☁️BizyAir Trellis Predict

What it is

This is where the actual image-to-3D magic happens. Feed it a photo, get back a 3D asset - no photogrammetry, no manual retopology, no turntable of forty photos. It's Microsoft's TRELLIS model doing the work, run through BizyAir's cloud so you don't have to fight the notoriously fiddly local install (CUDA pinning, spconv compilation, Visual Studio build tools - people have wiped their whole Python environment multiple times trying to get vanilla TRELLIS running on Windows).

TRELLIS works in two internal stages, and the node's inputs map straight onto them. First it generates a coarse "sparse structure" - think of it as deciding where the object's geometry lives in 3D space. Then it generates the "structured latent" that fills in appearance and fine detail on top of that skeleton. That's why you see two separate sets of guidance/steps sliders below.

The inputs that matter

• model - plug in the TRELLIS_MODEL from BizyAir Load Trellis.
• images - your reference photo(s). A clean shot with the subject fully visible and reasonably well-lit gives TRELLIS the best shot at a clean mesh.
• mode - single for one reference image, or multi if you're giving it several angles of the same object to reconstruct from. Per the node's own tooltip, multi-image mode lets you feed multiple reference angles for a more accurate 3D model.
• multimode - only matters in multi mode: stochastic or multidiffusion, controlling how the extra views get combined.
• ss_guidance_strength / ss_sampling_steps - guidance and step count for the sparse-structure stage (defaults 7.5 and 12).
• slat_guidance_strength / slat_sampling_steps - same idea for the structured-latent stage (defaults 3 and 12).
• seed - the usual, for reproducibility.
• masks (optional) - isolate the subject from its background before conversion, if your reference image has clutter around it.

You get two outputs, not one: trellis_gaussian (a 3D Gaussian splat representation - great for fast, photoreal-looking preview renders) and trellis_mesh (an actual textured mesh, the thing you'd import into Blender or a game engine). This node is flagged is_output_node: true, so results get saved/displayed automatically once the run finishes.

How to install it

ComfyUI Manager: search "BizyAir," install, restart. Manual route:

cd ComfyUI/custom_nodes
git clone https://github.com/siliconflow/BizyAir.git

Restart ComfyUI after. You'll need a BizyAir API key configured before any node in the pack - including this one - will actually execute; it's all a cloud call under the hood.

Common issues

Holes or gaps in the mesh. This shows up in TRELLIS generally, cloud or local - people running the vanilla local version hit the exact same artifact on thin or occluded geometry. It's usually the reference image's fault more than the model's: parts of the object the camera never saw well get guessed at, and the guess sometimes just doesn't close up. Try a cleaner photo, or bump the sampling steps on the affected stage.

Bumping into an unfamiliar "BizyAir" folder you didn't install. If you landed here after seeing that pop up unprompted from an unrelated workflow, it's not this node's doing directly - ComfyUI Manager's node-registry had a period where it misattributed missing nodes from other packs to BizyAir, which got it flagged as suspicious on r/comfyui more than once. It's a legitimate commercial pack from SiliconFlow, not malware; the registry bug was patched mid-2025.

Run fails immediately with no useful error. Check your API key first - this is entirely a cloud round-trip, so an auth failure or a run out of credits is the most common cause of a silent or generic failure here, not a bad parameter.

Two outputs, one workflow. Don't forget you're getting both a Gaussian splat and a mesh from one run - decide upfront which one your downstream tooling actually needs, since they're not interchangeable (splats render fast and look great from novel angles but aren't directly editable geometry the way a mesh is).