obsxrver/ComfyUI-Native-INT8_ConvRot

ComfyUI-Native-Int8-ConvRot

INT8 ConvRot models, converted to the native quantization format ComfyUI expects.

INT8 ConvRot currently offers one of the best quality-to-performance ratios of any quantization method. In my personal experience, INT8 ConvRot models provide quality close to BF16 at generation speeds matching or beating FP8_Scaled.

"INT8 ConvRot is row-wise INT8 with parameters and activations rotated before quantization via ConvRot." — ComfyUI-INT8-Fast Metrics.md

Quality Ranking

Per the latent-divergence benchmarks in Metrics.md:

GGUF Q8 > INT8 ConvRot > MXFP8 > FP8 >= INT8 Row > INT8 Tensorwise

Note: this is the general takeaway across all tested models. In several individual benchmarks (e.g. Anima, Flux2 Klein 9B, Qwen Image 2512), INT8 ConvRot actually scored better than GGUF Q8.

Requirements

• A ComfyUI version that includes native INT8 support (Comfy-Org/ComfyUI#14636, merged June 2026). Update if your loader reports an invalid quantization type.
• Models load with the standard Load Diffusion Model node — no custom node needed.

How to Quantize a Model to INT8 ConvRot

1. Install silveroxides' convert_to_quant:

   pip install -U convert-to-quant
   

   INT8 kernels require Triton (native on Linux; use triton-windows on Windows). PyTorch must be installed separately with the correct CUDA version.

2. Convert the model:

   ctq -i source_model_bf16.safetensors -o converted_model_int8_convrot.safetensors \
     --int8 --scaling_mode row --simple --convrot --convrot-group-size [64,256,1024] \
     --comfy_quant --save-quant-metadata --<model-arch-flag>
   

Notes

• --convrot-group-size accepts 64, 256, or 1024. It is recommended to choose a value that divides evenly into all of the model's layer dimensions.
• --<model-arch-flag> selects the layer-exclusion preset for your model architecture (e.g. --wan, --flux2, --zimage). Run ctq --help-filters (or ctq -hf) for the full list.

References

1. Reddit: "So is INT8-ConvRot the new hot thing?"
2. ComfyUI-INT8-Fast — Metrics.md (benchmark methodology & full tables)
3. Comfy-Org/Boogu-Image discussion #10
4. ComfyUI PR #14636 — Support int8 models
5. bertbobson/ComfyUI-INT8_ConvRot
6. silveroxides/convert_to_quant