Negative Prompts Explained: Eliminating Artifacts in Stable Diffusion & Midjourney

In Classifier-Free Guidance (CFG), latent diffusion models calculate two separate noise predictions during each denoising step: one conditioned on your positive prompt, and one conditioned on the negative prompt (or empty prompt). The final image vector is calculated as: `Vector = Negative + Guidance_Scale * (Positive - Negative)` By pushing the latent vector in the exact opposite direction of the negative embedding, you effectively banish those visual concepts from the final synthesized pixels.

Midjourney provides negative conditioning via the `--no` parameter. Multiple items should be listed without quotation marks or bullet points:

/imagine prompt: Minimalist Scandinavian living room with large Monstera plant, morning mist, warm wood textures, arch window --no people, clutter, frames, chandeliers, oversaturated colors --ar 16:9 --v 6.0

In Stable Diffusion (SDXL, SD 1.5), negative text prompts are fully weighted. You can use round brackets to amplify suppression: `(extra limbs:1.3), (worst quality:1.4)`. However, using pre-trained negative textual inversion embeddings like *EasyNegative* or *BadDream* compresses hundreds of undesirable quality vectors into a single clean token.

AI models struggle with hands because hands are articulated in thousands of distinct geometric poses with self-occluding fingers. Rather than writing "mutated hands, missing fingers, extra digits", the highest leverage approach is: * Specify physical action: "hands resting flat on mahogany table", "fingers loosely gripping an espresso mug". * Adjust camera distance: medium shots or wide shots have fewer pixels allocated to fingers, making imperfections less visible.

Over-suppressing latent space flattens dynamic range. If you negatively prompt "dark, shadows, contrast, noise, grain", the model is forced into sterile, washed-out plastic textures. Only apply negative prompts when an artifact actively appears in your test renders.