Understanding Negative Prompt Conditioning
Negative prompting allows digital artists to steer diffusion models away from undesirable latent probability vectors. Rather than trying to cram dozens of exclusions into your positive prompt, the negative conditioning tensor mathematically subtracts unwanted attributes during every denoising timestep.
1. Core Anatomy & Structural Negatives
For human subjects, structural integrity is paramount. Standard negative tokens should include:
extra digits, deformed fingers, fused hands, missing limbs, mutated anatomy, unnatural body proportions, asymmetric eyes, crossed eyes, plastic skin texture
2. Optical & Rendering Quality Negatives
To ensure high visual dynamic range and avoid muddy compression artifacts:
jpeg artifacts, low bitrate compression, pixelation, motion blur, blown out highlights, chromatic aberration, oversharpened, flat shading
3. Commercial Watermark & Typography Exclusions
Many base training sets contain stock photo signatures, artist watermarks, and decorative framing. Filter them systematically with:
watermark, signature, artist logo, copyright label, text overlay, border, picture frame, banner