The making of Myanmar Glam

One person, a home GPU, a few rented ones, open generative models and an agentic workflow. About a week.

The making-of, in HLG HDR. Narrated in a clone of the director's own voice, generated locally.

Best on an HDR screen: iPhone, iPad or a recent Mac in Safari, or Chrome on an HDR display. Other screens get a standard-range version automatically. The stills on this page are HDR too.

The point

Every frame of the couture film was generated. No camera, no set, no model on a runway, and no proprietary generation API: the images, the video, the HDR conversion and this narration all came from open models running on GPUs we control.

How far can one person push local generative AI, with an agentic workflow, a custom pipeline and tools built in-house?
Title card: how far can one person push local generative AI, over a dim HDR shot of the model The model in a gold beaded outfit at a candlelit vanity
The question behind the project, and a frame from the film it produced.

The system

Two tools hold it together. Pocket AI is where a shot gets built: references, prompt, settings. Tensor Master is the control room: it queues every job, ranks it, sends it to the home GPU or to rented cloud GPUs when the queue gets long, and keeps every take re-runnable from the gallery. An agentic workflow sits on top, writing the briefs, checking every take and sending the failures back to the queue.

Pocket AIbuild the shot · refs · prompt · settings
↓
Tensor Masterqueue · priorities · agentic QC · cost guard
↙ ↘
Home GPURTX 3090 · always on
Cloud GPUsrented RTX 5090s · by the hour
↘ ↙
Galleryevery take · edit & rerun
Same open models, wherever a job runs.

By the numbers

1,073jobs through Tensor Master
6days, start to final takes
426MiniMax H3 renders
69final takes, 8 looks
18rented cloud GPUs
$35cloud compute
GPU rendering time, 37 hours in all
home RTX 3090 · 30.1 hrented · 6.7 h
Diagram from the film: Pocket AI to Tensor Master to home and cloud GPUs to the gallery Stats card from the film: 1,073 jobs, 6 days, 37 GPU hours, 18 rented GPUs, 35 dollars
Scale up when the queue grows, shut it all down when it is done.

The pipeline

  1. Qwen Image 2.1Reference images: a rear view of every garment, location plates, a character sheet for the model.
  2. MiniMax H3Video in reference-to-video mode: 768×1344, 24 fps, 4-second takes.
  3. LTX-2.5 SDR → HDRAn ACES workflow that rebuilds the dynamic range at the takes' native size.
  4. RIFE 4.9Frame interpolation to 48 fps.
  5. Grade and deliveryPer-look 3D LUTs; Dolby Vision profile 8.4 plus HLG.
  6. MiniMax Music3Original score for the earlier cuts and for this making-of.
  7. CapCut AI EditThe final cut of the film, including its soundtrack.
The seven pipeline steps listed over an HDR shot End card on the jetty at dusk: one person, open models, own GPUs

What broke

Before and after: front buttons wrongly drawn on the back of a red dress, then the correct one-piece back Zoom on lace: upscaled version with moiré above, native-size version clean below
The back of the dress, fixed; moiré from an upscale against the native-size conversion.

Made with Pocket AI and Tensor Master, open-weight models on a home RTX 3090 and rented RTX 5090s, and an agentic workflow. Narration: Qwen3-TTS, cloned from the director's voice. Film soundtrack from CapCut; making-of score by MiniMax Music3.