PRACTICAL FIELD GUIDES
From setup
to usable asset.
Steps, checks and realistic trade-offs.
Version-aware paths for local inference, optimization and downstream 3D workflows.
Install TRELLIS.2 Locally
A source-first setup path for the official Linux and NVIDIA CUDA environment.
TRELLIS.2 in ComfyUI
Evaluate community custom nodes without confusing wrapper support with official TRELLIS.2 support.
TRELLIS.2 INT8 ConvRot in ComfyUI
Install and validate the new 5.25 GB INT8 ConvRot diffusion checkpoint without mistaking file size for total VRAM use.
TRELLIS.2 on Windows
Understand the gap between the official Linux baseline and community Windows routes before troubleshooting.
TRELLIS.2 Low-VRAM Route Planner
Choose between BF16, INT8 ConvRot, GGUF Q8, and GGUF Q4 by runtime and measured workflow — not a universal VRAM number.
Understand TRELLIS.2 GGUF Workflows
A practical way to assess community GGUF checkpoints, loader compatibility, and quality tradeoffs.
Bring a TRELLIS.2 GLB into Blender
Import, audit, repair, and optimize a generated GLB while preserving the original asset.
Optimize a Generated GLB for the Web
Reduce transfer and rendering cost while protecting silhouette, materials, and the intended camera distance.
Prepare a TRELLIS.2 Mesh for 3D Printing
Convert a visual surface into a verified solid with scale, wall thickness, orientation, and printer constraints.
TRELLIS.2 AMD GPU Compatibility
An evidence-graded matrix of which Radeon and Ryzen AI GPUs run TRELLIS.2, through which runtime, and on which operating system.
TRELLIS.2 with ROCm on Windows
What works, what is still work in progress, and why the stock ComfyUI wheels fail on a Radeon card under Windows.
Run TRELLIS.2 Without Python Using trellis.cpp
A native C++/GGML implementation of the full TRELLIS.2 pipeline with CUDA, ROCm and Vulkan backends on Linux and Windows.
Trellis Studio: A Desktop Front End for TRELLIS.2
Drag an image in, get a textured mesh out, with a local gallery and no command line or Python environment.
TRELLIS.2 Q8 Weights
A roughly 9.5–10 GB GGUF model set described as visually near-lossless, with fit determined by the complete measured workflow.
TRELLIS.2 Q4 Weights
A roughly 6 GB GGUF model set for tighter memory budgets, with a larger quality risk that must be checked on the target workflow.