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On-device and on-prem generation, enhancement, and batch — RTX-class private paths.
Local GPU image pipelines
Darkroom notes for private image AI.
Air-gapped art direction
Darkroom notes for private image AI.
Batch enhance in private
Darkroom notes for private image AI.
Faces and consent
Darkroom notes for private image AI.
Product frames under embargo
Darkroom notes for private image AI.
Style packs that stay on-box
Darkroom notes for private image AI.
Upscale without leakage
Darkroom notes for private image AI.
Private ControlNet workflows
Darkroom notes for private image AI.
Dataset hygiene
Darkroom notes for private image AI.
Evals for image quality
Darkroom notes for private image AI.
Color-managed workflows
Darkroom notes for private image AI.
Print-ready exports
Darkroom notes for private image AI.
Watermark and provenance
Darkroom notes for private image AI.
Client review loops
Darkroom notes for private image AI.
Local model choices in 2026
Darkroom notes for private image AI.
VRAM budgets that stick
Darkroom notes for private image AI.
Secure delete policies
Darkroom notes for private image AI.
Studio network layout
Darkroom notes for private image AI.
Local image AI in 2026
Compare a local image stack against representative briefs, quality gates and measured workstation performance.
Why faces should not hit random cloud APIs
Choose a face-image workflow by purpose, data path, access controls, retention and review.
RTX-class workstation notes
Darkroom notes for private image AI.
Handoff to brand systems
Darkroom notes for private image AI.
Training LoRAs without leaking the set
Track dataset permissions, training artifacts, model versions and evaluation before deploying a LoRA.