Denoising Diffusion Probabilistic Model for Super Resolution

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.

Report

An Efficient Approach to Super-Resolution with Fine-Tuning Diffusion Models.pdf

Demo :

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.

setup

create a virtualenv/conda is preferred.

pip install -r requirement.txt

Dataset for demo

We 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 16,128

Make sure to follow the same structure as provided in '/dataset' folder

Update the paths in config file.

Model Weights and Experiments

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]

Finetuning

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.json

Zeroshot

Navigate 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 demo

Infer

python infer.py -c [config path] #config/sr_sr3_16_128_AnimeF.json

Evaluation

python eval.py -p [path to results] #misc_results/sr_ffhq_AimeF_Finetuned_infer_celebhq_Iter_100K_results_230422_203929/results

Based on

• https://github.com/openai/guided-diffusion/tree/22e0df8183507e13a7813f8d38d51b072ca1e67c
• https://github.com/lucidrains/denoising-diffusion-pytorch
• https://github.com/rosinality/denoising-diffusion-pytorch

Authors

Shreshth Saini
Yu-Chih Chen
Krishna Srikar Durbha