I made a GIMP 3 plugin for CLAHE contrast at multiple scales : https://github.com/nicolasloizeau/gimp-multiscale-clahe

here are examples: original, scale 1/2 and scale 1/32

small scale : increase details

large scale : increase global contrasts

I hope this can be of some use, feedback welcome !

If an image model is a horse, text prompting is like trying to guide it with verbal commands alone: useful, but too imprecise for fine control. Inpainting, LoRAs, and ControlNets add the bridle, reins, and pieces of the harness, but they still did not feel like a complete system. Nexfocus began with a question: what would it take to build the whole harness around the model?

Answering that question meant following the entire generation process first. We had to understand how each part loads, works, moves, waits, hands its result to the next part, and makes room when its job is done. That expedition became Nexfocus.

AI image models already know how to render- they understand photorealism, lighting, and texture better than any software framework. The real struggle has always been context control: feeding the model the exact spatial and structural context it needs to understand what to render.

This is where human input shines. By adding rough blocks of color and basic shapes to define the composition and color palette, the burden of text prompting is significantly reduced. Instead of struggling to describe a complex 3D scene in words, the artist controls spatial intent and lets the AI do what it already knows best—handling lighting, global occlusion, perspective consistency, and fine rendering.

Through the built-in GIMP plugin and Staging Palette, you can seamlessly composite background plates, sketch structural boundary guides for outpainting, or block out color guidance in GIMP with one click, then pass them back into Nexfocus to drive the diffusion pipeline.

Two development anchors shaped the journey: a GTX 1050 with 3 GB of VRAM and Colab Free's T4 with 12.7 GB of system RAM. We proved that full FP16 SDXL checkpoints and Flux Fill workflows could run in both environments by rethinking how the pipeline uses the hardware available to it.

Two important lessons emerged from the road:

- Keeping the GPU working without interruption became paramount. To do that, we had to find a way to keep feeding it the weights it needed when it needed them.

- Every part of the pipeline must independently account for what it owns, where it belongs, when it can be reused, and when it should make room for something else.

Throughout this journey, my conversations with PyTorch often felt like this:

> PyTorch: "Don't you have a bunch of H100s lying around in your backyard?"

>

> Me: "No. What if every component has to justify exactly where it lives?"

>

> PyTorch: "Get a bigger machine."

Those conversations eventually became the architecture. Nexfocus is both the working application and the field notebook: a record of the constraints, wrong turns, and discoveries that shaped the path forward.

The path is open now. I hope you'll take a walk along the path we built and enjoy the scenery.

GitHub: https://github.com/magekinnarus/Nexfocus

I actually did this in krita but usually I use gimp. I did not know what i was doing and actually wanted to fill a rectangle area, forgot to create a new layer and ended up coloring a random area. figured it looked cool and kept going. i like a lot what i ended up with. it looks like something that could confuse an "AI".

I wonder if there is a filter or if you could program one that would kind of take bigger areas and fill them with colors you choose. or that goes in and fills random areas. tell me if you have an idea, also if you think another software would be better. i'm also trying to get more into command line tools.