| { |
| "model_type": "yeti", |
| "architecture": "Reconstruction AutoEncoder (RAE) + one-step Conditional Diffusion Transformer (C-DiT)", |
| "task": "real-world sRGB noise generation", |
| "framework": "pytorch-lightning", |
| "checkpoints": { |
| "rae.ckpt": "Reconstruction AutoEncoder", |
| "c_dit.ckpt": "Conditional Diffusion Transformer (main noise generator)", |
| "apbsn.ckpt": "AP-BSN self-supervised denoiser, trained on YeTI-generated noisy data only", |
| "apbsn_mix.ckpt": "AP-BSN denoiser trained on a 50:50 mix of YeTI-generated and real noisy data", |
| "mmbsn.ckpt": "MM-BSN self-supervised denoiser, trained on YeTI-generated noisy data only", |
| "mmbsn_mix.ckpt": "MM-BSN denoiser trained on a 50:50 mix of YeTI-generated and real noisy data" |
| }, |
| "paper": "https://arxiv.org/abs/2607.09193", |
| "code": "https://github.com/ByungWanLim/YeTI", |
| "dataset": "https://huggingface.co/datasets/BWLim/YeTI" |
| } |
|
|
