speedyrulz/PAN2-ComfyUI

PAN 2 single-file models for ComfyUI

Scripts that turn the IFM/PAN2 release (diffusers folders, about 81 GiB) into single .safetensors files in ComfyUI's quantized formats.

What you get

Target name	File	Size	Notes
transformer_int8_convrot	diffusion_models/PAN2_int8_convrot.safetensors	39.8 GiB	int8 ConvRot; the model's fp32 norm1.linear layers stay fp32
transformer_int8_convrot_full	diffusion_models/PAN2_int8_convrot_full.safetensors	27.4 GiB	int8 ConvRot, those layers quantized too
transformer_nvfp4	diffusion_models/PAN2_nvfp4.safetensors	15.5 GiB	NVFP4 (4-bit); runs natively on RTX 50 series, emulated elsewhere
te_int8_convrot	text_encoders/PAN2_qwen3_5_int8_convrot.safetensors	8.4 GiB	Qwen3.5-9B text encoder
te_nvfp4	text_encoders/PAN2_qwen3_5_nvfp4.safetensors	4.6 GiB	Qwen3.5-9B text encoder
vae_fp16	vae/PAN2_vae_fp16.safetensors	1.3 GiB	the fp32 VAE cast to fp16

Weight error against the original (relative, measured per layer): int8 ConvRot about 0.9%, NVFP4 about 9%.

Requirements

• Python 3.10+ with torch (2.1 or newer, CPU build is enough), safetensors, numpy, huggingface_hub
• Disk: the download (transformer 63 GiB, text encoder 15 GiB, VAE 2.7 GiB) plus the outputs you choose
• RAM: 16 GiB or more recommended. No GPU needed.

pip install torch safetensors numpy huggingface_hub

Use

Make everything, downloading what is needed into the Hugging Face cache:

python convert_pan2.py --out /path/to/ComfyUI/models

Only some files:

python convert_pan2.py --out /path/to/ComfyUI/models --only transformer_nvfp4 te_nvfp4 vae_fp16

From a copy you already downloaded (a folder containing transformer/, text_encoder/, vae/):

python convert_pan2.py --out /path/to/ComfyUI/models --src /path/to/PAN2

Files that already exist are skipped; add --overwrite to redo them. Each transformer file takes roughly ten minutes on a desktop CPU.

--device cuda does the math on the GPU. It is faster, but a few values can round differently, so the result may not be byte-identical to a CPU run.

Check a result

python verify_pan2.py /path/to/PAN2/transformer /path/to/ComfyUI/models/diffusion_models/PAN2_nvfp4.safetensors

It confirms that every tensor is present, that everything not quantized is bit-identical to the source, and prints the error of the quantized layers.

Using the files

These load in a ComfyUI build that has PAN 2 support (model code, the pan2 text encoder type, and loading of diffusers-layout Wan VAEs). Stock ComfyUI without that support will not recognise them. One such build is https://github.com/speedyrulz/ComfyUI.

• Diffusion model: Load Diffusion Model
• Text encoder: Load CLIP, type pan2
• VAE: Load VAE

What the script does

• Quantizes the large 2-D weights of the transformer blocks and the text refiner (for the text encoder: of its layers) and copies every other tensor unchanged.
• int8 ConvRot: each weight is rotated by a block Hadamard matrix of size 256, then stored as int8 with one scale per output row.
• NVFP4: 4-bit e2m1 values with one fp8 scale per block of 16 and one fp32 scale per tensor.
• NVFP4 text encoder: layers are marked full_precision_matrix_mult, and the token embedding table is stored as plain row-wise int8.
• The model's config.json is kept in the file's metadata.

PAN 2 itself is released by IFM under its own license; check the model page before redistributing converted files.