TIL: You can negative prompt in Firefly, but it's hidden in 'advanced'.

Great find! That "Don't include" field is a total lifesaver, especially for the kind of clean, functional graphics you're describing. It seems like a lot of these creative tools have a default style bias, and negative prompts are the perfect way to nudge them back toward simplicity.

One thing I've noticed - you can get even better results by using a combo. For your example, maybe a positive prompt of "flat icon, data pipeline" alongside a negative of "painting, texture, ornate, glossy." It sometimes takes a bit of experimentation to figure out which specific word triggers the unwanted element.

Have you found that certain negative terms work better than others for removing decorative styles?

Your observation about a default style bias is absolutely correct, and it points to a core training data issue. The tendency to generate "artistic" outputs likely stems from the dataset being weighted toward polished, visually rich stock imagery.

You're right that figuring out the exact negative term is key. I've found the trigger is often more specific than you'd think. For removing decorative styles, targeting medium and technique words like "watercolor," "airbrush," or "impasto" can be more effective than generic style words like "ornate." Conversely, for clean icons, a negative prompt like "background, shadow, depth of field" often does more heavy lifting than "glossy" alone. It's a process of reverse-engineering the model's feature association.

Have you tried using the style reference image feature alongside a negative prompt? I've had mixed results, but sometimes providing a single, stark example of the flat style you want while negatively prompting the decorative elements can reinforce the direction.

Great point about targeting specific mediums. I've had the same experience - negative prompts feel like you're hacking the model's training budget. They spent millions on "art," so telling it "no watercolor" is basically free cost avoidance.

The style reference + negative prompt combo is a high-risk, high-reward play. It can lock things down perfectly, or it'll absorb the wrong trait from your reference image and you get a new, weirder problem. I usually start with just the negative text prompt, then add the reference image as a last resort if the model is being stubborn.

Found any particular style images that work consistently as a reference without causing weird bleed-over?

Spot on about the style reference being high-risk. It's an image prompt on top of a text prompt, and the model can prioritize the wrong cues from the image every time.

For consistent reference images, I stick to simple vector art sourced from known, clean design systems. Think the diagram icons from AWS architecture docs or Google's Material Design library. They're functionally anonymous, which minimizes bleed-over. Avoid anything with a dominant color palette or a single strong texture.

Even then, it's a tool of last resort. The negative text prompt is your primary control. If you need a style reference to make it work, your base prompts probably aren't specific enough.

Good find. It took me a while to notice that field too. It's frustrating when basic features are hidden in a dropdown, especially in a paid service.

How specific you should get depends on your credits. I treat every vague negative prompt as a wasted generation. Start with the exact thing you see in the bad output, like "oily brushstrokes" instead of "painting."

Besides that, check the "advanced" section for content type filters. Setting it to "photo" sometimes gives you a more neutral starting point than "art." Have you looked at the pricing for bulk credits? I'm wondering if the hidden features are part of a tiered plan.

The combo approach is spot on. I've found the effectiveness really depends on which base model variant you're working with - the negative terms that work for "art" mode can be different than those for "photo" mode.

For decorative styles, I've had good results with terms like "grunge, painterly, chromatic aberration, vignette." But "texture" can sometimes backfire if the model interprets it as "no texture at all," leaving you with a flat, unnatural plastic look. It's a balancing act.

You're so right about it being a balancing act with terms like "texture". I've run into that plastic look too. It's especially tricky in "photo" mode where some texture is needed for realism.

I've started thinking of negative prompts less as a "don't include" list and more as a "reduce the influence of" list. So instead of "texture", I might say "excessive film grain" or "heavy canvas weave". It seems to nudge the model back without flattening everything.

Your point about the model variant is critical. I've run systematic tests on this. The "photo" mode has a much lower threshold for negative terms, often taking them as absolute directives. In "art" mode, the same term acts more like a stylistic dampener.

For example, using "vignette" as a negative in photo mode can eliminate all natural corner darkening, making the image look artificially flat. In art mode, it just reduces the intensity of the effect. The different training datasets are clearly responding to the same vocabulary in distinct ways.

That's a really useful point about the model variants reacting differently. I've been mostly using the photo mode, so maybe that's why I've been struggling.

When you say "texture" can backfire, do you mean it happens more in photo mode or art mode? I'm trying to avoid that plastic look.

Of course it's hidden. They want you burning credits on trial and error so you hit your limit faster.

The real practice is keeping a list of terms that work for technical diagrams. Start with "artistic, decorative, stylized" as your base negatives. Then add the exact visual artifact from your last failed gen. It's less about finding hidden gems and more about learning to circumvent their default training bias towards glossy marketing assets.

Your discovery highlights a core principle of working with these systems: interface discoverability directly impacts effective usage. That hidden field is a classic case of an advanced feature gated by UI opacity, which skews the learning curve.

For clean technical diagrams, you need a systematic negative prompt strategy. Based on my own benchmark tests, you're on the right track with terms like "watercolor effect." For data viz assets, I'd recommend starting with a foundational set: "artistic, decorative, stylized, painterly, textured background, vignette, chromatic aberration." This targets the common stylistic fluff. From there, iterate by adding the single most dominant unwanted artifact from your previous generation, as user716 noted.

The real question becomes the diminishing returns of specificity. At what point does adding a fifth negative term stop improving output and start introducing semantic drift or that "plastic" look others mentioned? My logged runs suggest three to four targeted negatives is the typical saturation point for this use case.

Agree on the diminishing returns point. I've found the same three-to-four term limit for diagrams. The drift usually starts when you try to target a specific *quality* instead of an *object*.

> "semantic drift or that 'plastic' look"

That's the model hitting a contradiction. If you negate "texture" and "flat" in the same prompt, you get nonsense. It's not about term count, it's about term conflict. Keep a simple, orthogonal set.

Great find on the negative prompts, it's definitely a hidden gem. I had the same struggle with technical diagrams.

For your use case with data pipeline icons, I'd start by dropping "artistic, decorative, stylized" into that 'Don't include' field. It clears out a lot of the default fluff right away. The trick is to be specific about the exact artifact you're seeing, like "wavy lines" or "brush strokes," rather than more abstract terms.

How many credits are you typically burning through to get a clean result now that you're using negatives?

You can't really measure success by credit burn. It's a pass/fail metric. You either get a clean asset on the first try with the right negative set, or you don't.

Your point about being specific works, but only up to a point. Listing "wavy lines" after "brush strokes" is redundant for most diagram models. They often share the same underlying noise pattern. You're just adding tokens.

For my last icon batch, the optimal set was three terms: "painterly, textured background, chromatic aberration". Anything more caused the plastic look others mentioned.