Extensions/ComfyUI-SeFiImage
ComfyUI Extension

ComfyUI-SeFiImage

ComfyUI nodes for SeFi-Image (Semantic-First Diffusion) text-to-image.

By AMXELA-Official·Created about a month ago·Updated about a month ago· 0
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ComfyUI-SeFiImage

ComfyUI custom nodes for SeFi-Image — a Semantic-First Diffusion text-to-image model family from Liu et al. (released July 2026). SeFi separates the latent into a semantic stream and a texture stream and denoises semantic structure slightly ahead of texture detail, giving the texture stream a cleaner structural anchor. The result is strong prompt following, text rendering and bilingual generation at a fraction of the usual training cost.

This pack wraps SeFi's official sefi inference package as two ComfyUI nodes so you can generate directly on the graph.

Features

  • Two simple nodes: a Loader and a Sampler — wire Loader → Sampler → SaveImage.
  • Supports every published checkpoint: 1B / 2B / 5B in Base, RL and Turbo flavours.
  • Weights download automatically from Hugging Face on first use.
  • No manual setup of the upstream package: the pack fetches the sefi source itself (stdlib tarball download — no git required on the host) and adds it to sys.path on first use.
  • The loaded pipeline is cached across executions, so repeated generations don't reload the model.

Installation

Via ComfyUI-Manager (recommended)

Use Install via Git URL in ComfyUI-Manager with:

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

Restart ComfyUI.

Dependencies

Installed from requirements.txt (most already ship with ComfyUI):

diffusers, transformers, accelerate, safetensors, huggingface_hub, omegaconf, pillow. PyTorch is provided by your ComfyUI install.

The upstream sefi package itself is not listed here because it is not pip-installable (it ships no packaging metadata). The pack downloads its source automatically — see How the sefi source is fetched.

Nodes

SeFi-Image Loader

Loads a SeFi inference pipeline. On first use it downloads the selected checkpoint from Hugging Face into SeFi's cache (outputs/model_weights/sefi_inference).

| Input | Type | Notes | |-------|------|-------| | checkpoint | choice | HF checkpoint id (see Models). | | dtype | choice | auto (let SeFi decide) / bfloat16 / float16 / float32. | | device | choice | auto / cuda / cpu. |

| Output | Type | |--------|------| | sefi_pipe | SEFI_PIPE |

SeFi-Image Sampler

Generates one image from a text prompt.

| Input | Type | Default | Notes | |-------|------|---------|-------| | sefi_pipe | SEFI_PIPE | — | From the Loader. | | prompt | string | — | Text prompt (multiline). | | steps | int | 4 | ~4 for Turbo; raise (e.g. 20–30) for Base/RL. | | guidance_scale | float | 1.0 | ~1.0 for Turbo; higher for Base/RL. | | width | int | 1024 | | | height | int | 1024 | | | seed | int | 0 | |

| Output | Type | |--------|------| | IMAGE | standard ComfyUI image (feed to SaveImage/PreviewImage). |

SeFi's inference API does not expose a negative prompt or batch-per-prompt, so those widgets are intentionally absent.

Models

Checkpoints are published under the SeFi-Image Hugging Face organization:

| Family | Sizes | HF id pattern | Suggested steps / guidance | |--------|-------|---------------|-----------------------------| | Turbo | 1B, 2B, 5B | SeFi-Image/SeFi-Image-{1,2,5}B-turbo | ~4 steps, guidance 1.0 | | Base | 1B, 2B, 5B | SeFi-Image/SeFi-Image-{1,2,5}B-Base | ~20–30 steps, higher guidance | | RL | 5B | SeFi-Image/SeFi-Image-5B-RL | as Base |

Recommended starting point: SeFi-Image-2B-turbo — fast and a good quality / VRAM balance.

VRAM

The upstream pipeline has no CPU-offload / low-VRAM path. Rough guidance:

| Model | Approx. VRAM (1024²) | |-------|----------------------| | 1B | comfortable on 8–12 GB | | 2B | fits ~12 GB | | 5B | likely OOM on 12 GB; needs more headroom |

If you OOM, drop to a smaller variant or reduce width/height.

Usage

  1. Add SeFi-Image Loader, pick SeFi-Image/SeFi-Image-2B-turbo, leave dtype/device on auto.
  2. Add SeFi-Image Sampler, connect sefi_pipe, type a prompt, keep steps=4, guidance_scale=1.0.
  3. Connect the Sampler's IMAGE output to a SaveImage (or PreviewImage).
  4. Queue. The first run downloads the checkpoint (several GB) — subsequent runs reuse the cached model.

How the sefi source is fetched

sefi is MIT-licensed but not published as a pip package. On first node execution (and, best-effort, at install time via install.py) the pack:

  1. Downloads https://codeload.github.com/jmliu206/SeFi-Image/tar.gz/refs/heads/main using Python's standard library (urllib + tarfile) — no git binary needed.
  2. Extracts it to SeFi-Image/ inside this pack directory.
  3. Adds that directory to sys.path so import sefi works.

This is done lazily so a network hiccup can never block ComfyUI startup — it simply retries on the next run. The fetched SeFi-Image/ folder is gitignored.

Troubleshooting

  • ModuleNotFoundError: sefi — the source fetch failed (no network on first run). Re-queue once you have connectivity, or manually git clone the SeFi repo into this pack's SeFi-Image/ subfolder.
  • CUDA out of memory — use a smaller checkpoint (2B or 1B), lower the resolution, or free VRAM before running.
  • Slow first run — expected: it's downloading multi-GB weights from Hugging Face. Later runs are fast.
  • Wrong-looking output on Base/RL — raise steps and guidance_scale; the defaults are tuned for Turbo.

Credits & License

  • Wrapper (this repo): MIT.
  • SeFi-Image model & sefi inference package: © the SeFi-Image authors (jmliu206/SeFi-Image), MIT. All model weights and inference logic belong to them; this repo only adds a thin ComfyUI node layer.

If you use SeFi-Image in your work, please cite the original authors.