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videocomposer

README.md

VideoComposer based on MindSpore

MindSpore implementation & optimization of VideoComposer: Compositional Video Synthesis with Motion Controllability.

Gallery

Different conditions to videos


Condition: image depth
Text input: "A black swan swam in the water"


Condition: local image
Text input: "A black swan swam in the water"


Condition: mask
Text input: "A black swan swam in the water"


Condition: motion
Text input: "A black swan swam in the water"


Condition: sketch
Text input: "A black swan swam in the water"

Motion transfer to videos

Text input: "Beneath Van Gogh's Starry Sky"


Text input: "A beautiful big silver moon on the water"


Text input: "A sunflower in a field of flowers"

Single sketch to videos with style

                

Style image                                                                                      Text input: "Red-backed Shrike lanius collurio"

Single sketch to videos without style

Text input: "A little bird is standing on a branch"

Image depth to videos without style

Text input: "Ironman is fighting against the enemy, big fire in the background, photorealistic"

Image depth to videos with style

                

Style image                                                                     Text input: "Van Gogh played tennis under the stars"


Inference scripts and configuration referring to scripts/run_infer.sh.

Main Features

  • Conditional Video Generation including the following tasks:
    • Motion transfer from a video to a single image (exp02)
    • Single sketch to videos with or without style guidance (exp03 and exp04)
    • Depth to video with or without style guidance (exp5 and exp6)
    • Generate videos based on multiple conditions: depth maps, local image, masks, motion, and sketch
  • Model Training (vanilla finetuning) supporting both Ascend 910 and 910*
  • Acceleration and Memory Reduction
    • Mixed Precision
    • Graph Mode for Training
    • Recompute

VideoComposer Architecture

Environment Setup

NOTES: The training code of VC is well tested on NPU 910 + MindSpore 2.2 (20230907) + CANN 7.0T2 + Ascend driver 23.0.rc3.b060*. Other mindspore and CANN versions may suffer from precision issues.

1. Framework Installation

  • For 910* NPU, please make sure the following packages are installed using the exact versions.
    1. CANN 7.0-T2. Version check:
        ll /usr/local/Ascend/latest
    1. Ascend driver 23.0.rc3.b060. Version check:
        cat /usr/local/Ascend/driver/version.info
    1. MindSpore 2.2 (20230907)
        pip show mindspore

2. Patching

For CANN 7.0T2, please disable AdamApplyOneFusionPasss to avoid overflow in training. It can be done by modifying /usr/local/Ascend/latest/ops/built-in/fusion_pass/config/fusion_config.json as follows:

{
    "Switch":{
	"GraphFusion":{
    		"AdamApplyOneFusionPass":"off",  # ==> add this line in the file
		"GroupConv2DFusionPass": "off",
		...
    },
    "UBFusion":{
	...
    }
}

3. Pip Package Installation

```shell
pip install -r requirements.txt
```

For `ffmpeg`, install by
```shell
conda install ffmpeg
```

If case you fail to install `motion-vector-extractor` via pip, please manually install it referring to the [official](https://github.com/LukasBommes/mv-extractor) repo.

Notes for 910: the code is also runnable on 910 for training and inference. But the number of frames max_frames for training should be changed from 16 to 8 frames or fewer due to memory limitation.

Prepare Pretrained Weights

The root path of downloading must be ${PROJECT_ROOT}\model_weights, where ${PROJECT_ROOT} means the root path of project.

Download the checkpoints shown in model_weights/README.md from https://download.mindspore.cn/toolkits/mindone/videocomposer/model_weights/ and https://download.mindspore.cn/toolkits/mindone/stable_diffusion/depth_estimator/midas_v3_dpt_large-c8fd1049.ckpt

Prepare Training Data

The training videos and their captions (.txt) should be placed in the following folder structure.

 ├── {DATA_DIR}
 │   ├── video_name1.mp4
 │   ├── video_name1.txt
 │   ├── video_name2.mp4
 │   ├── video_name2.txt
 │   ├── ...

Run examples/videocomposer/tools/data_converter.py to generate video_caption.csv in {DATA_DIR}.

python data_converter.py {DATA_DIR}

Format of video_caption.csv:

video,caption
video_name1.mp4,"an airliner is taxiing on the tarmac at Dubai Airport"
video_name2.mp4,"a pigeon sitting on the street near the house"
...

Inference

Online Inference

To run all video generation tasks on 910 or 910*, please run

bash scripts/run_infer.sh

On 910, to run a single task, you can pick the corresponding snippet of code in scripts/run_infer.sh, such as

# export MS_ENABLE_GE=1  # for 910*
# export MS_ENABLE_REF_MODE=1 # for 910* and Mindspore > 2.1
python infer.py \
    --cfg configs/exp02_motion_transfer_vs_style.yaml \
    --seed 9999 \
    --input_video "demo_video/motion_transfer.mp4" \
    --image_path "demo_video/moon_on_water.jpg" \
    --style_image "demo_video/moon_on_water.jpg" \
    --input_text_desc "A beautiful big silver moon on the water"

On 910*, you need to enable the GE Mode first by running export MS_ENABLE_GE=1. For Mindspore >2.1, you also need to enable the REF mode first by running export MS_ENABLE_REF_MODE=1.

It takes additional time for graph compilation to execute the first step inference (around 5~8 minutes).

Key arguments for inference

You can adjust the arguments in vc/config/base.py (lower-priority) or configs/exp{task_name}.yaml (higher-priority, will overwrite base.py if overlap). Below are the key arguments influencing inference speed and memory usage.

  • use_fp16: whether enable mixed precision inference

Offline (Mindspore Lite) Inference

Install Mindspore Lite

You need to have a Mindspore Lite Environment first for offline inference.

To install Mindspore Lite, please refer to Lite install

  1. Download the supporting tar.gz and whl packages according to the environment.

  2. Unzip the tar.gz package and install the corresponding version of the WHL package.

    tar -zxvf mindspore-lite-2.1.0-*.tar.gz
pip install mindspore_lite-2.1.0-*.whl

  3. Configure Lite's environment variables

    LITE_HOME is the folder path extracted from tar.gz, and it is recommended to use an absolute path.

    export LITE_HOME=/path/to/mindspore-lite-{version}-{os}-{platform}
export LD_LIBRARY_PATH=$LITE_HOME/runtime/lib:$LITE_HOME/tools/converter/lib:$LD_LIBRARY_PATH
export PATH=$LITE_HOME/tools/converter/converter:$LITE_HOME/tools/benchmark:$PATH

Export Mindspore Lite Model

For different tasks, you can use the corresponding snippet of the code in scripts/run_infer.sh, and change infer.py to export.py to save the MindIR model. Please remember to run export MS_ENABLE_GE=1 first on 910* and run export MS_ENABLE_REF_MODE=1 on 910* and Mindspore > 2.1 before running the code snippet.

# export MS_ENABLE_GE=1  # for 910*
# export MS_ENABLE_REF_MODE=1 # for 910* and Mindspore > 2.1
python export.py\
    --cfg configs/exp02_motion_transfer_vs_style.yaml \
    --input_video "demo_video/motion_transfer.mp4" \
    --image_path "demo_video/moon_on_water.jpg" \
    --style_image "demo_video/moon_on_water.jpg" \
    --input_text_desc "A beautiful big silver moon on the water"

The exported MindIR models will be saved at models/mindir directory. Once the exporting is finished, you need to convert the MindIR model to Mindspore Lite MindIR model. We have provided a script convert_lite.py to convert all MindIR models in models/mindir directory. Please note that on 910*, you need to unset MS_ENABLE_GE and MS_ENABLE_REF_MODE environmental variables before running the conversion.

unset MS_ENABLE_GE  # Remember to unset MS_ENABLE_GE on 910*
unset MS_ENABLE_REF_MODE  # Remember to unset MS_ENABLE_REF_MODE on 910* and Mindspore > 2.1
python convert_lite.py

Inference using the exported Lite models.

Then you can run the offline inference using the infer_lite.py for the given task, e.g,

python lite_infer.py\
    --cfg configs/exp02_motion_transfer_vs_style.yaml \
    --seed 9999 \
    --input_video "demo_video/motion_transfer.mp4" \
    --image_path "demo_video/moon_on_water.jpg" \
    --style_image "demo_video/moon_on_water.jpg" \
    --input_text_desc "A beautiful big silver moon on the water"

The compiling time is much shorter compared with the online inference mode.

Training

Standalone Training:

To run training on a specific task, please refer to scripts/run_train.sh.

After changing the task_name and yaml_file in the script for your task, run:

bash scripts/run_train.sh $DEVICE_ID

e.g. bash scripts/run_train.sh 0 to launch the training task using NPU card 0.

Under configs/, we provide several tasks' yaml files:

configs/
├── train_exp02_motion_transfer_vs_style.yaml
├── train_exp02_motion_transfer.yaml
├── train_exp03_sketch2video_style.yaml
├── train_exp04_sketch2video_wo_style.yaml
├── train_exp05_text_depths_wo_style.yaml
└── train_exp06_text_depths_vs_style.yaml

Taking configs/train_exp02_motion_transfer.yaml as an example, there is one critical argument:

video_compositions: ['text', 'mask', 'depthmap', 'sketch', 'single_sketch', 'motion', 'image', 'local_image']

video_compositions defines all available conditions:

  • text: the text embedding.
  • mask: the masked video frames.
  • depthmap: the depth images extracted from visual frames.
  • sketch: the sketch images extracted from visual frames.
  • single_sketch: the first sketch image from sketch.
  • motion: the motion vectors extracted from the training video.
  • image: the image embedding used as an image style vector.
  • local_image: the first frame extracted from the training video.

However, not all conditions are included in the training process in each of the tasks above. As defined in configs/train_exp02_motion_transfer.yaml,

conditions_for_train: ['text', 'local_image', 'motion']

conditions_for_train defines the three conditions used for training which are ['text', 'local_image', 'motion'].

Distributed Training

Please generate the HCCL config file on your running server at first referring to this tutorial. Then update scripts/run_train_distribute.sh by setting

rank_table_file=path/to/hccl_8p_01234567_xxx.json

After that, please set task_name according to your target task. The default training task is train_exp02_motion_transfer.

Then execute,

bash scripts/run_train_distribute.sh

Training in Step Mode

By default, training is done in epoch mode, i.e. checkpoint will be saved in every ckpt_save_interval epoch. To change to step mode, in train_xxx.yaml, please modify as:

dataset_sink_mode: False
step_mode: True
ckpt_save_interval: 1000

e.g., it will save checkpoints every 1000 training steps.

Currently, it's not compatible with dataset_sink_mode=True. It can be solved by setting sink_size=ckpt_save_intervel and epochs=num_epochs*(num_steps_per_epoch//ckpt_save_intervel) in model.train(...), which is under testing.

Supporting Annotation File Format

Both json and csv file are supported. JSON has a higher priority.

Key arguments for training

You can adjust the arguments in configs/train_base.py (lower-priority) or configs/train_exp{task_name}.yaml (higher-priority, will overwrite train_base.py if overlap). Below are the key arguments.

  • max_frames: number of frames to generate for each sample. Without memory reduction tricks, it can be set up to 8 for 910, and 16 for 910* for task-2 finetuning.
  • optim: optimizer name, adamw or momentum. Recommend momentum for 910 to avoid OOM and adamw for 910* for better loss convergence.
  • use_recompute: by enabling it, you can reduce memory usage with a small increase in time cost. For example, on 910, the max number of trainable frames per batch increases from 8 to 14 after recomputing is enabled.
  • root_dir: dataset root dir which should contain a csv annotation file. default is demo_video, which contains an example annotation file demo_video/video_caption.csv for demo traning.
  • num_parallel_workers: default is 2. Increasing it can help reduce video processing time cost if CPU cores are enough (i.e. num_workers * num_cards < num_cpu_cores) and Memory is enough (i.e. approximately, prefetch_size * max_row_size * num_workers < mem size)

Results

Training

The training performance for exp02-motion transfer is as follows.

NPU ** Num. Cards** Dataset Batch size ** Performance (ms/step)**
910* 1x8 WebVid 1 ~950
910* 8x8 WebVid 1 ~1100

Inference

The video generation speed is as follows.

NPU ** Framework ** ** Sampler ** ** Steps ** ** Performance (s/trial)**
910* MindSpore-2.2(20230907) DDIM 50 12
910* MindSpore-Lite-2.2(20230907) DDIM 50 11.6

Note that with MindSpore-Lite, the graph compilation time is eliminated.