A negative prompt should identify one unwanted visual concept, not become a universal wall of quality tokens. This protocol separates a compact base from symptom-specific modules and shows when the positive prompt must be fixed first.
Product support comes before prompt recipes
“Negative prompt” is not one universal control. Google documents a separate negativePrompt parameter for specific Imagen 3 models. Amazon Nova Canvas exposes negativeText and advises users to list the unwanted concept without words such as “no” or “without.” Stability AI’s legacy multi-prompting documentation uses negative weights. Other image interfaces may not expose an equivalent field.
Check the current model and interface before treating a syntax example as portable. Do not paste image negative terms into a video prompt by default: some video documentation explicitly recommends positive phrasing instead. This page covers still-image repair only.
Start with a positive baseline
The baseline should state the visible adult identity, full clothing, pose, frame, light, background, and absence of real-person reference. Generate and score it before adding an exclusion. If the baseline requests crossed limbs, hidden hands, a close crop, and several props, the negative field cannot tell the model what correct geometry should replace those conflicts.
Use the image prompt guide to separate identity, composition, pose, light, and style. A negative prompt is a repair layer after those positive decisions, not a substitute for them.
A compact base is a hypothesis, not a default truth
This is a testable starting module, not a universal “best” list. A service may ignore a term, interpret it differently, or already handle the symptom through another control. Provider-added provenance marks or watermarks must not be removed or bypassed; the module refers only to unintended marks generated inside the scene.
Keep style words, hand defects, facial defects, and background defects out of the base. That makes it possible to identify which module changed the result and avoids suppressing an intended illustrated or photographic treatment.
Seven symptom-based modules
| Module | Add only after observing | Negative terms to test | Required positive correction |
|---|---|---|---|
| Hands | Extra, fused, or distorted fingers | dedos extra, fused fingers, malformed hands | Both hands visible in one simple relaxed position |
| Limbs | Duplicated limbs or impossible joints | extremidades adicionales, duplicated limbs, impossible joints | One action, limited overlap, shoulders and hips facing one direction |
| Face | Duplicate face, asymmetrical eyes, or soft landmarks | duplicate face, distorted face, asymmetrical eyes | One face, three-quarter or front view, face in sharp focus |
| Crop | Head, cabello, hands, or feet cut off | cropped head, cropped hair, cropped hands, cropped feet | Name the shot size, visible boundary, headroom, and floor |
| Symbols | Pseudo-letters or signature-like marks inside the scene | texto, letters, signature-like marks | Remove signs, labels, screens, and text-bearing props |
| Background | Random objects, merged edges, or clutter | clutter, random objects, background artifacts | Name only two intentional objects and create subject separation |
| Style | Unwanted plastic, illustration, or oversharpened treatment | plastic texture, oversharpened, unwanted illustration | Describe one intended medium, material response, and light source |
Terms are examples for a controlled comparison, not a promise that a model will understand each phrase. If the crop module does not help, fix the aspect ratio and frame boundary. If hands still fail, use the pose and occlusion protocol before adding a longer list.
Pair every exclusion with a positive replacement
Amazon’s image prompting documentation explains that negation words inside the main prompt can draw attention to the very object a user intended to omit. A dedicated negative field can separate the unwanted concept, but the positive field still needs to describe what should occupy the frame. “No clutter” is weaker than naming a clean studio wall, one chair, and one softbox.
The same principle applies to anatomy. “No extra limbs” does not define a readable pose. State where both hands are, which leg carries weight, what the subject touches, and what is unobstructed. The negative module then acts as a narrow steering signal rather than the only geometry instruction.
The four-condition comparison
| Condition | Positive prompt | Negative field | Question |
|---|---|---|---|
| A — baseline | Unchanged approved prompt | Empty | Which defects occur without a negative module? |
| B — compact base | Same as A | Compact base only | Does focus, crop, or pseudo-text improve without harming intended detail? |
| C — targeted repair | Same as A plus one positive correction | Base plus one symptom module | Does the named defect improve while identity and composition remain stable? |
| D — ablation | Same as C | Remove the symptom module | Was the improvement repeatable or ordinary variation? |
Use the same seed when the service documents deterministic behavior and exposes that control. When no seed exists, keep every visible setting fixed and run multiple paired attempts. Do not claim a success rate from one attractive sample. Record accepted and rejected outputs and state the sample size.
How to score a module
- Target defect: improved, unchanged, or worse.
- Identity: face, age appearance, cabello, and wardrobe remain stable.
- Composition: shot size, viewpoint, crop, and subject placement remain stable.
- Anatomy: the repair does not create a new limb, joint, or contact failure.
- Style and texture: intended medium and material detail remain readable.
- Seguridad: the visible output remains an original fictional adult aged 25+ with no real-person likeness.
Keep a module only when the target improvement is larger than any new defect it introduces. El twelve-defect decision tree helps decide whether to revise, inpaint, regenerate, or reject.
Why giant negative lists fail as evidence
A long list changes many concepts at once. Even when the output looks different, it is impossible to tell which term mattered. The list may also contain contradictory style exclusions, duplicate tokens, aesthetic judgments, or terms that the current model does not support. Prompt length limits and token handling vary by product.
Start with no negative prompt, then a compact base, then one module. Remove terms that do not change the scored defect. This ablation step is the difference between a reusable repair protocol and cargo-cult copying.
When to stop prompting and edit a region
When one small region remains wrong but the rest of the image passes, a supported mask or inpainting workflow may be more efficient than regenerating the whole frame. When the silhouette, identidad, or camera is globally broken, return to the positive baseline instead. Do not use editing tools to retain an unsafe, age-ambiguous, or real-person-like result.
Review the ten-point realism diagnostic before saving or publishing. Realism is not only texture; it includes perspective, contact, light direction, material response, and consistent identity.
Primary sources and evidence limits
The documentation below establishes how specific products describe negative prompts, seeds, prompt structure, and masked editing. It does not prove that every service exposes those controls or responds identically. Check the current model and interface before copying a parameter literally.
- Google Cloud: Omit content using a negative prompt
- Amazon Nova Canvas: Negative prompts
- Amazon Nova Canvas prompting best practices
- Stability AI: Multi-prompting and negative weights
Frequently asked questions
What belongs in a compact base negative prompt?
Only recurrent technical exclusions supported by the model, such as unwanted pseudo-text, an unintended crop, or out-of-focus output. Keep anatomy and style modules separate until those defects appear.
Can a negative prompt fix a contradictory pose?
No. Simplify the positive pose, camera, and contact points first. Negative terms may suppress patterns, but they cannot define coherent geometry by themselves.
Should I copy the same negative prompt into video generation?
No. Controls differ by product. Some video models recommend positive motion phrasing and do not support negative prompts, so follow the current documentation for the exact model.
Reviewed July 2026 by the AI Porn XXX Editorial Team. Recheck product controls and documentation before repeating the protocol.