Haseeb ArshadHaseeb Arshad
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Creative AIProduction

Private Image Generation and Enhancement

A GPU-backed creative pipeline for private image generation and local image upscaling, exposed through authenticated web tools.

FooocusStable Diffusion XLRTX 5080PyTorchUpscaylCaddy

RTX 5080

GPU backend

SDXL

generation stack

Local

asset processing

The challenge

What the system needed to solve

Provide high-quality visual generation and restoration without uploading private source images or relying on metered cloud GPU APIs.

The build

Engineering decisions

01

Installed and validated Fooocus with a current CUDA/PyTorch stack on the dedicated Windows GPU workstation.

02

Bound the inference service to the private network and placed its browser route behind two-factor authentication.

03

Documented model bundles, data roots, boot-time recovery, GPU compatibility checks, and acceptance prompts.

04

Added Upscayl as a complementary local enhancement workflow for existing images.

Architecture

How the pieces connect

  1. 1Authenticated browser
  2. 2Caddy access policy
  3. 3Private GPU service
  4. 4Local models and output storage

Results

What changed

  • Generation and enhancement use owned hardware with predictable cost and private inputs.
  • The GPU service survives workstation restarts through a verified boot-time task.
  • Model and application state are separated from replaceable software components.