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LDMVFI: Video Frame Interpolation with Latent Diffusion Models

Duolikun Danier, Fan Zhang, David Bull

Project | arXiv | Video

Overview

We observe that most existing learning-based VFI models are trained to minimise the L1/L2/VGG loss between their outputs and the ground-truth frames. However, it was shown in previous works that these metrics do not correlate well with the perceptual quality of VFI. On the other hand, generative models, especially diffusion models, are showing remarkable results in generating visual content with high perceptual quality. In this work, we leverage the high-fidelity image/video generation capabilities of latent diffusion models to perform generative VFI.

Dependencies and Installation

See environment.yaml for requirements on packages. Simple installation:

conda env create -f environment.yaml

Pre-trained Model

The pre-trained model can be downloaded from here, and its corresponding config file is this yaml.

Preparing datasets

Training sets:

[Vimeo-90K] | [BVI-DVC quintuplets]

Test sets:

[Middlebury] | [UCF101] | [DAVIS] | [SNU-FILM]

To make use of the evaluate.py and the files in ldm/data/, the dataset folder names should be lower-case and structured as follows.

└──── <data directory>/
    ├──── middlebury_others/
    |   ├──── input/
    |   |   ├──── Beanbags/
    |   |   ├──── ...
    |   |   └──── Walking/
    |   └──── gt/
    |       ├──── Beanbags/
    |       ├──── ...
    |       └──── Walking/
    ├──── ucf101/
    |   ├──── 0/
    |   ├──── ...
    |   └──── 99/
    ├──── davis90/
    |   ├──── bear/
    |   ├──── ...
    |   └──── walking/
    ├──── snufilm/
    |   ├──── test-easy.txt
    |   ├──── ...
    |   └──── data/SNU-FILM/test/...
    ├──── bvidvc/quintuplets
    |   ├──── 00000/
    |   ├──── ...
    |   └──── 17599/
    └──── vimeo_septuplet/
        ├──── sequences/
        ├──── sep_testlist.txt
        └──── sep_trainlist.txt

Evaluation

To evaluate LDMVFI (with DDIM sampler), for example, on the Middlebury dataset, using PSNR/SSIM/LPIPS, run the following command.

python evaluate.py \
--config configs/ldm/ldmvfi-vqflow-f32-c256-concat_max.yaml \
--ckpt <path/to/ldmvfi-vqflow-f32-c256-concat_max.ckpt> \
--dataset Middlebury_others \
--metrics PSNR SSIM LPIPS \
--data_dir <path/to/data/dir> \
--out_dir eval_results/ldmvfi-vqflow-f32-c256-concat_max/ \
--use_ddim

This will create the directory eval_results/ldmvfi-vqflow-f32-c256-concat_max/Middlebury_others/, and store the interpolated frames, as well as a results.txt file in that directory. For other test sets, replace Middlebury_other with the corresponding class names defined in ldm/data/testsets.py (e.g. Ucf101_triplet).

To evaluate the model on perceptual video metric FloLPIPS, first evaluate the image metrics using the code above (so that the interpolated frames are saved in eval_results/ldmvfi-vqflow-f32-c256-concat_max), then run the following code.

python evaluate_vqm.py \
--exp ldmvfi-vqflow-f32-c256-concat_max \
--dataset Middlebury_others \
--metrics FloLPIPS \
--data_dir <path/to/data/dir> \
--out_dir eval_results/ldmvfi-vqflow-f32-c256-concat_max/ \

This will read the interpolated frames previously stored in eval_results/ldmvfi-vqflow-f32-c256-concat_max/Middlebury_others/ then output the evaluation results to results_vqm.txt in the same folder.

To interpolate a video (in .yuv format), use the following code.

python interpolate_yuv.py \
--net LDMVFI \
--config configs/ldm/ldmvfi-vqflow-f32-c256-concat_max.yaml \
--ckpt <path/to/ldmvfi-vqflow-f32-c256-concat_max.ckpt> \
--input_yuv <path/to/input/yuv> \
--size <spatial res of video, e.g. 1920x1080> \
--out_fps <output fps, should be 2 x original fps> \
--out_dir <desired/output/dir> \
--use_ddim

Training

LDMVFI is trained in two stages, where the VQ-FIGAN and the denoising U-Net are trained separately.

VQ-FIGAN

python main.py --base configs/autoencoder/vqflow-f32.yaml -t --gpus 0,

Denoising U-Net

python main.py --base configs/ldm/ldmvfi-vqflow-f32-c256-concat_max.yaml -t --gpus 0,

These will create a logs/ folder within which the corresonding directories are created for each experiment. The log files from training include checkpoints, images and tensorboard loggings.

To resume from a checkpoint file, simply use the --resume argument in main.py to specify the checkpoint.

Citation

@article{danier2023ldmvfi,
  title={LDMVFI: Video Frame Interpolation with Latent Diffusion Models},
  author={Danier, Duolikun and Zhang, Fan and Bull, David},
  journal={arXiv preprint arXiv:2303.09508},
  year={2023}
}

Acknowledgement

Our code is adapted from the original latent-diffusion repository. We thank the authors for sharing their code.