SeFi-Image Loader

SeFi-Image is the research model family that r/StableDiffusion spent June 2026 half-excited, half-arguing about. Built on "semantic-first diffusion" by Liu et al. (jmliu206), it separates the latent into a semantic stream and a texture stream, denoising structure slightly ahead of detail so the texture stream has a cleaner anchor. The headline claim: the 5B model was trained on roughly a tenth to a fifth of Z-Image's compute and still benchmarks near Qwen-Image and Z-Image. That's genuinely interesting research - and the community's honest verdict was that it's more interesting as a proof-of-concept than as your daily driver.

This loader is the part that makes trying it painless. The whole family - 1B, 2B, and 5B in Base, RL, and DMD2-distilled Turbo flavours - is one dropdown, and the first run just downloads whatever you pick. You reach for this node specifically because you want SeFi inside a ComfyUI graph without ever touching the upstream repo's CLI.

How it works

Three moving parts, and the pack does the awkward ones for you:

• Weights auto-download from Hugging Face on first load into SeFi's cache (outputs/model_weights/sefi_inference). Expect multi-GB on first run.
• The sefi package isn't pip-installable - it ships no packaging metadata. So the pack downloads its source as a GitHub tarball using Python's stdlib (urllib + tarfile, no git binary needed) and drops it on sys.path. Lazy, so a network hiccup never blocks ComfyUI startup; it just retries next run.
• The loaded pipeline is cached across executions, so re-queueing doesn't reload weights. The cache holds exactly one model - switch checkpoints and the old one is unloaded to free VRAM.

The inputs that matter

Only three, and you'll touch one of them.

• checkpoint - the 7 HF ids (SeFi-Image/SeFi-Image-{1,2,5}B-turbo, the Base variants, and 5B-RL). Start with SeFi-Image-2B-turbo: the README's recommended balance of speed, quality, and VRAM.
• dtype - leave on auto. Reach for bfloat16 if auto picks something wasteful on your card; float32 is only for debugging.
• device - auto is right. cuda/cpu exist if you need to force it.

Output: sefi_pipe

It outputs SEFI_PIPE - a custom type, not an IMAGE. Don't try to save from the loader; wire sefi_pipe into the SeFiSampler's matching input and then to a SaveImage. The loader's only job is to exist and hold the model in memory.

Installing it

Two routes, both standard:

ComfyUI-Manager - it may not be in the registry search yet (this pack is brand new), so use Install via Git URL:

https://github.com/AMXELA-Official/ComfyUI-SeFiImage

Then restart ComfyUI.

Manual:

cd ComfyUI/custom_nodes
git clone https://github.com/AMXELA-Official/ComfyUI-SeFiImage
cd ComfyUI-SeFiImage
pip install -r requirements.txt

The requirements are diffusers, transformers, accelerate, safetensors, huggingface_hub, omegaconf, pillow - most already ship with ComfyUI. The heavy lift is the weights download, not the install.

Where people get burned

• CUDA out of memory. The upstream pipeline has no CPU-offload path. Rough guide: 1B is comfortable on 8–12 GB, 2B fits ~12 GB, 5B likely OOMs on 12 GB. Drop to a smaller checkpoint or lower resolution.
• ModuleNotFoundError: sefi - the source fetch failed, usually no network on first run. Re-queue with connectivity, or manually clone jmliu206/SeFi-Image into the pack's SeFi-Image/ subfolder.
• Slow first run - that's the multi-GB download. Later runs reuse the cache.
• Wrong-looking output on Base/RL - that's not the loader's fault; you left the Turbo-tuned defaults in the sampler. See the SeFiSampler article.

This is a very young wrapper (first commit July 2026, zero search impressions), so treat it as early-adopter territory: the code is a thin, MIT-licensed layer and the model itself is MIT, but you're on the bleeding edge of a research release.