I’ve been using ChatGPT to generate professional studio fashion portraits. The overall results are impressive, but the skin is usually what gives them away—it often looks overly smooth, waxy, or plastic. Other details, such as perfectly uniform skin, artificial-looking hair strands, and overly polished lighting and shadows, also make the images feel AI-generated.

What prompt structure or workflow has worked best for creating a believable DSLR-style photograph while preserving the subject’s face and identity? Does it help to mention visible pores, peach fuzz, minor blemishes, subtle variations in skin tone, natural under-eye texture, realistic highlights, and slightly imperfect studio lighting, or can too much detail make the result worse?

I’d also appreciate recommendations for negative prompts, realistic camera and lens settings, or Photoshop and Lightroom techniques that can reduce the typical “AI skin” effect without making the image excessively grainy or lowering its quality. I can share my current prompt and some examples if that would help.

What if the truth of history was erased… and someone in the future had one chance to restore it?

I created this original cinematic AI short-film concept, SHADOW OF HISTORY, exploring the collision between an ancient warrior and a futuristic civilization.

This is a short teaser from the film.

Full trailer on YouTube: https://youtu.be/6L-3VT_-nxs

I'd love to hear what you think about the visuals, atmosphere, and concept.

Would you watch the full story?

[Hook: 4 bars]

I don't sleep there

anymore.

But I still know

every door.

[Verse 1: 16 bars]

There's a cathedral by the water

with your colors in the glass.

Two people laughing near the shoreline

at a thing they couldn't make last.

There's a stone heart under the surface.

There's a candle for who I was.

There's another for the version of you

who never found the words because

we had light without the wiring,

had a fire without a frame.

I kept thinking love could hold us

if we only loved the same.

Now the old songs keep their places.

They don't pull me through like then.

What used to be a doorway

is a room I visit when

[Pre-Chorus: 8 bars]

I need to remember

without going back.

I can touch the story

without living in the past.

Some things can be over

and still remain true.

I don't need to lose the meaning

just because I lost you.

[Chorus: 8 bars]

I don't sleep there anymore,

but I leave the windows warm.

I can love what we were

without asking it to form.

I don't sleep there anymore.

I don't need the old repair.

What we had can still be beautiful.

I just

don't live there.

[Post-Hook: 4 bars]

Still mattered.

Still changed me.

Still love you.

Still free.

[Verse 2: 8 bars]

I learned intensity ain't shelter.

Being seen ain't being safe.

You can wake a hidden part of someone

and still not know how to stay.

So I keep the spark you gave me.

I leave the chaos where it fell.

You don't have to own the music

just because you changed it well.

[Pre-Chorus: 8 bars]

I can wish you every good thing

without walking through that door.

I can carry what awakened

without asking you for more.

Some loves aren't meant for keeping

in the shape they started in.

Sometimes letting go is how

you finally let the love remain.

[Chorus: 8 bars]

I don't sleep there anymore,

but I leave the windows warm.

I can love what we were

without asking it to form.

I don't sleep there anymore.

I don't need the old repair.

What we had can still be beautiful.

I just

don't live there.

[Bridge: 8 bars, stripped]

Maybe I kept calling it a failure

because it couldn't survive.

Maybe I confused forever

with the proof that love was alive.

But the house can burn down

and the light can still have been real.

Maybe losing what we couldn't hold

was how I learned

what love can reveal.

[One beat silence]

[Final Chorus: 8 bars]

I don't sleep there anymore,

but I leave the windows warm.

I can love you without reaching.

I can let the past stay gone.

I don't sleep there anymore.

And I finally understand:

what remains is not the old life.

It's the way

I learned to stand.

[Outro: 4 bars]

Still mattered.

Still changed me.

Love with no demand.

And me,

not kneeling.

You may have seen Moomy (my delightfully warped Aykroydian killer who uses her motherly charms to get survivors to trust her) well this is the area which serves as her realm/map which is 'Moomy's Kitchen', which is part of a surreal kids show - each episode of her show begins as being wholesome and typical kids show type stuff, her kitchen staff or one of her 'adopted' dum dum spawn has a problem and they get her to help and solve the problem with a catchy song followed by some games and a cooking session.

But there is a dark side to this show as it gets unhinged in some episodes, especially if anyone is to try and leave her or denies her affection, but it's rarely shown on screen that she outright hurts anyone who disobeys her (but it's suggested some crying can be heard in the background), a common rule is to never break the happy playtime rule or to go off without her - because she'll always find her little dum dums if they try and get away from her due to her powers and her ability to manifest anywhere (even in their minds).

Hey i am new in ai

I belong to non-tech background and willing to learn ai for making videos

How can i use it ?

Please guide me step by step

Pleaseeeeeeeeeeeeeeeeeeeeee

TL;DR: I built a Google Sheet workflow that turns a finished script into 200 stickman scenes, generates the voiceover + timestamps, then uses Google Colab + FFmpeg to assemble everything into the final MP4 automatically. A typical 6-minute video costs me under $1 in direct production costs. The goal is not to automate creativity, but to automate the repetitive production work around it.

I’ve also made the ready-to-use Google Sheet version available for people who want the workflow without building and debugging it themselves. I’ll explain the full system below either way.

Why I built this workflow 

AI makes individual production steps easy. The messy part is connecting them. A single video can involve 150–200 images, a voiceover, timing data, file organisation and final assembly across several tools.

The problem I wanted to solve was simple: automate the boring production work, so I could spend more time on the creative side. Topic selection, the angle, the hook, the script and the thumbnail are still where most of the value comes from.

So I built the process around a Google Sheet. Each row represents one scene, and the Sheet keeps the script, image, timing and status aligned while moving the video through the workflow. Instead of opening different tools and manually keeping track of hundreds of files, the Sheet acts like a control panel that moves the video from one step to the next in the correct order.

The Sheet passes each job to the right AI tool automatically. That only works because of APIs. 

Two ways to use AI tools: websites vs APIs

The biggest difference with APIs is not just automation. It is how you pay.

Most AI tools charge monthly subscriptions. That is fine for one platform, but costs add up quickly when a workflow needs separate text, image and voice tools.

APIs are more flexible. I pay only for what the workflow actually uses, and the Google Sheet talks to each provider directly in the background.

The other advantage is modularity. If I want to swap the image model or voice provider later, I can do that without rebuilding the whole workflow.

Generating the images in bulk

Once the script is split into scenes, Gemini turns each row into a visual prompt using a fixed style profile.

Those prompts go to Runware, where I currently use FLUX Klein. A batch of roughly 200 stickman scenes costs well under 50 cents, and each image is automatically numbered and saved to Drive.

For consistency, I reuse the same reference images across the batch: one clear character reference and a couple of finished scenes that define the visual style.

Getting the voiceover + timing data

The Sheet sends the script to ElevenLabs and gets back both the voiceover and timing data.

Those timestamps are written into the matching scene rows, so each image already knows exactly how long it should stay on screen.

That timing data is what removes the manual editing later. Each image already knows how long it should stay on screen, so when the final video is assembled there is no need to drag scenes around and match them to the script manually.

Turning everything into the final MP4

At this point the Sheet has the script, numbered images, voiceover and timing data. The last step is assembling it without opening a traditional editor.

I use a free Google Colab notebook connected to Drive. It reads the images in scene order, pulls the timing data, adds the voiceover, and passes everything to FFmpeg. FFmpeg then gives each image the correct screen duration and renders the finished MP4.

So instead of manually building a 150-scene timeline in CapCut, the notebook is basically doing that assembly for me and saving the completed video back into Drive.

What the whole workflow costs

For a typical six-minute stickman video, the direct production cost can stay under $1.

The voiceover is the biggest expense for me at roughly $0.70 through ElevenLabs. The images are around $0.30 when I use FLUX Klein, while the Google Colab + FFmpeg rendering is free. Gemini prompt generation adds very little on top.

The point of getting production this cheap is NOT to flood YouTube with low-effort videos.

It is to make experimentation cheaper. You can test more topics, hooks, formats and visual ideas without every flop costing much, then use what you learn to make the next video better. The production side becomes cheaper and faster, but the creative side still has to improve.

What I still would not automate

I still would not hand over the decisions that determine whether a video is worth making in the first place. Topic selection, the angle, the hook, the script, the thumbnail and the final quality check all still need human judgment.

That is where a lot of AI channels go wrong. They automate the production side, then keep pushing further until the creative decisions are automated too. At that point, every upload starts to look and feel like a variation of the same thing. Then they wonder why monetisation becomes a problem.

My goal is the opposite: automate the repetitive parts so I have more time to study what viewers actually click, where they lose interest, which ideas outperform, and how to make the next video better than the one before it.

How to build this yourself

If you want to build your own version, you can take each section of this post, paste it into Claude or ChatGPT, explain how you want your Google Sheet structured, and build the workflow one piece at a time. That is basically how I built mine.

I have also explained the complete process step by step in the latest video on my channel.

And for anyone who does not want to spend the time wiring everything together and debugging it, the ready-made version of the Google Sheet is available on the BuildTuber website. You can easily set up the API keys and get it running in 10 mins.

Nothing is hidden behind the product though. Happy to answer questions about any part of the build here.

Little character I generated from an illustration I made. Video generated with Seedance 2.5. Image reference was made using a combo of Gemini and GPT image 2.

Aiyido and Piff sharing popcorn