INT8 row-wise quantized versions of Z-Image Base and Z-Image Turbo, using ConvRot (Hadamard-rotation outlier suppression) for improved quantization fidelity, plus a matching INT8 ConvRot quantization of the Qwen 3 4B text encoder. Converted with convert_to_quant for native ComfyUI compatibility.

ConvRot applies a group-wise Hadamard rotation to suppress weight outliers before quantization, improving INT8 fidelity versus plain per-tensor or per-row quantization alone. Critically, these conversions use --scaling_mode row, not tensor. Tensor-wise scaling computes a single scale factor for an entire weight matrix; even a small number of outlier values forces that global scale to widen, coarsening quantization precision across the rest of the matrix. In testing, this combination (ConvRot + tensor-wise scaling) produced visibly fuzzy, detail-smoothed output. Switching to row-wise scaling — which computes an independent scale per row, isolating outliers to the rows that contain them — resolved this and produced output sharpness matching or exceeding plain INT8 row-wise quantization.

If you encounter other ConvRot-quantized models with soft or "waxy" output, this scaling mode mismatch is the most likely culprit.

ctq -i <model>.safetensors -o <model>-int8-convrot.safetensors \
  --int8 --scaling_mode row --simple --low-memory \
  --convrot --convrot-group-size 64 \
  --zimage --comfy_quant --save-quant-metadata

The Qwen 3 4B text encoder was converted with the same flags, omitting --zimage (no architecture-specific preset needed for this text encoder; verify its native hidden dimensions divide cleanly by the chosen group size before quantizing).

ComfyUI's comfy_kitchen runtime requires the ConvRot Hadamard block size to be a power of 4 (4, 16, 64, 256, 1024…), not merely a power of 2. A group size of 64 was chosen because it divides cleanly into every 2D weight dimension in the Z-Image architecture, requiring no manual layer exclusions.

Load z_image_int8_convrot.safetensors or z_image_turbo_int8_convrot.safetensors with a standard UNETLoader node, and qwen_3_4b_int8_convrot.safetensors with a CLIPLoader node (type: your Z-Image workflow's text encoder type). No special ConvRot-aware nodes are required; the rotation metadata is embedded via --save-quant-metadata and read automatically by ComfyUI's mixed-precision quantization ops.

Converted and tested on an RTX 3070 (8GB VRAM) using --low-memory streaming conversion.

Quantization tooling: silveroxides/convert_to_quant Base models: Tongyi-MAI/Z-Image, Tongyi-MAI/Z-Image-Turbo