Stable-3D Generate 3D

Stable-3D Generate 3D is the payoff node of the pack - the one that actually produces the thing you came for: a real, downloadable 3D mesh as a GLB file. The "Stable-3D" branding makes it sound like a hosted service, but the name is a lie in the best way. Nothing here phones home; every weight lives on your disk, there's no API key, and the underlying model is MIT-licensed to boot. It takes normal maps (from Preprocess Image - it deliberately will not take your raw photo) and returns a mesh file path.

How it works

Under the hood this is Stable-X's Hi3DGen - the model from the June 2025 release thread that had r/StableDiffusion arguing about whether it was the SOTA open image-to-3D. It's a two-stage flow sampler:

1. Sparse structure stage: a flow sampler produces the coarse 3D skeleton - where the geometry actually is. This is the expensive, high-step-count stage and where most of the generation time goes.
2. Structured latent (SLAT) stage: decodes the fine surface into an actual mesh, then trimesh exports it as a GLB with the model oriented upright.

Feed it a single normal map and it runs single-image; feed it a batch (the pack's multiview example workflow does this with ImageBatch) and it switches to multi-image conditioning, which gives it more views to reason about and usually a sturdier result. A SPCONV_ALGO=native shim is set around generation for spconv on newer GPUs.

The inputs that matter

Five knobs, and two of them are the whole game:

• seed - -1 means random. Set a fixed value and you can reproduce a mesh exactly. And you'll want to: this node always re-runs, so with -1 every queue gives you a different mesh even with identical inputs.
• ss_sampling_steps (default 50) - the sparse-structure stage. This is the time sink. The author's tooltip says it plainly: more steps, more time. Drop to 25–30 for quick drafts.
• ss_guidance_strength (default 3.0) - CFG for the structure stage. Crank it to make the geometry hug the normal map; too high and things start to warp.
• slat_sampling_steps (default 6) and slat_guidance_strength (default 3.0) - the fine-surface stage. Cheap relative to the sparse stage, so leave them alone until you're chasing detail.

The output

One output: mesh_file_path, a STRING. It's not a tensor or a latent - it's the filename of the GLB written to your ComfyUI output folder (timestamped, something like 2026-08-16-143000_mesh.glb). Wire it into the Preview3D node used in the pack's example workflow to look at it in the UI, and grab the file from your output folder when you're happy with it.

Install and honest expectations

Install is the same as the rest of the pack - ComfyUI Manager (search "Stable3DGen") or:

cd ComfyUI/custom_nodes && git clone https://github.com/lerignoux/ComfyUI-Stable3DGen
cd ComfyUI-Stable3DGen && python -m pip install -r requirements.txt

Then restart. The dependencies are heavy: requirements.txt pins torch 2.5.1, spconv-cu120 and xformers, and the first run downloads gigabytes of weights into models/trellis and ~/.cache/torch/hub. This is not a realtime toy - a 50-step sparse pass on a consumer card is measured in minutes, and you want a 16GB-class GPU to be comfortable. And keep the release-thread perspective: closed-source Hunyuan 3D 2.5 draws sharper meshes, but you can't run that locally. This you can, and it ends with an actual GLB in your output folder. For an open, on-your-machine image-to-mesh pipeline, it's the one I'd reach for.