INT4 ConvRot Comfy Models — Winnougan

A collection of INT4 ConvRot-quantized diffusion, video, and upscaling models for ComfyUI, built to run comfortably on 8GB-class GPUs (developed and tested on an RTX 3070 Ti) without gutting output quality.

What's in this repo

Model	Type	Notes	Quant
Flux 2 Dev	Image diffusion	Base Flux 2 Dev checkpoint	INT4 convrot
Gemma 3 12B IT	LLM / Text	Instruction-tuned language model	INT4 convrot
Ideogram 4 Instant Comfy	Image diffusion	Comfy-optimized Ideogram 4 variant	INT8 convrot
Krea 2 Raw	Image diffusion	Base Krea 2 checkpoint, unmodified pipeline	INT4 convrot
Krea 2 Turbo	Image diffusion	Distilled/turbo variant, fewer steps	INT4 convrot
LTX-2.3 22B Distilled 1.1	Video diffusion	Transformer-only distilled LTX-2.3 build (22B)	INT4 convrot
Mistral 3 Small Flux 2	LLM / Multimodal	Mistral 3 Small integrated with Flux 2	INT4 convrot
SAM 3.1 Multiplex	Vision / Segmentation	Segment Anything Model 3.1 multiplex variant	INT8 convrot
SeedVR2 (7B)	Image and Video Upscaler	Full 7B variant	INT4 convrot
Sulphur Distilled	Video diffusion	Distilled checkpoint built off of LTX-2.3	INT4 convrot

All models are quantized to INT4 or INT8 using the Starnodes Model Converter (https://github.com/Starnodes2024/comfyui-starnodes-modelconverter).

Why ConvRot INT4

Standard INT8/INT4 row-wise quantization throws away a lot of precision on the weight matrices that matter most for visual fidelity. ConvRot groups weights along their largest power-of-4-compatible dimension before quantizing, which keeps much more of the original model's detail and reduces the artifacting you'd normally see from a naive INT4 cast. The trade-off is VRAM and disk savings big enough to run models like SeedVR2 7B and full video diffusion checkpoints on 8GB cards.

Requirements

ComfyUI (nighlty build)
If you're getting chronic errors update your Conda environment (I'm running Pytorch 2.12, cu132, Python 3.12, Flashattention/Sageattention and Triton 3.8)

Installation

Install ComfyUI-INT4-Fast into ComfyUI/custom_nodes/
Download the model(s) you want from this repo into the matching ComfyUI/models/diffusion_models/ (or appropriate folder for video/upscale models)
Load with the INT4 loader node from ComfyUI-INT4-Fast — do not use the standard checkpoint/UNETLoader nodes, they will not decode these correctly
See the Samples and Workflow folder in this repo for ready-to-use ComfyUI workflow JSONs and sample outputs

Quantization pipeline

Built with Starnodes power:

Grab the Starnodes model converter and do it yourself if you wish. It supports INT8 and INT4 convrot: Starnodes

License

Inherits the license terms of each respective base model (Krea 2, LTX-2.3, Sulphur 2, SeedVR2). Check each upstream model's license before commercial use.

Part of the ⚡ Winnougan quantization series.

Ideogram Non-Commercial Model Agreement Summary

Last Updated: June 3, 2026
Model Covered: Ideogram 4

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💡 Key Takeaway

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INT4-Convrot-Comfy-Models