Negative Prompt Examples: Copy-Ready Blocks That Work

A negative prompt is a second text box that tells the model what to steer away from: blur, extra fingers, stray text, a cartoon look you did not ask for. Most of the time a short negative of five to fifteen specific terms helps more than a long pasted list. A good baseline is low quality, worst quality, blurry, jpeg artifacts, watermark.

The catch is that only some models read a negative at all. Stable Diffusion models use it heavily. Some generators have no negative field at all, and for those you write the avoidance into the main prompt.

Below are copy-ready blocks by subject and style, the syntax Stable Diffusion users need, the terms that make images worse, and negatives for video and music. The last part covers what to do when a negative still misses a bad hand. You can paste any of these into the Deep Dream Generator image generator and compare the same prompt with and without the negative.

Which models actually read a negative prompt

A negative prompt only works where the model uses classifier-free guidance (CFG) at generation time. In that setup the model normally compares your prompt against an empty prompt and pushes the image toward yours. A negative prompt replaces that empty prompt with your list of things to avoid, so the image is pushed away from it. Models built without that comparison step have nothing to plug a negative into.

Stable Diffusion 1.5, SDXL and their fine-tunes rely on the negative, which is where weights come in. For newer model families, the lists that circulate about which ones ignore the field are community folklore: they disagree with each other and change with versions and interfaces. What is certain is that some generators have no negative field at all.

On Deep Dream Generator, pick a model and check whether a negative prompt box appears in the settings. If it does not, write the avoidance into the main prompt.

For models without a field, a few rules make the avoidance stick:

• Describe the alternative instead of naming the unwanted thing. a clean white wall works better than no graffiti. Naming graffiti in the prompt can make it more likely to appear.
• Use "without" or "avoid" sparingly, and only for one or two things. a product shot on seamless grey paper, without any text or logos is fine. Ten "no" clauses are not.
• Put the instruction near the subject it concerns. a woman holding a cup, both hands relaxed with five fingers each sits where the model reads hands.

Copy-ready negative blocks

Each block below is short on purpose, so the baseline plus any one block stays inside five to fifteen terms. Start with the baseline, add one block that fits your subject, and stop. If the result still shows a problem, add the one term that names it.

Baseline for any SD-style image

low quality, worst quality, blurry, jpeg artifacts, watermark

This overlaps with a widely shared universal negative, minus the anatomy terms. Those go in the next block, where you actually need them.

Portraits and hands

bad anatomy, bad hands, missing fingers, extra fingers, fused fingers, extra limbs, asymmetrical eyes, deformed face

Add long neck, disproportionate body only for full-body shots. In a head-and-shoulders crop those terms do nothing useful and dilute the rest.

Photoreal

cartoon, illustration, painting, 3d render, cgi, plastic skin, airbrushed, oversaturated

Illustration and painting

photograph, photorealistic, lens flare, film grain, depth of field blur

Anime and manga

Anime models drift toward a few specific faults: muddy line art, realistic skin texture creeping into a flat style, and doubled characters. This block targets those.

realistic, 3d, western cartoon, messy lineart, extra characters, duplicate, bad hands, blurry eyes

For cel-shaded results also add gradient shading, soft shading. For a manga page in black and white, add color, colored and leave out everything about skin.

Text, logos and frames

text, letters, words, caption, logo, signature, username, border, frame

Do not use this block when the image is meant to contain lettering, such as a poster or sign. It will fight your own prompt.

Landscapes and architecture

people, crowd, cars, power lines, tilted horizon, distorted perspective, hazy, oversaturated

Stable Diffusion syntax: weights and negative embeddings

In Automatic1111-style and ComfyUI prompt syntax, you can turn a negative term up or down. Each pair of parentheses raises a term's weight by about 1.1 times, and an explicit number sets it directly. Common weights sit between 0.5 and 1.6, and the same syntax works in the negative box:

(bad hands:1.4), (extra fingers:1.3), worst quality, blurry, watermark

Some practical limits:

• Weight one or two terms, not all of them. If everything is at 1.4, nothing stands out, and the image tends to look stiff and over-processed.
• Stay inside the common 0.5 to 1.6 range. If a term needs more than that to work, the problem is usually elsewhere in the prompt.
• `((bad hands))` and `(bad hands:1.21)` are roughly the same thing. Pick one style so your saved prompts stay readable.

Stable Diffusion users also often drop the name of a community negative embedding into the box: a small trained file meant to stand in for a list of faults. Treat that as community practice rather than a rule. Whether a given embedding suits your checkpoint is not something published lists settle, and embeddings can carry a style of their own, so compare results with and without one before you keep it in every prompt.

Negatives that make images worse

If your images got worse after pasting a 60-term list, the list is usually the cause. The negative is a direction the model pushes away from. The more crowded and vague that direction is, the less precise the push.

The usual offenders:

• Vague judgements. ugly, bad, boring, disgusting. The model has no single picture of "ugly", so the term pushes against a broad, unpredictable mix. Name the actual fault: asymmetrical eyes, muddy colours.
• Contradictions with your prompt. dark in the negative while the prompt asks for a night street. blurry in the negative while you want a shallow depth-of-field portrait with a soft background. The model splits the difference and you get neither.
• Negating your own subject. cat in the negative of a picture of a dog sitting next to a cat will distort the scene, not just remove the cat.
• Copied terms for other models. Anime-specific tags in a photoreal negative, or a negative embedding name typed into a model that has never heard of it.
• Length. Past roughly fifteen terms, each extra word gets less attention, and the important ones lose force.

Guidance scale (CFG) controls how hard the model pushes toward your prompt and away from your negative. A common value is 7.5. Raising it makes negatives bite harder, but it also adds contrast, saturation and a burnt look. If a negative is not working at 7, cut the list before you raise the scale. A shorter list at the same guidance is usually a cleaner fix than a long list at 11.

Run your own before and after test

The only way to know whether a negative helps your prompt on your model is to compare it against the same image without it. Nobody has published a reliable "this list improves images by X" figure. Results depend too much on the model and subject.

A fair test takes four or five generations:

1. Fix everything except the negative. Same model, same prompt, same aspect ratio and settings. If the model exposes a seed, set it to a fixed number.
2. Generate with an empty negative. This is your control.
3. Add the baseline block only. Generate again on the same seed.
4. Add one subject block, for example hands. Generate again.
5. Compare side by side, at full size, looking only at what the block targets. Ask whether the hands are better and whether anything else got worse.

If a block changes nothing, remove it, because it is costing you precision for no gain. On models without a fixed seed, generate three of each version and compare the sets instead of single images. Each generation costs energy, so a five-image test is a small, one-time expense per model. It saves you from pasting a useless list into hundreds of later prompts.

Negatives for video

Video models that take a negative use it to suppress motion faults rather than still-image faults. A starting block:

flickering, jittery motion, frame inconsistency, morphing, warping face, temporal incoherence, sudden cuts, extra limbs, text overlay, watermark

That set comes from the faults commonly listed for video generation: flickering, jitter, morphing and inconsistent frames. Many current video models have no negative field at all. For those, describe stable motion directly: slow steady dolly-in, the character's face stays consistent, smooth continuous motion.

For image-to-video, most of the work happens before you prompt. A clean start frame with correct hands and no stray text gives the model less to break. If one model keeps morphing, try another of the models in the video generator on a clean start frame. That is often more useful than piling negatives onto the one that keeps failing.

Negatives for music

Music generation is where negatives are most underused. The Deep Dream Generator AI music generator lists negative prompts under its creative controls, next to style directives. That matters most when you need a background track and the model keeps adding a singer.

Blocks to copy:

Background bed for YouTube or podcasts (no voice at all):

vocals, singing, lyrics, spoken word, choir, humming, vocal chops

Calm study or ambient track:

drums, heavy percussion, distorted guitar, sudden drops, build-ups, loud crescendo

Game loop or menu music:

fade out, long intro, vocals, key change, abrupt ending

Clean acoustic or lo-fi:

electronic synths, autotune, EDM drop, harsh high frequencies, clipping

The same rules as for images apply. Name instruments and structures, not vague judgements like "bad mix", and keep the list short. Also put the positive version in the main prompt: instrumental, solo piano and soft strings, steady tempo. A negative that says vocals works best alongside a positive that says instrumental. The output is 48 kHz stereo as of November 2026, so artifacts like clipping or harshness are worth listening for on headphones before you publish.

When the negative prompt is not enough

Sometimes everything about an image is right except one hand, or a smudge in the corner that looks like a half-formed watermark. Regenerating to fix it risks losing the composition you liked. Repairing the image is usually the better move.

• Inpainting. Mask only the bad hand or the smudge and regenerate that area with a short prompt such as relaxed hand, five fingers, holding the cup. The older AI Editor in the AI tools offers inpainting.
• Describe the edit. The AI Image Editor changes an image from a plain-language description: remove the text in the lower right corner or fix the left hand so it has five fingers. This is often the fastest fix for a single flaw.
• Upscale with artifact repair. The AI upscaler cleans up artifacts while enlarging, in three modes: Precise, Creative and Realismo. Precise mode goes up to 4K and stays close to the original. Creative mode goes up to 120MP and is guided by a prompt, so it can redraw fine detail. As of November 2026 there is a small free daily allowance, and credit packs start at about 12 upscales for $3.99.
• Rework the prompt itself. If the same fault appears in every attempt, the problem is usually the positive prompt. The AI Art Assistant in the AI tools lets you paste your prompt and the failing result and get suggestions. Image to Text can turn a reference you like into a prompt to start from.

A workable order is: fix hands and text by inpainting or editing first, then upscale once at the end. Upscaling first only enlarges the fault you then have to repair.

Frequently Asked Questions

What should I write in a negative prompt?

Write the specific faults you have actually seen in your results, using concrete words: extra fingers, watermark, oversaturated. Start with a short quality baseline and one block that matches your subject. Add terms only when a test shows you need them.

What is the best negative prompt for Stable Diffusion?

There is no single best list, because it depends on the checkpoint and subject. A strong starting point is the baseline low quality, worst quality, blurry, jpeg artifacts, watermark, plus (bad hands:1.3) if hands keep failing. Then add one subject block and test it on a fixed seed.

Do negative prompts work with Flux?

Do not assume they do. A negative only has an effect where the model uses the guidance step it plugs into, and reports about Flux variants vary by version and interface. If your interface shows no negative box, or a before-and-after test shows no change, describe what you want instead, for example clean background rather than no clutter.

Why did my images get worse after adding a long negative list?

Long lists dilute each term, and vague or contradictory words push the image in unpredictable directions. Cut the list to five to fifteen specific terms. Remove anything that conflicts with your main prompt, and bring any weights back inside the common 0.5 to 1.6 range.

What should be avoided in negative prompts?

Avoid vague words like ugly or bad, and avoid terms that contradict your own prompt. Leave out tags made for a different model or style, and don't name objects that belong in your scene. Also avoid weighting every term, which flattens the whole image.

How many terms should a negative prompt have?

Five to fifteen terms covers most images. Below five you may miss a recurring fault. Above fifteen the extra words rarely help and can weaken the ones that matter.

Can I use negative prompts for AI music?

Yes, where the music model supports them. The Deep Dream Generator music generator does, and it is most useful for keeping out vocals, drums or particular instruments. Pair the negative with a positive instruction such as instrumental for the most reliable result.

Final Thoughts

Negative prompts are a precise tool, not a magic list. Check that your model reads them, keep each block short and specific, and test with and without on a fixed seed so you know what each term is doing. Where a model has no negative field, write the alternative into the main prompt instead.

When a negative still misses a hand or a smudge, repair it with inpainting, an editing prompt or an artifact-fixing upscale. To see what each term is really doing, browse the available models, then run the same prompt, with and without your negative, across several of them in the image generator.