Update README.md

README.md

@@ -3,13 +3,16 @@
 <!-- [![arXiv](https://img.shields.io/badge/arXiv-2404.14396-b31b1b.svg)](https://arxiv.org/abs/2404.14396)-->
 
 [![Demo](https://img.shields.io/badge/ARC-Demo-blue)](https://arc.tencent.com/en/ai-demos/multimodal)
+[![Code](https://img.shields.io/badge/Github-Code-orange)](https://github.com/TencentARC/ARC-Hunyuan-Video-7B)
 [![Static Badge](https://img.shields.io/badge/Model-Huggingface-yellow)](https://huggingface.co/TencentARC/ARC-Hunyuan-Video-7B)
 [![Blog](https://img.shields.io/badge/ARC-Blog-green)](https://tencentarc.github.io/posts/arc-video-announcement/)
 
 <span style="font-size:smaller;">
 Please note that in our Demo, ARC-Hunyuan-Video-7B is the model consistent with the model checkpoint and the one described in the paper, while ARC-Hunyuan-Video-7B-V0 only supports video description and summarization in Chinese.
+Due to API file size limits, our demo uses compressed input video resolutions, which may cause slight performance differences from the paper. For original results, please run locally.
 </span>
 
+
 ## Introduction
 
 We introduce **ARC-Hunyuan-Video-7B**, a powerful multimodal model designed for _understanding real-world short videos_.
@@ -17,6 +20,8 @@ Understanding user-generated videos is actually challenging due to their complex
 information density in both visuals and audio, and fast pacing that focuses on emotional expression and viewpoint delivery.
 To address this challenge, ARC-Hunyuan-Video-7B
 processes visual, audio, and textual signals end-to-end for a deep, structured understanding of video through integrating and reasoning over multimodal cues.
+Stress test reports show an inference time of just 10 seconds for a one-minute video on H20 GPU, yielding an average of 500 tokens, with
+inference accelerated by the vLLM framework.
 
 Compared to prior arts, we introduces a new paradigm of **Structured Video Comprehension**, with capabilities including:
 
@@ -47,10 +52,12 @@ Specifically, ARC-Hunyuan-Video-7B is built on top of the Hunyuan-7B vision-lang
 ## News
 
 - 2025.07.25: We release the [model checkpoint](https://huggingface.co/TencentARC/ARC-Hunyuan-Video-7B) and inference code of ARC-Hunyuan-Video-7B including [vLLM](https://github.com/vllm-project/vllm) version.
-- 2025.07.25: We release the [API service](https://arc.tencent.com/en/document/ARC-Video-7B) of ARC-Hunyuan-Video-7B, which is supported by [vLLM](https://github.com/vllm-project/vllm). We release two versions: one is V0, which only supports video description and summarization in Chinese; the other is the version consistent with the model checkpoint and the one described in the paper.
+- 2025.07.25: We release the [API service](https://arc.tencent.com/zh/document/ARC-Hunyuan-Video-7B) of ARC-Hunyuan-Video-7B, which is supported by [vLLM](https://github.com/vllm-project/vllm). We release two versions: one is V0, which only supports video description and summarization in Chinese; the other is the version consistent with the model checkpoint and the one described in the paper.
 
 ## Usage
-
+### Dependencies
+- Our inference can be performed on a single NVIDIA A100 40GB GPU.
+- For the vLLM deployment version, we recommend using two NVIDIA A100 40GB GPUs.
 ### Installation
 
 Clone the repo and install dependent packages
@@ -58,17 +65,24 @@ Clone the repo and install dependent packages
 ```bash
 git clone https://github.com/TencentARC/ARC-Hunyuan-Video-7B.git
 cd ARC-Hunyuan-Video-7B
+# Install torch 2.6.0
+pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124
 pip install -r requirements.txt
 pip install git+https://github.com/liyz15/transformers.git@arc_hunyuan_video
 
-# For vllm, please follow the instructions below,
+# Install flash-attention based on your python version
+# If you are unable to install flash-attention, you can modify attn_implementation to "sdpa" in video_inference.py
+pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
+
+
+# (Optional) For vllm, please follow the instructions below,
 git submodule update --init --recursive
 cd model_vllm/vllm/
 export SETUPTOOLS_SCM_PRETEND_VERSION="0.8.5"
 wget https://wheels.vllm.ai/ed2462030f2ccc84be13d8bb2c7476c84930fb71/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
-export VLLM_PRECOMPILED_WHEEL_LOCATION=path_of_whl
+export VLLM_PRECOMPILED_WHEEL_LOCATION=$(pwd)/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
 pip install --editable .
-# Please install corresponding package based on your python version
+# Install flash-attention if you haven't installed it
 pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
 ```
 
@@ -77,6 +91,11 @@ pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.
 - Download [ARC-Hunyuan-Video-7B](https://huggingface.co/TencentARC/ARC-Hunyuan-Video-7B) including ViT and LLM and the original [whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) .
 
 ### Inference
+```bash
+# Our model currently excels at processing short videos of up to 5 minutes.
+# If your video is longer, we recommend following the approach used in our demo and API:
+# split the video into segments for inference, and then use an LLM to integrate the results.
+```
 
 #### Inference without vllm
 
@@ -96,9 +115,12 @@ python3 video_inference_vllm.py
 
 We also provide access to the model via API, which is supported by [vLLM](https://github.com/vllm-project/vllm). For details, please refer to the [documentation](https://arc.tencent.com/zh/document/ARC-Hunyuan-Video-7B).
 
-We release two versions: one is V0, which only supports video description and summarization in Chinese; the other is the version consistent with the model checkpoint and the one described in the paper, which is capable of multi-granularity timestamped video captioning and summarization, open-ended video question answering, temporal video grounding, and video reasoning ( It supports Chinese and English videos and particularly excels at Chinese).
+We release two versions: one is V0, which only supports video description and summarization in Chinese; the other is the version consistent with the model checkpoint and the one described in the paper, which is capable of multi-granularity timestamped video captioning and summarization, open-ended video question answering, temporal video grounding, and video reasoning (It supports Chinese and English videos and particularly excels at Chinese). 
+For videos longer than 5 minutes, we only support structured descriptions. We process these videos in 5-minute segments and use an LLM to integrate the inference results.
+
+If you only need to understand and summarize short Chinese videos, we recommend using the V0 version.
 
-If you only need to understand and summarize short Chinese videos, we recommend using the V0 version
+Due to video file size limitations imposed by the deployment API, we compressed input video resolutions for our online demo and API services. Consequently, model performance in these interfaces may slightly deviate from the results reported in the paper. To reproduce the original performance, we recommend local inference.
 
 ## Future Work
 
@@ -117,3 +139,5 @@ If you find the work helpful, please consider citing:
 }
 ```
 -->
+
+