| --- |
| library_name: diffusers |
| pipeline_tag: image-to-image |
| tags: |
| - polarization |
| - image-to-image |
| - diffusion |
| - controlnet |
| - lora |
| --- |
| |
| # 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: |
|
|
| ```bash |
| 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`. |
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|
