convert-to-quant 1.1.1

Convert safetensors weights to quantized formats (FP8, INT8) with learned rounding optimization

convert_to_quant

Convert safetensors weights to quantized formats (FP8, INT8, NVFP4, MXFP8) with learned rounding optimization for ComfyUI inference.

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Installation

pip install convert_to_quant

Or install from source:

git clone https://github.com/silveroxides/convert_to_quant.git
cd convert_to_quant
pip install -e .

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Requirements Summary

Feature	Requirement
Minimum (FP8/INT8)	Python 3.10+, PyTorch 2.8+, CUDA 12.8+
Full (NVFP4/MXFP8)	Python 3.12+, PyTorch 2.10+, CUDA 13.0+, comfy-kitchen
INT8 Kernels	Triton (Linux native, Windows via triton-windows)

[!IMPORTANT] PyTorch must be installed manually with the correct CUDA version for your GPU. This package does not install PyTorch automatically to prevent environment conflicts.

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Detailed Installation (GPU-Specific)

1. Install PyTorch

Visit pytorch.org to get the correct install command.

Examples:

# CUDA 13.0 (Required for Blackwell NVFP4/MXFP8)
pip install torch --index-url https://download.pytorch.org/whl/cu130

# CUDA 12.8 (Stable)
pip install torch --index-url https://download.pytorch.org/whl/cu128

# CPU only
pip install torch --index-url https://download.pytorch.org/whl/cpu

2. Optional: Triton (needed for blockwise INT8)

# Linux
pip install -U triton

# Windows (Example for torch>=2.9)
pip install -U "triton-windows<3.6"

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Quick Start

# Basic FP8 quantization with ComfyUI metadata (recommended)
convert_to_quant -i model.safetensors --comfy_quant

# INT8 Block-wise with SVD optimization
convert_to_quant -i model.safetensors --int8 --block_size 128 --comfy_quant

# Blackwell NVFP4 (4-bit)
convert_to_quant -i model.safetensors --nvfp4 --comfy_quant

Load the output .safetensors file in ComfyUI like any other model.

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Supported Quantization Formats

Format	CLI Flag	Hardware	Optimization
FP8 (E4M3)	(default)	Ada/Hopper+	Learned Rounding (SVD)
INT8 Block-wise	--int8	Any GPU	Learned Rounding (SVD)
INT8 Tensor-wise	--int8 --scaling_mode tensor	Any GPU	High-perf _scaled_mm
NVFP4 (4-bit)	--nvfp4	Blackwell	Dual-scale optimization
MXFP8	--mxfp8	Blackwell	Microscaling (E8M0)

For a deep dive into how these formats work, see FORMATS.md.

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Model-Specific Presets

Model	Flag	Notes
Flux.2	--flux2	Keep modulation/guidance/time/final high-precision
T5-XXL	--t5xxl	Decoder removed
Hunyuan Video	--hunyuan	Attention norms excluded
WAN Video	--wan	Time embeddings excluded

(See --help-filters for a full list of presets)

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Documentation

• 📖 MANUAL.md - Complete usage guide with examples and troubleshooting
• 📚 FORMATS.md - Technical reference for quantization formats
• 🧪 DEVELOPMENT.md - Changelog and research notes
• 📋 AGENTS.md - Developer guide & registry architecture

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Key Features

• Learned Rounding: SVD-based optimization minimizes quantization error.
• Bias Correction: Automatic bias adjustment using synthetic calibration data.
• Model-Specific Support: Exclusion lists for sensitive layers (norms, embeddings).
• Three-Tier Quantization: Mix different formats per layer using --custom-layers.

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Advanced Usage

Layer Config JSON

Define per-layer settings with regex patterns:

convert_to_quant -i model.safetensors --layer-config layers.json --comfy_quant

Scaling Modes

# Block-wise scaling for better accuracy
convert_to_quant -i model.safetensors --scaling-mode block --block_size 64 --comfy_quant

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Acknowledgements

Special thanks to:

• Clybius – For Learned-Rounding inspiration.
• lyogavin – For ComfyUI int8_blockwise support.

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License

MIT License