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realrebelai/comfyui_rebels_sefi

Analysis updated 2026-05-18

★ 10PythonAudience · developerComplexity · 4/5LicenseSetup · moderate

TLDR

Custom ComfyUI nodes to run the SeFi-Image AI art model on modest 8GB graphics cards.

Mindmap

mindmap
  root((ComfyUI Rebels SeFi))
    What it does
      Adds SeFi-Image nodes
      Runs on 8GB VRAM
      Base and Turbo modes
    Tech stack
      Python
      ComfyUI
      Qwen3-VL text encoder
    Use cases
      Generate AI images locally
      Run on modest hardware
      Use GGUF quantized models
    Audience
      ComfyUI users
      AI image generation hobbyists

Code map

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What do people build with it?

USE CASE 1

Generate AI images locally in ComfyUI using the SeFi-Image model on a modest graphics card.

USE CASE 2

Run a faster Turbo version of the model for quicker image generation with fewer steps.

USE CASE 3

Use smaller GGUF quantized model files to save disk space and memory.

USE CASE 4

Automatically balance model memory between GPU and system RAM on limited hardware.

What is it built with?

PythonComfyUIPyTorchDiffusersGGUF

How does it compare?

realrebelai/comfyui_rebels_sefiaarav90-cpu/fetchitadammartinez271828/x4-analysis
Stars101010
LanguagePythonPythonPython
Setup difficultymoderateeasyeasy
Complexity4/52/52/5
Audiencedeveloperdevelopergeneral

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 1h+

Requires a ComfyUI install, a compatible GPU with at least 8GB VRAM, and manually downloading model weight files.

The node code is MIT licensed and free to use, but the actual model weight files are non-commercial only, meaning personal or research use is fine but commercial use is not allowed.

In plain English

This project adds custom building blocks, called nodes, to ComfyUI, a visual tool people use to build AI image generation pipelines. Specifically, it lets ComfyUI run an image generating AI model called SeFi-Image, in both a full quality version and a faster Turbo version, and it is built to work on modest hardware, as little as 8GB of graphics card memory and 16GB of system memory. The underlying model works by splitting its internal representation into two related streams, one handling overall structure and one handling fine detail, denoising them on slightly different timings so the structure settles first and detail fills in after. Only part of that representation actually gets turned into a final picture. The text prompt itself is understood by a separate text reading model called Qwen3-VL. To install, the user copies this folder into ComfyUI's custom nodes folder, restarts ComfyUI, and updates a few Python packages the newer model needs. Then they must separately download the actual model weight files, transformer, text encoder, and a matching VAE file, from Hugging Face, and place each into the correct ComfyUI models folder. The README stresses that the special VAE file made for this model must be used, since a generic substitute produces noticeably worse images. Inside ComfyUI, this adds a loader node with dropdown menus to pick the model files, and a sampler node with prompt, steps, guidance, size, and seed settings to actually generate images. The loader automatically manages how much of the model stays on the graphics card versus streams from system memory, based on how much free video memory it detects, so it can adapt itself to different hardware. Text encoding runs on the processor by default and is cached per prompt, so repeated use of the same prompt is much faster after the first run. A troubleshooting section covers common issues like missing files or outdated components. This is aimed at people already using ComfyUI for AI image generation who want to run this specific model efficiently on consumer grade hardware. The node pack and its included code are shared under the MIT license, but the actual model weight files carry a separate non-commercial license, so free personal or research use is allowed, but not for commercial products, unless the README's license terms are respected.

Copy-paste prompts

Prompt 1
Walk me through installing this custom node pack into my existing ComfyUI setup.
Prompt 2
Explain which model files I need to download from Hugging Face and where each one goes.
Prompt 3
Help me understand the difference between the Base and Turbo versions of SeFi-Image.
Prompt 4
Explain what the blocks_on_gpu and weight_dtype settings do in the Rebels SeFi Loader node.
Prompt 5
What does this project's license actually allow me to do with the generated images and the model weights?

Frequently asked questions

What is comfyui_rebels_sefi?

Custom ComfyUI nodes to run the SeFi-Image AI art model on modest 8GB graphics cards.

What language is comfyui_rebels_sefi written in?

Mainly Python. The stack also includes Python, ComfyUI, PyTorch.

What license does comfyui_rebels_sefi use?

The node code is MIT licensed and free to use, but the actual model weight files are non-commercial only, meaning personal or research use is fine but commercial use is not allowed.

How hard is comfyui_rebels_sefi to set up?

Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.

Who is comfyui_rebels_sefi for?

Mainly developer.

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