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viralvfx/comfyui-int4-fast

Analysis updated 2026-05-18

32PythonAudience · developerComplexity · 3/5Setup · moderate

TLDR

A ComfyUI add-on that shrinks AI image models to 4-bit precision for faster, lighter generation on compatible GPUs.

Mindmap

mindmap
  root((ComfyUI-INT4-Fast))
    What it does
      INT4 model quantization
      Faster GPU inference
      LoRA compatibility
    Tech stack
      Python
      ComfyUI custom node
      Tensor Cores
    Use cases
      Load INT4 models
      Quantize on the fly
      Save quantized checkpoints
    Audience
      ComfyUI users
      Developers

Code map

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What do people build with it?

USE CASE 1

Load a diffusion model in compressed INT4 format to speed up image generation and cut memory use.

USE CASE 2

Quantize a standard float model checkpoint to INT4 automatically the first time it loads.

USE CASE 3

Save a quantized model checkpoint to disk so future loads skip the conversion step.

USE CASE 4

Use LoRA customizations with an INT4-quantized model without breaking compatibility.

What is it built with?

PythonComfyUITensor Cores

How does it compare?

viralvfx/comfyui-int4-fastautolearnmem/automembilly-ellis/exr-imageio-poc
Stars323232
LanguagePythonPythonPython
Setup difficultymoderatehardmoderate
Complexity3/55/53/5
Audiencedeveloperresearcherresearcher

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

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires an up to date ComfyUI install plus the comfy-kitchen package and a Tensor Core capable GPU.

In plain English

ComfyUI-INT4-Fast is an add-on for ComfyUI, a visual tool many people use to run AI image generation models. This add-on lets those image generation models run in a compressed format called INT4, which shrinks each number the model uses down to just 4 bits instead of the usual larger sizes. The payoff is that models load faster, use less memory, and run more quickly on graphics cards that support a feature called Tensor Cores, without needing a separate, more powerful setup. It builds directly on an earlier project by another developer, called ComfyUI-INT8-Fast, adapting that same approach from 8-bit compression down to the more aggressive 4-bit version. The add-on can take a standard model file and shrink it to this format automatically the first time you load it, or you can save the shrunk version to disk so future loads are instant. It also handles the more delicate parts of a model, like its first and last layers, by keeping those in a slightly less aggressive 8-bit format so quality does not suffer where it matters most. If you use LoRA files, small add-on tweaks that customize how a model draws, this tool automatically adjusts them so they still work correctly with the compressed model. The package adds two new building blocks inside ComfyUI: one to load a model in this compressed format, and one to save a model out in that same format after processing. The developer notes that your very first image generation after loading a model will take longer, since the system needs to set up some behind the scenes optimizations, but every generation after that should be noticeably faster. To use it, you need ComfyUI already installed and updated, plus a required companion package called comfy-kitchen. This tool is meant for people who are already comfortable running ComfyUI and want faster, lighter image generation on supported hardware.

Copy-paste prompts

Prompt 1
Help me install ComfyUI-INT4-Fast and comfy-kitchen into my ComfyUI custom_nodes folder.
Prompt 2
Explain the difference between the Load Diffusion Model INT4 and Save Int4 Model nodes.
Prompt 3
Walk me through using the verified Krea2 Turbo INT4 model with this custom node.
Prompt 4
Help me understand why my first generation after loading a model is slower than later ones.

Frequently asked questions

What is comfyui-int4-fast?

A ComfyUI add-on that shrinks AI image models to 4-bit precision for faster, lighter generation on compatible GPUs.

What language is comfyui-int4-fast written in?

Mainly Python. The stack also includes Python, ComfyUI, Tensor Cores.

How hard is comfyui-int4-fast to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is comfyui-int4-fast for?

Mainly developer.

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