GenPolar

Official model weights for GenPolar: Stokes-Informed Diffusion for Robust Linear Polarization Estimation.

GenPolar estimates the linear Stokes components from an intensity/RGB observation and derives the degree and angle of linear polarization analytically.

Released files

File	Purpose	Required for final inference
genpolar_one_step.pth	Distilled one-step UNet and ControlNet	Yes
genpolar_vae_encoder_lora/	VAE encoder LoRA adapter used by the one-step model	Yes
genpolar_stage1_teacher.pth	Stage-one diffusion teacher used for distillation and stage-one reproduction	No

The .pth files are inference-focused exports. They contain only unet_state_dict and controlnet_state_dict, plus small format metadata. Optimizer states, training counters, the fake score model, training arguments, and duplicate state dictionaries are intentionally excluded.

All released tensors retain their original FP32 values.

Base model

The code initializes model components from runwayml/stable-diffusion-v1-5 before loading the GenPolar state dictionaries.

One-step inference

Place the downloaded files under release_weights/, then run:

python inference.py \
  --ckpt_path ./release_weights/genpolar_one_step.pth \
  --vae_lora_dir ./release_weights/genpolar_vae_encoder_lora \
  --input_folder ./data/DfP \
  --results_folder ./results \
  --fp16

The input directory format and full environment setup are documented in the GenPolar source repository.

Integrity

SHA-256 hashes are provided in SHA256SUMS.