ComfyUI-mflux-AnyModel

Run any mflux model inside ComfyUI on Apple Silicon (MLX/Metal). A single loader and sampler pair drives every mflux model family — FLUX.1, FLUX.2 Klein, Qwen-Image, Z-Image, Ideogram 4, FIBO, ERNIE-Image — plus their image-conditioned variants, through one consistent interface.

The node is built around a capability registry: for the selected model it inspects the real generate_image signature and forwards only the parameters that model actually accepts. Parameters a model ignores are dropped with a note instead of silently corrupting the result, and an input image is never handed to a model that cannot use it.

Why this exists

Most mflux models are driven the same way, but not all. Ideogram 4 is preset-driven: its step count, per-step guidance schedule, and noise schedule are calibrated together, so passing a loose steps/guidance value silently replaces the calibrated schedule and degrades the image with no warning. Edit and image-to-image variants each require a different image argument (masked_image_path, depth_image_path, redux_image_paths, controlnet_image_path, image_paths).

This node encodes those differences once, so the common case stays trivial and the edge cases fail loudly and clearly rather than producing a wrong image.

Requirements

• Apple Silicon (M1–M5). MLX and Metal only; there is no CUDA path.
• ComfyUI.
• mflux >= 0.18.0 (installed automatically as a dependency).

Installation

ComfyUI Manager: search for ComfyUI-mflux-AnyModel and install.

Manual:

cd ComfyUI/custom_nodes
git clone https://github.com/fxd0h/ComfyUI-mflux-AnyModel
ComfyUI/.venv/bin/pip install "mflux>=0.18.0"

Restart ComfyUI.

Nodes

Node	Purpose
mflux Model Loader	Resolve a model (builtin alias, HuggingFace repo, or local path) with quantization and an optional LoRA chain. Outputs a typed MFLUX_MODEL handle that carries the model and its capability profile.
mflux Sampler	Generate from the handle. Reads the capability profile and forwards only valid parameters. Outputs the image and an info string listing what was forwarded or dropped.
mflux LoRA	Chainable LoRA feeder (local file, HuggingFace repo, or repo:filename.safetensors). Stack several to compose.
mflux Image	Typed image feeder: a primary image plus an optional mask (for fill) and an optional depth/control map (for depth and controlnet models).
mflux Upscale (SeedVR2)	One-step SeedVR2 upscaler. Loads its own model.

Supported models

Text-to-image (loader + sampler, no image input):

dev, schnell, krea-dev, qwen, z-image, z-image-turbo, flux2-klein-4b, flux2-klein-9b, ernie-image, ernie-image-turbo, fibo, fibo-lite, ideogram4.

Image-conditioned (loader + sampler + mflux Image):

dev-kontext (instruction edit), dev-fill (inpaint, needs a mask), dev-depth (depth-guided, needs a depth map), dev-redux (image reference), dev-controlnet-canny (needs a control image), qwen-image-edit, fibo-edit.

A HuggingFace repo or local path can be typed into the loader's model_path to run a model that is not in the dropdown; it is dispatched to the right architecture by name, and rejected with a clear message if it is not a sampler model (for example a SeedVR2 upscaler).

How the capability system works

On load, the node resolves the alias to the correct mflux variant class (so a fill or controlnet model is never silently run as plain text-to-image), then builds a CapabilityProfile:

• the set of generate_image parameters, read by introspection;
• which parameters are required, and which image roles the model declares;
• a small table of facts that introspection cannot reveal — which models are preset-driven, and where negative_prompt is accepted but ignored.

The sampler then applies three rules:

1. Hard-block an input image on a model that does not accept one, and a missing required image on a model that needs one.
2. For preset-driven models, ignore steps/guidance in auto mode (matching the mflux CLI) and note it. override mode forwards them and warns that the calibrated schedule is being replaced.
3. Drop with a note any parameter the model accepts but ignores, and forward only the arguments the model's generate_image actually declares.

This is verified by self-tests that introspect the installed mflux package, so they catch upstream signature changes rather than drifting.

Notes on mflux features

The node is fork-agnostic: it adapts to whatever mflux is installed. Capabilities added by recent mflux work — LyCORIS LoKr LoRA loading, Z-Image sigma schedules and shift, Qwen edit fixes — are exposed automatically when present and simply absent when not.

Running the tests

python tests/test_dispatch.py
python tests/test_sampler.py

No weights are downloaded; the tests only introspect the installed mflux package.

Credits

• mflux by Filip Strand — the MLX implementation this node drives.
• ComfyUI.

License

MIT. See LICENSE.