Moebius Model Loader

Two nodes in this pack, and this is the boring half - in the best way. Moebius Model Loader loads a Moebius inpainting checkpoint plus the shared PixelHacker VAE and hands the pair to Moebius Inpaint as a pipe. What makes it worth its own page is the dropdown: it doubles as a one-click downloader, so your first run is "pick an entry, click, wait for ~900 MB," not "go hunt HuggingFace for the right file path."

Moebius, for context, is hustvl's 0.22B diffusion inpainting specialist (ECCV 2026), distilled from a 10B-parameter teacher and small enough that it runs on cards that can't load a FLUX Fill. When it released in June 2026 the r/StableDiffusion thread was part enthusiasm ("great for blemish removal on gpu-poor machines"), part healthy skepticism about the "0.2B at 10B performance" marketing. The weights are MIT; this pack is Apache-2.0.

The one input that matters: model_name

The loader has exactly two inputs and only one you'll think about. model_name lists any checkpoint you've dropped into ComfyUI/models/moebius/, plus four (download) entries that fetch the official weights from HuggingFace on first use:

• (download) pretrained - the general model, fine-tuned on a large mixed corpus. Your default for everyday object removal.
• (download) ft_places2 - scenes, buildings, landscapes, streets, interiors.
• (download) ft_celebahq - studio-style portraits.
• (download) ft_ffhq - face close-ups; glasses, hands-in-front-of-face, occlusion repair.

All four are the same architecture trained on different data, so pick by content. The rule of thumb from the author: the face checkpoints shine when the face fills the frame - if the face is small inside a bigger scene, pretrained or ft_places2 usually beat them. Every (download) choice also pulls the shared VAE (~335 MB, from the separate PixelHacker repo) automatically, and it's shared across all four checkpoints, so you download it once.

The other input, dtype, defaults to fp32 and the tooltip is the whole argument: the model is small, fp32 is cheap, and the upstream authors recommend it for best quality. Reach for fp16/bf16 only if you're genuinely VRAM-starved on a card that can't fit fp32.

The output

One output: pipe (MOEBIUS_PIPE), a custom type that only Moebius Inpaint consumes. That's the entire graph: Moebius Model Loader → Moebius Inpaint. There's no secret routing to other packs.

Install and the download flow

ComfyUI Manager (search "Moebius Inpainting") or:

cd ComfyUI/custom_nodes
git clone https://github.com/b2renger/ComfyUI_moebius_inpainting
pip install -r ComfyUI_moebius_inpainting/requirements.txt

restart, and you're done - no extra model setup. The requirements are all common dependencies (diffusers, einops, timm, opencv-python, huggingface_hub, numpy, Pillow). Torch is deliberately not pinned, because the upstream repo pins torch==2.7.1+cu130 and letting that into your ComfyUI install would fight whatever torch build ComfyUI portable ships. There's no flash-linear-attention in the requirements either - it's a distillation-teacher dependency, irrelevant to inference. If you'd rather place files by hand, the loader also picks up whatever you drop into models/moebius/ (it filters out the VAE folder and huggingface-hub bookkeeping), and the (download) entries vanish once the matching checkpoint is present locally.

Where people get tripped up

• First run downloads. Pick a (download) entry and the first load grabs the checkpoint (~900 MB) plus the VAE; it prints progress to the console. That's not a hang, that's HuggingFace being on the other end of your connection.
• The checkpoint layout matters if you self-host files: models/moebius/pretrained/diffusion_pytorch_model.bin, and so on, with the VAE under models/moebius/vae/. Get the subfolder wrong and the loader won't see it.
• GPU support is whatever your torch supports - pure PyTorch, no custom kernels, works on Blackwell (RTX 50xx), Ada (RTX 40xx), older cards, CPU, and Apple Silicon.