Stable Diffusion Prompt for Realistic Skin

  • Use short, specific prompts focusing on keywords like “high resolution”, “soft lighting”, “film grain”, etc. Avoid long, overly detailed prompts.
  • Include both positive descriptors (e.g. “healthy skin”) as well as negative descriptors (e.g. “no blemishes”) to narrow down the image generation.
  • Specify lighting, angle/perspective, and other photographic details.

Models

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  • Use photorealistic models like RealisticVision_V13 or a blend of models (e.g. URPM + RealisticVision) rather than the base v1.5 model.

Fixing Defects

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  • Add keywords to fix issues like “bad anatomy”, “extra fingers”, etc. Use negative prompts to exclude common defects.
  • Control image noise, skin texture, pore visibility, etc. for more natural, realistic skin.

Advanced Techniques

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  • Incorporate LoRA, hypernetworks, textual inversion and other techniques to further improve realism.
  • Try different sampling methods like DPM++ 2M Karras. Adjust CFG scale and steps.
  • Use upscalers to increase output resolution and enhance fine details.

The key is iteratively refining both the text prompt and model/settings to narrow down the image generation towards more and more realistic skin. Checking the sample images from the search results can provide examples of techniques to try.

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