.gitmodules

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-[submodule "InstantMesh"]
-	path = InstantMesh
-	url = https://github.com/TencentARC/InstantMesh.git

.python-version

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-3.12

CLAUDE.md

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-# Claude notes — instamesh-docker
-
-Agent context for this repo. Read before non-trivial changes.
-
-## What this is
-
-Docker wrapper around upstream [TencentARC/InstantMesh](https://github.com/TencentARC/InstantMesh)
-for single-image → 3D mesh generation. **Not** a fork of InstantMesh
-itself — this repo owns the docker build + compose plus a small set of
-patches that fix known upstream build breakage.
-
-## Repo shape
-
-```
-instamesh-docker/
-  InstantMesh/          # UNTRACKED — upstream clone. Operators run
-                        # `git clone https://github.com/TencentARC/InstantMesh.git`
-                        # once before `docker compose build`. Not a submodule.
-  docker/
-    Dockerfile          # patched (see "Patches applied" below)
-    compose.yml         # GPU compose, named volume for model cache, port 43839
-    requirements.txt    # InstantMesh's pip deps, kept here so the Dockerfile
-                        # can ADD them without changing the upstream tree
-  main.py, pyproject.toml, uv.lock, .python-version
-                        # leftover uv scaffolding from initial repo init;
-                        # NOT part of the build path. Don't extend these.
-  README.md
-  CLAUDE.md
-```
-
-`InstantMesh/` is intentionally untracked. Don't add it as a submodule
-or vendor it into source control — keep the upstream boundary clean.
-
-## Patches applied (vs. upstream)
-
-The Dockerfile is forked from InstantMesh's docker config with three
-deliberate changes — call out any churn around these:
-
-1. **CUDA aligned to 12.1.** Base image is
-   `nvidia/cuda:12.1.0-runtime-ubuntu22.04`; conda installs `cuda` from
-   `nvidia/label/cuda-12.1.0`; pip installs `torch==2.1.0 + cu121` and
-   `xformers==0.0.22.post7`. All four must stay in sync. Don't bump CUDA
-   without re-pinning all of them.
-2. **Duplicate `conda create` removed.** Upstream's Dockerfile created
-   the conda env twice; the second one nuked the first's pinned
-   packages. Don't reintroduce.
-3. **`onnxruntime` added.** Missing from upstream's `requirements.txt`
-   (InstantMesh issue #175). Installed as a separate `pip install` step
-   after the bulk requirements install so it's not silently dropped by
-   the `|| true` below.
-
-## Intentional `|| true` on the bulk pip install
-
-```dockerfile
-RUN pip install --no-cache-dir -r requirements.txt || true
-```
-
-This is **on purpose**. The requirements include `nvdiffrast` via a git
-URL that needs `--no-build-isolation`, which pip's bulk install won't
-do. We let that line fail, then install `nvdiffrast` correctly:
-
-```dockerfile
-RUN pip install git+https://github.com/NVlabs/nvdiffrast.git --no-build-isolation
-```
-
-Don't "fix" the bulk install by removing `|| true` — the build will
-hard-fail on nvdiffrast.
-
-## Hardware contract
-
-- **NVIDIA GPU required**, exposed through Docker via the nvidia
-  runtime. InstantMesh wants **≥ 12 GB VRAM** comfortably; smaller cards
-  may OOM during inference.
-- **CUDA 12.1** — see patch #1 above.
-
-## Build is slow + brittle
-
-Expect a 15+ minute first build. Conda + nvdiffrast compilation +
-PyTorch wheels add up. Triggers that re-run the slow layers:
-- Editing `docker/requirements.txt` invalidates the conda install layer.
-- Editing the Dockerfile's `apt-get install` line redoes the whole
-  thing from a fresh base image.
-
-Lean on Docker's layer cache; don't restructure for "cleanliness"
-without measuring the rebuild cost.
-
-## Compose
-
-```yaml
-ports:
-  - "43839:43839"      # Gradio app, host-published
-volumes:
-  - instantmesh-models:/workspace/models   # weights cache (named volume)
-```
-
-No host-network mode, no bind mounts for code (the upstream tree is
-COPYed at build time). If you need to iterate on InstantMesh source,
-edit `InstantMesh/` locally and rebuild — there's no dev-mode wiring.
-
-## Where outputs go
-
-InstantMesh's Gradio app writes generated meshes inside the container
-(under `/workspace/instantmesh/`). There's **no bind mount** for
-outputs in `compose.yml` — operator downloads results from the Gradio
-UI. If you find yourself wanting to bind-mount an outputs dir, mirror
-`hunyuan3d-sunnie/docker/outputs/`.
-
-## Don't-touch zones
-
-- The CUDA 12.1 pins (base image / conda label / PyTorch wheel index /
-  xformers wheel) — load-bearing as a set.
-- The `|| true` on bulk requirements install — required for nvdiffrast.
-- The `onnxruntime` separate install line — load-bearing per upstream #175.
-- The untracked `InstantMesh/` directory — let operators reclone.
-
-## Bigger picture
-
-Personal/one-off project, GPU-only. Runs on the dev tower; not part of
-the homelab production stack. Sibling project `hunyuan3d-sunnie/` uses
-a different model (Hunyuan3D-2) for the same general shape of problem
-on a 10 GB card — pick that one if VRAM is tight.

InstantMesh

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-Subproject commit 08822c52fdc399b93ea00e4fa9e596344ed52ccc

README.md

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-# instamesh-docker
-
-Docker wrapper around [TencentARC/InstantMesh](https://github.com/TencentARC/InstantMesh)
-for single-image → 3D mesh generation. Patches upstream's docker setup to
-fix a couple of known build issues (missing `onnxruntime`, duplicate
-conda env create) and pins CUDA to 12.1 to match the InstantMesh
-PyTorch / xformers wheels.
-
-## Requirements
-
-- NVIDIA GPU with CUDA 12.1 support and ≥ 12 GB VRAM (InstantMesh is
-  not VRAM-friendly).
-- Docker + the NVIDIA Container Toolkit (`nvidia` runtime exposed to
-  compose).
-- Disk: several GB for the conda env + model weights.
-- Network egress on first build to pull miniconda, PyTorch wheels, and
-  the nvdiffrast / InstantMesh dependencies (long build — 15+ minutes
-  is normal).
-
-## Quick start
-
-```bash
-# Clone the upstream model repo into this directory (the Dockerfile
-# COPYs it via the build context; not a git submodule). Operator-managed;
-# not committed.
-git clone https://github.com/TencentARC/InstantMesh.git
-
-# Build and run
-cd docker
-docker compose build
-docker compose up
-
-# Gradio UI at http://localhost:43839
-```
-
-## Project structure
-
-```
-instamesh-docker/
-  InstantMesh/         # cloned upstream — operator-managed, not committed
-  docker/
-    Dockerfile         # patched build (CUDA 12.1, onnxruntime fix)
-    compose.yml        # GPU compose, named volume for model cache
-    requirements.txt   # Python deps installed inside the conda env
-  README.md
-  CLAUDE.md
-```
-
-## Notes
-
-- First run downloads model weights into the `instantmesh-models`
-  named volume (mounted at `/workspace/models`). Several GB.
-- The build uses miniconda inside the container with a `python=3.10`
-  env named `instantmesh` — not the host's Python. The repo-root
-  `pyproject.toml` / `uv.lock` / `.python-version` are leftover uv
-  scaffolding and are not part of the build path.
-- See `CLAUDE.md` for the agent-facing gotchas: the patched bits, the
-  intentional `|| true` on the bulk requirements install, and the CUDA
-  pin reasoning.

uv.lock

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-version = 1
-revision = 3
-requires-python = ">=3.12"
-
-[[package]]
-name = "instamesh-docker"
-version = "0.1.0"
-source = { virtual = "." }