convert_to_quant

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

---

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 .

---

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
Bundled legacy INT8 runtime kernels	Optional Triton (Linux native, Windows via triton-windows)
ConvRot	SciPy (installed automatically)

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.

---

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 (bundled legacy/custom runtime kernels)

Weight conversion and current ComfyUI tensor/row INT8 layouts do not require Triton. Install it only when using the bundled Triton runtime kernels.

# Linux for torch 2.12+
pip install -U "triton<3.8"

# Windows for torch 2.10 and 2.11
pip install -U "triton-windows<3.7"
# Windows for torch 2.12+
pip install -U "triton-windows<3.8"

3. Optional: Blackwell formats (NVFP4/MXFP8)

pip install -U "comfy-kitchen>=0.2.18,<0.3"

---

Quick Start

Use the command 'ctq -hf' to view arguments for layer exclusion presets for various models

# All examples include metadata and comfy_quant layers for ComfyUI compatible quantization.
# Examples utilize low memory overhead argument to reduce peak RAM/VRAM usage.

# Basic FP8 Tensorcore quantization without learned rounding
ctq -i model.safetensors -o model-fp8mixed.safetensors --comfy_quant --save-quant-metadata --simple --low-memory

# INT8 Row-Wise quantization without learned rounding
ctq -i model.safetensors -o model-int8mixedrow.safetensors --int8 --scaling_mode row --comfy_quant --save-quant-metadata --simple --low-memory

# Blackwell MXFP8 quantization without learned rounding
ctq -i model.safetensors -o model-mxfp8mixed.safetensors --mxfp8 --comfy_quant --save-quant-metadata --simple --low-memory

Use In Code As Module

# Example modular usage of INT8 Row-Wise quantization of Flux2 Klein 9B
from convert_to_quant import quantize

quantize(
    input="./flux-2-klein-9b.safetensors",
    output="./flux-2-klein-9b-int8mixedrow.safetensors",
    comfy_quant=True,
    save_quant_metadata=True,
    verbose="VERBOSE",
    low_memory=True,
    int8=True,
    scaling_mode="row",
    flux2=True,
    simple=True,
    calib_samples=8192
)

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

---

Supported Quantization Formats

Format	CLI Flag	Hardware	Optimization
FP8 (E4M3)	(default)	Ada/Hopper+	Learned Rounding (SVD)
INT8 Block-wise	--int8	QuantOps runtime	Learned Rounding (SVD)
INT8 Tensor-wise	--int8 --scaling_mode tensor	Turing+ runtime	Native _scaled_mm path
INT8 ConvRot	--int8 --scaling_mode row --convrot --convrot-group-size 256	Turing+ runtime	Native row-wise layout with Hadamard rotation
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.

---

Model-Specific Presets

Model	Flag	Notes
Flux.1	--flux1	Keep modulation/guidance/time/final/input layers high-precision
Flux.2	--flux2	Keep modulation/guidance/time/final high-precision
FLUX.2 Klein	--flux_klein	Flux-sensitive layers without guidance input
ERNIE Image	--ernie_image	Protect author-recommended time/AdaLN/boundary/projection layers
Gemma 4	--gemma4	Protect embeddings, K/V, audio, vision, and multimodal projectors
Qwen3.5	--qwen35	Protect size-specific boundaries, embeddings, and complete visual stack
Boogu	--boogu	Protect embeddings and norm projections
LTX 2 / 2.3	--ltxv2, --ltx2, --ltx2_3	Protect boundary blocks, connectors, VAE, and vocoder
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)

---

Key Features

• Learned Rounding: SVD-based optimization minimizes quantization error.
• Bias Correction: Automatic bias adjustment using synthetic calibration data.
• Lower INT8 Peak Memory: In-place learned-rounding finalization avoids a second full-size float working tensor.
• Model-Specific Support: Exclusion lists for sensitive layers (norms, embeddings).
• Three-Tier Quantization: Mix different formats per layer using --custom-layers.

---

Advanced Usage

Exclude Layer Option

Define specific excluded layers with regex patterns for models with no exclusion preset(This is just example):

ctq -i model.safetensors --exclude-layers "(double_blocks.[01]|final_layer|txt_attn.proj)" --comfy_quant

Scaling Modes

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

---

Acknowledgements

Special thanks to:

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

---

License

MIT License