Z-Image vs Nano Banana 2 vs FLUX.2: which makes the most realistic AI photos?

What “realistic” actually means

People tend to use realism as a synonym for skin detail. That is why so many “photo-real” prompts produce a face covered in pores that still looks like a render. A believable photo is a stack of agreements: anatomy, lens perspective, light direction, material response, depth of field, motion, and the small accidents a camera would plausibly record.

A model can be excellent at attractive single-person portraits and weak at two people touching. It can nail a fresh text-to-image prompt and then quietly redesign the subject during an edit. It can win a blind-preference benchmark because its defaults are polished, while being a pain to direct across a consistent series.

For this comparison, “realistic” means five separate things:

• First-pass fidelity: does the image resemble a plausible camera capture?
• Instruction fidelity: did it respect the actual scene, not just the mood?
• Edit stability: can it change one element without replacing the person?
• Series consistency: can you repeat the identity and visual language?
• Workflow control: can you inspect, reproduce, and modify the process?

That last point is why an API model and a local checkpoint should not be ranked as if the download button were a minor detail. It changes the job.

The model families compared

Names matter here. “FLUX.2” is not one model. Neither is “Nano Banana.” Google currently uses Nano Banana branding across multiple Gemini image models, while Black Forest Labs ships different FLUX.2 sizes and licenses. Record the exact model ID in every comparison. Otherwise your result will be impossible to reproduce a month later.

When Z-Image is the right pick

Z-Image-Turbo is the easy recommendation for someone who wants local photographic output without turning a workstation into a datacenter. The official project describes a six-billion-parameter family, and the distilled Turbo model is designed for an eight-step workflow with zero guidance. The project also positions it for roughly 16GB-class consumer hardware, although quantization, resolution, VAE behavior, and the rest of your node graph still affect real memory use.

Its useful advantage is not “more pores.” It tends to get to a coherent photographic baseline quickly. That makes it a good daily driver for portraits, lifestyle shots, and prompt exploration where you want to keep seeds, swap LoRAs, batch variations, and retain the workflow inside the output metadata.

The Turbo recipe is also where people sabotage it. Do not paste in the CFG and step count from an SDXL workflow. A distilled model expects its own sampling recipe. Starting with eight steps and guidance at zero is not an aesthetic opinion; it is the model’s intended operating point. Change one variable after you have a baseline.

Choose Z-Image Base instead when training or deep customization is the real objective. The non-distilled base costs more inference time but gives fine-tuning a more suitable target. Turbo is the output-first choice; Base is the workshop.

When Nano Banana 2 is the right pick

Nano Banana 2 is the practical choice when the job sounds like a conversation: “keep this person and this room, replace the jacket, move the lamp, make the light feel like late afternoon, and do not touch the framing.” Google’s Gemini image models combine generation and editing behind an API, so they can reason over references and revision instructions without a hand-built local inpainting graph.

The current lineup needs translating. Google documents Nano Banana 2 as the Gemini 3.1 Flash Image model and Nano Banana Pro as Gemini 3 Pro Image; there are also faster or older Flash-family choices. Model IDs are more durable than nicknames, so store both in your production logs.

This is not a downloadable checkpoint hiding behind a cute name. ComfyUI can call the service through API nodes, but the generation still runs on Google’s infrastructure. You inherit its request pricing, quotas, safety system, product changes, and data-handling terms. That may be completely reasonable for marketing edits and client iteration. It may be unacceptable for sensitive source material or an offline pipeline.

I would choose Nano Banana 2 before Nano Banana Pro for everyday revision work, then escalate difficult compositions to Pro. Paying the premium on every draft is rarely smart. Use the expensive model when a cheaper pass has shown you what is actually failing.

When FLUX.2 is the right pick

FLUX.2 is the broadest family in this comparison, which is both its strength and the source of bad recommendations. FLUX.2 Klein 4B is not a miniature FLUX.2 Max with every trade-off preserved. It is the small, fast branch for generation, editing, and local iteration. The 4B release uses Apache 2.0 terms, making it the cleanest starting point here for a small team that needs a permissive trainable local model.

Pick the distilled Klein checkpoint when speed is the product. Pick Klein Base when you plan to train a LoRA or need behavior that distillation has compressed away. For serious LoRA work, that distinction matters more than a few points on a public image leaderboard.

FLUX.2 Dev aims much higher but is a 32-billion-parameter model. Running it locally at a comfortable precision is a different hardware class, and its non-commercial license must be read before shipping paid work. Quantized community workflows may make inference fit; they do not rewrite the license and they are not equivalent to full-precision training.

The hosted Pro, Flex, and Max options are worth testing when high-end instruction fidelity or multi-reference composition earns back the API cost. Do not assume the most expensive endpoint wins your portrait brief. It may be better at the parts your test barely measures.

Where ComfyUI fits

ComfyUI is not a fourth realism model. It is the graph where you load a model, connect its text encoder and VAE, choose sampling parameters, add control or reference conditioning, and save the recipe. Saying “made with ComfyUI” tells you about the workbench, not the engine.

A sane local comparison graph stays deliberately boring:

1. Load the exact official checkpoint, text encoder, and VAE expected by that model.
2. Use its documented scheduler, guidance, and step starting point.
3. Generate at a comparable native pixel area and aspect ratio.
4. Disable face detailers, sharpening, upscalers, and style LoRAs.
5. Save seed, prompt, node versions, model hash, and workflow JSON.

Only after that baseline should you add ControlNet-style structure, IP-adapter/reference systems, identity LoRAs, or a second pass. Those tools may improve the finished image, but they make a poor model comparison because the surrounding stack starts doing the work.

API nodes can put Nano Banana or hosted FLUX endpoints in the same canvas. That is useful operationally, not proof that the model has become local. Never place an API key directly into a workflow you plan to share.

How to run a fair realism test

A single glamorous headshot is a vibe check. A useful test set has scenes that expose different failure modes. Use at least six prompts and keep the intent constant while adapting syntax only where a provider requires it.

Blind the results before rating them. Score instruction match, photographic coherence, identity, edit preservation, and obvious defects separately. Also record time and money per accepted result. A fast model that gives one usable frame in four can beat a premium model that is marginally prettier but costs ten times more to explore.

Mistakes that ruin the comparison

• Using one generic sampler recipe. Eight distilled steps for Z-Image Turbo and a base-model training workflow are not interchangeable.
• Comparing a polished API output with a raw local latent. Match final resolution and disclose any built-in enhancement or external upscale.
• Calling every variant “FLUX.2.” Record Klein 4B Base, Klein distilled, Dev, Flex, Pro, or Max. Their hardware and licenses are materially different.
• Testing only attractive faces. Add interaction, full-body, editing, and repeated-character cases. Portrait defaults hide structural weaknesses.
• Treating public rankings as laboratory measurements. Arena votes are a useful preference signal, but presentation, unknown prompts, and model updates move the result. They do not answer your workflow question.
• Ignoring privacy and terms. A hosted model sees a request that a local checkpoint never receives. Check the provider’s current data terms before uploading client or identity references.

My recommendations by workflow

Start with the smallest option that clears the brief. Move up only when you can name the failure: poor edit locality, weak text, identity drift, insufficient control, or a quality ceiling. “This one is newer” is not a production requirement.

Once a model is chosen, the next realism gains usually come from lighting, restraint, and a cleaner workflow—not adding ten copies of “ultra realistic” to the prompt. The companion guides on realistic portrait prompting and plastic skin cover that part in detail.