# Download the weights from this repo, plus the Gemma text encoder
hf download milo01/INT4-Convrot-Comfy-Models --local-dir models/INT4-Convrot-Comfy-Models
hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b# Fast pipeline (distilled model, no distilled LoRA needed)
uv run python -m ltx_pipelines.distilled \
--distilled-checkpoint-path models/INT4-Convrot-Comfy-Models/<distilled-checkpoint>.safetensors \
--spatial-upsampler-path models/INT4-Convrot-Comfy-Models/<spatial-upsampler>.safetensors \
--gemma-root models/gemma-3-12b \
--prompt "A beautiful sunset over the ocean" \
--output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8# HQ pipeline (two-stage, higher quality)
uv run python -m ltx_pipelines.ti2vid_two_stages_hq \
--checkpoint-path models/INT4-Convrot-Comfy-Models/<checkpoint>.safetensors \
--distilled-lora models/INT4-Convrot-Comfy-Models/<distilled-lora>.safetensors 0.8 \
--spatial-upsampler-path models/INT4-Convrot-Comfy-Models/<spatial-upsampler>.safetensors \
--gemma-root models/gemma-3-12b \
--prompt "A beautiful sunset over the ocean" \
--output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8INT4 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
- Install
ComfyUI-INT4-FastintoComfyUI/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 Workflowfolder 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.
Part of the ⚡ Winnougan quantization series.
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# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen