wiro/ ask-video
ask-video
ask-video
Convert the video data into textual descriptions.
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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}
}
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}
}
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