AI Media Automation

what it is

A software and automation layer over a local ComfyUI generative pipeline — taking a complex, fiddly local AI setup and turning it into something reproducible and scriptable.

problem / objective

Local generative pipelines are fragile: model files scattered across directories, workflows encoded in UI state, environments that work until they don't. The objective was to treat the pipeline like software — parameterized, versioned where it matters, and deployable from a script instead of a afternoon of clicking.

environment

• Local GPU host running ComfyUI
• Python for orchestration and tooling
• Model and LoRA assets managed outside the UI's default sprawl

architecture / design

The automation layer sits between the operator and ComfyUI: templates define prompt structures, configuration defines which models and LoRAs a run uses, and workflow graphs are parameterized rather than rebuilt by hand. Deployment is scripted — environment setup, config, and launch are repeatable steps, not tribal knowledge.

technical implementation

• Model / LoRA management — organized asset layout and config-driven selection instead of ad-hoc file juggling
• Prompt templating — reusable prompt structures with parameterized inputs
• Workflow parametrization — ComfyUI graph definitions driven by config rather than manual UI editing
• Reproducible deployment — setup, config, and launch scripts that take the pipeline from nothing to running

security considerations / methodology

Everything runs locally — no reliance on hosted inference, no assets or prompts leaving the machine. Dependencies are pinned enough that the environment is rebuildable rather than a snowflake.

interesting problems encountered

• Dependency and version fragility in the local-AI stack — pinning and scripting the environment was most of the battle
• Managing large model assets sanely — organization and dedup matter at that file size

what i built

• A template/config-driven automation layer over ComfyUI
• Deployment scripts covering environment setup through launch

results

The pipeline runs from scripts — "works on my machine" became "runs from a script."

evidence

$ pipeline architecture — templated prompts dispatch into the ComfyUI backend, post-processing feeds a review queue

what i learned / next steps

Practical experience taking a messy real-world software system and making it reproducible — the same discipline as any deployment automation work. It's also deliberate proof on this portfolio that the software work isn't only security-flavored.