INT4-Convrot-Comfy-Models

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

All models are quantized to INT4 using 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

1. Install ComfyUI-INT4-Fast into ComfyUI/custom_nodes/
2. Download the model(s) you want from this repo into the matching ComfyUI/models/diffusion_models/ (or appropriate folder for video/upscale models)
3. Load with the INT4 loader node from ComfyUI-INT4-Fast — do not use the standard checkpoint/UNETLoader nodes, they will not decode these correctly
4. 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

Links

• 🎥 YouTube: tutorials and walkthroughs for this collection
• 💬 Discord: community, support, and early access
• 🩷 Patreon / ☕ Ko-fi: support ongoing quantization work
• 🤗 More models: huggingface.co/Winnougan

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.

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Part of the ⚡ Winnougan quantization series.