CyberFreezeDev
Loading
We study how to turn generated clips into finished, on-brand videos at a predictable cost and turnaround.
We test whether chained video, image, voice and music generators plus editing automation produce business videos a client signs off, at a quotable cost.
Can a pipeline of video, image, voice and music generators plus automated editing produce short business videos that a client accepts, with a cost and turnaround we can quote in advance?
A single text-to-video model gives you a few seconds of footage. A usable ad, product explainer or social clip needs a script, a shot list, consistent characters or products across shots, a voiceover, music, captions, brand colours and a final edit. Each step can be generated or automated, and each step can fail in its own way.
Our internal tooling, which grew out of our Vibe Editor and AI Mini Studio projects, already chains generated video, images, voice and music with editing automation. This track tests that chain systematically, and the findings shape our generative video and image studio retainers.
Weekly reels, Shorts and ads outpace shoot budgets; generated footage can fill the gap if quality holds.
Reels, Shorts, product pages and ads all want fresh video every week. A small business cannot book a shoot for each one. Generated footage can fill the gap if the quality holds.
What matters is the cost of an approved second, rejected clips included.
Generators are priced per clip or per second, but many clips get thrown away. The number that matters to a client is what a finished, approved second of video costs, including the rejects.
Model licences differ and platforms ask for disclosure, so each shot's model must be tracked.
Each model has its own licence terms for commercial use, and platforms such as YouTube ask creators to disclose realistic altered or synthetic content. A pipeline has to track which model made which shot.
Wrong logos, garbled text and extra fingers must be caught before posting.
Generated video can produce wrong logos, garbled on-screen text, extra fingers or a product that looks subtly different from the real one. Someone has to catch that before it is posted.
Local work runs on the lab GPU. Heavier video models run through hosted generation services, called from our own tooling, which logs the credits each generation spends.
Every test starts from a written brief, the same way a client job would, and ends with a reviewer deciding whether the cut is usable.
| Metric | How we measure it | What counts as success |
|---|---|---|
| Keep rate | Generations attempted for each shot that makes the final cut | Few enough retries that the shot cost is predictable |
| Cost per finished second | Credits from the ledger, including rejected takes, divided by final runtime | A figure we can quote before the job starts |
| Wall-clock turnaround | Time from approved brief to reviewable cut, with machine time and human time logged apart | Fits a weekly content calendar |
| Prompt adherence | Reviewer scores each shot against the shot list on a fixed rubric | Shows what was asked, with no invented objects |
| Consistency across shots | Same character, product or set checked side by side across every shot | A viewer would not notice it was generated shot by shot |
| Artefacts | Checklist: hands, faces, on-screen text, logos, flicker, warping | Nothing on the checklist visible at normal playback speed |
| Audio fit | Voiceover timing against cuts, music level under speech, pronunciation of names | No re-record needed |
Brief types in the test set: a 15 second product ad, a 45 second explainer with voiceover, and a captioned social clip in vertical format.
Public facts first, then the hypotheses we are testing. We have not published our own measurements yet.
Wan 2.1's smaller model needs 8.19 GB, just above our card, so local runs need offloading or quantization.
The authors of Wan 2.1 state their smaller 1.3 billion parameter text-to-video model needs 8.19 GB of VRAM and makes a 5 second 480p clip in about 4 minutes on an RTX 4090. Our 8 GB card is just below that line, so local video generation will need offloading or quantization, and will be slower.
Generators make seconds of footage, so editing many shots tells the story.
Current generators produce clips of seconds, not minutes. Anything longer is an edit of many shots, which is why the editing automation matters as much as the generator.
Hypothesis: most rejects come from characters or products drifting between shots.
We expect most rejected takes to come from a character or product drifting between shots, not from single-shot quality. Reference images and fixed seeds should help; we are measuring how much.
Hypothesis: adding titles and captions in the edit removes garbled on-screen text.
On-screen text generated inside video is often garbled. Adding titles, prices and captions in the editing step should remove a whole class of rejects.
Log every generation, retry and credit for three briefs, then score the cuts.
Log every generation, retry and credit for each brief, then score the cuts with the rubric above.
Measure where stills and upscaling on the 8 GB card save cost without slowing delivery.
Images and upscaling fit on the 8 GB card far more easily than video. We will measure where local work saves cost without slowing delivery.
Voiceover and pronunciation tests, including Indian names, come from the voice AI track.
Voiceover quality and pronunciation tests come from our voice AI research, including Indian names and brand terms.
Notes will be published in the R&D / Labs notes section of our blog. Sample frames are published only from our own test briefs, never from client work.
| Note | Published when | What it contains |
|---|---|---|
| Method note | Before any scored cut | The three brief types, the review rubric, the artefact checklist and how cost per finished second is counted |
| Brief write-ups | When each brief type has been run end to end | Generations attempted per kept shot, credits spent including rejects, turnaround split into machine and human time, and the rubric scores, with model names and versions |
| Local versus hosted | After the stills and upscaling comparison | Time and cost per image and per upscaled clip on the 8 GB card against the hosted service, same inputs |
| Cost model | Once at least three briefs per type are logged | The method for estimating cost per finished second for a new brief, with the ledger data it was fitted on |
Not in one step. Generators make short clips. A finished ad needs a script, consistent shots, voice, music, captions and an edit. Our pipeline automates those steps and a person reviews the cut before it goes out.
It depends on each model's licence and on the platform's rules. Some open models allow commercial use, some do not, and platforms such as YouTube ask for disclosure of realistic synthetic content. We track which model made each shot so this can be checked.
No. Video generation and editing automation is offered as a service on retainer through our generative video and image studio. This lab track is where we test the methods.
Yes. Finished cuts can go straight into Cybe Social, our scheduler, which posts one upload to every connected account.
Last updated