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README.md

@@ -5,6 +5,8 @@ tags:
 - text-to-image
 - image-to-image
 - inpainting
+- sdxl
+- stable-diffusion-xl
 - qwen-image
 - qwen-image-2512
 - flux
@@ -18,16 +20,17 @@ 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).
+High-fidelity **ConvRot INT8** quantized weights for multi-condition ControlNet models across diverse generative architectures (**SDXL 1.0**, **Qwen-Image**, and **FLUX.1-dev**).
 
 ---
 
 ## 🌟 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:
+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 / disk footprint while preserving precise 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.
+- **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**.
 
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@@ -35,16 +38,17 @@ This repository hosts high-quality **ConvRot INT8** quantized weights for multi-
 
 | Filename | Base Architecture | Base Model | Supported Conditions | Quantization | File Size | License |
 | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
+| `controlnet-union-pro-max-sdxl-1.0_convrot_int8.safetensors` | SDXL 1.0 | [xinsir/controlnet-union-sdxl-1.0](https://huggingface.co/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](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 |
 
 ---
 
-## 🛠️ Model Features
+## 🛠️ Key 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.
+- **All-in-One Multi-Condition Control**: Unified architectures enabling single or blended conditioning inputs (Pose, Depth, 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.
 
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@@ -70,9 +74,10 @@ Place the downloaded `.safetensors` files into your ComfyUI `models/controlnet/`
 ## 📜 Credits & License
 
 ### Base Models & Research
+- **SDXL ControlNet Union:** [xinsir/controlnet-union-sdxl-1.0](https://huggingface.co/xinsir/controlnet-union-sdxl-1.0) by **xinsir**
 - **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)
+- **Base Architectures:** [Stability AI SDXL 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) & [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)
 
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