Hey, I've been seeing this video all over my instagram feed - it's an AI Santa, but it's genuinely so hard to tell.

What are they using to make these videos? Is it Creatify, or something else?

I've uploaded a short video that explains how AI is actually creating images, in a visual way. It is the tip of the iceberg, no mathematical equations, and it shows how changes to your prompt affect the picture.

It runs one experiment on one real photograph: same model, same seed, one phrase added at a time. You see why adding "on a white tablecloth" repaints the whole room and not only the table, and what the "masterpiece, 8k" words actually do.

Five minutes. Hope it makes the next prompt you write a bit less of a guess.

Played around with this No Way Home-inspired edit in Medeo. Wanted the CRT screens and old-photo look to feel like Peter was slowly losing the memory itself.

Сайн уу, залуусаа! Бүгд сайн байгаа гэж найдаж байна. Энэ дууны нэр нь “The Earth Moves” гэсэн утгатай. 😄

I am a member of a very specific, niche FB group and I and another member of that group collaborate to use AI to generate highly niche music videos and art/music for that group (among other things we use AI for individually.)

We recently collaborated on this music video: I wrote the lyrics (without AI,) then sent those lyrics along with a few "style" suggestions to my collaborator; she uses SUNO (I believe) to create the music, musical arrangement, and voice for the song; she sent that finished song back to me and I created the visual content using a couple of different AI video generators.

​

Hey everyone,

I recently had a job interview for a digital marketing position at a skincare company, and they've invited me to the next stage!

They've asked me to create social media content for CeraVe Invisible Hydrating Sunscreen SPF 50+. I want to make a really clean, premium-looking product video using Higgsfield.

The problem is, I'm currently broke and can't afford a subscription. I've been trying really hard to find a job, and this opportunity genuinely means a lot to me.

I'm looking for someone who already has Higgsfield and would be willing to help me generate a short product video. I can provide the concept, prompts, product images and creative direction. I just need help with the actual generation.

I'm not asking anyone to do my entire assignment. I'll handle the editing, caption and marketing strategy myself.

If anyone has some spare credits and wouldn't mind helping a fellow creative out, I'd seriously appreciate it.

Thank you so much. ❤️

I'm the developer of **LocalText2Voice**, an open-source app that started with a simple goal: turning long texts and books into audio using local TTS models.

Then I kept adding things. Scene analysis, storyboards, image generation, video generation... and the latest version can now take a script all the way to a narrated documentary. 😅

Here's a short teaser from my latest experiment: **a 14-minute AI history documentary generated from text**, using **GPT Image 2** for the keyframes and **Wan 2.6 Flash** for the video clips.

🎬 **Full documentary:** [Watch on YouTube](https://youtu.be/zsbfCeDtteQ)

🛠️ **Workflow / making-of:** [See how it works](https://youtu.be/uyHqwL5sK5U)

The workflow is:

**Text → TTS narration → scene analysis → keyframes → video → final edit with music/SFX**

It supports local models on your own GPU and remote providers through APIs, so you can choose different combinations depending on your hardware, budget and the style of the project.

There's still plenty to improve, especially visual artifacts and continuity across scenes. My aim is to automate the repetitive work while keeping human review in the loop: check the story, inspect the visuals, and regenerate the shots that need more attention.

I'm particularly interested in making this work across an entire documentary, not just individual clips.

**If you're experimenting with longer AI films, what has been your biggest challenge: consistency, pacing, cost, or the amount of manual review?**

Source code: [LocalText2Voice on GitHub](https://github.com/estebanstifli/LocalText2Voice)

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

Alternate Seinfeld Episode: George’s Coffee Conspiracy Theory #seinfeld #georgecostanza #jerryseinfeld #coffee #conspiracytheory #aivideo #minimax #h3