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license: other
tags:
- comfyui
- quantization
- int4
- convrot
- diffusion
- image-generation
- video-generation
- upscaling
- krea
- ltx
- seedvr2
---
# 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 |
|---|---|---|---|
| **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 1.1 Distilled** | Video diffusion | Distilled LTX-2.3 build | INT4 convrot |
| **Sulphur 2 Base** | Video diffusion | Base checkpoint built off of LTX-2.3 | INT4 convrot |
| **SeedVR2 (7B)** | Image and Video Upscaler | Full 7B variant | INT4 convrot |
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](https://github.com/Starnodes2024/comfyui-starnodes-modelconverter)
## 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](https://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.
---
*Part of the ⚡ Winnougan quantization series.* |