I got sick of cloud AI credits, so wrapped Hunyuan3D into a fully offline, local C# engine.

Reddit r/LocalLLaMA / 3/30/2026

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Key Points

A local game-development project wraps the Hunyuan3D-2 approach into a fully offline pipeline to avoid cloud AI paywalls, uploads, and subscription constraints that can conflict with studio NDAs.
The pipeline combines Hunyuan DiT for geometry generation with a Paint VAE for texturing, and uses XAtlas for UV unwrapping plus Nvdiffrast for FP32 rasterization.
To improve texture quality, it intercepts albedo outputs, applies a VRAM-safe 4K tiled upscaler locally, and synthesizes procedural PBR normal maps before exporting GLB assets.
The author built a C# UI that sandboxes a Python/PyTorch environment, disables automatic Hugging Face hub “auto-heal” behavior, and runs entirely from a local 24GB model folder for true offline operation.
Hardware and performance considerations are emphasized, including the need for at least ~8GB VRAM (tested on an RTX 3080 10GB) and PyTorch expandable segments to reduce VRAM fragmentation; the project is planned for a Steam release with a $45 perpetual license and a free demo.

Hey everyone,

I know this sub is heavily focused on local text models, but I wanted to share a local compute project for the 3D and gamedev side. The current 3D AI generation market (like Meshy or Rodin) is an absolute nightmare of API paywalls, $120/mo subscriptions, and cloud-upload requirements that instantly violate studio NDAs.

I run a small indie game studio, and we needed to generate background props and mobs offline. So, I spent the last few months ripping apart the Hunyuan3D-2 architecture and building a custom, fully localized wrapper around it.

The Architecture:

The Core:

Hunyuan DiT for geometry + Paint VAE for texturing.

The Pipeline:

Running the raw model is a mess, so we integrated XAtlas for smart, engine-ready UV unwrapping and Nvdiffrast for FP32 rasterization.

The Textures:

Native outputs are usually blurry, so the pipeline intercepts the albedo, runs a VRAM-safe 4K tiled upscaler, and synthesizes a procedural PBR normal map locally before wrapping the GLB.

The Wrapper:

A C# UI that sandboxes the Python/PyTorch environment. I surgically killed the huggingface_hub auto-heal scripts. It relies entirely on a physical 24GB local models folder. If you pull your ethernet cable out, it still generates perfectly.

Hardware:

I'm running this on my own rig with an RTX 3080 10GB. Because of the 4K upscaler and the massive DiT model, you realistically need 8GB+ VRAM, otherwise the C# UI will likely hang on the final phase. I had to force PyTorch expandable segments just to mitigate VRAM fragmentation.

We packaged the whole pipeline up as "Jupetar" on Steam, pending review from Valve. It’s a one-off $45 perpetual license because we refuse to add to the SaaS fatigue.

There will be a free Demo version live specifically so people can test the VRAM limits and generation speed on their own hardware before buying.

Would love to hear what the local-compute crowd thinks of the pipeline architecture, or if anyone has tips on further optimizing local PyTorch VRAM fragmentation for 3D tasks!

Link attached below if you want to follow this project:

Source: Steam https://share.google/d3FmRxIVF64A9oo5F

submitted by /u/Blackie-Chan101
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