Initial release

docker/Dockerfile.demo

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+# Demo/runtime image: the reference image (CUDA 12.8 runtime + toolchain)
+# plus a Go toolchain, so both libtrellis2.so (CUDA) and the Go demo server
+# build against the SAME glibc/libstdc++ that run them. NixOS host binaries
+# don't survive inside the container (different dynamic loader), hence this.
+#
+# Build:  docker build -f docker/Dockerfile.demo -t trellis2-demo docker
+# Usage:  see scripts/demo.sh
+FROM trellis2-ref
+
+# The mounted source tree's CMake fetches its own pinned CGAL + Boost header
+# set. The image only provides the archive tools, so dependency versions have
+# one owner instead of drifting between the demo and downstream builds.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        ca-certificates curl unzip \
+    && rm -rf /var/lib/apt/lists/*
+
+ADD https://go.dev/dl/go1.24.4.linux-amd64.tar.gz /tmp/go.tgz
+RUN tar -C /usr/local -xzf /tmp/go.tgz && rm /tmp/go.tgz
+ENV PATH=/usr/local/go/bin:$PATH
+# go.sum is committed; `go build` fetches the single dep (purego) on demand.
+# A committed server/vendor/ dir (if present) is used automatically.

docker/Dockerfile.ref

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+# Python reference environment for parity dumps.
+#
+# Deliberately stock-PyTorch only: no FlexGEMM / flash-attn / o-voxel / CuMesh
+# CUDA extensions. Sparse attention and sparse conv are monkeypatched to dense
+# equivalents by scripts/ref_common.py (batch=1 makes them equal), so reference
+# activations can be produced on GPU or CPU without custom kernels — that also
+# keeps the RTX 5070 Ti (sm_120) out of extension-build trouble.
+#
+# Build:  docker build -f docker/Dockerfile.ref -t trellis2-ref docker
+# Run :   docker run --rm --device nvidia.com/gpu=all \
+#             -v "$PWD":/work -v "$PWD/../python/TRELLIS.2":/trellis2 \
+#             -e PYTHONPATH=/trellis2 -w /work trellis2-ref python scripts/...
+FROM pytorch/pytorch:2.7.1-cuda12.8-cudnn9-devel
+
+RUN pip install --no-cache-dir \
+    "transformers==4.57.1" \
+    safetensors \
+    pillow \
+    numpy \
+    easydict \
+    opencv-python-headless \
+    trimesh \
+    tqdm \
+    imageio \
+    gguf