README.md

metadata

license: cc-by-4.0
language:
  - en
base_model:
  - ByteDance/sd2.1-base-zsnr-laionaes5
pipeline_tag: image-text-to-image
tags:
  - SPAD
  - Photons
  - Generative
  - ISP
datasets:
  - aRy4n/eXtreme-Deformable
  - aRy4n/real-color-SPAD-indoor6
metrics:
  - type: accuracy
    split: test
    task:
      type: video-to-image
      name: Burst Reconstruction

gQIR: Generative Quanta Image Reconstruction

Aryan Garg¹, Sizhuo Ma², Mohit Gupta¹

¹ University of Wisconsin-Madison ² Snap, Inc

All model weights are available here now!

Color-Model Name	Stage	Bit Depth	🤗 Download Link
qVAE	Stage 1	1-bit	1965000.pt
Adversarial Diffusion LoRA-UNet	Stage 2	1-bit	state_dict.pth
qVAE	Stage 1	3-bit	0105000.pt
Adversarial Diffusion LoRA-UNet	Stage 2	3-bit	state_dict.pth
FusionViT	Stage 3	3-bit	fusion_vit_0050000.pt

Code at github.com/Aryan-Garg/gQIR

ArXiv Version: arxiv.org/abs/2602.20417

Cite Us:

@InProceedings{garg_2026_gqir,
    author    = {Garg, Aryan and Ma, Sizhuo and  Gupta, Mohit},
    title     = {gQIR: Generative Quanta Image Reconstruction},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2026},
}