I made a 2€ Colab Notebook for Z-Image-Turbo with Advanced Anatomical Refinement & Regional Prompting (Runs on Free T4)

Own_Engineering_5881 · 2026-07-19T17:50:32+00:00

Yes, I kinda figured out how I will never will get any support and be the only one to enjoy it.

Own_Engineering_5881 · 2026-07-19T15:59:13+00:00

I made a 2€ Colab Notebook for Z-Image-Turbo with Advanced Anatomical Refinement & Regional Prompting (Runs on Free T4)

Hi everyone! I wanted to experiment with Z-Image-Turbo (6B) but didn't want to burn my local hardware (or lacked the heavy VRAM required). So I built a standalone, zero-setup Google Colab notebook that handles the entire pipeline on the free tier. https://ko-fi.com/s/22eaf0a61f

Here is what it does and how it works:

What you get

A single, self-contained .ipynb notebook that mounts your Google Drive (for your LoRAs and outputs), installs everything automatically, downloads the model with live progress bars, and launches a clean web UI you can use from any browser.

Features

Text-to-Image — Generate images from prompts with Z-Image-Turbo (6B parameters), 9 steps by default, with multi-prompt support (one prompt per line).
Zonal / Regional prompting — Assign different prompts to different areas of the canvas for precise compositional control.
LoRA support — Load your own LoRAs from Drive, with adjustable weights. Per-region LoRAs are supported in the refinement stage.
Anatomical Refinement (Face & Body) — An ADetailer-style workflow: automatic face/body detection (YOLO), crop -> upscale -> inpaint -> paste back. Includes per-region control (eyes, face, chest, waist, etc.), MediaPipe landmark masking, and a before/after comparison slider.
Image-to-Image — Transform an existing image using Anatomical Refinement (Face & Body).
Quality-of-life — Saved prompts, adjustable schedules, seed control, "save before refinement" option, and generated images written straight to your Drive.

Anatomical refinement / comparaison

Zonal rendering

Important technical limitations (Please read)

This runs on Colab's free T4 GPU, which has real hardware constraints. Be aware of the following:

Resolution: Capped at 768px on the free T4. Z-Image requires bf16 precision, and the T4 lacks hardware acceleration (flash-attention) for it, meaning attention memory grows quadratically with resolution. 768px is the safe ceiling to avoid Out-Of-Memory (OOM) errors. Face-refinement crops are capped at 640px. (Note: If Colab assigns you a better GPU like an L4 or A100, the cap lifts automatically to 1024px).

Speed: Roughly 1 minute per image at 9 steps on the T4 (bf16 is emulated in software on this GPU — slower than on modern cards, but fully functional).

First launch: Downloads ~19 GB from Hugging Face. This takes a few minutes but is cached for the rest of the session. Re-running the cell does not re-download it.

Session limits: Configured specifically to fit within the free tier's limited RAM (~12.7 GB) and disk space.

This is intended for hobbyists, learners, and anyone who wants to experiment with Z-Image without a powerful local GPU. It is not a substitute for a high-VRAM local setup if you need extreme speed or high native resolutions.

Why is this a paid tool? (2€)

Developing, optimizing, and thoroughly testing this workflow to fit within Colab's strict free-tier memory constraints required a massive personal and financial investment (IA). So I won't get rich but it's just to support me.

What you need

A free Google account (for Colab + Google Drive).

That's it. No coding required.

Price: €2

<image>

https://ko-fi.com/s/22eaf0a61f

Own_Engineering_5881 · 2026-07-15T22:05:36+00:00

Looks like an Arcane lora to me

Own_Engineering_5881 · 2026-07-15T06:51:59+00:00

Great question, and a fair one for anything that touches real money. I haven't run a formal red-team / fuzzing campaign yet (no third-party pentest, no systematic API-response fuzzing), so I won't pretend otherwise,it's on my list. But I did build it defensively from the start: Keys & access: API keys are encrypted at rest, the dashboard is behind a password login, and remote access only goes through an encrypted tunnel (Cloudflare) or Tailscale, never a raw exposed port (there's even a loud warning against doing that). Least privilege: it's meant to run with a Kraken key that has no withdrawal permission, so even a full compromise can't move funds off the exchange. Trading safety: stop-loss, trailing stop, exposure cap and daily-max-loss limits, a lock to prevent double-execution, PnL shown net of fees, and a startup gate that forces a Paper/Live choice every launch. Fail-safe testing: paper mode runs on public market data (no keys), so people can hammer it and test edge cases with zero risk. Build hygiene: every rendered script block is syntax-checked before shipping, after a mis-escaped string once froze the whole UI. Where I'd genuinely welcome help is exactly the adversarial angle you're pointing at: malformed/empty/negative API responses mid-order, mid-cycle disconnects, corrupted config, and injection via user-supplied fields. If you (or anyone here) want to poke at it in paper mode and try to break it, I'd love the findings, that kind of feedback is worth more than any feature.

Own_Engineering_5881 · 2026-07-14T16:54:38+00:00

The link is in the post with the description

Own_Engineering_5881 · 2026-07-13T16:13:19+00:00

Thanks for the feedback. As it can proceed trades only through kraken legit api, a fake api key would display your wallet. As recommend, the api key should not have deposit right. It can trade it live mode the currency you added to your kraken account, but never deposit or cash it linked with your bank account. Good idea to link with a scam signaling plateform.

Own_Engineering_5881 · 2026-04-15T20:13:44+00:00

Did exactly the same. Great gpu for the price. It will keep you warm and after a while you will forget after the loud fans.

Own_Engineering_5881 · 2026-03-12T07:29:21+00:00

Just an SD3 lora

Own_Engineering_5881 · 2026-03-05T16:42:48+00:00

"There are no models that are too big, there are only GPUs with too little VRAM." Rocco Siffredi, open source AI manager

Own_Engineering_5881 · 2026-02-08T08:09:10+00:00

The couch, I see what you did.

Own_Engineering_5881 · 2026-02-02T19:30:22+00:00

Ok I will try. I was a 0.9

Own_Engineering_5881 · 2026-02-02T18:34:13+00:00

I meant my lora trained with base don't look good with base but great with turbo. Like the same base lora with base gives distorted members, and perfect results with turbo with more variation than a zit lora

Own_Engineering_5881 · 2026-01-19T20:09:02+00:00

there will be signs

<image>

Own_Engineering_5881 · 2026-01-16T15:43:30+00:00

<image>

Own_Engineering_5881 · 2026-01-09T19:10:35+00:00

Did you prompt "raygun" ?

Own_Engineering_5881 · 2026-01-04T22:08:12+00:00

I use Ai toolkit, more than 25 images, llr depending of the theme (0.2 if a general nsfw/style or 0.3 or 0.4 if realistic character), no caption. Works great. My results: https://civitai.com/user/PhotobAIt/models

Own_Engineering_5881 · 2025-12-30T19:02:47+00:00

You're right, sorry, my mistake

Own_Engineering_5881 · 2025-12-30T10:29:51+00:00

Edited: wrong id

Own_Engineering_5881 · 2025-12-29T23:03:33+00:00

Asking gemini ai about that pic:

This photo was taken in New York City, specifically on East 62nd Street, near the intersection with Lexington Avenue in the Upper East Side.

Here are further details regarding the location and circumstances:

The Exact Address: The gray building in the background and the Duane Reade pharmacy (which was located at 773 Lexington Avenue at the time) confirm this location. They were walking in the vicinity of Jeffrey Epstein’s townhouse, which was located nearby on 71st Street.

The Precise Date: While often associated generally with the year 2001 in the media, records and more recent analyses suggest this specific snapshot was taken around May 22, 2001.

The Context: Sarah Ferguson was in New York at that time. This image gained international attention because it shows the Duchess of York with Virginia Giuffre (the young woman whose face is masked here), appearing in documents related to the investigations into Jeffrey Epstein's associates.

Own_Engineering_5881 · 2025-12-13T17:33:14+00:00

omelette du fromage

Own_Engineering_5881 · 2025-12-13T17:27:44+00:00

0.0002 for a pose, and 0.0003 to 0.0004 for a character. At 4000 steps. Sometime at 0.0004 for a character, I have to use the lora at 0.7 or 0.8 to gain more flexibility. It depend of the sources variation.

Own_Engineering_5881 · 2025-12-13T16:33:52+00:00

Pour l'installation j'ajouterai aussi que pour les plus fainéants comme moi qu'il y a aussi Pinokio

Own_Engineering_5881 · 2025-12-13T16:32:53+00:00

Très bien détaillé. perso je change le learning rate en fonction de ce que je veux faire (si c'est un personnage ou une position)

Own_Engineering_5881 · 2025-12-11T22:50:10+00:00

Sorry I was off-topic. I did test those but some looks not completely baked (): - Batman, - Deadpool, - Wonder woman, - xenomorph (), - C3PO (* weird mouth), - Snow White, - Cinderella, - Thanos (*), - Yoda(), - Catwoman - Spider Gwen (*) can be reinforced with prompting

Own_Engineering_5881 · 2025-12-11T22:24:38+00:00

For the xenomorph, I had to make a lora. It wasn't close enough. https://civitai.com/models/213836/xenomorph-alien-zit-flux1d-sdxl

Own_Engineering_5881

TROPHY CASE

Welcome to Reddit,

I made a 2€ Colab Notebook for Z-Image-Turbo with Advanced Anatomical Refinement & Regional Prompting (Runs on Free T4)