workflow-templates-broken-zap-2026-06-08/site/knowledge/concepts/samplers.md

Samplers & Schedulers

Samplers are the algorithms that iteratively denoise a random latent into a coherent image, while schedulers control the noise schedule — how much noise is removed at each step. Together they determine the image's quality, speed, and visual character. Choosing the right combination is one of the most impactful decisions in any generation workflow.

How It Works in Hanzo Studio

• Key nodes: KSampler (main sampling node), KSamplerAdvanced (provides control over start/end steps for multi-pass workflows)
• Typical workflow pattern: Load model → connect conditioning → configure sampler/scheduler/steps → sample → decode

Key Settings

• sampler_name: The denoising algorithm. Common choices include euler (fast, good baseline), euler_ancestral (more creative variation), dpmpp_2m (balanced quality and speed), dpmpp_2m_sde (high quality, slightly slower), dpmpp_3m_sde (very high quality), and uni_pc (fast convergence)
• scheduler: Controls the noise reduction curve. normal is linear, karras front-loads noise reduction for better detail, exponential and sgm_uniform (recommended for SDXL) are also available
• steps (1–100): Number of denoising iterations. 20–30 is typical; more steps give diminishing returns. Flux and LCM models need far fewer (4–8 steps)

Tips

• euler + normal is the safest starting combination for any model
• dpmpp_2m + karras is a popular choice when you want higher quality with minimal speed cost
• Ancestral samplers (euler_ancestral, any _sde variant) produce different results each run even with the same seed — useful for exploration, but not for reproducibility
• Flux and LCM models converge much faster; using 20+ steps with them wastes time without improving quality