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Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems

Sojin Lee*, Dogyun Park*, Inho Kong, Hyunwoo J. Kim†.

ECCV 2024 Oral

This repository contains the official PyTorch implementation of DAVI: Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems accepted at ECCV 2024 as an oral presentation.

Our framework allows efficient posterior sampling with a single evaluation of a neural network, and enables generalization to both seen and unseen measurements without the need for test-time optimization. We provide five image restoration tasks (Gaussian deblur, 4x Super-resolution, Box inpainting, Denoising, and Colorization) with two benchmark datasets (FFHQ and ImageNet).

Setting

Please follow these steps to set up the repository.

1. Clone the Repository

git clone https://github.com/mlvlab/DAVI.git
cd DAVI

2. Install Environment

conda create -n DAVI python==3.8
conda activate DAVI
conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=12.1 -c pytorch -c nvidia

pip install accelerate ema_pytorch matplotlib piq scikit-image pytorch-fid wandb

3. Download Pre-trained models and Official Checkpoints

We utilize pre-trained models from FFHQ (ffhq_10m.pt) and ImageNet (256x256_diffusion_uncond.pt) obtained from DPS and guided_diffusion, respectively.

  • Download the checkpoints for FFHQ and ImageNet from this Google Drive Link.
  • Place the pre-trained models into the model/ directory.
  • Place our checkpoints into model/official_ckpt/ffhq or model/official_ckpt/imagenet.

4. Prepare Data

For amortized optimization, we use the FFHQ 49K dataset and the ImageNet 130K dataset, which are subsets of the training datasets used for the pre-trained models. These subsets are distinct from the validation datasets (ffhq_1K and imagenet_val_1K) used for evaluation.

  • FFHQ 256x256

    • data/ffhq_1K and data/ffhq_49K.

    We downloaded the FFHQ dataset and resized it to 256x256, following the instructions on the ffhq-dataset public site.

    We use 00000-00999 as the validation set (1K) and 01000-49999 (49K) as the training set.

  • ImageNet 256x256

    • data/imagenet_val_1K and data/imagenet_130K.

    We downloaded the ImageNet 100 dataset and use its training set.

  • Measurements as numpy format

    • data/y_npy

    During amortized training, we load a subset of the training set to monitor the convergence of the training process.

    You can specify degradation types using the --deg option.

    • Gaussian Deblur gaussian
    • 4x Super-resolution sr_averagepooling
    • Box inpainting inpainting
    • Denoising deno
    • Colorization colorization
    python utils/get_measurements.py --deg gaussian --data_dir data/ffhq_49K

Overall directory

├── results
│
├── models
│ ├── ffhq_10m.pt # FFHQ for training
│ ├── 256x256_diffusion_uncond.pt # ImageNet for training
│ └── official_ckpt # For Evaluation
│     ├── ffhq
│     │   ├── gaussian_ema.pt
│     │   ├── sr_averagepooling_ema.pt
│     │   ├── ...
│     │   ├── ...
│     ├── imagenet
│     │   ├── gaussian_ema.pt
│     │   ├── sr_averagepooling_ema.pt
│     │   ├── ...
│     └── └── ...
│
├── data # including training set and evaluation set
│ ├── ffhq_1K # FFHQ evluation
│ ├── imagenet_val_1K # ImageNet evluation
│ ├── ffhq_49K # FFHQ training
│ ├── imagenet_130K # ImageNet training
│ └── y_npy
│         ├── ffhq_1k_npy
│         │   ├── gaussian
│         │   ├── sr_averagepooling
│         │   ├── ...
│         │   └── ...
│         ├── imagenet_val_1k_npy
│         │   ├── gaussian
│         │   ├── sr_averagepooling
│         │   ├── ...
└─────────└── └── ...

Evaluation

1. Restore degraded images

  • You can specify the directory of measurements with --y_dir data/y_npy
accelerate launch --num_processes=1 eval.py --eval_dir data/ffhq_1K --deg gaussian --perturb_h 0.1 --ckpt model/official_ckpt/ffhq/gaussian_ema.pt

2. Evaluate PSNR,LPIPS and FID

  • PSNR and LPIPS 
    python utils/eval_psnr_lpips.py
  • FID: pytorch-fid 
    python -m pytorch_fid source_dir recon_dir

Train with MultiGPU

  • To check training logs, use the --use_wandb flag.
accelerate launch --multi_gpu --num_processes=4 train.py --data_dir data/ffhq_49K/ --model_path model/ffhq_10m.pt --deg gaussian --t_ikl 400 --weight_con 0.5 --reg_coeff 0.25 --perturb_h 0.1