README.md

metadata

license: other
language:
  - en
base_model:
  - meta-llama/Meta-Llama-3.1-8B-Instruct
pipeline_tag: video-text-to-text
inference: false
library_name: transformers

中文阅读

CogVLM2-Llama3-Caption

Code | 🤗 Hugging Face | 🤖 ModelScope

Typically, most video data does not come with corresponding descriptive text, so it is necessary to convert the video data into textual descriptions to provide the essential training data for text-to-video models. CogVLM2-Caption is a video captioning model used to generate training data for the CogVideoX model.

Usage

import io

import argparse
import numpy as np
import torch
from decord import cpu, VideoReader, bridge
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_PATH = "THUDM/cogvlm2-llama3-caption"

DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[
    0] >= 8 else torch.float16

parser = argparse.ArgumentParser(description="CogVLM2-Video CLI Demo")
parser.add_argument('--quant', type=int, choices=[4, 8], help='Enable 4-bit or 8-bit precision loading', default=0)
args = parser.parse_args([])


def load_video(video_data, strategy='chat'):
    bridge.set_bridge('torch')
    mp4_stream = video_data
    num_frames = 24
    decord_vr = VideoReader(io.BytesIO(mp4_stream), ctx=cpu(0))

    frame_id_list = None
    total_frames = len(decord_vr)
    if strategy == 'base':
        clip_end_sec = 60
        clip_start_sec = 0
        start_frame = int(clip_start_sec * decord_vr.get_avg_fps())
        end_frame = min(total_frames,
                        int(clip_end_sec * decord_vr.get_avg_fps())) if clip_end_sec is not None else total_frames
        frame_id_list = np.linspace(start_frame, end_frame - 1, num_frames, dtype=int)
    elif strategy == 'chat':
        timestamps = decord_vr.get_frame_timestamp(np.arange(total_frames))
        timestamps = [i[0] for i in timestamps]
        max_second = round(max(timestamps)) + 1
        frame_id_list = []
        for second in range(max_second):
            closest_num = min(timestamps, key=lambda x: abs(x - second))
            index = timestamps.index(closest_num)
            frame_id_list.append(index)
            if len(frame_id_list) >= num_frames:
                break

    video_data = decord_vr.get_batch(frame_id_list)
    video_data = video_data.permute(3, 0, 1, 2)
    return video_data


tokenizer = AutoTokenizer.from_pretrained(
    MODEL_PATH,
    trust_remote_code=True,
)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    torch_dtype=TORCH_TYPE,
    trust_remote_code=True
).eval().to(DEVICE)


def predict(prompt, video_data, temperature):
    strategy = 'chat'

    video = load_video(video_data, strategy=strategy)

    history = []
    query = prompt
    inputs = model.build_conversation_input_ids(
        tokenizer=tokenizer,
        query=query,
        images=[video],
        history=history,
        template_version=strategy
    )
    inputs = {
        'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
        'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'),
        'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'),
        'images': [[inputs['images'][0].to('cuda').to(TORCH_TYPE)]],
    }
    gen_kwargs = {
        "max_new_tokens": 2048,
        "pad_token_id": 128002,
        "top_k": 1,
        "do_sample": False,
        "top_p": 0.1,
        "temperature": temperature,
    }
    with torch.no_grad():
        outputs = model.generate(**inputs, **gen_kwargs)
        outputs = outputs[:, inputs['input_ids'].shape[1]:]
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return response


def test():
    prompt = "Please describe this video in detail."
    temperature = 0.1
    video_data = open('test.mp4', 'rb').read()
    response = predict(prompt, video_data, temperature)
    print(response)


if __name__ == '__main__':
    test()

License

This model is released under the CogVLM2 LICENSE. For models built with Meta Llama 3, please also adhere to the LLAMA3_LICENSE.

Citation

🌟 If you find our work helpful, please leave us a star and cite our paper.

@article{yang2024cogvideox,
  title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer},
  author={Yang, Zhuoyi and Teng, Jiayan and Zheng, Wendi and Ding, Ming and Huang, Shiyu and Xu, Jiazheng and Yang, Yuanming and Hong, Wenyi and Zhang, Xiaohan and Feng, Guanyu and others},
  journal={arXiv preprint arXiv:2408.06072},
  year={2024}
}