How to Use Negative Prompts in Stable Diffusion (2025)

You’ve copied that massive list of negative prompts everyone swears by: “bad anatomy, poorly drawn hands, extra fingers, missing limbs, ugly, deformed…” – 50 words deep. You hit generate. The result? Worse than before. Hands still broken. Image quality actually degraded.

Here’s what nobody tells you: overloading negative prompts often makes things worse, not better.

Community testing on HuggingFace revealed something uncomfortable. A user ran 15 iterations with and without extensive hand-fix negatives. Result? “Basically zero effect on the quality of hand generation.” Different seeds, same broken fingers. The popular negative prompt lists we all copy? They might just be changing your luck, not your quality.

Why Negative Prompts Fail When You Need Them Most

Negative prompts work differently than most tutorials claim. They’re a hack – literally. The AUTOMATIC1111 technical documentation explains it: during CFG sampling, they replace unconditioned noise with noise predicted by your negative text. That wasn’t part of the original training.

You’re steering a system by feeding it instructions it wasn’t designed to receive. Which works… sometimes. When the model expects it.

SD 1.5 learned on 512-800 pixel images with specific anatomical biases. Negatives help there. SDXL learned different patterns and doesn’t rely on the same unconditioned approach. Adding 200 negative terms? You’re confusing it.

The SD 1.5 vs SDXL Negative Prompt Split

Not all Stable Diffusion models handle negatives the same way. This matters more than prompt syntax.

Civitai’s SDXL embedding creators found that “SDXL no longer needs a long list of negative prompts, some models even perform worse when you use them.” That 200-word negative prompt list you’re using? Optimized for SD 1.5. On SDXL? Degrading your output.

What Actually Works: The Three-Term Strategy

Forget the lists. Start here:

Negative prompt: blurry, low quality, watermark

Three terms. Run a batch of 4-6 images. Compare quality to your previous 50-term monster.

You’ll likely see improvement. Why? Concrete visual patterns – “blurry” and “watermark” map to actual visual artifacts SD learned during training. No confusion – fewer terms mean clearer guidance. Seed variation matters more – you’re letting the model breathe.

Specific issues persist? Add one or two targeted terms:

Negative prompt: blurry, low quality, watermark, extra fingers, fused hands

Still having hand problems after that? The issue isn’t your negative prompt. It’s the model’s training data. No amount of negatives will fix what the model never learned correctly.

Pro tip: Test the same seed with and without each negative term individually. If removing a term doesn’t change the output, that term is dead weight. Delete it.

The Abstract Term Trap

Community analysis on Stable Diffusion Tutorials revealed why certain negatives feel useless: the model has no learned concept for abstract quality judgments.

“Ugly” – SD doesn’t know what’s ugly. It wasn’t trained on labeled “ugly” images. “Bad anatomy” – too vague. Bad how? Extra limbs? Wrong proportions? Pick one. “Poorly drawn” – compared to what reference? The model can’t interpret this.

These terms survive in prompt lists because they sound helpful. Testing shows they “often do little to improve features and can result in inconsistent outcomes.” Replace abstract terms with specific visual descriptors. Instead of “ugly face,” use “asymmetrical eyes, distorted mouth.” Instead of “bad hands,” use “extra fingers, missing thumb.”

Emphasis Syntax: Platform Roulette

Every tutorial shows you this:

(keyword:1.3)

That tells Stable Diffusion to weight “keyword” 30% higher. It’s in AUTOMATIC1111. It’s in Stable Diffusion Art’s examples. Looks official.

The catch? Only works in AUTOMATIC1111 and a handful of interfaces. ComfyUI? Different syntax. NightCafe? Doesn’t support it. Many web UIs ignore it entirely. Stable Diffusion Art’s own warning: “keyword emphasis in AUTOMATIC1111 is not universally supported by all services.”

Before you spend 20 minutes tweaking (hands:1.5) vs (hands:1.8), check if your platform actually reads that syntax.

When to Skip Negatives Entirely

Sometimes the best negative prompt is none at all. Skip them if: you’re using SDXL Turbo or LCM models (CFG 1-2 range makes them ineffective) – your positive prompt is already highly specific and detailed (negatives add confusion) – you’re after abstract or experimental results (negatives restrict creative interpretation) – the model was fine-tuned for a specific style (negatives can break the fine-tune).

Testing this is simple: generate 4 images with your full negative prompt, then 4 without any negatives. If quality doesn’t drop, you don’t need them.

CFG Scale Amplifies Everything

High CFG + long negatives = trouble. CFG controls how closely sampling follows your prompt. At CFG 15+, the model already commits hard to your instructions. Adding 50 negative terms on top? You’re compounding rigidity. Result: oversaturated colors, artifacts, loss of detail.

Running CFG 12+? Cut your negative prompt to 3-5 terms maximum. At CFG 7-8, you have more room for negatives – but still don’t go wild.

Testing Methodology That Actually Works

Stop guessing. Test properly:

Lock your seed – use the same seed for all comparisons, or you’re just comparing luck. Change one variable – test one negative term at a time, not 10 changes at once. Generate 4 minimum – single images lie. Batch of 4 shows patterns. Document what fails – if “bad hands” did nothing across 6 seeds, stop using it.

Test 1: Positive prompt only, seed 12345, CFG 7
Test 2: Same + "blurry, low quality"
Test 3: Same + "blurry, low quality, extra fingers"
Test 4: Same + full 50-term negative list

Compare Test 2 vs Test 4. If Test 4 isn’t meaningfully better, you’ve proven that your long list is useless for this prompt.

The goal isn’t to find the “perfect” negative prompt. It’s to find the minimum effective set for your specific model, prompt, and style.

The Honest Limitations

Negative prompts can’t fix everything. They can’t fix hands if the model never learned correct hand anatomy. They can’t create detail that isn’t in the training data. Can’t override fundamental model biases.

What they do: reduce obvious artifacts, filter out watermarks, nudge away from low-quality outputs. That’s it. Added 15 hand-specific negatives and fingers are still fused? The issue is the model.

At that point: use a different model, try inpainting to fix the region manually, or generate 20 images and cherry-pick the one that got hands right by accident.

Your Next Prompt

Delete your current negative prompt. Start fresh with these three terms: blurry, low quality, watermark. Generate a batch. Add one targeted term only if you see a specific recurring problem. Test with and without that term to verify it actually helps.

Document what works for your model and style. That custom 5-term list you build through testing? It’ll outperform any generic 200-term copypasta.

FAQ

Do negative prompts work the same way across all Stable Diffusion models?

No. Test your specific model.

Why do popular negative prompt lists include so many abstract terms like “ugly” or “bad anatomy”?

Because they sound helpful, not because they work. SD wasn’t trained with labeled “ugly” images, so it has no learned concept of what that means visually. Remember that HuggingFace testing from earlier? Abstract terms had zero measurable effect. Stick to concrete visual descriptors instead: “extra fingers,” “blurry,” “watermark.”

Can I fix hands with negative prompts?

Sometimes, but HuggingFace community testing found that extensive hand-fix negatives had “basically zero effect” across 15 iterations. If “extra fingers, fused hands” doesn’t work after 4-6 generations, the problem is the model’s training data. One debugging session with hand fixes: burned through 20 seeds, got maybe 3 usable results. At that point, generate more images and cherry-pick, use inpainting, or switch models.