horde-engine 3.3.3

A wrapper around ComfyUI to allow use by AI Horde.

horde-engine

horde-engine wraps a pinned commit of ComfyUI so the AI Horde can run ComfyUI-designed inference pipelines programmatically. It is the default inference backend for AI Horde since v1.0.0.

Python >=3.12. GPU/CUDA required. The package name on PyPI is horde-engine; the Python module is hordelib.

Community: AI Horde Discord.

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Quick start

1. Install horde-engine together with a PyTorch build for your hardware (see Choosing a PyTorch build). For NVIDIA:

   # CUDA 13+ driver -> torch 2.12.0 (the default line):
   pip install --extra-index-url https://download.pytorch.org/whl/cu130 horde-engine
   # CUDA 12.x driver -> the newest CUDA 12.8 build is torch 2.11.0:
   pip install --extra-index-url https://download.pytorch.org/whl/cu128 horde-engine "torch==2.11.0"
   

2. Set AIWORKER_CACHE_HOME to a model directory (see Model directory layout).

3. Call hordelib.initialise() once per process, then use the public API:

   import hordelib
   hordelib.initialise()
   
   from hordelib.api import HordeLib, ImageGenPayload
   
   horde = HordeLib()
   payload = ImageGenPayload(
       prompt="an ancient llamia monster",
       model="Deliberate",
       sampler_name="k_dpmpp_2m",
       cfg_scale=7.5,
       ddim_steps=25,
       width=512,
       height=512,
       seed=123456789,
   )
   result = horde.basic_inference_single_image(payload)
   result.image.save("test.png")
   

hordelib.initialise() wipes sys.argv -- parse command-line arguments first. It also forbids dev-mode paths containing spaces (do not clone the repo to a path with spaces) and raises if you try to import any ComfyUI-touching code before calling it.

For more usage patterns, see the public API docs and the example scripts in examples/. For complete API surface reference, consult the test suite under tests/.

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Installation

Requires an NVIDIA GPU with CUDA. On Linux, install the NVIDIA CUDA Toolkit first. Systems with 16 GB RAM or less need swap space and may need an enlarged /tmp:

sudo mount -o remount,size=16G /tmp

From PyPI

# CUDA 13+ driver -> torch 2.12.0 (the default line):
pip install --extra-index-url https://download.pytorch.org/whl/cu130 horde-engine
# CUDA 12.x driver -> the newest CUDA 12.8 build is torch 2.11.0:
pip install --extra-index-url https://download.pytorch.org/whl/cu128 horde-engine "torch==2.11.0"

From source (development)

git clone https://github.com/Haidra-Org/hordelib.git
cd hordelib
uv sync   # installs the latest torch (2.12.0, the CUDA 12.6 build) -- see "Choosing a PyTorch build"

Choosing a PyTorch build

horde-engine does not pin torch: it declares torchvision as a version range, and torchvision pins the exact matching torch in its metadata, so installing the library pulls the latest supported torch (2.12.0 -> torchvision 0.27.0). You only pick the build (which CUDA/ROCm wheels) for your hardware. torch and torchvision are both routed per-build in [tool.uv.sources] so they always share one CUDA build (a torchvision left on generic PyPI would pull a different CUDA build and break the install).

torchaudio is not installed by default: it is the only package in the trio with no +cu132 wheel, and audio generation is currently unsupported. ComfyUI's eager import torchaudio is satisfied by a lazy stub (hordelib.utils.torch_build) when it is absent; audio operations raise a clear error only if actually used. Install a matching torchaudio ad-hoc if you need audio (cu126/cu130/cpu only).

For development, uv sync --extra cu126 installs the CUDA 12.6 build of torch 2.12.0. It runs on any CUDA 12.6+ driver and, via NVIDIA driver backward-compatibility, on CUDA 13 drivers too -- the broadest single build. Pick a different build with the matching extra:

uv sync --extra cu132   # exact CUDA 13.2 build
uv sync --extra cu130   # CUDA 13.0/13.1 build (also runs on a 13.2 driver)
uv sync --extra cu126   # CUDA 12.6+ build (also runs on CUDA 13 drivers)
uv sync --extra cpu     # no GPU; ~100x slower, testing only

With pip, select the index with --extra-index-url (install torch and torchvision from the same index):

Hardware	index	Notes
NVIDIA, CUDA 13.2 (exact)	cu132	torch 2.12.0; no torchaudio wheel (audio unavailable on this build).
NVIDIA, CUDA 13.0/13.1 driver	cu130	torch 2.12.0; also runs on a 13.2 driver.
NVIDIA, CUDA 12.6+ driver	cu126	torch 2.12.0; the only CUDA-12 build of 2.12.0.
NVIDIA, CUDA 12.x, older torch	cu128	No 2.12.0 wheel; caps at torch 2.11.0 (add "torch==2.11.0").
AMD (Linux only)	rocm6.4	torch 2.9.1 only (see note below).
No GPU	cpu	~100x slower; testing only.

ROCm: only torch 2.9.1 (ROCm 6.4) is currently installable. PyTorch publishes torch 2.10-2.12 on rocm7.x but not the matching pytorch-triton-rocm (3.6.0/3.7.0) those wheels hard-depend on, so they cannot be resolved yet. Install it ad-hoc: uv pip install torch==2.9.1 torchvision pytorch-triton-rocm --extra-index-url https://download.pytorch.org/whl/rocm6.4.

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Architecture (v3)

The v3 refactor replaced the legacy dict-heavy pipeline machinery with a typed, testable, registry-driven layer. The key pieces:

Layer	Location	Role
Public API	hordelib/api.py	Declared consumer surface. Workers import from here only.
Bootstrap	hordelib/initialisation.py	initialise() -- manifest installer, path patching, ComfyUI import, monkeypatches.
Typed pipeline	hordelib/pipeline/	ImageGenPayload (pydantic, clamp-dont-reject), PipelineTemplate/ParamBinding, priority-ordered PipelineRegistry, pure graph patch functions, IO/model resolution. All GPU-free and unit-tested.
Execution bridge	hordelib/execution/	ExecutionBackend protocol, in-process ComfyUI backend, VRAM monkeypatches, pure graph utilities.
Environment manifest	hordelib/installation/	manifest.json pins ComfyUI commit + external custom nodes. EnvironmentInstaller checks out/clones idempotently.
ComfyUI bridge	hordelib/comfy_horde.py	Comfy_Horde -- the actual ComfyUI process. do_comfy_import() applies monkeypatches.
Model managers	hordelib/model_manager/	Per-category model records (pydantic), download/validate/lookup, LoRA/TI adhoc cache.
Pipeline JSON	hordelib/pipelines/	Backend-format pipeline files executed at runtime.
Pipeline designs	hordelib/pipeline_designs/	ComfyUI-app-editable source workflows (convert to pipelines/).
Custom nodes	hordelib/nodes/	First-party Horde nodes (HordeCheckpointLoader, HordeImageOutput) and vendored compat nodes. External nodes (controlnet aux, QR) are git-pinned in the manifest.

Pipeline flow

dict payload --> normalize (horde_compat) --> ImageGenPayload
  --> resolve_context (model files, loras, TIs)
  --> registry.select (predicate + priority --> PipelineTemplate)
  --> materialize (bindings + patch steps --> ComfyGraph)
  --> backend.run_pipeline --> ResultingImageReturn

Key invariants

• initialise() before everything. Importing HordeLib or any ComfyUI-touching code before initialise() raises RuntimeError. The test suite enforces this.
• Public API only. Consumers import from hordelib.api. Anything not re-exported there is internal and may change without notice. Enforced by tests/meta/test_public_api_contract.py.
• Clamp, don't reject. Payload validation coerces bad values to defaults rather than raising -- the Horde contract requires tolerance.
• Node titles are the parameter namespace. A KSampler titled sampler produces dotted parameters sampler.cfg, sampler.seed, etc. Renaming a title silently breaks parameter mapping.
• Pipeline selection is registry-driven. Predicates + explicit priorities replace the old if/elif tree. Adding a new baseline requires one family module, one template, one register call, and one inference test. See Adding a baseline.

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Model directory layout

AIWORKER_CACHE_HOME must follow the AI Horde directory structure:

<AIWORKER_CACHE_HOME>/
    clip/
    codeformer/
    compvis/
        Deliberate.ckpt
        ...
    controlnet/
    embeds/
    esrgan/
    gfpgan/
    safety_checker/

Models are managed through SharedModelManager (download, validate, list available). See examples/download_all_sd_models.py for bulk downloading.

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Development

Requirements: git, uv, AIWORKER_CACHE_HOME set to a model directory, and a CUDA GPU.

Build, lint, test

The whole toolchain lives in one uv-managed .venv. Sync it once (installs the CUDA 12.6 build of torch 2.12.0 via [tool.uv.sources]), then drive tools with uv run --no-sync:

uv sync                          # one-time, and after dependency changes

# Full test suite. The import guard runs first in a clean process (it asserts no ComfyUI
# import happens before initialise()), then the rest of the suite runs:
uv run --no-sync pytest -x tests/meta -k test_no_initialise_comfy_horde
uv run --no-sync pytest -x tests --cov --ignore=tests/meta --durations=20

uv run --no-sync pytest -k <pattern>           # run a subset
uv run --no-sync pre-commit run --all-files    # lint + format gate (black + ruff + mypy)
uv build                                       # build sdist + wheel

(Equivalently, activate .venv and call pytest / pre-commit directly.)

The first test run downloads many multi-GB models and is very slow. Most CI/agent sandboxes cannot run the full inference suite; prefer uv run --no-sync pre-commit run --all-files for quick feedback.

Tests require a CUDA GPU, AIWORKER_CACHE_HOME, CIVIT_API_TOKEN, and the committed images_expected/ reference images. Image-output tests compare generated images against references via cosine/histogram similarity. Set HORDELIB_SKIP_SIMILARITY_FAIL=1 to downgrade similarity failures to skips on different hardware.

Key test areas

Path	What it covers
tests/pipeline/	Payload models, graph operations, template integrity, registry selection -- all GPU-free
tests/execution/	Graph utilities, modality seams -- GPU-free
tests/meta/	Public API contract, initialise()-before-use guard
tests/test_horde_inference_*.py	Full GPU inference with image-similarity oracles
tests/installation/	Manifest round-trip, installer idempotency

Designing and converting pipelines

Design pipelines in the ComfyUI web app (cd ComfyUI && python main.py, then http://127.0.0.1:8188/). Assign stable title attributes to nodes -- these become the dotted parameter names. Save the design in hordelib/pipeline_designs/ as pipeline_<name>.json.

Convert to backend format by running the pipeline through the embedded ComfyUI (it auto-saves comfy-prompt.json), then save the result in hordelib/pipelines/ with the same filename.

Do not hand-edit pipelines/*.json if a pipeline_designs/ source exists.

Updating ComfyUI or external custom nodes

Change the relevant commit SHA in hordelib/installation/manifest.json:

• comfyui_ref for ComfyUI itself
• ref for each external custom node

Full 40-character SHAs only. The next hordelib.initialise() (or python -m hordelib.installation) applies the update. Run the full test suite afterwards.

Local wheel build

uv build        # writes dist/horde_engine-<version>.tar.gz and -<version>-py3-none-any.whl

The version is derived from git tags by hatch-vcs: a clean checkout sitting exactly on a vX.Y.Z tag builds X.Y.Z; anything else builds a dev version (e.g. 1.2.3.devN). ComfyUI is not bundled into the wheel -- it is fetched per hordelib/installation/manifest.json on first initialise(). To smoke-test the wheel away from the source tree (so the repo's hordelib/ cannot shadow the installed package), install it into a throwaway venv created outside the repo.

Release process

Releases are cut by pushing a version tag. The tag is the version (hatch-vcs), and the release workflow builds with uv build and publishes to PyPI via trusted publishing (then creates a GitHub Release for the tag).

git checkout main && git pull
git tag vX.Y.Z          # must be vX.Y.Z and sit on the commit you want released
git push origin vX.Y.Z

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Documentation

Document	Content
docs/public_api.md	Declared consumer surface and key patterns
docs/adding-a-baseline.md	Checklist for supporting a new model architecture
docs/modality-readiness.md	Audit of audio/video modality seams
docs/plans/major-overhaul-1.md	v3 phased refactor plan and status
AGENTS.md	Comprehensive codebase map, conventions, and sensitive areas

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License

GNU Affero General Public License v3.0

Acknowledgments

horde-engine builds on a large number of open-source projects:

• ComfyUI (GPLv3) -- the graph/nodes inference engine this project wraps.
• comfyui_controlnet_aux (Apache 2.0) -- ControlNet preprocessors, installed via the manifest.
• ComfyUI-layerdiffuse -- layer diffusion nodes (vendored with compat fixes).
• facerestore_cf -- face restoration nodes (vendored with compat fixes).
• ComfyQR -- QR code generation, installed via the manifest.
• horde-sdk and horde-model-reference -- payload types and model metadata consumed by this library.