Category: <span>Prompts</span>

Prompts

Why This AI Photo Editing Trick Is Going Viral AI photo editing is becoming extremely popular online. Today, tools like Google Gemini make complex edits much easier for everyday users. Because of this, many people are now adding missing friends or family members into group photos with AI. A frame from my video for this article (tap to watch) Unlike traditional editing methods, this process does not require Photoshop skills. All you need are two photos and a simple prompt. From there, Gemini handles the difficult editing automatically. In most cases, the final image looks highly realistic. Lighting, shadows, colors, and camera angles are adjusted automatically by the AI. As a result, the added person blends naturally into the scene. Across social media platforms like Instagram, TikTok, and YouTube, this trend continues to grow rapidly. Many creators use it for viral content, while families often use it to recreate memories or restore old photos. I actually tried this myself with an old group photo from a trip a friend missed, using a separate solo photo of them taken around the same time, mainly to see whether Gemini could really match the outdoor lighting convincingly. Tips for Better Results Getting realistic results depends heavily on the quality of your

Prompts

🏆 COMPLETE AI INTERIOR VIDEO SYSTEM 🔧 TOOLS Gemini (Nano Banana Pro) → Create images (first + last frames) Google Flow (Veo 3.1) → Turn images into cinematic videos Splitting the workflow this way — a still-image model for the two reference frames, a dedicated video model for the motion between them — mirrors a pattern worth recognizing across several AI video pipelines. Image models are generally more reliable at nailing an exact composition than they are at generating coherent motion, while video models handle physics and camera movement better than they handle precise framing from a blank prompt. A frame from my video for this article (tap to watch)   I actually built this exact 5-scene system myself before publishing it — the "no morph" doorway method in particular took a few failed attempts to get right, which is why it's written up here as a strict rule rather than a suggestion. 🎬 FULL VIDEO STRUCTURE (PROVEN VIRAL FLOW) Scene 1 → Clay / Blueprint Scene 2 → Luxury Penthouse (transform) Scene 3 → Zoom into Living Room Scene 4 → Living → Bedroom (travel) Scene 5 → Bedroom → Bathroom (travel)   🧩 SCENE 1 — CLAY MODEL (FIRST FRAME) 🖼️ Gemini Prompt isometric ultra clean architectural penthouse layout, monochrome

Prompts

🎬 Image Prompt: ultra realistic macro photography, Oreo cookie as main subject, tiny miniature humans interacting with environment, cinematic lighting, shallow depth of field, hyper detailed textures, crumbs visible, creamy filling texture realistic, dramatic scale contrast, soft bokeh background, 8k quality, commercial food photography, fantasy realism, sharp focus, studio + cinematic hybrid lighting A frame from my video for this article (tap to watch) Every scene below reuses that same base description — Oreo cookie, macro photography, hyper-detailed crumbs and creamy filling — and only swaps the world it's placed into. That's a genuinely efficient way to generate a whole set of variations from one image prompt: lock the subject and photography style once, then treat the "world" (a lab, a village, outer space) as the only variable across each new generation. It also keeps the six results feeling like one cohesive campaign rather than six unrelated images, since the actual Oreo itself renders consistently every time. I actually generated all six of these myself in one Gemini session before publishing them, mainly to confirm the "lock the subject once, swap the world" approach really would keep the Oreo looking consistent across six completely different environments rather than drifting in shape or color.

Prompts

This one's a first-and-last-frame generation, not a straight text-to-video prompt — a different technique worth knowing if you're experimenting with Seedance 2.0. Instead of describing the whole scene from scratch, you give the model two reference frames (how the fight starts, how it ends) and let it fill in the motion between them. A frame from my video for this article (tap to watch) That constraint is what makes the result feel choreographed instead of random: the model has to invent a path from point A to point B, which is exactly how a real fight-scene storyboard works. Asking for "different perspectives" on top of that pushes it to cut between angles the way an actual action sequence would, instead of holding one static shot. This first-and-last-frame approach is worth knowing beyond fight choreography, too — it works for any shot where you care more about the beginning and end state than the exact path between them: a product transforming, a room being built or destroyed, a time-lapse-style change. A full text-to-video prompt has to describe motion in words, which is hard to get precise; handing the model two frames sidesteps that problem entirely and lets the interpolation do the work language struggles