ControlNet Models (ConvRot INT8)

High-fidelity ConvRot INT8 quantized weights for multi-condition ControlNet models across diverse generative architectures (Illustrious-XL / SDXL, SDXL 1.0, Qwen-Image, and FLUX.1-dev).


🌟 Model Overview

This repository hosts high-quality ConvRot INT8 quantized weights for all-in-one ControlNet Union models. By applying orthogonal Hadamard rotation prior to per-channel INT8 quantization, these models effectively eliminate activation outlier distortion and drastically reduce VRAM and disk footprint while preserving precise structural control fidelity:

  • CN-anytest4_illustrious2 (Variants A & B): Multi-purpose all-in-one Anytest v4 ControlNet models fine-tuned for Illustrious-XL / SDXL by 2vXpSwA7, offering high-precision anime and illustration structure guiding.
  • controlnet-union-pro-max-sdxl-1.0: The comprehensive all-in-one ControlNet Union model for SDXL 1.0 by xinsir, supporting 10+ control conditions in a single compact file.
  • Qwen-Image-2512-Fun-Controlnet-Union-2602: Multi-condition ControlNet Union model (5 layer blocks) for the Qwen-Image-2512 architecture.
  • FLUX.1-dev-ControlNet-Union-Pro-2.0: Next-generation unified 7-in-1 ControlNet for the FLUX.1-dev architecture by Shakker Labs.

πŸ“¦ Available Models

Filename Base Architecture Base Model Supported Conditions Quantization File Size License
CN-anytest4_illustrious2_A_convrot_int8.safetensors Illustrious-XL / SDXL 2vXpSwA7/iroiro-lora (Anytest v4 Variant A) Multi-condition (Canny, Lineart, Depth, Pose, Structure) ConvRot INT8 ~1.30 GB Fair AI / OpenRAIL++-M
CN-anytest4_illustrious2_B_convrot_int8.safetensors Illustrious-XL / SDXL 2vXpSwA7/iroiro-lora (Anytest v4 Variant B) Multi-condition (Canny, Lineart, Depth, Pose, Structure) ConvRot INT8 ~1.30 GB Fair AI / OpenRAIL++-M
controlnet-union-pro-max-sdxl-1.0_convrot_int8.safetensors SDXL 1.0 xinsir/controlnet-union-sdxl-1.0 OpenPose, Depth, Canny, Lineart, Anime Lineart, Scribble, Soft Edge, Normal, Segment, Tile, Inpaint ConvRot INT8 ~1.30 GB OpenRAIL++-M / Apache-2.0
Qwen-Image-2512-Fun-Controlnet-Union-2602_convrot_int8.safetensors Qwen-Image-2512 alibaba-pai/Qwen-Image-2512-Fun-Controlnet-Union Canny, HED, Depth, Pose, MLSD, Scribble, Gray, Inpaint ConvRot INT8 ~1.64 GB Apache-2.0
FLUX.1-dev-ControlNet-Union-Pro-2.0_convrot_int8.safetensors FLUX.1-dev Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0 Canny, Depth, Pose, Blur, Gray, Soft Edge, Low Quality ConvRot INT8 ~2.00 GB Other / Non-Commercial

πŸ› οΈ Key Features

  • All-in-One Multi-Condition Control: Unified architectures enabling single or blended conditioning inputs (Pose, Depth, Lineart, Canny, Tile, etc.) without switching heavy checkpoints during workflow execution.
  • ConvRot INT8 Precision: Leverages orthogonal Hadamard rotations to redistribute channel-wise outlier spikes uniformly across dimensions, preventing quantization error buildup in deep control layers.
  • VRAM & Storage Optimization: Slashes VRAM consumption and disk footprint by ~50% to 70% compared to unquantized FP16 checkpoints, allowing seamless multi-ControlNet workflows on consumer GPUs.

πŸš€ Usage in ComfyUI

To load and execute these ConvRot INT8 ControlNet models in ComfyUI, please use the dedicated loader node from the ComfyUI-HSWQ-Loader-and-Tools extension:

Installation

Clone the repository into your ComfyUI custom_nodes directory:

cd ComfyUI/custom_nodes
git clone https://github.com/ussoewwin/ComfyUI-HSWQ-Loader-and-Tools.git

Place the downloaded .safetensors files into your ComfyUI models/controlnet/ directory and load them using the dedicated ControlNet loader node.


πŸ“œ Credits & License

Base Models & Research


Disclaimer: These models are provided for optimization, workflow acceleration, and research purposes. Please adhere to the licenses and terms of the respective base models.

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