Steps: Controlling refinement in image generation

Introduction

The steps parameter defines how many iterations the model performs during generation. Each step refines the output further, gradually turning noise into a coherent image. More steps generally mean more detail, but with diminishing returns and longer generation times.

This parameter appears across all generation tasks (text-to-image, image-to-image, inpainting, and outpainting) and its behavior is consistent: higher step counts produce more refined output at the cost of speed.

How steps work

In diffusion-based models, each step removes a portion of noise from the image. In flow-matching models (like FLUX and Z-Image), each step moves the output along a learned trajectory from noise to data. Regardless of the internal mechanism, the progression follows the same pattern:

Steps 0

A giant jellyfish floats through a sunlit canyon, its tendrils trailing softly across the rock walls as dust dances in the air

A giant jellyfish floats through a sunlit canyon, its tendrils trailing softly across the rock walls as dust dances in the air

A giant jellyfish floats through a sunlit canyon, its tendrils trailing softly across the rock walls as dust dances in the air

A giant jellyfish floats through a sunlit canyon, its tendrils trailing softly across the rock walls as dust dances in the air

A giant jellyfish floats through a sunlit canyon, its tendrils trailing softly across the rock walls as dust dances in the air

A giant jellyfish floats through a sunlit canyon, its tendrils trailing softly across the rock walls as dust dances in the air

The generation process follows these phases:

• Early steps (1-5): Establish basic composition, rough shapes, and color distribution.
• Middle steps (5-15): Form recognizable objects, define spatial relationships, and develop textures.
• Later steps (15-30): Refine details, enhance coherence, and develop subtle lighting.
• Final steps (30+): Polish fine details and smooth transitions, with increasingly subtle changes.

Request structure

The steps parameter is a number passed at the top level of your generation request.

[
  {
    "taskType": "imageInference",
    "model": "civitai:101055@128078",
    "positivePrompt": "A giant jellyfish floating through a sunlit canyon",
    "steps": 30,
    "width": 1024,
    "height": 1024
  }
]

Recommended ranges by architecture

Different model architectures have different sweet spots for step count:

Steps in image-to-image tasks

In image-to-image , inpainting , and outpainting , the steps parameter interacts with strength. The model doesn't run all the steps. It starts partway through the denoising schedule based on the strength value.

For example, with steps: 40 and strength: 0.5, the model only performs the last 20 steps. This means higher step counts become more important at lower strengths, since fewer actual steps are used for refinement.

Scheduler interaction

The choice of scheduler affects how much value you get from additional steps:

• Fast-converging schedulers (Euler, UniPC, LCM): Reach good quality at 15-25 steps. Extra steps add little.
• Detail-oriented schedulers (DPM++ 3M, DPM++ 2M SDE): Continue refining up to 40-50 steps.
• Stochastic schedulers (Euler Ancestral, DPM++ SDE): Don't converge. More steps produce different results, not necessarily better ones.

Tips

1. Start at 25-30 steps for standard models. This hits the sweet spot for most SD 1.5, SDXL, and FLUX models. Only go higher if you see visible improvement.
2. Use 4-8 steps for distilled models. LCM, Lightning, Schnell, and similar architectures are designed for low step counts. Running them at 30 steps wastes compute.
3. Increase steps when using low strength. In image-to-image at strength: 0.3, a step count of 50 gives you ~15 actual refinement steps, while 20 would give you only ~6.
4. Match steps to your scheduler. A fast scheduler like Euler at 15 steps is great for prototyping. Switch to DPM++ 2M Karras at 30 steps for final renders.
5. Profile your use case. In batch processing, cutting from 30 to 20 steps can halve your costs with minimal quality loss. Test and find your acceptable threshold.