Deep dive into CFG scale, sampling methods, steps, and other parameters that control image generation quality and style.
Image generation models have several parameters that affect output quality, style, and consistency. Understanding these is crucial for professional-grade results.
What it does: Controls how strictly the model follows your prompt vs. its own creativity.
| CFG Value | Effect |
|---|---|
| 1-3 | Very creative, may ignore parts of prompt |
| 4-7 | Sweet spot — follows prompt while staying natural |
| 8-12 | Strict prompt following, may look artificial |
| 13-20 | Over-saturated, artifacts, "deep fried" look |
Rule of thumb: Start at 7, lower for creative/artistic, raise for precise control.
What it does: How many denoising iterations the model performs.
| Steps | Quality vs Speed |
|---|---|
| 10-15 | Fast preview, lower quality |
| 20-30 | Good balance for most use cases |
| 30-50 | Higher detail, diminishing returns |
| 50+ | Rarely needed, wastes compute |
The seed controls the random noise pattern. Same prompt + same seed = same image (mostly).
Uses:
Important: Going above native resolution causes artifacts. Use upscaling (img2img, Real-ESRGAN) instead.
Many LoRAs require specific trigger words — always check the model card on CivitAI or HuggingFace.
Sign in to join the discussion.
No comments yet. Share your thoughts on this article.