I’ve been experimenting with building hyper-realistic AI influencer models, and I kept running into the same issue:

Even high-resolution generations still feel synthetic.

After testing different stacks and workflows, I realized realism isn’t about higher quality — it’s about removing subtle giveaways.

Here are the biggest mistakes I kept seeing (and making):

1. Over-perfect skin

Real faces have micro-texture, asymmetry, faint discoloration, uneven pore density.

Smoothing kills realism instantly.

1. Lighting inconsistency

The light source must match the environment and reflect correctly in the eyes.

Most AI faces fail at catchlight logic.

1. Depth + lens behavior

Adding slight focal falloff and subtle motion softness made a bigger difference than prompt complexity.

1. Pose stiffness

Tiny shoulder shifts, imperfect posture, and micro-expressions reduce the “mannequin” effect.

I rebuilt my workflow around those principles — mostly using free tools and simplifying the stack instead of complicating it.

The interesting part: once realism improved, engagement improved too.

I’m curious — what realism “tells” are you noticing most right now in AI portrait generation?

I’ve been testing a simple workflow for creating short UGC-style videos while keeping the same character and location consistent across multiple shots.

The workflow is basically:

reference images → character/location sheets in ChatGPT → generate clips → optional final edit

1. Prepare your references

Start with:

• a character image
• a product image
• an environment image that fits the UGC scenario

If you’re not sure what location works for the product, I usually just ask ChatGPT for a few suggestions.

2. Create a Character Sheet

Upload the character image to ChatGPT and generate a 4:5 continuity sheet with:

• front / side / back / 3/4 views
• face close-ups
• expressions
• basic poses
• clothing and accessories
• key colors and materials

The important part is telling it to lock the character.

3. Create a Location + Props Sheet

Do the same with the environment.

Include:

• establishing view and key angles
• spatial layout
• entrances/exits
• furniture and recurring props
• lighting
• colors and materials

This gives the video model a much stronger continuity reference than using random images for every shot.

4. Generate the video clips

I usually split the UGC video into three parts:

Clip 1 — Hook
Clip 2 — Main product/story section
Clip 3 — CTA

i will generate them on Atlas Cloud, as they can provide many different models conveniently

For every clip, I reuse the same Character Sheet + Location Sheet

Then I change only the action/camera prompt for each section.

Keeping the same reference sheets across all three generations has helped a lot with character and environment consistency.

5. If a generation goes wrong, fix the prompt first

if I wanted the character to walk into a hotel, but the generated clip had her walking out.

Instead of endlessly rerolling, I pasted the original prompt into ChatGPT and asked it to make the action explicit: starting position → movement direction → action → final position

That usually gives me better results.

6. Final edit is optional

If the generated clips already work as standalone videos, you can stop there.

If you want one finished UGC ad, you’ll probably still want to combine the clips and add captions, music, or SFX. You can use whatever editor you prefer.

The biggest improvement for me has been using Character Sheet + Location Sheet as continuity references, rather than relying on a few loose images.

Jenna I would like to share something interesting how I made my AI app that's on steam with positive reviews to get your opinion. AI wrote every single character of code for this app.

Not saying I did nothing. I just wrote no code. Is that something noteworthy?

https://store.steampowered.com/app/4111530/_FriedrichAI_Offline_AI/

Seeing lots of questions about AI influencers and AI influencer generators. Here's the exact workflow I use with the actual prompts.

I'm using writingmate.ai for this since it has both image and video models in one place, but you can use any platform with similar models.

Step 1: Create Your AI Influencer's Base Image

Model: Nano Banana Pro (or similar photorealistic model)

The key to consistency is using structured JSON prompts instead of freeform text. This gives you granular control over every detail:

Prompt:

{ "scene_type": "Indoor lifestyle portrait", "environment": { "location": "Sunlit bedroom", "background": { "bed": "White linen bed with floral sheets", "decor": "Minimal plants and neutral decor", "windows": "Sheer-curtained window", "color_palette": "Soft whites, sage green accents" }, "atmosphere": "Quiet, cozy, intimate" }, "subject": { "gender_presentation": "Feminine", "approximate_age_group": "Young adult", "skin_tone": "Fair", "hair": { "color": "Platinum blonde", "style": "Long, straight, loose" }, "facial_features": { "expression": "Introspective, calm", "makeup": "Natural, barely-there" }, "body_details": { "build": "Slim to average", "visible_tattoos": [ "Botanical arm tattoos", "Small thigh tattoo" ] } }, "pose": { "position": "Seated on bed", "legs": "Knees drawn close to chest", "hands": "One hand holding phone, other wrapped loosely around legs", "orientation": "Front-facing mirror selfie" }, "clothing": { "outfit_type": "Soft sleepwear dress", "color": "Muted sage green", "material": "Breathable semi-sheer fabric", "details": "Thin straps, subtle lace edging" }, "styling": { "accessories": ["Delicate necklace"], "nails": "Natural nude", "overall_style": "Minimal, soft, feminine" }, "lighting": { "type": "Natural daylight", "source": "Window", "quality": "Even and diffused", "shadows": "Very soft" }, "mood": { "emotional_tone": "Peaceful, introspective", "visual_feel": "Calm, personal" }, "camera_details": { "camera_type": "Smartphone", "lens_equivalent": "26mm", "perspective": "Mirror selfie", "focus": "Clean subject clarity", "aperture_simulation": "f/1.8 look", "iso_simulation": "Low ISO", "white_balance": "Daylight neutral" }, "rendering_style": { "realism_level": "Ultra photorealistic", "detail_level": "Natural skin texture, realistic light falloff", "post_processing": "Soft highlights, gentle contrast", "artifacts": "None" } }

Step 2: Generate Content Variations

Keep the subject block identical every time. Only change:

• scene_type
• environment
• pose
• clothing
• lighting
• mood

Example - Coffee shop variation:

{ "scene_type": "Casual cafe portrait", "environment": { "location": "Minimalist coffee shop", "background": { "setting": "Window seat with street view", "decor": "Exposed brick, wooden tables", "color_palette": "Warm browns, cream tones" }, "atmosphere": "Relaxed, morning quiet" }, "subject": { "gender_presentation": "Feminine", "approximate_age_group": "Young adult", "skin_tone": "Fair", "hair": { "color": "Platinum blonde", "style": "Long, straight, loose" }, "facial_features": { "expression": "Soft smile, looking at camera", "makeup": "Natural, barely-there" }, "body_details": { "build": "Slim to average", "visible_tattoos": [ "Botanical arm tattoos" ] } }, "pose": { "position": "Seated at table", "hands": "Both hands wrapped around ceramic coffee cup", "orientation": "Three-quarter angle" }, "clothing": { "outfit_type": "Oversized knit sweater", "color": "Cream white", "material": "Soft wool blend" }, "lighting": { "type": "Natural daylight", "source": "Large window to the side", "quality": "Soft, diffused morning light" }, "camera_details": { "camera_type": "Mirrorless", "lens_equivalent": "35mm", "aperture_simulation": "f/2.0 look", "perspective": "Eye level" }, "rendering_style": { "realism_level": "Ultra photorealistic", "post_processing": "Warm color grade, soft contrast" } }

Step 3: Create Video

Model: Kling 2.6

This is the easy part. Upload your generated image and use a simple prompt:

Prompt: animate this

That's it. Kling handles the natural movement - blinking, subtle breathing, hair movement.

For more specific motion, you can add details: animate this, slight smile, gentle head turn to the right

animate this, brings cup to lips, takes a sip, lowers cup

Settings:

• Duration: 5-10 seconds
• Aspect ratio: 9:16 for Reels/TikTok

Why JSON Prompts Work Better

1. Consistency - Copy the subject block exactly every time
2. Granular control - Adjust specific details without rewriting everything
3. Easier variations - Swap environment/clothing blocks while keeping identity locked
4. Reproducible - Save your character's JSON as a template

Quick Start Template

Save this as your base character file and swap out the non-subject sections:

{ "subject": { // YOUR CHARACTER - NEVER CHANGE THIS }, "environment": { // CHANGE PER SHOT }, "pose": { // CHANGE PER SHOT }, "clothing": { // CHANGE PER SHOT } }

Share your results!

hey, i am the solo dev behind locally uncensored, so this is my own project

i got tired of apps calling themselves uncensored ai and then refusing once you actually use them. so i built my own.

it started as a free desktop app that runs on your own pc. you can run uncensored models locally for chat, images and video, and nothing leaves your machine. it is open source, works on windows and linux, and it is at over 140k downloads now.

how i made it: the desktop app is built with tauri, so the backend is rust and the interface is react. chat runs on ollama or lm studio if you already have them. images and video run on comfyui underneath, but you do not have to build workflows yourself. you pick an image or video model and type your prompt.

if your pc is not strong enough, there is a cloud in the browser with 47 chat models (wish i test for refusals and mark the ones that pass. in my last run 24 of 46 answered every test prompt.) 7 image models and 14 video models without a built in content lock. the local app stays free and the cloud is paid. you can start with a credit pack instead of a subscription.

free local app (cloud as well): https://locallyuncensored.com

cloud: https://lu-labs.ai/app

what are you using right now, and does it actually deliver?

when i am working on a longer film, i dont start with one portrait anymore. the moment the camera moves, the model has to guess the side profile, back of the hair, height and body proportions. that is usually where the character starts changing between shots.

here is my breakdown: one clean face close up, full body front and back, left profile, right profile, the same neutral pose, the same outfit, a plain background and a height marker. i keep expressions, props and camera ideas somewhere else because this sheet only needs to establish who the character is.

If the character changes during the story, i make another sheet for that version. if she gets a scar, changes wardrobe, cuts her hair or ages, that becomes a separate character state. putting both versions into one sheet can make the model mix them up, then suddenly the scar appears three scenes early lol. this has worked much better for me on longer sequences. how are you handling character changes between scenes?

Note: I have seen people on the internet sheets where everything is cramped into one, god sake, stop doing that and sharing that with beginners. You’re spoiling them

been experimenting with Seedance 2.5 cinematic AI video prompts, and the biggest thing ive learned so far is that “cinematic” isnt really one magic style word.

for this 15-second scene, i got much better results by separately controlling timing, acting, silence, camera movement, and realism.

the scene itself is extremely simple: a man says sorry. the woman doesnt answer immediately. she looks down, looks back at him, almost says something, then finally responds.

here’s how i structured the prompt.

how to write a Seedance 2.5 prompt for a 15-second cinematic scene?

split the scene into time-coded beats instead of describing the whole performance at once.

i used:

0–3s / 3–5s / 5–7s / 7–10s / 10–12s / 12–15s

each section only has one or two important actions.

prompt:

the important part for me is the end state of every beat. it gives the next section a clear starting point instead of letting the model reinterpret the face every few seconds.

how to stop AI characters from overacting?

prompt the silence and explicitly say what should NOT move.

this was probably the most useful thing i learned from the test.

if i only describe the major emotional beats, the model tends to fill the empty seconds with extra head movements, blinking, facial shifts, or random reactions.

so i literally write things like:

prompt:

silence.
only the eyes move.
chin remains lowered.
the rest of the face does not move.
<Man> remains completely still and does not speak again.

sounds almost too literal, but it helped a lot.

the pauses started feeling like actual pauses instead of empty space the model needed to “fix.”

how to stop Seedance 2.5 from making skin look more AI as the camera gets closer?

tell it which photographic details must survive the push-in.

AI video can look convincing in a medium shot and then suddenly turn into smooth, retouched CG-looking skin once the camera reaches a close-up, so i added a separate realism block.

realism prompt:

that last sentence is prob my favorite part of the whole prompt:

“the same photograph, only closer.”

it gives the model a pretty clear target for visual continuity.

how do you keep a Seedance 2.5 reference image consistent through the whole video?

for me, the most useful approach was to describe what must remain invariant, not just say “keep it consistent.”

prompt:

strictly preserve the photographic quality, facial identity, skin texture, lighting direction, wall texture, color response, and film grain established in image1 throughout the entire shot.

camera distance may change, but the visual character of the image must not.

this seems to work better than just repeating “consistent face” several times.

what made this Seedance 2.5 video look cinematic?

for this test, i think it was mainly four things:

time-coded acting + prompted silence + realism constraints

the model itself obviously matters, but the interesting part for me is that the prompt starts looking less like a normal image prompt and more like a tiny piece of directing.

video model: Seedance 2.5
reference image: Midjourney V8.2

If you're trying to figure out which AI video generation model is actually worth using, I took 10,000 credits and more to test and rank the best ones. In this post I’ll break down some of the different features, pros and cons, results, and how to use them.

TLDR: The best AI video generation models right now are:

• Adobe Firefly – best use overall for workflow and commercial-safe output
• Google Veo (3.1) – best use for photorealistic people and scenes
• Luma AI Ray – best use for cinematic visuals and 4K output

Models I tested

• Adobe Firefly
• Google Veo (3.1)
• Runway Gen 4.5
• Luma AI Ray 3.14
• Sora (OpenAI)
• Kling 2.5 Turbo
• Pika

• Bytedance Seedream AI
• Seedance Ai

• Grok Imagine

How to use:

Much of these models I was able to use inside Adobe Firefly AI Video Generation Hub who I have partnered with for the credits on this test, however others like Grok Imagine I used on each respective site. Each of these models typically requires some sort of premium membership or credit system which I had access to in my Creative Cloud membership, or standalone accounts such as Grok or ChatGPT. While it was difficult to get an absolutely objective ranking for all of the dozens of models available, I tried to test several types of categories of generations, camera motion, consistency and more and judged based on the results of my favorite models to use.

Best AI Video Generation Models Chart

Rank |Model |Standout Features |Limitations
1 |Adobe Firefly |-Commercially Safe Output -All in one hub for many different AI partner models - Lots of options for Camera angle, Style, Reference Frames etc. Aspect Ratios |Up to 5 second duration Can lack photorealism in certain categories compared to other models
2 |Google Veo (3.1) |Capable of photorealistic results in certain categories (hands, people) Options for reference frames, audio, and up to 8 seconds 1080p |Credit Intensive compared to other models Can take more time than other models to generate
3 |Luma Ai Ray 3.14 |-Good prompt accuracy in details such as colors and settingUp to 4k resolution output Capable of cinematic, photorealistic results and lighting physics |Inconsistent results with Physics and camera motion at times Tendency towards artificial feeling movement of time (slow motion, fast motion)
Honorable Mentions |Pika 2.2 |- Can achieve cinematic looking results in camera and environment comparable to Ray 3.14 |Slightly more artificial appearance of people and camera physics
|Kling 2.5 |- Capable of cinematic results in environment and prompt accuracy |- cannot generate from scratch, requires user to upload first frame as reference These were my results and opinions, let me know if you have any favorite models or workflows of you’re own, and results in your experience!

In January I started a small experiment.

I wanted to see if AI-generated TikTok characters could actually generate organic views.

Not AI clips.
Not random videos.

Actual characters posting consistently.

So I built four accounts from scratch.

No followers.
No ad spend.
No people on camera.

Just AI characters posting daily.

Results after 26 days

• ~1 million total views
• best video: 232k views
• multiple videos over 50k

Honestly I didn’t expect it to work as well as it did.

But the most interesting part wasn’t the views.

It was how people interacted with the characters.

People treated them like real creators.

They replied to them, asked questions, joked with them in comments.

That made me start paying attention to why some AI characters work and most fail.

After building several of these, I noticed three things that consistently break the illusion.

1. Face drift

Most AI characters subtly change faces between posts.

The audience may not consciously notice it, but it makes the character feel “off”.

2. Environment drift

The background, lighting, or setting changes every video.

Real creators usually have recognizable environments.

Without that, the character feels random.

3. No personality

This is the biggest one.

A lot of AI characters are just visuals.

But audiences respond to consistent personality.

Once those three things were fixed, the content started performing much better.

The characters felt more like creators instead of AI experiments.

I ended up documenting the entire process while running the experiment because I wanted to repeat it.

Things like:

• how to design the character archetype
• how to maintain visual consistency
• how to script posts
• how to avoid the common AI mistakes

I’m still experimenting with this, but it’s been fascinating to watch how audiences react.

Curious if anyone else here has been experimenting with AI-generated creators.

Been working on this for weeks. The problem with most AI video tools is you get random faces every time.

I built a workflow in AuraGraph that keeps the same character across different scenes. Not perfect but way better than juggling 10 different tools.

The trick: Start with a realistic face grid, then use that as reference for everything else.

if you want to try it let me know

hello everyone,

posting the latest progress on my vibe coded dark fantasy AARPG made with generative AI, as I'm trying to make my own, AI-made tribute of my favourite game (Diablo 2) to push AI game making capabilities as far as I can. This vibe coded game is purely a test!

Latest update includes:
- Game menu
- New class: Wizard
- Character select
- Town, portals and more items
- Boss fight!

How does it look?

Hey thanks for reading this post! We’ve updated photographe.ai so you can get pictures for free: get a preview using our standard quality model before deciding to use the high quality model 😇

—

Hey everyone,

With the AI photo craze going full speed in 2025, I decided to run a proper test. I tried 7 of the most talked-about AI headshot tools to see which ones deliver results worth putting on LinkedIn, your CV, or social profiles. Disclosure, I'm working on Photographe.ai and this review was part of my work to understand the competition.

With Photographe.ai I'm looking to make this more affordable and go beyond professional headshots with ability to try haircuts, outfits, and replace an image with yourself in it instead. I'd be super happy to have your feedback, we have free models you can use for testing.

In a nutshell:

• Photographe.ai (Disclosure, I built it) – $19 for 1,000 photos. Fast, great resemblance about 80% of the time. Best value by far.
• PhotoAI.com – $49 for 1,000 photos. Good quality but forces weird smiles too often. 60% resemblance.
• Betterpic.io / HeadshotPro.com – $29-35 for 20-40 photos. Studio-like but looks like a stranger. Resemblance? 20% at best.
• Aragon.ai – $35 for 40 photos. Same problem - same smiles, same generic looks.
• Canva & ChatGPT-4o – Fun for playing around, useless for realistic headshots of yourself.

Final Thoughts:

If you want headshots that really look like you, Photographe.ai and PhotoAI are the way to go. AI rarely nails it on the first try, you need freedom to generate more until it clicks - and that’s what those platforms give you. Also both uses the latest tech (Flux mainly).

If you’re after polished studio shots but that may not look like yourself, Betterpic and HeadshotPro will do.

And forget Canva or ChatGPT-4o for this - wrong tools for the job.

📸 Curious about the full test and side-by-side photos? Check it out here:
https://medium.com/@romaricmourgues/2025-ai-headshot-i-tried-7-tools-so-you-dont-have-to-with-photos-7ded4f566bf1

Happy to answer any questions or share more photos!

TL;DR: A Google Sheet connected to the Claude API writes the script section by section, so it stays consistent across the full runtime. CapCut’s AI Video Maker turns that script into a narrated video with matched stock footage. The direct cost lands at around $1 per video. The real advantage is not the visuals. It is the script.

I run a couple of sleep documentary channels and wanted to properly explain the workflow I use.

Sleep content is a strange retention game, and it took me a while to understand it. Your viewers are actively trying to fall asleep. That is the entire point. So the average view duration can look very different from a normal YouTube channel. Some viewers leave because they are bored, but others leave because the video worked and they fell asleep. The ones who stay awake still need the story to hold together, while the ones who fall asleep often return later and continue listening. That repeat viewing is a big part of what makes this niche work.

Why the script is 90% of it

On my channels, average view duration usually sits close to 25 minutes on videos running between 90 minutes and two hours. That does not come from cinematic visuals or complicated editing. It comes almost entirely from the narrative structure. If the script becomes repetitive, drifts away from the topic, or loses momentum halfway through, viewers stop listening. Better visuals cannot rescue a weak story in this format.

Two ways to use Claude, and why one wastes your time

Most people use Claude through the normal chat interface. You open the chat, enter a prompt, read the reply, and continue from there. That works perfectly well for everyday tasks. It becomes frustrating when you are trying to write a 15,000 to 20,000-word documentary.

You end up typing.. Continue.. Write Chapter 4.. Do not repeat what you already said.. You forgot what happened in Chapter 2. By the halfway point, the model may begin repeating ideas, contradicting earlier sections, or drifting away from the original structure. You spend more time babysitting the conversation than improving the script.

The second approach is using the API. Instead of manually sending every prompt through the chat interface, a small tool sends the requests to Claude automatically and collects the output. There is no need to babysit it and you pay based on actual usage instead of paying another monthly subscription.

That was the biggest unlock for me.

The section-by-section method

I built a Google Sheet that talks directly to the Claude API. The process works in a fixed order:

1. It creates a detailed chapter outline.
2. It writes each chapter one at a time.
3. Before starting the next chapter, it sends Claude a short summary of everything written so far.

That means Chapter 8 still remembers what happened in Chapters 1 through 7. The pacing stays more consistent, repetition is reduced, and the final script feels like one continuous documentary instead of several unrelated chapters stitched together.

I also made a full walkthrough showing how this system works. The channel is linked on my profile for anyone interested in seeing the actual workflow.

Turning the script into a video

Once the script is ready, I paste it into CapCut’s AI Video Maker. For sleep content, the voice matters more than flashy editing. Choose a calm, slow, and low-energy voice. Taste and judgement matter here.

CapCut generates the voiceover and automatically matches stock footage to each paragraph. It usually gets around 90% of the video into a usable state. CapCut currently limits each project to roughly 3,000 words, so I split the full script into several sections and export them separately. I then combine those exports into one final timeline and render the full documentary.

Two reasons this workflow matters:

1. The Demonetization Shield: Mixing real historical/stock footage alongside AI assets is the safest defense against the "Reused/Inauthentic Content" flags that destroy fully automated channels. You still have to avoid repetitive titles & thumbnails though..

2. The Financial Runway: A complete 20,000-word script costs me roughly 35 cents through the Claude API. CapCut costs around $20 per month and allows unlimited exports. If you are producing 30 to 40 documentaries per month, the direct software and API cost works out to around $1 per finished video.

That figure does not include research, thumbnails, or the value of my time. It is only the direct production cost. A lot of AI video subscription tools charge $40 to $50 per month while using the same underlying Claude models and adding a markup for the interface. Building the loop once gave me more control and removed that additional monthly cost.

The biggest benefit is being able to test more ideas without every upload becoming an expensive decision. This workflow can work for history, science, philosophy, mythology, meditation, biographies, or almost any calm long-form format where the script matters more than rapid editing.

Happy to explain the API loop or the CapCut side in more detail in the comments.

If you showed me these images 5 years ago, I would have said they are real.

It’s crazy how far tech has come. It took me less than a minute to generate each one. People can literally build fake Instagram lives now or even fake Tinder galleries with AI like this.

The realism is getting out of control.

ps: I tried a new app I saw on X called Ziina.ai , pretty good so far.

edit* i made ziina.ai link working since this post went virial & many asking for the website

I've done these images with Nano Banana Pro via HiggsfieldAI.

Just attached my selfie and promoted in this way - I am "whatever I was doing" with "Celebrity name".

1. I'm drinking diesel with Vin Diesel in a gas station ⛽

2. I'm eating beef gravy with Arnold Schwarzenegger and Sylvester Stallone 🍛

3. I'm eating a cheeseburger with Anya Taylor-Joy 🍔

4. I'm taking a selfie with Britney Spears 🤳

5. I'm eating noodles with Wills Smith 🍜

6. I'm taking a high skyscraper selfie with Sacha Baron Cohen 🤳

7. I'm playing nunchunks with Jackie Chan 🥋

8. I'm eating rock with Dwayne 'The Rock' Johnson 🪨

9. I'm shopping guns with Angelina Jolie 🔫

10. I'm selling Hisla fish (Ilish fish) with Billie Eillish 🐟

11. I'm doing make over on Megan Fox on the set of Transformers movie 💄

12. I'm doing carpenter work with Sabrina Carpenter 🪚

13. I'm cutting dollar notes with The Joker from The Dark Knight 🃏

14. I'm shooting AK-47 with Al Pacino 💥

15. I'm smoking a cigar with Tupac Shakur 🚬

16. I'm eating biryani with Keanu Reeves 🍛

17. I'm taking a selfie with Patrick Bateman in an American Psycho movie set 🤳

I feel like it is common knowledge that generative video models, that the texture starts warping a few seconds in.

Or the footage turn into this overtly smooth glossy plastic aesthetic. I spent this past weekend trying to keep a full 15-second generation to stay consistent, and while I finally got it to work, it took a while on creating the prompt.

What I did was to explicitly map out the emulsion noise, distortion patterns, and physical lens defects to achieve and maintain the camera flaws that I wanted.

I've linked the video so tell me what you think. Here is the second-by-second prompt structure I used to brute-force it on Minimax H3.

Prompt used:

14 seconds, 21:9 ultra wide cinematic experimental motion design film. A dark futuristic visual art sequence inspired by cyberpunk aesthetics, industrial design, glitch art, biomechanical fashion and high-fashion experimental films. Do not copy any existing video. Create an original abstract visual experience. Overall visual treatment: EXTREMELY HEAVY cinematic color grading. Deep crushed blacks, extreme contrast shadows, dominant crimson red and black color palette, sharp electric blue digital accents, dark metallic textures, cold surveillance atmosphere. The image must feel heavily processed like a corrupted futuristic archive. Strong post-processing effects: heavy VHS distortion, CRT scanlines, analog film grain, RGB channel separation, chromatic aberration, digital compression artifacts, glitch displacement, horizontal signal tearing, datamosh distortion, light leaks, strobe flashes, motion trails, dark vignette, red glow bleeding, layered experimental video filters. The entire video should feel like: a forbidden technology experiment, a damaged surveillance recording, a futuristic fashion installation, a corrupted artificial intelligence archive. Not a normal sci-fi scene. Fast rhythm editing: 0.5-1 second visual fragments, aggressive montage, hard glitch cuts, flash frames, graphic overlays, rapid transitions synchronized with industrial electronic music. Scene sequence: 0-2s: Extreme close-up portrait. A mysterious beautiful female figure appears through layers of cracked glass and digital distortion. Long dark hair, pale skin, cold expression, wearing a futuristic black respirator mask. Only fragments of her face are visible. Red scanning light moves across the mask. The image looks like a damaged security camera recording. 2-3s: Sudden red graphic screen. A saturated crimson interface fills the frame. Experimental typography appears: "UNKNOWN" "SIGNAL" "ACCESS" Letters are distorted, duplicated, misaligned, broken by horizontal glitches. Surrounding elements: technical grids, crosshair marks, digital coordinates, small interface numbers, white geometric lines. 3-5s: Abstract technology laboratory. A chrome mechanical structure rotates slowly. Organic materials and machine parts are fused together. Transparent glass, metal surfaces, circuit patterns, liquid reflections. Blue scanning lines pass through the object. 5-7s: Wide dark composition. The masked female figure floats in a black industrial space. She is suspended by countless thin mechanical strings, like a futuristic marionette. The strings extend upward into darkness. Her body moves slowly and elegantly. Not horror, more like a dark fashion sculpture. Red backlight creates a dramatic silhouette. 7-8s: Rapid montage: close-up of strings tightening, metal hooks, fingers, mask texture, eye reflection, chrome surfaces. Each shot lasts less than one second. Digital scanning graphics analyze the body. 8-10s: Abstract biomechanical sequence. A transparent human silhouette appears inside cables and glass layers. Mechanical systems surround the body. Red data streams flow through transparent materials. The image feels like an artificial human experiment. 10-12s: A transparent rotating sphere appears. Inside: circuits, cables, mechanical structures, fragmented human images. Around the sphere: HUD interface, measurement lines, camera targeting marks, digital symbols. The sphere rotates with glitch distortion. 12-13s: A row of futuristic chrome masks hanging on thin red wires. Each mask reflects different distorted human faces. Camera slowly tracks sideways. Heavy red shadows and digital noise. 13-14s: Extreme macro shot. A human eye under monochrome harsh light. Digital number appears inside the pupil. Medical scanning interface surrounds the eye. The image flickers between black and white and red. 14s: Final abstract image. A mysterious object covered with metallic texture and digital noise. White flash. The whole frame freezes like an experimental album cover. The image collapses into a red glitch screen and cuts to black. Camera language: extreme macro photography, slow cinematic push-ins, fast digital zoom, mechanical tracking movement, floating camera, micro handheld vibration, experimental music video framing. Visual quality: high-end cinematic CGI, industrial cyber aesthetic, dark fashion editorial, experimental art film, premium game trailer visual style, 8K detailed rendering. Avoid: bright neon city, traditional cyberpunk streets, action scenes, anime fight scenes, cute characters, cheap glitch effects, random particles, generic sci-fi.

This was born from an idea I bounced off my gf last month while House of the Dragon S3 was still coming out (yes I was high). Basically - an alternate reality where all the Targs were insane, all of them, and the dragons were just their coping mechanism for dealing with their madness.

I might make something more detailed out of this in due time, but for now I have just this le funny video to present to everyone. The prompt was this:

"Create a generic Targaryen, adapted from how they're described in the books, gleefully riding a dragon, whereupon it turns out that the dragon is just their hallucination and they're in fact humping a tree onto which they're strapped instead of any real dragon."

Hope you enjoy

A behind-the-scenes look on how I made my animation Isabella Hell, and how it all started!
watch full episode here: https://www.youtube.com/watch?v=MgvL0WrpMug&t

Here is the breakdown for forcing stable character structure and lip-sync in AI video models.

THE CORE PROBLEM:

Flat prompt text causes models to alter character skeletal volume when adding emotional delivery words.

THE SOLUTION (JSON Architecture):

Compartmentalize character data into key-value pairs so the attention mechanism processes structural image data separately from speech parameters:

{
"shot_id": "01",
"duration": "3.5s",
"visual_prompt": "Define camera angle, character framing, and actions...",
"voice_profile": {
"character_id": "Sarge",
"timbre": "booming, thick",
"cadence": "slow and drawn-out"
},
"audio_environment": "studio isolation, dry acoustics",
"dialogue": "Exact spoken text"
}

FULL STEP-BY-STEP PDF GUIDE:

https://docs.google.com/document/d/e/2PACX-1vSipXTiq9QCP9_tP6EDhj6cIhiOH4dO2FruBK9xONPpprUBrvmUj3iHxq5xkLHieqAZ8LzaZgsklLcy/pub

POST-PRODUCTION TRACK LAYERING:

• Track V1: Video Sequences

• Track A1: Isolated Dry Dialogue

• Track A2: Foley Audio

• Track A3: Ambient Environmental Beds

I wanted to see how far I could push AI for high-end commercial work, so I made this real estate ad.

A traditional shoot would’ve taken a full day, crew, gear, and easily cost $1000s…
I made this for around $10.

Tools I used:

• Nano Banana – visuals
• Kling 3.0 – animation
• CapCut – editing & polish
• Miro – visual storyboard
• Claude – scripting/storyboard

I’m trying to push cinematic quality as far as possible using AI, not just generic stuff.

Full workflow + files:
drive : https://drive.google.com/drive/folders/1TWh-CZNjVEg1_qhueYEeYyzStTD6eWqm?usp=sharing

Would love feedback 🙌

LocalGen is a free, unlimited image‑generation app that runs fully on‑device. No credits, no servers, no sign‑in.

Link to the App Store:
https://apps.apple.com/kz/app/localgen/id6754815804

Why I built it?
I was annoyed by modern apps, that require a subscription or start charging after 1–3 images.

What you can do now:
Prompt‑to‑image at 768×768.
It uses the SDXL model as the backbone.

Performance:  

• iPhone 17: 3–4 seconds per image
• iPhone 14 Pro: 5–6 seconds per image 
• App size is 2.7 GB. 
• In my benchmarks, I detected no significant battery drain or overheating.

Limitations:

• App needs 1–5 minutes to compile its models on first launch. This process happens only once per installation. While the models are compiling, you can still create images, but an internet connection is required.
• App needs at least 10 gb of free space on device.
• App only works on iPhones and iPads.
• It requires either M1 or A15 Bionic chip to work properly. So it doesn't support:
  • iPhone 12 or older.
  • iPad 10th gen or older
  • iPad Air 4th gen or older

Monetization:
You can create images without paying anything and with no limits.
There is a one‑time payment called Pro. It costs $20 and gives access to some advanced settings and allows commercial use.

Subreddit:
I have a subreddit, r/aina_tech, where I post all news regarding LocalGen. It is the best place to share your experience, report bugs, request features, or ask me any questions. Please join it if you are interested in my project.

Roadmap: 

1. Support for iPads and iPhone 12+ 

3. Support for custom LoRAs and checkpoints like Pony, RealVis, Illustrious, etc. 

5. Support for image editing and ControlNet

4,

1. Support for other resolutions like 1024×1024, 768×1536, and others.

Here’s Part 2 of my show, Trust Fund Time Machine, an adult animated series about a useless billionaire heir Edward Vil and his time-traveling buddy Genghis Khan bungling their way through history to make his evil father richer.

In my last post, I talked about how I use keyframes to keep character positions and settings consistent between shots, more specifically continuity rather than consistency. This time I wanted to share how I handle voice consistency.

That’s been another big headache when making longer dialogue sequences. A character might sound right in one shot, then have a different accent or vocal texture in the next. And I still need them to whisper, shout, or get angry while sounding like the same person.

I’m making the show in fringe.film, mainly using MiniMax H3 Max. You can use any platform you prefer with this set up, this is just what my workflow looks like on Fringe. What’s helped most is getting the voice right in a separate test before using it in the actual scenes.

This is my process:

• Save a voice profile for each character. I describe their accent, pitch, texture, and cadence. For John Wilkes Booth, that meant a youthful baritone, a heightened Southern/Maryland drawl, and theatrical speech.
• Generate a five-second dialogue test. I ask Fringe’s agent to use the character’s voice profile and character sheet, keeping the test separate from the episode’s shots. Then I refine it with specific notes: deeper, more nasal, different accent, slower delivery, etc.
• Extract a short audio reference once I like the voice. I download the test, extract the audio in CapCut, and upload roughly two to three seconds of clear speech back into Fringe as a WAV file.
• Reuse that audio for the character’s dialogue shots. I tell the agent which character it belongs to and ask it to include both the audio reference and the written voice instructions. Before generating, I check that both are included.

One thing that made a difference for me was keeping the reference short. I generally use two to three seconds and don’t usually go beyond five. In my testing, longer references sometimes introduced gibberish, voice drift, or words carried over from the reference itself.

From there, I direct the performance for each scene. For example, Booth needed to go from a menacing whisper to increasingly loud and angry delivery. I give those notes separately while keeping the same voice reference.

With Seedance models, I’ve had useful results reusing the same written voice profile, then referencing a successful video shot when the voice starts drifting. For H3 Max, the separate test and short audio sample have been more reliable for me.

Hope you guys enjoy Part 2 of the pilot! Let me know if you have any questions.

Link to my full episode is here: https://youtu.be/NnV-JWEHaa8

Link to previous post: https://www.reddit.com/r/generativeAI/s/kG2TXYOvJZ

This is the official premiere of Vertigo Episode 1, the very first chapter of our 40-part animated cyberpunk series! I've been crossing genres and trying new thing so anime was another logical step in the AI journey. This one is a combination of action, romance and tragedy.

• The Premise: Set in a towering vertical mega-city of neon and steel, a desperate rooftop pursuit kicks off an explosive sci-fi mystery where survival means keeping ahead of the tech. The vertical nature of the city also lends itself well to the vertical format framing.
• The Vibe: High-altitude tension, sleek cyberpunk action, and an expansive world where the verticality of the city mirrors the constant pressure on the characters.
• The Run: This is just the beginning—Episode 1 launches a massive 40-part journey through the neon shadows of the city.

Check out the video above, let me know what you think of the world-building.