WiREDiff: a Wind Resolution-Enhancing Diffusion Model

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To access the processed machine-learning wind speed dataset used for this exploration, visit the Caltech Data Respository page: raw data

Training/Resume Training

# Use sr.py and sample.py to train the super resolution task and unconditional generation task, respectively.
# Edit json files to adjust network structure and hyperparameters
python sr.py -p train -c config/sr_sr3.json

Test/Evaluation

# Edit json to add pretrain model path and run the evaluation 
python sr.py -p val -c config/sr_sr3.json

# Quantitative evaluation alone using SSIM/PSNR metrics on given result root
python eval.py -p [result root]

Inference Alone

Set the image path like steps in Own Data, then run the script:

# run the script
python infer.py -c [config file]

N-Conffusion

Fill the pretrained model weights into the finetuning config file. For training N-Conffusion on the task of super-resolution, run:

python3 sr_finetune_bounds.py -p calibration -c config/finetune_bounds_16_128_conffusion.json --enable_wandb --finetune_loss quantile_regression

Once the finetuning is complete, we can test the finetuned bounds. For this, replace the value of EXPERIMENT_NAME of the bounds_resume_state argument in the file test_finetune_bounds_16_128_conffusion.json with the actual experiment name of the finetuned run (i.e. have it point to the dir with the saved checkpoints).

Finally, run the following command

python3 test_finetuned_bounds.py --enable_wandb

Weights and Biases

The library now supports experiment tracking, model checkpointing and model prediction visualization with Weights and Biases. You will need to install W&B and login by using your access token.

pip install wandb

# get your access token from wandb.ai/authorize
wandb login

W&B logging functionality is added to the sr.py, sample.py and infer.py files. You can pass -enable_wandb to start logging.

• -log_wandb_ckpt: Pass this argument along with -enable_wandb to save model checkpoints as W&B Artifacts. Both sr.py and sample.py is enabled with model checkpointing.
• -log_eval: Pass this argument along with -enable_wandb to save the evaluation result as interactive W&B Tables. Note that only sr.py is enabled with this feature. If you run sample.py in eval mode, the generated images will automatically be logged as image media panel.
• -log_infer: While running infer.py pass this argument along with -enable_wandb to log the inference results as interactive W&B Tables.

You can find more on using these features here

Acknowledgments

• The SR3 implementation is based on this unofficial implementation of SR3.
• The implementation of the calibration and evaluation metrics is based on this official implementation of im2im-uq.
• The implementation of N-Conffusion is based on this