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Local AI Image Generation

The pipeline behind a solo operator's image stack. A Mac writes bespoke prompts and reviews outputs, a Windows box with the dGPU runs the diffusion models, a feedback ledger turns hundreds of reviews into a prompt taxonomy. Notes on Flux Schnell vs Z-Image Turbo vs Qwen, swap-pressure safety, cluster-uniform visual systems, and the tooling that keeps the loop from eating the week.

4 postsFor: Creative technologists and solo operators generating production imagery locally

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Z-Image Turbo vs Flux Schnell is not a drop-in swap

IMAGE AI·APR 24·10 MIN

Z-Image Turbo vs Flux Schnell is not a drop-in swap

Z-Image Turbo vs Flux Schnell: same prompt, different brain. Rich layered prompts that land in Flux collapse in Z-Image. Empirical evidence across rounds.

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mflux ate my Mac: the safety wrapper I wish I'd written first

IMAGE AI·APR 24·13 MIN

mflux ate my Mac: the safety wrapper I wish I'd written first

Running mflux in a loop crashed my Mac mid-batch. Here is the mflux memory safety wrapper I wrote after: swap gate, cooldowns, timeouts, pkill escape.

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The image feedback ledger that became a prompt taxonomy

IMAGE AI·APR 24·12 MIN

The image feedback ledger that became a prompt taxonomy

Across 21 review rounds and ~500 AI image generations, a small JSON ledger became an AI image prompt taxonomy. Here is what it caught and missed.

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Put this to work

Running Flux, Z-Image, and Qwen locally without the cloud-API bill.

> See the Operator's Stack

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some problems.

Instead of briefing four vendors, you work with one person across brand, code, infrastructure, compliance, and growth. You get dated receipts, published pricing, and an agent library you own after the engagement ends. You work with me directly. That’s kind of the whole point.

or email direct hello@michaeldishmon.com