ComfyUI-Cyberdelia-Quantizer

ComfyUI Cyberdelia Quantizer

<p align="center"> <img src="assets/icon.png" alt="Cyberdelia Quantizer icon" width="200"> </p>

An independent ComfyUI custom node for converting original FP16/BF16 diffusion checkpoints to native mixed-precision safetensors files.

The main compatibility fix is deliberately simple: every quantized weight gets an explicit marker beside the exact physical layer name:

model.diffusion_model.blocks.0.attn.to_q.weight
model.diffusion_model.blocks.0.attn.to_q.weight_scale
model.diffusion_model.blocks.0.attn.to_q.comfy_quant

The prefix is never stripped. This prevents the central-metadata prefix mismatch that can make Forge Neo treat an INT8 checkpoint as FP16 and report unexpected SingleStreamDiT keys.

Formats

| Option | Storage | Runtime notes | | --- | --- | --- | | FP8_E4M3 | FP8 E4M3 | Portable FP8 storage; full-precision matmul for compatibility | | FP8_E5M2 | FP8 E5M2 | Wider range, lower mantissa precision; full-precision matmul | | NVFP4 | packed 4-bit | Native acceleration on NVIDIA Blackwell (SM 10.0+) | | MXFP8 | block-scaled FP8 | Native acceleration on NVIDIA Blackwell (SM 10.0+) | | INT8 | per-channel INT8 | Native ComfyUI format; accelerated on NVIDIA Turing or newer | | INT8_CONVROT | rotated per-channel INT8 | Recommended broad option; NVIDIA Turing or newer | | INT4_CONVROT | packed rotated INT4 | Smaller; NVIDIA Turing or newer; profile-sensitive | | W4A8_INT8 | grouped 4-bit weights / INT8 runtime | Requires a recent kitchen backend; Ampere-class path or newer |

NVFP4 and MXFP8 files still load on unsupported hardware, but ComfyUI may fall back to dequantized computation instead of providing a speed-up. FP8 in this node uses the official compatibility-oriented marker full_precision_matrix_mult=true.

Requirements

• A current ComfyUI installation (version 0.34 or newer is recommended).
• The comfy-kitchen version pinned by that ComfyUI release.
• An original dense FP16/BF16 .safetensors diffusion model.

Do not manually upgrade only comfy-kitchen while leaving ComfyUI behind. Update ComfyUI and its requirements together so their quantization contracts match.

Do not use an FP8, INT8, GGUF, NVFP4, or other already-quantized checkpoint as the source. The node rejects recognized quantized inputs instead of silently applying a second lossy conversion.

Installation

ComfyUI Manager

After the first Comfy Registry release, search for Cyberdelia Quantizer in ComfyUI Manager, install it, and restart ComfyUI.

Git

From the ComfyUI custom_nodes directory:

git clone https://github.com/cyberdeliaAI/ComfyUI-Cyberdelia-Quantizer.git

Manual installation

Download a release archive and extract it as:

ComfyUI/custom_nodes/ComfyUI-Cyberdelia-Quantizer

Then update ComfyUI's normal requirements and restart ComfyUI. No separate node dependencies are installed because Torch, safetensors, and comfy-kitchen must match the host ComfyUI build.

Nodes

🍳 Cyberdelia Quantizer

1. Select the original model in model_name.
2. Select the matching model_profile.
3. Select the output quant_format.
4. Leave output_filename empty to use the source filename plus a format suffix.
5. Run once with dry_run=true if you want a plan without writing a file.
6. Keep strict=true for release builds.

The output is written beside the source model. Existing files are protected unless overwrite=true. Saving uses a temporary file, validates its marker/weight/sidecar contract, and only then moves it to the final filename.

🔎 Cyberdelia Quant Check

This checks only the safetensors header and tiny JSON markers. It identifies:

• exact per-layer .comfy_quant packaging;
• legacy central _quantization_metadata;
• the model.diffusion_model.* prefix mismatch;
• orphan markers and missing side tensors.

It does not load the model weights into RAM.

Profiles

Dedicated profiles are included for Krea2, Anima, Z-Image Base/Turbo, Flux 1/2, Qwen Image, Wan 2.2, LTX-2, ACE-Step, Boogu Image, Chroma, ERNIE Image, Ideogram 4, MiniMax H3, and SeedVR. A conservative generic profile is also available.

A profile controls which sensitive layers stay in their original source dtype. Shape-incompatible layers are also kept unchanged and listed in the result. The Qwen Image 2512 profile keeps its sensitive txt_mlp/txt_mod weights in FP8 E4M3 when a more aggressive format is selected.

Design differences from the Tritant converter

• Writes exact per-layer .comfy_quant tensors; no prefix-stripped central map.
• Adds real FP8 E4M3 and FP8 E5M2 output modes (MXFP8 remains separate).
• Reads source tensors one at a time instead of loading the complete dense source checkpoint before conversion.
• Preserves excluded and non-floating tensors exactly instead of casting every unquantized tensor to BF16.
• Aborts on layer failures by default instead of returning a misleading success.
• Refuses accidental source overwrite and uses an atomic validated save.
• Chooses the largest valid ConvRot group per layer (256, 128, 64, 32, or 16).

Tests

The dependency-free contract tests can be run outside ComfyUI:

python -m unittest discover -s tests -v

They specifically guard against reintroducing the prefix-stripping bug.

License and upstream references

Apache-2.0. See NOTICE for attribution. This project is not an official Comfy Org project. The checkpoint contract follows the public ComfyUI native quantization loader and comfy-kitchen layouts.

Repository: https://github.com/cyberdeliaAI/ComfyUI-Cyberdelia-Quantizer