Negative Prompting
Specify what to exclude from AI-generated images — suppressing unwanted artifacts, styles, and quality issues to achieve higher fidelity and more controlled results.
Introduced: Negative prompting became a standard technique with the rise of Stable Diffusion in 2022. The concept uses classifier-free guidance to steer image generation away from unwanted features. Users discovered that specifying what to avoid — blurry, distorted, extra fingers, watermark — dramatically improved output quality. The approach emerged organically from community experimentation as practitioners shared negative prompt lists that consistently produced cleaner, more polished results across a wide range of subjects and styles.
Modern LLM Status: Negative prompting is a standard feature in most image generation interfaces. DALL-E 3 and Midjourney v6 handle many common quality issues automatically through improved training and built-in quality filters, but explicit negative prompts remain essential for fine-grained control over style, composition, and artifact avoidance. Tools like Stable Diffusion XL, Flux, and ComfyUI continue to offer dedicated negative prompt fields, and the technique has become part of every serious image generation workflow. Understanding what to exclude is now considered as important as knowing what to include.
Define by Exclusion
Positive prompts tell the model what to create. Negative prompts tell the model what to avoid. Together, they define a precise corridor in the generation space — narrowing the vast range of possible outputs to exactly what you want.
The key insight: image generation models can accidentally include unwanted elements because their training data contains imperfect images. Photographs with watermarks, illustrations with anatomical errors, and compositions with distracting backgrounds all exist in the datasets these models learned from. Without explicit guidance, the model may reproduce these flaws.
Negative prompts counteract this by explicitly penalizing features you want to suppress — from common artifacts like extra limbs and blurriness to stylistic elements such as certain color palettes or artistic styles you want to avoid. Think of it as sculpting: you are not just adding clay, you are also carving away everything that does not belong.
Classifier-free guidance (CFG) is the engine behind negative prompting. During image generation, the model produces two predictions: one guided by your positive prompt, and one guided by your negative prompt. The final image is generated by amplifying the difference between these two predictions. A higher CFG scale means stronger adherence to both what you want and what you want to avoid. The sweet spot typically falls between 7 and 12 for most generation tasks.
The Negative Prompting Process
Four steps from concept to controlled generation
Craft the Positive Prompt
Begin by describing what you want to see in the generated image. Be specific about subject, composition, lighting, style, and mood. The positive prompt is your creative vision — the negative prompt will protect it from common pitfalls.
“Professional portrait of a woman in a sunlit garden, soft bokeh background, natural lighting, Canon EOS R5, 85mm lens”
Identify Potential Problems
Anticipate the common artifacts and unwanted elements that typically appear for this type of image. Portraits often produce distorted hands and asymmetric faces. Landscapes may introduce over-saturated colors or HDR artifacts. Product shots can include unwanted reflections or busy backgrounds. Experience with your chosen model helps you predict these failure modes.
For portraits: distorted hands, extra fingers, asymmetric eyes, unnatural skin texture, watermarks, text overlays
Write the Negative Prompt
List the excluded features, artifacts, and quality issues as comma-separated terms. Organize them by category: quality issues first (blurry, low resolution), then anatomical problems (extra fingers, distorted face), then stylistic exclusions (cartoon, anime, oversaturated). Prioritize the most impactful terms at the beginning of the list.
“blurry, low quality, distorted hands, extra fingers, watermark, text, oversaturated, cartoon, deformed face, unnatural pose”
Balance Guidance Strength
Adjust the classifier-free guidance (CFG) scale to control how strongly the negative prompt influences generation. Low values (5–7) give the model more creative freedom but weaker artifact suppression. High values (10–15) enforce strict adherence but can produce overly rigid or washed-out results. Test incrementally to find the balance point for your specific use case.
Start at CFG 7.5 for general use. Increase to 10–12 if artifacts persist. Reduce to 5–6 if the image looks overly constrained or loses natural variation.
See the Difference
How negative prompts transform output quality
Without Negative Prompt
Portrait of a person in a garden
The model produces an image with distorted hands showing six fingers, a slightly blurry background that lacks depth, faint watermark artifacts in the lower corner, and oversaturated greens that look artificial rather than natural.
With Negative Prompt
Portrait of a person in a garden
Negative: blurry, distorted hands, extra fingers, watermark, low quality, oversaturated, cartoon
A clean, natural portrait with correct hand anatomy showing five fingers per hand. The garden background has pleasant bokeh with true-to-life colors. No watermark artifacts appear, and the overall image quality is sharp with balanced saturation throughout.
Negative Prompting in Action
Real-world applications across different image categories
Professional headshot of a middle-aged man in a navy suit, studio lighting, shallow depth of field, neutral gray background, sharp focus on eyes, corporate photography style
deformed face, asymmetric eyes, extra fingers, distorted hands, blurry, out of focus, watermark, text, logo, low resolution, jpeg artifacts, unnatural skin, plastic skin, oversaturated, cross-eyed, double chin artifact, malformed ears
Portrait generation is particularly prone to facial asymmetry and hand deformities. By explicitly naming these common failure modes, the model steers away from the regions of its latent space that produce these artifacts. The skin-related terms prevent the waxy, plastic look that plagues many AI portraits.
Panoramic mountain vista at golden hour, alpine meadow with wildflowers in foreground, snow-capped peaks, dramatic clouds, National Geographic photography style, 24mm wide-angle lens
HDR artifacts, tone mapping, over-processed, oversaturated, text overlay, watermark, signature, frame, border, vignette, lens flare, chromatic aberration, noise, grain, haze, smog, people, buildings, power lines, fences
Landscape images in training data frequently contain heavy HDR processing and text overlays from stock photography. The negative prompt suppresses these processing artifacts while also excluding human-made elements that would break the natural wilderness feel. Specifying “over-processed” prevents the hyper-real look that makes AI landscapes look artificial.
Clean product photography of a minimalist wristwatch on white surface, soft studio lighting, slight shadow beneath, commercial catalog style, 100mm macro lens, high detail on dial and strap texture
busy background, cluttered scene, reflections, glare, brand logos, text, watermark, other products, hands, wrist, low quality, blurry, out of focus, distorted proportions, warped edges, color cast, harsh shadows
Product photography demands isolation and clarity. The negative prompt prevents the model from adding contextual elements like hands or competing objects that appear frequently in training data product shots. Excluding brand logos and text avoids hallucinated branding, while suppressing reflections and glare keeps the focus on material texture and form.
When to Use Negative Prompting
Knowing when exclusion improves your results
Perfect For
Adding standard quality exclusions — blurry, low resolution, watermark, artifacts — to every generation produces consistently cleaner results across all subject matter.
When a model consistently produces the same unwanted element — extra fingers in portraits, text in landscapes — negative prompting directly targets and eliminates these patterns.
When you want photorealism but the model keeps drifting toward illustration, or you want watercolor but it produces digital art, negative prompts enforce stylistic boundaries.
Exclude specific compositional elements — borders, frames, text overlays, multiple subjects — to ensure the generated image matches your intended layout precisely.
Skip It When
If your positive prompt consistently produces satisfactory results without artifacts or quality issues, adding negative terms introduces unnecessary complexity and may over-constrain the output.
Some API-only models and newer architectures do not offer a dedicated negative prompt field. In these cases, incorporate exclusions into your positive prompt using phrases like “without” or “no.”
During early ideation or brainstorming phases, heavy negative prompting can restrict the model’s creative range. Let the model explore freely first, then add negative constraints to refine promising directions.
Use Cases
Where negative prompting delivers the most value
Professional Photography Generation
Eliminate watermarks, compression artifacts, and unnatural lighting from AI-generated photos intended for professional use in marketing materials or publications.
Character Design Consistency
Suppress anatomical deformities and style drift when generating character art across multiple poses, ensuring consistent proportions and visual identity throughout a project.
Architectural Visualization
Exclude impossible geometry, floating elements, and perspective distortions from building renders to produce physically plausible architectural concepts and interior designs.
Medical Illustration
Remove stylistic embellishments, inaccurate anatomy, and artistic license from medical diagrams to maintain clinical accuracy and educational clarity in healthcare materials.
Technical Diagrams
Suppress photorealistic textures, decorative elements, and unnecessary detail from technical diagrams and schematics where clarity and precision are paramount.
Brand-Consistent Marketing Assets
Exclude competing brand colors, off-brand styles, and unwanted visual elements to generate marketing imagery that aligns with established brand guidelines and visual identity systems.
Where Negative Prompting Fits
From basic text-to-image to advanced compositional control
Image generation follows a natural refinement path: start with a clear positive prompt, add negative terms to suppress problems, introduce structural controls like ControlNet for precise composition, and use multi-prompt techniques for complex scenes with multiple subjects. Each layer adds precision but also adds complexity. Negative prompting is the most accessible and universally applicable refinement step — it works with every diffusion-based model and requires no additional tools or preprocessing.
Related Techniques
Complementary approaches for image generation control
Refine Your Image Generation
Build and refine your prompts with our interactive tools, or explore other image generation frameworks to expand your creative control.