If you have ever tried to generate a picture of your own cat rather than just "a cat," you already know the specific frustration this article is about. The lighting looks fine. The color is close. And then you look at the paws, or the whiskers, or the eyes, and something is quietly wrong in a way that would never happen in an actual photo of your actual cat. Pet portraits are one of the most common things people ask AI image tools to make, and also one of the outputs people are pickiest about, because unlike a stock photo of a generic tabby, you know exactly what your cat looks like. A stranger would not notice a sixth toe. You will.

This is not a list of ten prompt ideas to copy and paste. It is an explanation of why cats specifically trip up image models, what to actually say in a prompt to get a result that holds up to scrutiny, and how to keep one particular cat looking like itself across more than one image, which is a different and harder problem than getting a single nice picture.

Why cats are genuinely one of the harder subjects

The reason AI image generators mangle hands is well documented at this point: fingers are small relative to the frame, their exact position depends entirely on what the hand is doing, and the captions attached to training photos almost never describe hand anatomy in detail. Nobody labels a photo "woman holding coffee cup, four fingers wrapped around handle, thumb extended." The caption just says "woman holding coffee cup," so the model never learns that finger count and position actually matter, only that something hand shaped tends to appear near a cup.

Cats run into the same wall, for the same underlying reason. A paw resting on a windowsill occupies a small part of the frame. The exact number of visible toes, whether the paw is flat or curled, how the fur parts around each toe, none of that gets described in the caption that trained the model on that photo. The caption says "cat on windowsill," not "four toes visible, slightly spread, fur parting between the second and third toe." Whiskers are worse still: thin, high contrast lines against fur or background, present in almost every cat photo but essentially never mentioned in a caption, so the model has seen millions of cats with whiskers and never once been told anything specific about how many there are or where they attach. It has learned that whisker like lines belong somewhere near a cat's face, not a real structural understanding of a whisker pad.

The result is the same pattern you see with hands: the model defaults to a statistically safe blur of "something whisker like near the muzzle" and "a paw shaped form with some number of toe like divisions," and most of the time that blur looks convincing at a glance and falls apart under a close look. Eyes have a version of this too, though it is subtler. Cats have vertical slit pupils in bright light that widen toward round in low light, and a reflective layer behind the retina, the tapetum lucidum, that produces the glowing eyeshine you see in flash photos at night. A model trained on a mix of daylight photos, flash photos, and stylized art tends to average these together, which is why AI cat eyes often look slightly uncanny even when nothing is technically wrong with them: a round pupil that reads as bright daylight paired with a glow that only makes sense in a dark room, sitting in the same eye.

Fur adds one more layer that hands do not have to deal with: direction. Real fur grows in a consistent pattern across the body, with the grain changing direction at specific points, the whorl behind the ears, the ruff around the neck, the longer britches on the back legs. A photographer or painter tracks that grain instinctively. A diffusion model generates the whole image close to simultaneously rather than building it up the way a person draws fur strand by strand, so texture that should flow in one continuous direction can quietly reverse itself across a seam, especially in longer haired cats where the effect is more visible.

None of this means AI cannot produce a genuinely good cat picture. It means the parts that go wrong are small, specific, and predictable once you know what to look for, and a lot of them can be headed off with a more specific prompt before you even get to fixing anything after the fact.

A prompt that actually works, versus one that does not

Here is a prompt that produces roughly what most people start with:

"A cute cat sitting on a windowsill, sunlight, realistic style."

That is not a bad sentence, but it gives the model almost nothing to anchor on beyond "cat" and "sunlight." Every detail the model fills in, the breed, the coat pattern, the pose, the exact quality of the light, the expression, is a guess, and every guess is a chance for the paws, whiskers, or eyes to land in the statistically safe but slightly wrong territory described above.

Here is the same idea written specifically:

"A photorealistic portrait of a silver classic tabby cat with dense, plush fur, sitting in an upright loaf position on a wooden windowsill. Soft window light comes from the left, creating a gentle rim light along the edge of its fur and a warm highlight on its whiskers. Its pupils are narrow vertical slits reacting to the bright light. Ears relaxed and slightly forward, one front paw tucked under its chest, the other paw resting flat with its toes visibly spread. Shot on an 85mm lens with a shallow depth of field, background softly blurred, natural color grading."

Notice what changed. "Cat" became a named coat pattern, classic tabby rather than mackerel or spotted, which gives the model an actual visual category to match instead of an average of every cat photo it has seen. "Sitting" became "loaf position," a specific, recognizable pose rather than an open ended word that could mean almost any posture. The light direction is stated, which lets the pupil shape and the rim light both make sense together instead of contradicting each other. And the paw is described in a way that pushes the model toward rendering visible, separated toes rather than a rounded blob, because the prompt is explicitly asking for that detail rather than leaving it to chance.

This does not guarantee a perfect result every time. Nothing does. But it moves the odds meaningfully, because you have replaced a dozen open guesses with a dozen specific instructions, and specific instructions are exactly what these models respond to best.

Describing fur in a way that actually changes the output

"Fluffy" and "detailed fur" are the two words that show up in almost every generic cat prompt, and they do almost nothing, because the model has no idea what specific texture "detailed" is supposed to mean. What actually helps is naming the coat pattern and describing texture the way a groomer or a vet tech would.

Name the actual pattern instead of just a color. Tabby comes in a few recognizable layouts: mackerel (narrow stripes), classic or blotched (swirled, marbled patches), spotted, and ticked (each hair banded, no clear stripe). There is also tuxedo, tortoiseshell, calico, colorpoint (a Siamese style pattern with a darker face, ears, paws, and tail against a lighter body), solid, and bicolor. Any of these is a stronger anchor for the model than "orange cat" or "black and white cat," because it points to an actual, recognizable visual category rather than leaving color placement to chance.

Describe density and length together, not separately. "Short, dense fur that lies flat, with a slightly longer ruff around the neck" reads very differently to the model than "long fur," even though both are technically about length. Mentioning where the fur changes, a fluffier ruff at the neck, longer britches on the back legs, a slight whorl behind the ears, gives the model actual structure to follow rather than one uniform texture painted over the whole body.

Mention how light interacts with the fur, not just that fur exists. "Individual strands catching the light along the back" or "a soft halo of backlit fur along the silhouette" tells the model you want fine strand level detail visible, which pushes it away from the smoothed over, slightly plastic look that under specified fur prompts tend to produce.

Pose, lighting, and the details that carry personality

Cat owners have a whole vocabulary for posture that most prompts never use. "Sitting" covers everything from alert and upright to half asleep, and the model has to guess which you mean. Loaf position (paws tucked under the body, a compact rounded shape) reads completely differently from a stretch, mid roll, or the loose sprawl of a cat fully asleep on its side. Using the actual term gets you closer to the specific image in your head than a vague verb does, the same way naming a coat pattern beats naming a color.

Ears and whiskers do a lot of the emotional work in a cat photo, and they are worth describing directly rather than leaving to the "cute" or "happy" adjectives that do not translate into anything visual. Ears forward and upright reads as alert or curious. Ears rotated sideways, sometimes called airplane ears, reads as uncertain or annoyed. Ears flattened back against the head reads as scared or irritated. Whiskers pushed forward reads as interested or focused on something; whiskers pinned back against the face reads as tense. None of this is guesswork, it is standard cat body language, and naming it in a prompt gives the model an actual expression to aim for instead of a generic "happy cat face."

Lighting deserves the same treatment, and it needs to be consistent with everything else in the prompt. Window light from one side produces a directional highlight and a visible shadow side, good for showing texture and fur detail. Overcast or diffuse light flattens shadows and renders color more accurately, which suits a coat pattern you want rendered faithfully. Golden hour side light adds warmth and a strong rim light along the edge of the fur. The detail that trips people up is pupil shape: if you describe bright daylight, narrow vertical pupils make sense; if you describe a dim room or evening light, wider, more oval pupils do; if you separately ask for glowing eyeshine, understand that is a flash photography effect, and asking for it alongside soft natural window light will read as contradictory to the model, which is part of why AI cat eyes sometimes look faintly wrong even in an otherwise well lit image.

Keeping one specific cat consistent across more than one image

Getting one good picture is one problem. Getting five pictures of a cat that all look like the same cat is a different and genuinely harder one, and it comes up constantly, someone wants their actual cat rendered in a few different poses or settings, or reimagined in a different style, and finds that the coat markings, eye color, or even the basic proportions drift from one generation to the next.

The most reliable fix is a real reference photo of the actual cat, when the tool you are using supports image based generation or editing rather than text only prompting. A written description, no matter how precise, is still an interpretation. A reference photo of your actual cat's actual markings removes that layer of interpretation entirely and gives the model something concrete to match instead of reconstruct from adjectives.

The second fix, and the one that matters even without a reference photo, is reusing the exact same wording for anything that describes the cat's identity rather than the scene. If your first prompt says "a silver classic tabby with a white chest patch and one white front paw," that exact phrase should appear in every later prompt for the same cat, word for word. Changing "silver classic tabby" to "grey tabby" in a later prompt might read as the same thing to you, but to the model it is a different set of tokens, which is often enough to shift the markings slightly. Only change the parts of the prompt that describe the scene, the pose, the lighting, the setting, and leave the identity description untouched.

The third habit worth building is treating any generation that actually nailed the cat's look as an anchor, and building forward from that exact prompt rather than starting over each time. If a result got the coat pattern, eye color, and proportions right, that prompt is now your template. Copy it in full and change only the one line describing pose or setting for the next image, instead of rewriting the whole description from memory. This is the same core technique that keeps an illustrated character consistent across a set of anime style images, reference material plus exact reused wording plus treating a working result as the template for what comes after, and the mechanics do not change just because the subject is a cat instead of a drawn character. The guide on keeping an anime character consistent walks through this in more depth if you want the fuller version of the workflow.

What still commonly goes wrong

Even with a specific prompt and a reference image, some failures show up often enough to be worth recognizing on sight rather than being surprised by them.

Paws are still the most common casualty, especially in any pose that is not a simple flat sit. A curled sleeping paw, a paw mid step, or two front paws close together in a loaf position are where you are most likely to see a fused shape with an unclear or wrong number of toes. The fix is rarely a better paw specific adjective, it is usually simplifying the pose, a flat, clearly separated paw is much more likely to render correctly than a tucked or overlapping one.

Whiskers show up as either floating disconnected from the muzzle, oddly symmetric in a way real whisker pads never quite are, or occasionally doubled, with a faint extra set appearing near the eyes or cheeks where they do not belong. This tends to get worse the more stylized or painterly the request, since a stricter photorealistic prompt gives the model less room to invent.

Eyes are where the mismatch described earlier shows up most, a pupil shape that does not match the described lighting, or two eyes that are subtly different shapes or colors from each other. This is worth actually checking at full size before you use an image, because it reads as more unsettling than almost any other error once you notice it.

Fur direction seams appear mostly on longer haired cats or in dramatic side lighting, a visible line where the texture direction changes abruptly rather than flowing naturally, usually around the shoulder or the base of the tail.

And on more complex generations, occasionally something extra appears that should not be there at all, a second partial tail, a strange shape hidden in a fur pattern, or a faint second face if the prompt asked for more than one cat in frame. None of these mean the prompt was wrong, they are simply the model's ordinary failure mode when it is working with underdescribed detail, and knowing to check for them before you use the image saves a lot of after the fact disappointment.

If a generation fails in a way that goes beyond one of these specific details, an outright rejection, a garbled result, or something that does not resemble the prompt at all, that is a different and more general problem, and the troubleshooting guide covers the common causes and fastest fixes for that category of issue.

A quick checklist before you generate

Name the actual coat pattern (classic tabby, mackerel tabby, tortoiseshell, colorpoint, and so on) instead of just a color.
Describe fur density, length, and where it changes, rather than one blanket word like "fluffy."
Use real posture terms, loaf position, mid stretch, curled asleep, instead of a vague verb like "sitting."
Name ear and whisker position to carry expression, rather than relying on "cute" or "happy."
Make sure lighting direction and pupil shape are consistent with each other, and do not combine flash style eyeshine with soft natural daylight in the same prompt.
For a specific real cat, use a reference photo when the tool supports it, reuse identity wording exactly across prompts, and keep whichever result nailed the look as your template going forward.
Check paws, whiskers, and eyes at full size before using an image, they are the most likely place for a small error to hide.

Can I generate pictures of my actual cat, not just a generic one?

Yes, though it depends on giving the model something concrete to work from. A detailed text description gets you closer than a vague one, but a real reference photo of your cat, used with a tool that accepts image input rather than text only, removes most of the guesswork around the exact markings, eye color, and proportions. Enhance AI's image editor supports prompted edits on an existing image, which is a useful starting point if you already have a decent photo of your cat and want to place it in a new setting or style rather than generating a lookalike from scratch.

Why do the whiskers come out wrong even when everything else looks right?

Whiskers are thin, high contrast, and present in nearly every cat photo the model ever trained on, but essentially never described in the captions attached to those photos. The model has learned that whisker like marks belong somewhere near a cat's face without ever being taught how many there are or exactly where they attach, so it fills in a plausible looking approximation rather than an anatomically consistent one. This tends to improve with a more photorealistic, less stylized prompt, since stylization gives the model more license to invent.

Why does my cat's eye color or coat markings change between generations?

Every new generation is a fresh interpretation of your prompt, so any wording you change, even a small substitution like "grey" for "silver," can shift how the model renders markings and color. The fix is to keep the exact phrase describing your cat's identity identical across every prompt, and only vary the part describing pose, setting, or lighting. A reference image tightens this further, since it gives the model something to match rather than reinterpret.

What is the fastest way to fix one bad paw or whisker without regenerating the whole image?

Regenerating from scratch risks losing everything that was already right along with the one detail that was wrong. The Change Region tool in Enhance AI's image editor is built for exactly this, it masks just the area you select and regenerates only that section, so you can fix a fused paw or a stray whisker while leaving the rest of the portrait untouched. If the issue is something you want removed entirely rather than corrected, like an unwanted object in the background, the Magic Eraser tool in the same editor handles that instead.

Which Enhance AI model should I use for a realistic pet portrait versus a stylized one?

For a photorealistic result, models built around photographic realism, like the Seedream family or Nano Banana 2, tend to hold up well on the fine texture and lighting detail a real photo needs. If you want something more painterly or illustrated, the Flux model family is a strong fit. GPT Image 2 is worth trying when your prompt is long and has a lot of specific, separate instructions to follow, since it tends to track detailed, multi part descriptions closely. If you want a clean, flat, sticker or icon style cat rather than anything photographic, Recraft V4 outputs true vector art, which is a different look entirely and works well for that specific case. Enhance AI runs 250 plus models in total, so it is worth trying more than one on the same prompt if the first result is close but not quite right.

Is there a real difference between prompting for a photo style cat versus an illustrated one?

Yes, and the biggest difference is how much you should lean on lighting and lens language versus purely descriptive language. A photorealistic prompt benefits from camera and lighting terms, an 85mm lens, shallow depth of field, window light from the left, because those terms tell the model to draw on its photographic training rather than its illustration training. A stylized or painterly request benefits more from naming the actual style or medium, watercolor, colored pencil, flat vector illustration, since that steers the model toward a completely different visual vocabulary. Mixing the two, asking for camera lens language and a flat illustration style in the same prompt, tends to produce a muddled result that is not fully committed to either.

Cats are a genuinely hard subject for AI to get exactly right, for reasons that are specific and understandable rather than random. Naming the actual coat pattern, describing fur and posture the way someone who owns a cat actually talks about them, and keeping lighting and pupil logic consistent will get you a noticeably better first result. And when a generation gets most of the way there but leaves one paw or whisker wrong, fixing just that region rather than starting over is usually the faster and better path, whether that is through Enhance AI's image editor or simply a more carefully anchored next prompt.