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Forked from official repository for Flexible Diffusion Modeling of Long Videos

Edit summary

Added features:

  • latent diffusion

Added dependencies:

  • diffusers
  • transformers

Running Latent Diffusion with Carla

Sample run command: python scripts/video_train.py --batch_size=1 --max_frames=5 --dataset=carla_no_traffic_2x_encoded --num_res_blocks=1 --num_workers=4 --save_interval=1000 --sample_interval=1000 --num_channels=64 --diffusion_space=latent.

Note: To run latent diffusion with Carla dataset with video whose latent embeddings have already been normalized, follow the Carla installation procedure, run cd datasets/carla then run python encode_latent.py --path=no-traffic --normalize. Then, when running scripts/video_train.py provide the flag --dataset=carla_no_traffic_2x_encoded.

Full overview

Usage

Tested with Python 3.10 in a conda environment. We require the Python packages mpi4py torch torchvision wandb blobfile tqdm moviepy imageio diffusers as well as the command line tool ffmpeg. This repository itself should also be installed by running

pip install -e .

For sampling with the adaptive sampling schemes (adaptive-autoreg and adaptive-hierarchy-2), also install the lpips package with e.g. pip install lpips.

To run scripts/video_fvd.py, tensorflow and tensorflow_hub are required. Install with e.g. pip install tensorflow==2.8 tensorflow_hub==0.12.0.

This repo logs to wandb, using the wandb entity/username and project name set by:

export WANDB_ENTITY=<...>
export WANDB_PROJECT=<...>

And add a directory for checkpoints to be saved in:

mkdir checkpoints

Preparing Data

CARLA Town01

Download CARLA Town01 with our download script as follows.

cd datasets/carla
bash download.sh
cd ../..

Training

Running on CARLA Town01

We train models on CARLA Town01 on an A100 GPU with the command:

python scripts/video_train.py --batch_size=2 --max_frames 20 --dataset=carla_no_traffic --num_res_blocks=1

Debugging/running on smaller GPUs

To train on smaller GPUs without exceeding CUDA memory limits, you can try reducing --max_frames from e.g. 20 to 5, --batch_size from 2 to 1, and --num_channels from the default 128 to e.g. 32.

Debugging with faster feedback loops

All of the previous suggestions for running on smaller GPUs will usually also speed up training. To more quickly check for major issues with training, you can decrease --sample_interval from the default 50000 to e.g. 1000 so that samples are logged to wandb more often, and decrease --diffusion_steps from the default 1000 to e.g. 32 so that logging samples is faster.

Resuming after training ends/fails

After training for more than save_interval iterations (50000 by default), we can kill and resume training from the latest checkpoint with:

python scripts/video_train.py <ORIGINAL ARGUMENTS> --resume_id <WANDB ID OF RUN WE ARE RESUMING>

e.g.

python scripts/video_train.py --batch_size=2 --max_frames 20 --dataset=carla_no_traffic --num_res_blocks=1 --resume_id 1v1myd4c

Downloading pretrained checkpoints

We release a checkpoint for the Carla Town01 dataset which can be downloaded as follows

mkdir -p checkpoints/pretrained/1v1myd4b/
cd checkpoints/pretrained/1v1myd4b/
wget https://www.cs.ubc.ca/~wsgh/fdm/carla-fdm-ckpts/1v1myd4b/ema_0.9999_550000.pt
cd ../../..

and then sampled from as described in the following section by replacing <CHECKPOINT PATH> with checkpoints/pretrained/1v1myd4b/ema_0.9999_550000.pt. This checkpoint is different from those reported in our preprint, but we have verified that it produces similar results, with FVD scores of 124 when sampling with Hierarchy-2 and 246 when sampling with Autoreg.

Sampling

Checkpoints are saved throughout training to paths of the form checkpoints/<WANDB ID>/model<NUMBER OF ITERATIONS>.pt and checkpoints/<WANDB ID>/ema_<EMA RATE>_<NUMBER OF ITERATIONS>.pt respectively. Best results can usually be obtained from the exponential moving averages (EMAs) of model weights saved in the latter form. Given a trained checkpoint, we can sample from it with a command like

python scripts/video_sample.py <CHECKPOINT PATH> --batch_size 2 --sampling_scheme <SAMPLING SCHEME> --stop_index <STOP INDEX> --n_obs <N OBS>

which will sample completions for the first test videos, each conditioned on the first frames (where may be zero). The dataset to use and other hyperparameters are inferred from the specified checkpoint. The should be one of those defined in improved_diffusion/sampling_schemes.py, most of which are described in our preprint. Options include, "autoreg", "long-range", "hierarchy-2", "adaptive-autoreg", "adaptive-hierarchy-2". The final command will look something like:

python scripts/video_sample.py checkpoints/2f1gq6ud/ema_0.9999_550000.pt --batch_size 2 --sampling_scheme autoreg --stop_index 100

Experimenting with different sampling schemes

Our sampling schemes are defined in improved_diffusion/sampling_schemes.py, including all those presented in our preprint. To add a new one, create a new subclass of SamplingSchemeBase with a next_indices function (returning a pair of vectors of observed and latent indices) in this file. Add it to the sampling_schemes dictionary (also in the same file) to allow your sampling scheme to be accessed by scripts/video_sample.py.

For debugging, you can visualise the indices used by a sampling scheme with

python scripts/video_sample.py <ANY CHECKPOINT PATH> --sampling_scheme <SAMPLING SCHEME> --just_visualise

The --just_visualise flag tells the script to save a .png visualisation to the visualisations/ directory instead of sampling videos.

Directory structures

Checkpoints are saved with the following directory structure

checkpoints
├── .../<wandb id>
│   ├── model_<step>.pt
│   ├── ema_<ema_rate>_<step>.pt
│   └── opt_<step>.pt
└── ... (other runs)

Optionally, to better organise the results directories, you can make more descriptive directory names and move the checkpoints into them as follows. The results directory (containing samples etc.) will mirror this structure.

checkpoints
├── <descriptive path>
|   ├── <wandb id>
│   |   └── ema_<ema_rate>_<step>.pt
|   └── ... (other runs with same descriptive path)
└── ... (other runs)

After running sampling scripts, a results directory will be created with structure:

results
├── <descriptive path>
│   ├── <wandb id>
│   │   ├── <checkpoint name>
│   │   │   ├── <sampling scheme descriptor>
│   │   │   │   ├── samples
│   │   │   │   │  ├── <name-1>.npy
│   │   │   │   │  ├── <name-2>.npy
│   │   │   │   │  ├── ...
|   └── ... (other runs with same descriptive path)
└── ... (other runs)

FVD scores and video files created by scripts/video_fvd.py and scripts/video_make_mp4.py will also be saved in the results/<descriptive path>/<wandb id>/<checkpoint name>/<sampling scheme descriptor> directory.

Computing FVD scores

After drawing sufficiently many samples (we use 100 for results reported in our preprint), FVD scores can be computed with

python scripts/video_fvd.py  --eval_dir results/<descriptive path>/<wandb id>/<checkpoint name>/<sampling scheme descriptor> --num_videos <NUM VIDEOS>

Running this script will print the FVD as well as saving it in a file at results/<descriptive path>/<wandb id>/<checkpoint name>/<sampling scheme descriptor>.

View-able video formats

Videos produced by scripts/video_sample.py are saved in .npy format. Save a video (or grid of sampled videos) in a .gif or .mp4 format with

python scripts/video_make_mp4.py --eval_dir results/<descriptive path>/<wandb id>/<checkpoint name>/<sampling scheme descriptor>

Interpretable metrics on CARLA Town01

To compute the WD and PO metrics on CARLA Town01 video samples, download our regressor to world coordinates using

mkdir checkpoints/carla-regressor/
cd checkpoints/carla-regressor/
wget https://www.cs.ubc.ca/~wsgh/fdm/carla-regressor-ckpts/classifier_checkpoint.pth 
wget https://www.cs.ubc.ca/~wsgh/fdm/carla-regressor-ckpts/regressor_checkpoint.pth
cd ../../

The regressor and classifier are run in tandem so both are required. After downloading them, you can map generated samples to the corresponding sequences of CARLA world coordinates with

python scripts/video_to_world_coords.py --eval_dir results/<descriptive path>/<wandb id>/<checkpoint name>/<sampling scheme descriptor> --regressor_path checkpoints/carla-regressor/regressor_checkpoint.pth --classifier_path checkpoints/carla-regressor/classifier_checkpoint.pth

This will regress to world coordinates for each sampled video and save them to the results/<descriptive path>/<wandb id>/<checkpoint name>/<sampling scheme descriptor>/coords/ directory. Similarly, you can obtain the regressor's output on the dataset with

python scripts/video_to_world_coords.py --dataset_dir datasets/carla/no-traffic/ --regressor_path checkpoints/carla-regressor/regressor_checkpoint.pth --classifier_path checkpoints/carla-regressor/classifier_checkpoint.pth

We recommend using the pretrained classifier and regressor weights linked to above. To instead retrain these models run

python scripts/carla_regressor_train.py --data_dir datasets/carla/no-traffic --is_classifier True

for the classifier and

python scripts/carla_regressor_train.py --data_dir datasets/carla/no-traffic --is_classifier False

for the regressor.

Link to original (pre-refactor) codebase

This is a refactored version of our original codebase with which the experiments in the preprint were run. This refactored codebase is cleaner and with less changes from the Improved DDPM repo it is based on, as well as having an architectural simplification vs our original codebase (we removed positional encodings). We have reproduced the main results with this refactored codebase. Much of code in this repository which is listed as being committed by wsgharvey was originally written by saeidnp or vmasrani. "Optimizing" sampling schemes as described in our preprint is currently only implemented in the original codebase.