No official implementation of Image Super-Resolution via Iterative Refinement(SR3) is available. Thus we implement our version and take inspiration from existing github repos on Diffusion Models.
So far we only experiment with 4X super-resolution 16x16 --> 128x128.
An Efficient Approach to Super-Resolution with Fine-Tuning Diffusion Models.pdf
please open and run the demo.ipynb for step by step process to run the inferece, finetuning, training, and evaluation. please download the weights (Weights.zip) from OneDrive. Unzip it and copy the folders and paste them in the current location or location of this Readme file.
create a virtualenv/conda is preferred.
pip install -r requirement.txtWe have create a separate folder "/dataset" which should contain all datasets. For demo we have added few samples for all reference dataset.
Also, if you have your own data and wants to try model on that please prepare the data first with:
python data/prepare_data.py --path [dataset root] --out [output root] --size 16,128Make sure to follow the same structure as provided in '/dataset' folder
Update the paths in config file.
Please check following folder for all model weights.
deno_model_weight
Although Demo provides proper guidance; if need to change or play around with finetuning, one can change the path for pretrained model in config file here:
"resume_state": [your pretrained path]config file have path for pretrained model. for details check demo.ipynb
python sr.py -p train -c [config path] #config/sr_sr3_16_128_AnimeF.jsonNavigate to DDNM folder and execute the following command. for details check demo.ipynb
python main.py --ni --simplified --config imagenet_256.yml --path_y 'exp/datasets/imagenet/imagenet' --eta 0.85 --deg 'sr_averagepooling' --deg_scale 4.0 --sigma_y 0 -i demopython infer.py -c [config path] #config/sr_sr3_16_128_AnimeF.jsonpython eval.py -p [path to results] #misc_results/sr_ffhq_AimeF_Finetuned_infer_celebhq_Iter_100K_results_230422_203929/results- https://github.com/openai/guided-diffusion/tree/22e0df8183507e13a7813f8d38d51b072ca1e67c
- https://github.com/lucidrains/denoising-diffusion-pytorch
- https://github.com/rosinality/denoising-diffusion-pytorch
Shreshth Saini
Yu-Chih Chen
Krishna Srikar Durbha