https://github.com/iva-mzsun/glober

https://github.com/iva-mzsun/glober

Last synced: 13 days ago
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• Host: GitHub
• URL: https://github.com/iva-mzsun/glober
• Owner: iva-mzsun
• License: other
• Created: 2023-03-03T01:45:40.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-11T08:50:10.000Z (10 months ago)
• Last Synced: 2024-08-01T18:41:07.410Z (3 months ago)
• Language: Python
• Size: 930 KB
• Stars: 9
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-diffusion-categorized - [Code

README

        
# BLOBER

The official code for the paper: 

GLOBER: Coherent Non-autoregressive Video Generation via Global Guided Video Decoder

[ARXIV](https://arxiv.org/abs/2309.13274) [DEMO](https://iva-mzsun.github.io/GLOBER)

## Conda Environment

```

conda env create -f environment.yaml

pip install -r requirements.txt

```

## Train Script

```

# Train scripts for both the auto-encoder and generator are the same

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py --base $CFG --logdir experiments/ # --ckpt path/to/ckpt

```

## Sample&Evaluation Scripts

We follow the implementation of StyleGAN-V(https://github.com/universome/stylegan-v) for evaluation.

```

# AutoEncoder

bash scripts/script_for_sample_3c.sh $CUR $CUDA $TOTAL $CFG $EXP $PTH $UC_FRAME $UC_VIDEO $UC_DOMAIN

bash scripts/script_for_fvd_3c.sh $EXP $UCFRAME $UCVID $UCDOMAIN $PTH $CUDA

# Generator

bash scripts/script_for_sample.sh $CFG $EXP $PTH $UC $CUR $TOTAL $CUDA

bash scripts/script_for_fvd.sh $EXP $UC $PTH $CUDA

```

## Checkpoints

Will be released soon.

## Test generation speed of prior methods

VIDM: \url{https://github.com/MKFMIKU/vidm}

VDM: \url{https://github.com/lucidrains/video-diffusion-pytorch}

VideoFusion: \url{https://huggingface.co/docs/diffusers/main/en/api/pipelines/text_to_video}

TATS:  \url{https://github.com/SongweiGe/TATS}