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Video Caption

English | 简体中文

The folder contains codes for dataset preprocessing (i.e., video splitting, filtering, and recaptioning), and beautiful prompt used by EasyAnimate. The entire process supports distributed parallel processing, capable of handling large-scale datasets.

Meanwhile, we are collaborating with Data-Juicer, allowing you to easily perform video data processing on Aliyun PAI-DLC.

Table of Content

Quick Start

Setup

AliyunDSW or Docker is recommended to setup the environment, please refer to Quick Start. You can also refer to the image build process in the Dockerfile to configure the conda environment and other dependencies locally.

# pull image
docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:easyanimate

# enter image
docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:easyanimate

# clone code
git clone https://github.com/aigc-apps/EasyAnimate.git

# enter video_caption
cd EasyAnimate/easyanimate/video_caption

Data Preprocessing

Data Preparation

Place the downloaded videos into a folder under datasets (preferably without nested structures, as the video names are used as unique IDs in subsequent processes). Taking Panda-70M as an example, the entire dataset directory structure is shown as follows:

📦 datasets/
├── 📂 panda_70m/
│   ├── 📂 videos/
│   │   ├── 📂 data/
│   │   │   └── 📄 --C66yU3LjM_2.mp4
│   │   │   └── 📄 ...

Video Splitting

EasyAnimate utilizes PySceneDetect to identify scene changes within the video and performs video splitting via FFmpeg based on certain threshold values to ensure consistency of the video clip. Video clips shorter than 3 seconds will be discarded, and those longer than 10 seconds will be splitted recursively.

The entire workflow of video splitting is in the stage_1_video_splitting.sh. After running

sh scripts/stage_1_video_splitting.sh

the video clips are obtained in easyanimate/video_caption/datasets/panda_70m/videos_clips/data/.

Video Filtering

Based on the videos obtained in the previous step, EasyAnimate provides a simple yet effective pipeline to filter out high-quality videos for recaptioning. The overall process is as follows:

  • Scene transition filtering: Filter out videos with scene transition introduced by missing or superfluous splitting of PySceneDetect by calculating the semantic similarity accoss the beginning frame, the last frame, and the keyframes via CLIP or DINOv2.
  • Aesthetic filtering: Filter out videos with poor content (blurry, dim, etc.) by calculating the average aesthetic score of uniformly sampled 4 frames via aesthetic-predictor-v2-5.
  • Text filtering: Use EasyOCR to calculate the text area proportion of the middle frame to filter out videos with a large area of text.
  • Motion filtering: Calculate interframe optical flow differences to filter out videos that move too slowly or too quickly.

The entire workflow of video filtering is in the stage_2_video_filtering.sh. After running

sh scripts/stage_2_video_filtering.sh

the semantic consistency score, aesthetic score, text score, and motion score of videos will be saved in the corresponding meta files in the folder easyanimate/video_caption/datasets/panda_70m/videos_clips/.

Note

The computation of semantic consistency score depends on the openai/clip-vit-large-patch14-336. Meanwhile, the aesthetic score depends on the google/siglip-so400m-patch14-384 model. Please run HF_ENDPOINT=https://hf-mirror.com sh scripts/stage_2_video_filtering.sh if you cannot access to huggingface.com.

Video Recaptioning

After obtaining the aboved high-quality filtered videos, EasyAnimate utilizes InternVL2 to perform video recaptioning. Subsequently, the recaptioning results are rewritten by LLMs to better meet with the requirements of video generation tasks. Finally, an advanced VideoCLIP-XL model is used to filter out (video, long caption) pairs with poor alignment, resulting in the final training dataset.

Please download the video caption model from InternVL2 of the appropriate size based on the GPU memory of your machine. For A100 with 40G VRAM, you can download InternVL2-40B-AWQ by running

# Add HF_ENDPOINT=https://hf-mirror.com before the command if you cannot access to huggingface.com
huggingface-cli download OpenGVLab/InternVL2-40B-AWQ --local-dir-use-symlinks False --local-dir /PATH/TO/INTERNVL2_MODEL

Optionally, you can prepare local LLMs to rewrite the recaption results. For example, you can download Meta-Llama-3-8B-Instruct by running

# Add HF_ENDPOINT=https://hf-mirror.com before the command if you cannot access to huggingface.com
huggingface-cli download NousResearch/Meta-Llama-3-8B-Instruct --local-dir-use-symlinks False --local-dir /PATH/TO/REWRITE_MODEL

The entire workflow of video recaption is in the stage_3_video_recaptioning.sh. After running

CAPTION_MODEL_PATH=/PATH/TO/INTERNVL2_MODEL REWRITE_MODEL_PATH=/PATH/TO/REWRITE_MODEL sh scripts/stage_3_video_recaptioning.sh

the final train file is obtained in easyanimate/video_caption/datasets/panda_70m/videos_clips/meta_train_info.json.

Beautiful Prompt (For EasyAnimate Inference)

Beautiful Prompt aims to rewrite and beautify the user-uploaded prompt via LLMs, mapping it to the style of EasyAnimate's training captions, making it more suitable as the inference prompt and thus improving the quality of the generated videos. We support batched inference with local LLMs or OpenAI compatible server based on vLLM for beautiful prompt.

Batched Inference

  1. Prepare original prompts in a jsonl file easyanimate/video_caption/datasets/original_prompt.jsonl with the following format:

    {"prompt": "A stylish woman in a black leather jacket, red dress, and boots walks confidently down a damp Tokyo street."}
{"prompt": "An underwater world with realistic fish and other creatures of the sea."}
{"prompt": "a monarch butterfly perched on a tree trunk in the forest."}
{"prompt": "a child in a room with a bottle of wine and a lamp."}
{"prompt": "two men in suits walking down a hallway."}

  2. Then you can perform beautiful prompt by running

    # Meta-Llama-3-8B-Instruct is sufficient for this task.
# Download it from https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct or https://www.modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct to /path/to/your_llm

python caption_rewrite.py \
    --video_metadata_path datasets/original_prompt.jsonl \
    --caption_column "prompt" \
    --batch_size 1 \
    --model_name /path/to/your_llm \
    --prompt prompt/beautiful_prompt.txt \
    --prefix '"detailed description": ' \
    --saved_path datasets/beautiful_prompt.jsonl \
    --saved_freq 1

OpenAI Server

  • You can request OpenAI compatible server to perform beautiful prompt by running

    OPENAI_API_KEY="your_openai_api_key" OPENAI_BASE_URL="your_openai_base_url" python beautiful_prompt.py \
    --model "your_model_name" \
    --prompt "your_prompt"

  • You can also deploy the OpenAI Compatible Server locally using vLLM. For example:

    # Meta-Llama-3-8B-Instruct is sufficient for this task.
# Download it from https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct or https://www.modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct to /path/to/your_llm

# deploy the OpenAI compatible server
python -m vllm.entrypoints.openai.api_server serve /path/to/your_llm --dtype auto --api-key "your_api_key"

    Then you can perform beautiful prompt by running

    python -m beautiful_prompt.py \
    --model /path/to/your_llm \
    --prompt "your_prompt" \
    --base_url "http://localhost:8000/v1" \
    --api_key "your_api_key"