INT4 Convrot Comfy Models

--- 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.*