Gemini 2.5 Flash Image is Insane… (Nano Banana Released!)

Table of Contents

• 🍌 What is Nano Banana (Gemini 2.5 Flash)?
• 🔄 3D, Multiview & Composition Tests
• 🧑‍🚀 Character Consistency & Editing
• ✂️ Image Editing: Remove, Replace, Add
• 🎨 Raw Generation & Style Transfer
• ⏱️ Continuity, Thinking Mode & Material Change
• 📈 Benchmarks, Announcements & Leaderboard Performance
• ⚙️ How to Use Nano Banana (Practical Guide)
• 🖥️ Sponsored Infrastructure & Hardware — Why It Matters
• ⚠️ Limitations & Gotchas
• 💡 Creative Use Cases & Ideas
• ❓ FAQ
• 🔚 Conclusion

🍌 What is Nano Banana (Gemini 2.5 Flash)?

Nano Banana is the image generation and editing preview of Google’s Gemini 2.5 Flash. Think of it as a single model that can do both raw generation from text prompts and extremely robust image editing and inpainting. Across a wide range of tests—3D rotations, multiview composition, character consistency, physics-aware reflections, photo restoration, thumbnails, and even continuity sequences—this model handled tasks that used to be flaky with surprising precision.

“It really is the best image generation and editing model I have ever used.” — Matthew Berman

That quote sums up my experience. What sets Nano Banana apart is how well it understands real-world constraints like lighting, reflections, reflections in glasses, the backside of objects, accurate UI screens on devices, and keeping a single character consistent across multiple edits and angles. It’s not perfect, but the jump in reliability and realism is massive.

🔄 3D, Multiview & Composition Tests

One of the first things I checked was how well the model could imagine different perspectives and maintain fidelity when asked to rotate or render multiple views within a single image.

• Phone flip experiment: I took a thumbnail where Marques Brownlee (MKBHD) is holding two phones and asked the model to flip the phones over. Nano Banana correctly generated the back of the iPhone, recognized the iOS icons and layout, and also made the Android phone’s notch and OS look appropriately Android. Tiny icon artifacts appeared in places, but overall the system’s ability to predict the correct back-of-device visuals and operating systems was jaw-dropping.
• Coke can multiview: I asked the model to show a can of Coke from three different angles and saw flawless results—perfect logo alignment, condensation droplets, and consistent light reflections. This speaks to an internal understanding of cylindrical geometry and texture continuity.
• Character rotation: I tested with a group of stylized characters and asked to rotate the rightmost two 180 degrees. The model initially flipped three instead of two and made a few internal consistency mistakes (like exposed organs appearing/disappearing). A re-run with clearer instructions produced a much better result—so prompt specificity still matters.
• Back-of-head generation: I uploaded a selfie and asked for the back view; Nano Banana generated a convincing rear-of-head that matched my hairstyle and lighting context, which is a tricky conditional generation task that many models struggle with.

Overall: the model excels at predicting the unseen side of objects and people, which opens a lot of creative and production workflows—product photography, 3D asset creation, and visual effects pre-visualization included.

🧑‍🚀 Character Consistency & Editing

Character consistency is where Nano Banana really flexes. I put the model through a battery of tests designed to stress whether the same subject remains recognizable across multiple edits, poses, and contexts.

• Self-portrait edits: I uploaded my photo and asked the model to add a can of Coke to my hand. The initial can was slightly small, but the character remained consistent with the source image—same face, same posture.
• Reflective glasses: I uploaded sunglasses and asked the model to put them on me. Not only did it place the glasses, but the reflections in the lenses correctly showed yellow flowers from the scene. That required the model to infer that the flower field likely exists in front of me, then to render a plausible reflection on a curved, reflective surface. This is a big step forward in environmental awareness.
• Back-of-person: After adding accessories, I asked the model to generate the back of the same person. It produced a believable backside that matched hair, jacket, and lighting—again, impressive.
• Multiple poses and multiview comic: I fed two stylized characters and asked for three poses; the model produced coherent poses, a thumbs-up frame, and a four-panel comic that kept the main character consistent across panels (with the cat moving from windowsill to lap—nice touch!).
• Scene compositing and faking: I showed how the model could place characters into a soundstage and stage the whole scene convincingly, including background actors and period-appropriate gear—creating an image that looks like an on-set photograph.

These capabilities make Nano Banana a powerful tool for storytellers, marketers, and indie filmmakers who need consistent characters across scenes without expensive photoshoots or 3D rigs.

✂️ Image Editing: Remove, Replace, Add

Editing real photos—inserting people, removing people, and blending additions—has long been a fraught space for generative models. Nano Banana handles these tasks with new levels of polish.

• Remove people: I experimented with an iconic photo (founders of OpenAI). I asked the model to remove the person on the right—poof, gone. I then removed the person on the left—gone again—with shadows, textures, and background continuity looking natural. The model kept lighting and grain consistent.
• Add people: I uploaded my photo and asked to add myself into the founders’ image. The model inserted me with an expression and posture that matched the rest of the group, and it appropriately matched shadows and hand positions. It even adjusted my facial expression to look cohesive with the others in the scene.
• Facial manipulations: I tried a variety of facial edits—adding a ZZ Top style beard, making me cry (which looked fake), then removing the tears. You can do playful things like stick bananas in ears and get plausible results. Face replacement remains blocked or unreliable (so don’t expect the model to swap any face perfectly), but constrained edits and additions work extremely well.
• Background removal: I tested background removal on a photo of Sam Altman and the model removed the background with near-perfect precision. I also asked for a bust-sized crop and got a good result (minor sleeve inconsistencies, but passable).

Takeaway: Nano Banana is a strong inpainting and compositing tool, great for editorial use and photo retouching. Just be mindful of edge cases—hands, tiny reflections, or nuanced facial micro-expressions can still produce artifacts.

🎨 Raw Generation & Style Transfer

It’s not just editing—Nano Banana can create compelling imagery from nothing. I tried prompts designed to mimic uncurated, candid captures and to stress physics and stylistic rules.

• Street scene freeze-frame: I prompted for a random real-world moment frozen in time. The model generated a dynamic scene full of narrative details: spilled coffee, a dog on a leash (sometimes floating—one failure), a kite in power lines, and a pigeon eying a hotdog. Depth-of-field was handled well—foreground water bottle blurred by bokeh, while the main action was crisp.
• Fun generations: banana wearing costumes, a cat whose fur looks like moss—both worked nicely and suggest strong creative potential for memes and stylized art.
• Physics and reflections: A car photo generated from a front-facing prompt showed the photographer reflected perfectly in the grill and the wet-ground reflections of tires and lights—significant progress in rendering physically consistent reflections.
• Style swaps: I used the model for thumbnail-style edits and surprised-face generation. It can replace backgrounds with solid colors, alter facial expressions, and add stylistic lighting on the face to create eye-catching thumbnails—very useful for content creators who want to iterate quickly.

⏱️ Continuity, Thinking Mode & Material Change

Some of the most mind-bending features are Nano Banana’s ability to produce logical progressions and to selectively change material or state without altering unrelated elements.

• Candle sequence: I asked for a three-part image—an unlit candle, a burning candle, and a melted candle. The model produced a convincing progression with coherent lighting and reflections, including realistic melt patterns and reflections in the holder.
• Burger decay “thinking mode”: I tried a prompt that explicitly asked the model to imagine a timeline—fresh burger to years-later decay. The model generated intermediate frames with increasing mold and decomposition, demonstrating an ability to simulate temporal progression. This suggests internal “thinking” or planning is being leveraged for image sequences.
• Material swap (teapot): I created a teapot made of transparent ice steaming with tea, then asked the model to keep everything the same but make the teapot metal. The smoke and scene remained identical while only the material changed—this selective editing is both practical and artistically powerful.
• Meme generation and counting: Quick meme edits, like placing objects on a whiteboard with text, were trivial and accurate. I tested human anatomy by prompting two hands in a handshake—Nano Banana produced five convincing fingers on both hands with skin texture and even tiny hairs. The model handled counting and finger count better than many predecessors.

These features are huge for storyboarding, sequential art, scientific visualization, and any task that needs consistent transformations across a series of frames.

📈 Benchmarks, Announcements & Leaderboard Performance

Google has been public about Gemini 2.5 Flash’s launch: Sundar Pichai shared the rollout, and the model sits at the top of LM Arena’s image edit leaderboard with a massive Elo jump—nearly a 200-point increase compared to prior models. That’s not just hype; the leaderboard performance aligns with my hands-on findings: this model marks a material improvement on prior generative image models.

The practical impact of that ranking is that many tasks that once required complex pipelines (manual editing + generative augmentation) can now be consolidated into a single model-driven workflow, saving time and production cost.

⚙️ How to Use Nano Banana (Practical Guide)

If you want to try Nano Banana right now, here are the two main entry points and some tips for getting the best results.

Access via AI Studio (google.ai/studio)

• Open AI Studio and find the featured models list.
• Select “Gemini 2.5 Flash image preview” (sometimes called Nano Banana).
• Turn off any auto-settings you don’t want; I personally turn off autosave or autosamples to keep control.
• Key controls to experiment with: top_k (controls diversity), guidance scale (controls adherence to prompt vs creativity), and the canvas/inpainting tool for selective edits.

Access via Gemini Chat UI

• Open Gemini and choose “Choose your model” → pick “Fast/All Round/Help: Gemini 2.5 Flash.”
• Click the three-dot menu and select “Image generation” to access the image editing and inpainting tools directly in the chat interface.

Prompting tips that worked well

• Be specific but not overly prescriptive for character rotations—e.g., “Rotate the two rightmost characters 180 degrees, keep lighting and background intact.”
• For multiview, request “three views in one image” and specify relative angles (“front, three-quarter, back”).
• To trigger thinking mode for sequences, prompt explicitly with a timeline and ask for significant moments (e.g., “create a chronological series of 4 moments between fresh and decayed”).
• For material changes, instruct “everything the same except change the teapot material to metal.” This maintains scene continuity.
• If an edit fails, re-run with minor clarifications. The model can produce different outcomes across generations, and iteration often yields the best result.

Pro tip: Save seeds and iterations if the platform exposes them. That helps you reproduce or tweak results without starting over.

🖥️ Sponsored Infrastructure & Hardware — Why It Matters

High-capacity image models require serious infrastructure. Nevius has rolled out NVIDIA Blackwell GPU clusters optimized for these next-gen models—offering up to 30x faster inference and 4x faster training compared to previous-generation H100 setups. That throughput matters when you’re batching hundreds or thousands of image inferences for a product pipeline or when training your own finetuned models.

Dell Technologies also highlighted laptops and workstations with NVIDIA RTX Pro Blackwell chips—these are great for creators doing local inference, rapid prototyping, or running mixed workloads that blend CPU preprocessing and GPU rendering. The point here is simple: cutting-edge models perform better when matched with cutting-edge hardware.

⚠️ Limitations & Gotchas

No model is perfect, and Nano Banana has a few consistent limitations worth calling out so you know what to expect in production:

• Small artifacting: icons, tiny light blobs, or leaves might disappear across frames or variations (I saw a leaf missing in some replicated apple images).
• Micro-expression realism: edits like adding tears can still look fake unless carefully prompted and iterated.
• Face replacement: replacing a face with another specific face remains unreliable or blocked. The model does excellent editing of expressions and accessories, but identity swaps are not a solved product feature here.
• Complex internal anatomy: in early rotations of characters, internal details (like exposed organs) could disappear or inconsistently appear when the same character is rotated—so use caution with medically detailed or anatomically complex transformations.
• Floating objects or unnatural physics: occasionally a leash or small object appears to float if the model misinterprets depth cues—double-check critical details.

Despite those caveats, the overall fidelity and reliability are far beyond what we saw in earlier generations.

💡 Creative Use Cases & Ideas

Here are some practical and creative ways to use Nano Banana in real-world workflows:

1. Thumbnail rapid prototyping: Replace backgrounds, intensify facial expressions, and add stylized lighting to iterate thumbnails without new photoshoots.
2. Product photography and multiviews: Generate multiple angle shots (e.g., cans, phones, watches) from a single shoot to populate e-commerce galleries.
3. Character sheets for games and animation: Generate consistent poses and angles from a single concept image for 3D modelers and riggers.
4. Editorial retouching and compositing: Remove or add subjects to archival images, restore and colorize old photos with remarkable accuracy.
5. Storyboarding and sequence creation: Use continuity and material change features to create timelines and visualize transformations.
6. Meme and social content creation: Quick meme generation and style swaps for social channels, with strong control over composition and text styles.

❓ FAQ

Is Nano Banana available to the public?

Access was rolled out in the Gemini interface and AI Studio as a preview. Availability may vary by account and region. If you have access to Gemini 2.5 Flash or the image preview in AI Studio, you can try it today by selecting the Gemini 2.5 Flash image preview in the featured models list.

What are the best prompts to maintain character consistency?

Be explicit about the character: upload a clear reference image, then include instructions like “Preserve facial features, hair, and outfit. Keep lighting and shadow consistent with the original photo.” Use a combination of reference images and descriptive prompts and iterate until the model locks in the desired look.

Can it replace a person’s face with someone else?

No—face-to-face replacement is either blocked or unreliable in practice. The model excels at altering expressions and adding accessories, but swapping a face with a targeted identity is not a supported feature you should expect to work well.

What controls are important in the UI?

Look for sampling controls (top_k, temperature), guidance/CFG scale, and mask/inpainting tools. Those are the levers that let you trade off creativity for fidelity. Saving seeds or versions is useful when available.

Can Nano Banana handle sequences and temporal continuity?

Yes. The model can produce chronological image sequences and maintain progressive changes across frames (e.g., candle burning, food rotting). You can trigger a “thinking mode” by requesting significant moments across a timeline, and the model will attempt to generate coherent intermediate frames.

How good is photo restoration and colorization?

Very good. I restored and colorized multiple damaged historical photos with impressive results. Some images become slightly stylized after colorization, but the repairs and color choices were accurate and useful for archival restoration.

What should I watch out for when using it commercially?

Always check licensing and platform TOS. Respect privacy and likeness rights when editing photos of people. Be cautious with content that could be used to mislead or defame—ethical use is as important as technical ability.

Gemini 2.5 Flash, aka Nano Banana, is a watershed moment for image generation. From precise object rotations and believable multiview product shots to character-consistent edits, convincing reflections, photo restoration, and temporal sequence generation—the model is a leap forward. Small artifacts and edge cases remain, but the overall reliability, especially in physics-aware reflections and character continuity, is unprecedented.

If you’re a creator, marketer, or developer working with visual content, you need to experiment with this model. Use it to speed up iteration, reduce the need for expensive reshoots, and open creative directions that were previously impractical. Pair it with capable hardware (NVIDIA Blackwell GPUs, Dell workstations) if you’re doing high-volume or local inference.

Finally, a quick heads-up: prompt design and iteration still matter. Nano Banana is powerful, but the smartest results come from a thoughtful loop of prompt, generation, tweak, and regenerate. Try the features I highlighted—3D multiviews, continuity sequences, material swaps, and comic panels—and you’ll see why I’m so excited.

If you want to dig deeper, I encourage you to try the model in AI Studio or Gemini, iterate on your prompts, and share your favorite results. I’ll keep testing and sharing what I discover—there’s a lot more to explore with Nano Banana.

See you in the next one.