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license: apache-2.0
base_model:
- alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1
- alibaba-pai/Qwen-Image-2512-Fun-Controlnet-Union
- InstantX/Qwen-Image-ControlNet-Union
base_model_relation: quantized
tags:
- comfyui
- controlnet
- int8
- convrot
- quantized
- z-image
- qwen-image
---
# ControlNet models — INT8 ConvRot
INT8 ConvRot quantized ControlNet models for **Z-Image** and **Qwen-Image**, in ComfyUI-native `.comfy_quant` format. Same quality as the bf16 originals, roughly half the VRAM and disk.
**Requires the loader patch:** stock ComfyUI cannot load INT8 ControlNet files (the ControlNet and model-patch loaders never got native INT8 support). Install
**https://github.com/0xBeycan/ComfyUI-ConvRot-ControlNet** — it patches the stock loader nodes, adds no new nodes, and leaves bf16 / fp8 ControlNets untouched.
## Files
| File | Put in | Load with | bf16 → INT8 | VRAM saved | PSNR vs bf16 |
|---|---|---|---|---|---|
| `Z-Image-Turbo-Fun-Controlnet-Union-2.1_int8_convrot.safetensors` | `models/model_patches/` | `ModelPatchLoader` | 6.71 → 3.36 GB | −3.0 GB | 42.0 dB |
| `Qwen-Image-2512-Fun-Controlnet-Union-2602_int8_convrot.safetensors` | `models/controlnet/` | `Load ControlNet Model` | 3.51 → 1.82 GB | −1.6 GB | 36.0 dB |
| `Qwen-Image-InstantX-ControlNet-Union_int8_convrot.safetensors` | `models/controlnet/` | `Load ControlNet Model` | 3.54 → 1.83 GB | −1.6 GB | 44.8 dB |
Each file was compared against its bf16 original at the same seed and settings. Generation speed was identical on the test hardware — the gain is memory, not time. Older GPUs (30/40 series) may see a speed-up from the INT8 kernel; not tested.
Test setup: RTX 5090, ComfyUI 0.33.1, PyTorch 2.10.0+cu130.
## Usage
1. Install [ComfyUI-ConvRot-ControlNet](https://github.com/0xBeycan/ComfyUI-ConvRot-ControlNet) into `custom_nodes/` and restart ComfyUI. Confirm the console shows `[ConvRot-ControlNet] patched 3/3 loaders`.
2. Drop the file into the folder from the table above.
3. In your existing workflow, select the `_int8_convrot` file in the same loader node you already use. Nothing else changes.
## Requirements
- ComfyUI ≥ 0.33 (native `int8_tensorwise` support in `comfy.quant_ops`)
- [ComfyUI-ConvRot-ControlNet](https://github.com/0xBeycan/ComfyUI-ConvRot-ControlNet)
## How they were made
Quantized with [silveroxides/convert_to_quant](https://github.com/silveroxides/convert_to_quant): INT8, ConvRot rotation (group size 256), **row-wise scaling** (`--scaling_mode row` — tensor-wise scaling breaks LoRA compatibility and softens output), `.comfy_quant` metadata. The zero-initialised / low-magnitude control-injection layers are kept in bf16 in every model, since quantizing them mutes the conditioning. The full recipe and per-model exclusion lists are in the loader repo's README.
## Sources
- [alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1](https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1)
- [alibaba-pai/Qwen-Image-2512-Fun-Controlnet-Union](https://huggingface.co/alibaba-pai/Qwen-Image-2512-Fun-Controlnet-Union) (2602 release)
- [InstantX/Qwen-Image-ControlNet-Union](https://huggingface.co/InstantX/Qwen-Image-ControlNet-Union)
All three originals are Apache-2.0; these quantized files carry the same license.
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