File size: 3,769 Bytes
fc0be1f 5353762 f38bc94 5353762 f38bc94 fc0be1f f38bc94 5353762 f38bc94 5353762 f38bc94 5353762 f38bc94 5353762 f38bc94 c699991 f38bc94 5353762 f38bc94 d15adef f38bc94 5353762 aeb5083 5353762 aeb5083 d15adef aeb5083 d15adef f38bc94 d15adef f38bc94 5353762 f38bc94 c699991 f38bc94 5353762 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ---
license: other
tags:
- controlnet
- text-to-image
- image-to-image
- inpainting
- qwen-image
- qwen-image-2512
- flux
- flux.1-dev
- quantized
- int8
- convrot
- comfyui
pipeline_tag: image-to-image
---
# ControlNet Models (ConvRot INT8)
High-fidelity **ConvRot INT8** quantized weights for ControlNet models across diverse architectures (Qwen-Image, FLUX.1-dev).
---
## ๐ Model Overview
This repository hosts high-quality **ConvRot INT8** quantized weights for multi-condition ControlNet Union models, designed to significantly reduce VRAM and storage footprint while preserving fine-grained structural control fidelity:
- **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.
---
## ๐ฆ Available Models
| Filename | Base Architecture | Base Model | Supported Conditions | Quantization | File Size | License |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| `Qwen-Image-2512-Fun-Controlnet-Union-2602_convrot_int8.safetensors` | Qwen-Image-2512 | [alibaba-pai/Qwen-Image-2512-Fun-Controlnet-Union](https://huggingface.co/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](https://huggingface.co/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 |
---
## ๐ ๏ธ Model Features
- **Multi-Condition Control**: Unified architectures allowing single or combined conditioning inputs (Canny, Depth, Pose, etc.) without swapping model checkpoints.
- **ConvRot INT8 Precision**: Preserves high-frequency details and directional feature integrity through randomized orthogonal Hadamard rotations, eliminating outlier distortion common in standard INT8 quantization.
- **Significant Footprint Reduction**: Delivers ~50% to 70% VRAM and storage savings compared to unquantized FP16 checkpoints.
---
## ๐ 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](https://github.com/ussoewwin/ComfyUI-HSWQ-Loader-and-Tools)
### Installation
Clone the repository into your ComfyUI `custom_nodes` directory:
```bash
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.
---
## ๐ Credits & License
### Base Models & Research
- **Qwen-Image ControlNet Union:** [alibaba-pai/Qwen-Image-2512-Fun-Controlnet-Union](https://huggingface.co/alibaba-pai/Qwen-Image-2512-Fun-Controlnet-Union) & [aigc-apps/VideoX-Fun](https://github.com/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](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0) & [InstantX Team](https://huggingface.co/InstantX) (FLUX.1-dev Non-Commercial License)
- **Base Architecture:** [black-forest-labs/FLUX.1-dev](https://huggingface.co/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.
|