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, Z-Image-Turbo, Qwen-Image, and FLUX.1-dev).
🌟 Model Overview
This repository hosts high-quality ConvRot INT8 quantized weights for all-in-one ControlNet Union and dedicated ControlNet 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.
- Z-Image-Turbo-Fun-Controlnet-Union-2.1-lite-2601-8steps: Multi-condition ControlNet Union model (lite 6-block architecture, 8-step distilled) for the Z-Image-Turbo architecture by alibaba-pai, providing lightweight and fast structure control (Canny, Depth, Pose, Color, etc.).
- Z-Image-Turbo-Fun-Controlnet-Tile-2.1-lite-2601-8steps: Dedicated high-resolution Tile and upscaling ControlNet model (lite architecture, 8-step distilled) for the Z-Image-Turbo architecture by alibaba-pai, optimized for super-resolution detail enhancement.
- Qwen-Image-2512-Fun-Controlnet-Union-2602: Multi-condition ControlNet Union model (5 layer blocks) for the Qwen-Image-2512 architecture.
- Qwen-Image-ControlNet-Inpainting: Dedicated inpainting and editing ControlNet model for the Qwen-Image 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.
📦 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 | ~1.40 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 | ~1.40 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 | ~1.40 GB | OpenRAIL++-M / Apache-2.0 |
Z-Image-Turbo-Fun-Controlnet-Union-2.1-lite-2601-8steps_convrot_int8.safetensors |
Z-Image-Turbo | alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1 | Multi-condition (Canny, Depth, Pose, Color, Scribble, Inpaint) | ConvRot INT8 | ~1.01 GB | Apache-2.0 |
Z-Image-Turbo-Fun-Controlnet-Tile-2.1-lite-2601-8steps_convrot_int8.safetensors |
Z-Image-Turbo | alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1 | Tile, Super-Resolution, Detail Enhancing | ConvRot INT8 | ~1.01 GB | 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 | ~1.76 GB | Apache-2.0 |
Qwen-Image-ControlNet-Inpainting_convrot_int8.safetensors |
Qwen-Image | alibaba-pai/Qwen-Image-ControlNet-Inpainting | Inpainting, Image Editing | ConvRot INT8 | ~2.12 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 | ~2.14 GB | Other / Non-Commercial |
🛠️ Key Features
- All-in-One Multi-Condition Control: Unified architectures enabling single or blended conditioning inputs (Pose, Depth, Lineart, Canny, Tile, Inpainting, 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% compared to unquantized FP16 checkpoints, allowing seamless multi-ControlNet workflows on consumer GPUs.
🚀 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:
- Extension Repository: ComfyUI-HSWQ-Loader-and-Tools
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.
📜 Credits & License
Base Models & Research
- Illustrious Anytest ControlNet: 2vXpSwA7/iroiro-lora by 2vXpSwA7
- SDXL ControlNet Union: xinsir/controlnet-union-sdxl-1.0 by xinsir
- Z-Image-Turbo ControlNet Models: alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1 & aigc-apps/VideoX-Fun (Apache-2.0)
- Qwen-Image ControlNet Models: alibaba-pai/Qwen-Image-2512-Fun-Controlnet-Union, alibaba-pai/Qwen-Image-ControlNet-Inpainting & aigc-apps/VideoX-Fun (Apache-2.0)
- FLUX.1-dev ControlNet Union Pro 2.0: Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0 & InstantX Team (FLUX.1-dev Non-Commercial License)
- Base Architectures: Illustrious-XL, Stability AI SDXL 1.0 & black-forest-labs/FLUX.1-dev
Disclaimer: These models are provided for optimization, workflow acceleration, and research purposes. Please adhere to the licenses and terms of the respective base models.