update readme for video caption

README.md

@@ -27,7 +27,7 @@ Experience the CogVideoX-5B model online at <a href="https://huggingface.co/spac
   controllability. With this release, the CogVideoX series now supports three tasks: text-to-video, video extension, and
   image-to-video generation. Feel free to try it out [online](https://huggingface.co/spaces/THUDM/CogVideoX-5B-Space).
 - 🔥🔥 **News**: ```2024/9/19```: The caption model used in the CogVideoX training process to convert video data into text
-  descriptions, [cogvlm2-llama3-caption](https://huggingface.co/THUDM/cogvlm2-llama3-caption), is now open-source. Feel
+  descriptions, [CogVLM2-Caption](https://huggingface.co/THUDM/cogvlm2-llama3-caption), is now open-source. Feel
   free to download and use it.
 - 🔥 **News**: ```2024/9/16```: We have added an automated video generation tool! You can now use local open-source
   models + FLUX + CogVideoX to automatically generate high-quality videos. Feel free

README_ja.md

@@ -25,7 +25,7 @@
 - 🔥🔥 **ニュース**: ```2024/9/19```: CogVideoXシリーズの画像生成ビデオモデル **CogVideoX-5B-I2V**
   をオープンソース化しました。このモデルでは、背景として画像を入力し、プロンプトと組み合わせてビデオを生成でき、より強力なコントロール性を提供します。これで、CogVideoXシリーズは、テキスト生成ビデオ、ビデオ拡張、画像生成ビデオの3つのタスクをサポートしています。ぜひ [オンラインでお試しください](https://huggingface.co/spaces/THUDM/CogVideoX-5B-Space)。
 - 🔥🔥 **ニュース**: ```2024/9/19```：CogVideoX
-  のトレーニングプロセスで、ビデオデータをテキストに変換するためのキャプションモデル [cogvlm2-llama3-caption](https://huggingface.co/THUDM/cogvlm2-llama3-caption)
+  のトレーニングプロセスで、ビデオデータをテキストに変換するためのキャプションモデル [CogVLM2-Caption](https://huggingface.co/THUDM/cogvlm2-llama3-caption)
   がオープンソース化されました。ぜひダウンロードしてご利用ください。
 - 🔥 **ニュース**: ```2024/9/16```: 自動動画生成ツールを追加しました！オープンソースのローカルモデル + FLUX + CogVideoX
   を使用して、高品質な動画を自動生成できます。ぜひ[お試しください](tools/llm_flux_cogvideox/llm_flux_cogvideox.py)。

README_zh.md

@@ -27,7 +27,7 @@
   。该模型可以将一张图像作为背景输入，结合提示词一起生成视频，具有更强的可控性。
   至此，CogVideoX系列模型已经支持文本生成视频，视频续写，图片生成视频三种任务。欢迎前往在线[体验](https://huggingface.co/spaces/THUDM/CogVideoX-5B-Space)。
 - 🔥🔥 **News**: ```2024/9/19```: CogVideoX 训练过程中用于将视频数据转换为文本描述的 Caption
-  模型 [cogvlm2-llama3-caption](https://huggingface.co/THUDM/cogvlm2-llama3-caption)
+  模型 [CogVLM2-Caption](https://huggingface.co/THUDM/cogvlm2-llama3-caption)
   已经开源。欢迎前往下载并使用。
 - 🔥 **News**: ```2024/9/16```: 我们添加自动化生成视频工具，你可以使用本地开源模型 + FLUX + CogVideoX
   实现自动生成优质视频，欢迎[体验](tools/llm_flux_cogvideox/llm_flux_cogvideox.py)

tools/caption/README.md

@@ -3,11 +3,37 @@
 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.
 
+## Update and News
+- 🔥🔥 **News**: ```2024/9/19```: The caption model used in the CogVideoX training process to convert video data into text
+  descriptions, [CogVLM2-Caption](https://huggingface.co/THUDM/cogvlm2-llama3-caption), is now open-source. Feel
+  free to download and use it.
+
+
+## Video Caption via CogVLM2-Caption
+
+🤗 [Hugging Face](https://huggingface.co/THUDM/cogvlm2-llama3-caption) | 🤖 [ModelScope](https://modelscope.cn/models/ZhipuAI/cogvlm2-llama3-caption/) 
+
+CogVLM2-Caption is a video captioning model used to generate training data for the CogVideoX model.
+
+### Install
+```shell
+pip install -r requirements.txt
+```
+
+### Usage
+
+```shell
+python video_caption.py
+```
+
+Example:
+<div align="center">
+    <img width="600px" height="auto" src="./assests/CogVLM2-Caption-example.png">
+</div>
+
 ## Video Caption via CogVLM2-Video
 
-<p align="center">
+[Code](https://github.com/THUDM/CogVLM2/tree/main/video_demo) | 🤗 [Hugging Face](https://huggingface.co/THUDM/cogvlm2-video-llama3-chat) | 🤖 [ModelScope](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-chat) | 📑 [Blog](https://cogvlm2-video.github.io/) ｜ [💬 Online Demo](http://cogvlm2-online.cogviewai.cn:7868/)
-🤗 <a href="https://huggingface.co/THUDM/cogvlm2-video-llama3-chat">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-chat">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://cogvlm2-video.github.io/">Blog</a> &nbsp&nbsp ｜ <a href="http://cogvlm2-online.cogviewai.cn:7868/">💬 Online Demo</a>&nbsp&nbsp
-</p>
 
 CogVLM2-Video is a versatile video understanding model equipped with timestamp-based question answering capabilities.
 Users can input prompts such as `Please describe this video in detail.` to the model to obtain a detailed video caption:
@@ -16,3 +42,26 @@ Users can input prompts such as `Please describe this video in detail.` to the m
 </div>
 
 Users can use the provided [code](https://github.com/THUDM/CogVLM2/tree/main/video_demo) to load the model or configure a RESTful API to generate video captions.
+
+## Citation
+
+🌟 If you find our work helpful, please leave us a star and cite our paper.
+
+CogVLM2-Caption:
+```
+@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-Video:
+```
+@article{hong2024cogvlm2,
+  title={CogVLM2: Visual Language Models for Image and Video Understanding},
+  author={Hong, Wenyi and Wang, Weihan and Ding, Ming and Yu, Wenmeng and Lv, Qingsong and Wang, Yan and Cheng, Yean and Huang, Shiyu and Ji, Junhui and Xue, Zhao and others},
+  journal={arXiv preprint arXiv:2408.16500},
+  year={2024}
+}
+```

tools/caption/README_ja.md

@@ -2,11 +2,37 @@
 
 通常、ほとんどのビデオデータには対応する説明文が付いていないため、ビデオデータをテキストの説明に変換して、テキストからビデオへのモデルに必要なトレーニングデータを提供する必要があります。
 
+## 更新とニュース
+- 🔥🔥 **ニュース**: ```2024/9/19```：CogVideoX
+  のトレーニングプロセスで、ビデオデータをテキストに変換するためのキャプションモデル [CogVLM2-Caption](https://huggingface.co/THUDM/cogvlm2-llama3-caption)
+  がオープンソース化されました。ぜひダウンロードしてご利用ください。
+## CogVLM2-Captionによるビデオキャプション
+
+🤗 [Hugging Face](https://huggingface.co/THUDM/cogvlm2-llama3-caption) | 🤖 [ModelScope](https://modelscope.cn/models/ZhipuAI/cogvlm2-llama3-caption/) 
+
+CogVLM2-Captionは、CogVideoXモデルのトレーニングデータを生成するために使用されるビデオキャプションモデルです。
+
+### インストール
+```shell
+pip install -r requirements.txt
+```
+
+### 使用方法
+```shell
+python video_caption.py
+```
+
+例:
+<div align="center">
+    <img width="600px" height="auto" src="./assests/CogVLM2-Caption-example.png">
+</div>
+
+
+
 ## CogVLM2-Video を使用したビデオキャプション
 
-<p align="center">
+[Code](https://github.com/THUDM/CogVLM2/tree/main/video_demo) | 🤗 [Hugging Face](https://huggingface.co/THUDM/cogvlm2-video-llama3-chat) | 🤖 [ModelScope](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-chat) | 📑 [Blog](https://cogvlm2-video.github.io/) ｜ [💬 Online Demo](http://cogvlm2-online.cogviewai.cn:7868/)
-🤗 <a href="https://huggingface.co/THUDM/cogvlm2-video-llama3-chat">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-chat">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://cogvlm2-video.github.io/">ブログ</a> &nbsp&nbsp ｜ <a href="http://cogvlm2-online.cogviewai.cn:7868/">💬 オンラインデモ</a>&nbsp&nbsp
+
-</p>
 
 CogVLM2-Video は、タイムスタンプベースの質問応答機能を備えた多機能なビデオ理解モデルです。ユーザーは `このビデオを詳細に説明してください。` などのプロンプトをモデルに入力して、詳細なビデオキャプションを取得できます：
 <div align="center">
@@ -14,3 +40,26 @@ CogVLM2-Video は、タイムスタンプベースの質問応答機能を備え
 </div>
 
 ユーザーは提供された[コード](https://github.com/THUDM/CogVLM2/tree/main/video_demo)を使用してモデルをロードするか、RESTful API を構成してビデオキャプションを生成できます。
+
+## Citation
+
+🌟 If you find our work helpful, please leave us a star and cite our paper.
+
+CogVLM2-Caption:
+```
+@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-Video:
+```
+@article{hong2024cogvlm2,
+  title={CogVLM2: Visual Language Models for Image and Video Understanding},
+  author={Hong, Wenyi and Wang, Weihan and Ding, Ming and Yu, Wenmeng and Lv, Qingsong and Wang, Yan and Cheng, Yean and Huang, Shiyu and Ji, Junhui and Xue, Zhao and others},
+  journal={arXiv preprint arXiv:2408.16500},
+  year={2024}
+}
+```

tools/caption/README_zh.md

@@ -2,11 +2,38 @@
 
 通常，大多数视频数据不带有相应的描述性文本，因此需要将视频数据转换为文本描述，以提供必要的训练数据用于文本到视频模型。
 
+## 项目更新
+- 🔥🔥 **News**: ```2024/9/19```: CogVideoX 训练过程中用于将视频数据转换为文本描述的 Caption
+  模型 [CogVLM2-Caption](https://huggingface.co/THUDM/cogvlm2-llama3-caption)
+  已经开源。欢迎前往下载并使用。
+
+## 通过 CogVLM2-Caption 模型生成视频Caption
+
+🤗 [Hugging Face](https://huggingface.co/THUDM/cogvlm2-llama3-caption) | 🤖 [ModelScope](https://modelscope.cn/models/ZhipuAI/cogvlm2-llama3-caption/) 
+
+CogVLM2-Caption是用于生成CogVideoX模型训练数据的视频caption模型。
+
+### 安装依赖
+```shell
+pip install -r requirements.txt
+```
+
+### 运行caption模型
+
+```shell
+python video_caption.py
+```
+
+示例：
+<div align="center">
+    <img width="600px" height="auto" src="./assests/CogVLM2-Caption-example.png">
+</div>
+
 ## 通过 CogVLM2-Video 模型生成视频Caption
 
-🤗 [Hugging Face](https://huggingface.co/THUDM/cogvlm2-video-llama3-chat) | 🤖 [ModelScope](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-chat) | 📑 [Blog](https://cogvlm2-video.github.io/) ｜ [💬 Online Demo](http://cogvlm2-online.cogviewai.cn:7868/)
+[Code](https://github.com/THUDM/CogVLM2/tree/main/video_demo) | 🤗 [Hugging Face](https://huggingface.co/THUDM/cogvlm2-video-llama3-chat) | 🤖 [ModelScope](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-chat) | 📑 [Blog](https://cogvlm2-video.github.io/) ｜ [💬 Online Demo](http://cogvlm2-online.cogviewai.cn:7868/)
 
-CogVLM2-Video 是一个多功能的视频理解模型，具备基于时间戳的问题回答能力。用户可以输入诸如 `请详细描述这个视频` 的提示语给模型，以获得详细的视频Caption：
+CogVLM2-Video 是一个多功能的视频理解模型，具备基于时间戳的问题回答能力。用户可以输入诸如 `Describe this video in detail.` 的提示语给模型，以获得详细的视频Caption：
 
 
 <div align="center">
@@ -14,3 +41,27 @@ CogVLM2-Video 是一个多功能的视频理解模型，具备基于时间戳的
 </div>
 
 用户可以使用提供的[代码](https://github.com/THUDM/CogVLM2/tree/main/video_demo)加载模型或配置 RESTful API 来生成视频Caption。
+
+
+## Citation
+
+🌟 If you find our work helpful, please leave us a star and cite our paper.
+
+CogVLM2-Caption:
+```
+@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-Video:
+```
+@article{hong2024cogvlm2,
+  title={CogVLM2: Visual Language Models for Image and Video Understanding},
+  author={Hong, Wenyi and Wang, Weihan and Ding, Ming and Yu, Wenmeng and Lv, Qingsong and Wang, Yan and Cheng, Yean and Huang, Shiyu and Ji, Junhui and Xue, Zhao and others},
+  journal={arXiv preprint arXiv:2408.16500},
+  year={2024}
+}
+```

tools/caption/requirements.txt

@@ -0,0 +1,23 @@
+decord>=0.6.0
+#根据https://download.pytorch.org/whl/torch/，python版本为[3.8,3.11]
+torch==2.1.0
+torchvision== 0.16.0
+pytorchvideo==0.1.5
+xformers
+transformers==4.42.4
+#git+https://github.com/huggingface/transformers.git
+huggingface-hub>=0.23.0
+pillow
+chainlit>=1.0
+pydantic>=2.7.1
+timm>=0.9.16
+openai>=1.30.1
+loguru>=0.7.2
+pydantic>=2.7.1
+einops
+sse-starlette>=2.1.0
+flask
+gunicorn
+gevent
+requests
+gradio

tools/caption/video_caption.py

@@ -0,0 +1,108 @@
+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()