, DPM++ SDE): high quality in relatively few steps - a popular modern default.
- DDIM: deterministic and good for reproducible, controlled results.
Why it matters
The sampler changes both how many steps you need and the final look. Some samplers are deterministic (the same seed always gives the same image); ancestral samplers inject extra noise, so results vary more. If you want clean reproducibility, prefer a deterministic sampler and lock the seed.
Try it in the generator
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Related terms
- StepsSteps (sampling steps) are the number of denoising passes the model runs to turn noise into an image. More steps can mean more detail, but past a point they only add time, not quality.
- SeedA seed is the number used to initialize the random noise an image is built from. The same seed plus the same prompt and settings produces the same image every time, which makes results reproducible.
- DenoisingDenoising is the core operation of a diffusion model: at each step it predicts and removes a little noise, gradually turning a random field into a clear image.
- Diffusion modelA diffusion model is the type of AI that powers most modern image generators. It learns to turn random noise into a coherent image by reversing a step-by-step noising process.