WAS/was-node-suite-weights

WAS Node Suite weights

A stable mirror of the checkpoints the WAS Node Suite Power Preprocessor loads, so a node does not break when a third-party copy is moved or removed. Nothing here is trained by this project. Every file keeps the licence of the work it came from, and each is listed with its origin below.

denoise/

File	Original	Licence
scunet_color_real_psnr.pth	cszn/SCUNet, Kai Zhang	Apache-2.0
NAFNet-SIDD-width32.pth	megvii-research/NAFNet	MIT, with Apache-2.0 for BasicSR
NAFNet-SIDD-width64.pth	megvii-research/NAFNet	MIT, with Apache-2.0 for BasicSR

hdr/

Converted to safetensors for native ComfyUI inference.

File	Original	Licence
hdrcnn.safetensors	gabrieleilertsen/hdrcnn, Gabriel Eilertsen	BSD-3-Clause

low_light/

File	Original	Licence
hvi-cidnet-generalization.safetensors	Fediory/HVI-CIDNet	MIT
hvi-cidnet-sice.safetensors	Fediory/HVI-CIDNet	MIT
hvi-cidnet-sony-total-dark.safetensors	Fediory/HVI-CIDNet	MIT
hvi-cidnet-fivek.safetensors	Fediory/HVI-CIDNet	MIT
retinexformer-ntire.pth	caiyuanhao1998/Retinexformer	MIT
retinexformer-lol-v1.pth	same	MIT
retinexformer-lol-v2-real.pth	same	MIT
retinexformer-lol-v2-synthetic.pth	same	MIT
retinexformer-fivek.pth	same	MIT
retinexformer-sid.pth	same	MIT
retinexformer-smid.pth	same	MIT
retinexformer-sdsd-indoor.pth	same	MIT
retinexformer-sdsd-outdoor.pth	same	MIT
darkir-m.pt	cidautai/DarkIR	MIT

birefnet/

File	Original	Licence
birefnet/General.safetensors	ZhengPeng7/BiRefNet	MIT
birefnet/General-HR.safetensors	ZhengPeng7/BiRefNet	MIT
birefnet/General-dynamic.safetensors	ZhengPeng7/BiRefNet	MIT
birefnet/General-reso_512.safetensors	ZhengPeng7/BiRefNet	MIT
birefnet/DIS.safetensors	ZhengPeng7/BiRefNet	MIT
birefnet/DIS-TR_TEs.safetensors	ZhengPeng7/BiRefNet	MIT
birefnet/COD.safetensors	ZhengPeng7/BiRefNet	MIT
birefnet/HRSOD.safetensors	ZhengPeng7/BiRefNet	MIT
birefnet/Matting-HR.safetensors	ZhengPeng7/BiRefNet	MIT
birefnet/Portrait.safetensors	ZhengPeng7/BiRefNet	MIT

ben2/

File	Original	Licence
ben2/ben2-base.safetensors	PramaLLC/BEN2	MIT

optical_flow/

FlowSeek's released checkpoints, converted to safetensors with every tensor unchanged. Each carries the Depth Anything V2 Small weights FlowSeek reads depth with. LICENSE-FlowSeek and NOTICE-FlowSeek.md beside them are upstream's own.

File	Original	Licence
optical_flow/flowseek_t_ct.safetensors	flowseek_T_CT.pth from mattpoggi/flowseek, Matteo Poggi and Fabio Tosi	Apache-2.0
optical_flow/flowseek_t_tskh.safetensors	flowseek_T_TartanCT_TSKH.pth, same	Apache-2.0

models/kandinsky6/

Kandinsky 6 for ComfyUI, in ComfyUI's own folder layout: each subfolder goes into the matching folder under ComfyUI/models. The transformers are ComfyUI int8 ConvRot and W6A8 quantisations; everything else is byte for byte the upstream file. models/kandinsky6/NOTICE.md names each source commit and every SHA-256. The licence files sit beside the files they cover.

File	Original	Licence
models/kandinsky6/diffusion_models/kandinsky6_lite_distill_5s_int8_convrot.safetensors	kandinskylab/Kandinsky-6.0-Lite-distill-5s-Diffusers, Kandinsky Lab, quantised for ComfyUI (int8_convrot)	MIT
models/kandinsky6/diffusion_models/kandinsky6_lite_distill_5s_w6a8.safetensors	kandinskylab/Kandinsky-6.0-Lite-distill-5s-Diffusers, Kandinsky Lab, quantised for ComfyUI (w6a8)	MIT
models/kandinsky6/diffusion_models/kandinsky6_lite_5s_int8_convrot.safetensors	kandinskylab/Kandinsky-6.0-Lite-5s-Diffusers, Kandinsky Lab, quantised for ComfyUI (int8_convrot)	MIT
models/kandinsky6/diffusion_models/kandinsky6_lite_5s_w6a8.safetensors	kandinskylab/Kandinsky-6.0-Lite-5s-Diffusers, Kandinsky Lab, quantised for ComfyUI (w6a8)	MIT
models/kandinsky6/diffusion_models/kandinsky6_pro_distill_5s_int8_convrot.safetensors	kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers, Kandinsky Lab, quantised for ComfyUI (int8_convrot)	MIT
models/kandinsky6/diffusion_models/kandinsky6_pro_distill_5s_w6a8.safetensors	kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers, Kandinsky Lab, quantised for ComfyUI (w6a8)	MIT
models/kandinsky6/diffusion_models/kandinsky6_pro_5s_int8_convrot.safetensors	kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers, Kandinsky Lab, quantised for ComfyUI (int8_convrot)	MIT
models/kandinsky6/diffusion_models/kandinsky6_pro_5s_w6a8.safetensors	kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers, Kandinsky Lab, quantised for ComfyUI (w6a8)	MIT
models/kandinsky6/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors	Qwen/Qwen2.5-VL-7B-Instruct, as repackaged by Comfy-Org	Apache-2.0
models/kandinsky6/text_encoders/clip_l.safetensors	openai/clip-vit-large-patch14, as repackaged by Comfy-Org	MIT
models/kandinsky6/vae/kandinsky6_audio_vae.safetensors	MMAudio's 44.1 kHz autoencoder and BigVGAN v2, as packed by Kandinsky Lab	CC BY-NC 4.0, non-commercial (MMAudio); MIT (BigVGAN)

The video VAE Kandinsky 6 decodes with, hunyuan_video_vae_bf16.safetensors, is at Comfy-Org/HunyuanVideo_repackaged under the Tencent Hunyuan Community License.

Citations

Zhang et al., Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, 2022.
Chen et al., Simple Baselines for Image Restoration, ECCV 2022.
Yan et al., HVI: A New Color Space for Low-light Image Enhancement, CVPR 2025.
Cai et al., Retinexformer: One-stage Retinex-based Transformer for Low-light
Image Enhancement, ICCV 2023.
Feijoo et al., DarkIR: Robust Low-Light Image Restoration, 2024.
Zheng et al., Bilateral Reference for High-Resolution Dichotomous Image
Segmentation, 2024.
Poggi and Tosi, FlowSeek: Optical Flow Made Easier with Depth Foundation Models
and Motion Bases, ICCV 2025.
Yang et al., Depth Anything V2, NeurIPS 2024.
Kandinsky Lab, Kandinsky 6, 2026.
Cheng et al., MMAudio: Taming Multimodal Joint Training for High-Quality
Video-to-Audio Synthesis, CVPR 2025.
Lee et al., BigVGAN: A Universal Neural Vocoder with Large-Scale Training, ICLR 2023.
Bai et al., Qwen2.5-VL Technical Report, 2025.
Radford et al., Learning Transferable Visual Models From Natural Language
Supervision, ICML 2021.