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https://github.com/opengvlab/internvl
[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的可商用开源多模态对话模型
https://github.com/opengvlab/internvl
gpt gpt-4o gpt-4v image-classification image-text-retrieval llm multi-modal semantic-segmentation video-classification vision-language-model vit-22b vit-6b
Last synced: about 1 month ago
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[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的可商用开源多模态对话模型
- Host: GitHub
- URL: https://github.com/opengvlab/internvl
- Owner: OpenGVLab
- License: mit
- Created: 2023-11-22T08:08:08.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-01T03:51:40.000Z (about 1 month ago)
- Last Synced: 2024-08-01T13:45:44.882Z (about 1 month ago)
- Topics: gpt, gpt-4o, gpt-4v, image-classification, image-text-retrieval, llm, multi-modal, semantic-segmentation, video-classification, vision-language-model, vit-22b, vit-6b
- Language: Python
- Homepage: https://internvl.readthedocs.io/en/latest/
- Size: 35.4 MB
- Stars: 4,537
- Watchers: 45
- Forks: 352
- Open Issues: 93
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ChatGPT-repositories - InternVL - [CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4V. 接近GPT-4V表现的可商用开源模型 (NLP)
README
# 
InternVL Family: Closing the Gap to Commercial Multimodal Models with Open-Source Suites —— A Pioneering Open-Source Alternative to GPT-4o
[\[🆕 Blog\]](https://internvl.github.io/blog/) [\[🚀 InternVL2 Blog\]](https://internvl.github.io/blog/2024-07-02-InternVL-2.0/) [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[📖 Document\]](https://internvl.readthedocs.io/en/latest/) [\[🌐 API\]](./document/How_to_use_InternVL_API.md) [\[🚀 Quick Start\]](#quick-start-with-huggingface)
[\[📜 InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821) [\[📖 1.0 中文解读\]](https://zhuanlan.zhihu.com/p/702946079) [\[📖 1.5 中文解读\]](https://zhuanlan.zhihu.com/p/699439759) [\[📖 2.0 中文解读\]](https://zhuanlan.zhihu.com/p/706547971)
[Switch to the Chinese version (切换至中文版)](/README_zh.md)
## News 🚀🚀🚀
- `2024/07/18`: 🔥🔥 InternVL2-40B achieved SOTA performance among open-source models on the [Video-MME](https://github.com/BradyFU/Video-MME) dataset, scoring 61.2 when inputting 16 frames and 64.4 when inputting 32 frames. It significantly outperforms other open-source models and is the closest open-source model to GPT-4o mini.
- `2024/07/18`: 🔥 InternVL2-Pro achieved the SOTA performance on the [DocVQA](https://rrc.cvc.uab.es/?ch=17&com=evaluation&task=1) and [InfoVQA](https://rrc.cvc.uab.es/?ch=17&com=evaluation&task=3) benchmarks.
- `2024/07/04`: 🚀 We release the [InternVL2 series](https://huggingface.co/collections/OpenGVLab/internvl-20-667d3961ab5eb12c7ed1463e). InternVL2-Pro achieved a 62.0% accuracy on the MMMU benchmark, matching the performance of leading closed-source commercial models like GPT-4o. The free API of this model can be applied by filling ([application form](https://docs.google.com/forms/d/e/1FAIpQLSfMCzhPr1OOEKau_6jwTU0EiZMSFckDo-HMlc_hUudhF_97rw/viewform?usp=sf_link)) / ([申请表](https://wj.qq.com/s2/14910502/25a4/)). Other models are available at [HF link](https://huggingface.co/collections/OpenGVLab/internvl-20-667d3961ab5eb12c7ed1463e).
- `2024/06/19`: We propose Needle In A Multimodal Haystack ([MM-NIAH](https://github.com/OpenGVLab/MM-NIAH)), the first benchmark designed to systematically evaluate the capability of existing MLLMs to comprehend long multimodal documents.
- `2024/05/30`: We release [ShareGPT-4o](https://sharegpt4o.github.io/), a large-scale dataset that we plan to open-source with 200K images, 10K videos, and 10K audios with detailed descriptions.
- `2024/05/29`: We release the Mini-InternVL series, which includes two chat models: [Mini-InternVL-Chat-2B-V1-5](https://huggingface.co/OpenGVLab/Mini-InternVL-Chat-2B-V1-5) and [Mini-InternVL-Chat-4B-V1-5](https://huggingface.co/OpenGVLab/Mini-InternVL-Chat-4B-V1-5). These models achieve impressive performance with minimal size: the 2B model delivers 80% of the performance with only 8% of the model size, and the 4B model achieves 90% of the performance with just 16% of the model size. For more details, please check our [blog](https://internvl.github.io/blog/2024-05-25-Mini-InternVL-1.5/).
- `2024/05/28`: Thanks to the [lmdeploy](https://github.com/InternLM/lmdeploy) team for providing AWQ quantization support. The 4-bit model is available at [OpenGVLab/InternVL-Chat-V1-5-AWQ](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5-AWQ).
- `2024/05/13`: InternVL 1.0 can now be used as the [text encoder](https://huggingface.co/OpenGVLab/InternVL-14B-224px) for diffusion models to support multilingual generation natively in over 110 languages worldwide. See [MuLan](https://github.com/mulanai/MuLan) for more details.
- `2024/04/18`: InternVL-Chat-V1-5 has been released at [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5), approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc.
- `2024/02/27`: InternVL is accepted by CVPR 2024 (Oral)! 🎉
- `2024/02/24`: InternVL-Chat models have been included in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit).
- `2024/02/21`: [InternVL-Chat-V1-2-Plus](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2-Plus) achieved SOTA performance on MathVista (59.9), MMBench (83.8), and MMVP (58.7). See our [blog](https://internvl.github.io/blog/2024-02-21-InternVL-1.2/) for more details.
- `2024/02/12`: InternVL-Chat-V1-2 has been released. It achieves 51.6 on MMMU val and 82.3 on MMBench test. For more details, please refer to our [blog](https://internvl.github.io/blog/2024-02-21-InternVL-1.2/) and [SFT data](./internvl_chat#prepare-training-datasets). The model is now available on [HuggingFace](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2), and both training / evaluation data and scripts are open-sourced.
- `2024/01/24`: InternVL-Chat-V1-1 is released, it supports Chinese and has stronger OCR capability, see [here](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1).
- `2024/01/16`: We release our [customized mmcv/mmsegmentation/mmdetection code](https://github.com/OpenGVLab/InternVL-MMDetSeg), integrated with DeepSpeed, which can be used for training large-scale detection and segmentation models.
## TODO List
- [ ] Support vLLM and Ollama
- [x] Rebuild documents using readthedocs
- [x] Support fine-tuning different LLMs with LoRA
- [ ] Support video and PDF input in online demo
- [ ] Release InternVL2 with VisionLLMv2 integration
- [x] Release `requirements.txt` for InternVL2
- [x] Release training / evaluation code for InternVL2 series
- [x] Release Streamlit web UI for InternVL1.5 and InternVL2
## Documents
- Get Started
- Installation: [\[Environment\]](https://internvl.readthedocs.io/en/latest/get_started/installation.html) [\[requirements.txt\]](./requirements.txt)
- Evaluation Data Preparation: [\[InternVL Evaluation\]](https://internvl.readthedocs.io/en/latest/get_started/eval_data_preparation.html)
- Chat Data Format: [\[Meta File\]](https://internvl.readthedocs.io/en/latest/get_started/chat_data_format.html#meta-file) [\[Pure Text\]](https://internvl.readthedocs.io/en/latest/get_started/chat_data_format.html#pure-text-data) [\[Single-Image\]](https://internvl.readthedocs.io/en/latest/get_started/chat_data_format.html#single-image-data) [\[Multi-Image\]](https://internvl.readthedocs.io/en/latest/get_started/chat_data_format.html#multi-image-data) [\[Video\]](https://internvl.readthedocs.io/en/latest/get_started/chat_data_format.html#video-data)
- Local Chat Demo: [\[Streamlit Demo\]](https://internvl.readthedocs.io/en/latest/get_started/local_chat_demo.html#streamlit-demo) [\[Gradio Demo\]](https://internvl.readthedocs.io/en/latest/get_started/local_chat_demo.html#gradio-demo) [\[LMDeploy Demo\]](https://internvl.readthedocs.io/en/latest/get_started/local_chat_demo.html#lmdeploy-demo)
- InternVL Family
- InternVL 2.0: [\[Introduction\]](https://internvl.readthedocs.io/en/latest/internvl2.0/introduction.html) [\[Quick Start\]](https://internvl.readthedocs.io/en/latest/internvl2.0/quick_start.html) [\[Finetune\]](https://internvl.readthedocs.io/en/latest/internvl2.0/finetune.html) [\[Evaluation\]](https://internvl.readthedocs.io/en/latest/internvl2.0/evaluation.html) [\[Deployment\]](https://internvl.readthedocs.io/en/latest/internvl2.0/deployment.html)
- InternVL 1.5: [\[Introduction\]](https://internvl.readthedocs.io/en/latest/internvl1.5/introduction.html) [\[Quick Start\]](https://internvl.readthedocs.io/en/latest/internvl1.5/quick_start.html) [\[Finetune\]](https://internvl.readthedocs.io/en/latest/internvl1.5/finetune.html) [\[Evaluation\]](https://internvl.readthedocs.io/en/latest/internvl1.5/evaluation.html) [\[Deployment\]](https://internvl.readthedocs.io/en/latest/internvl1.5/deployment.html)
- InternVL 1.2: [\[Introduction\]](https://internvl.readthedocs.io/en/latest/internvl1.2/introduction.html) [\[Quick Start\]](https://internvl.readthedocs.io/en/latest/internvl1.2/quick_start.html) [\[Finetune\]](https://internvl.readthedocs.io/en/latest/internvl1.2/finetune.html) [\[Evaluation\]](https://internvl.readthedocs.io/en/latest/internvl1.2/evaluation.html)
- InternVL 1.1: [\[Introduction\]](https://internvl.readthedocs.io/en/latest/internvl1.1/introduction.html) [\[Quick Start\]](https://internvl.readthedocs.io/en/latest/internvl1.1/quick_start.html) [\[Evaluation\]](https://internvl.readthedocs.io/en/latest/internvl1.1/evaluation.html)
- InternVL 1.0: [\[Classification\]](https://internvl.readthedocs.io/en/latest/internvl1.0/classification.html) [\[CLIP-Benchmark\]](https://internvl.readthedocs.io/en/latest/internvl1.0/clip_benchmark.html) [\[Segmentation\]](https://internvl.readthedocs.io/en/latest/internvl1.0/segmentation.html) [\[InternVL-Chat-LLaVA\]](https://internvl.readthedocs.io/en/latest/internvl1.0/internvl_chat_llava.html) [\[InternVL-G\]](https://internvl.readthedocs.io/en/latest/internvl1.0/internvl_g.html)
## Compared with SOTA VLLMs
## Model Zoo
#### Multimodal Large Language Model (InternVL 2.0)
Model Name
Vision Part
Language Part
HF Link
MS Link
Document
InternVL2‑1B
InternViT‑300M‑448px
Qwen2‑0.5B‑Instruct
🤗 link
🤖 link
📖 doc
InternVL2‑2B
InternViT‑300M‑448px
internlm2‑chat‑1‑8b
🤗 link
🤖 link
📖 doc
InternVL2‑4B
InternViT‑300M‑448px
Phi‑3‑mini‑128k‑instruct
🤗 link
🤖 link
📖 doc
InternVL2‑8B
InternViT‑300M‑448px
internlm2_5‑7b‑chat
🤗 link
🤖 link
📖 doc
InternVL2‑26B
InternViT‑6B‑448px‑V1‑5
internlm2‑chat‑20b
🤗 link
🤖 link
📖 doc
InternVL2‑40B
InternViT‑6B‑448px‑V1‑5
Nous‑Hermes‑2‑Yi‑34B
🤗 link
🤖 link
📖 doc
InternVL2-Llama3-76B
InternViT‑6B‑448px‑V1‑5
Hermes‑2‑Theta‑
Llama‑3‑70B
🤗 link
🤖 link
📖 doc
#### InternVL2-Pro API
We welcome everyone to use our API for research. For better management, please submit ([application form](https://docs.google.com/forms/d/e/1FAIpQLSfMCzhPr1OOEKau_6jwTU0EiZMSFckDo-HMlc_hUudhF_97rw/viewform?usp=sf_link)) / ([申请表](https://wj.qq.com/s2/14910502/25a4/)) to obtain free API access.
#### Multimodal Large Language Model (InternVL 1.0-1.5)
Model
Date
HF Link
MS Link
Note
Mini‑InternVL‑Chat‑4B‑V1‑5
2024.05.28
🤗 link
🤖 link
🚀🚀 16% of the model size, 90% of the performance
Mini‑InternVL‑Chat‑2B‑V1‑5
2024.05.19
🤗 link
🤖 link
🚀 8% of the model size, 80% of the performance
InternVL‑Chat‑V1‑5
2024.04.18
🤗 link
🤖 link
support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc.
InternVL‑Chat‑V1‑2‑Plus
2024.02.21
🤗 link
🤖 link
more SFT data and stronger
InternVL‑Chat‑V1‑2
2024.02.11
🤗 link
🤖 link
scaling up LLM to 34B
InternVL‑Chat‑V1‑1
2024.01.24
🤗 link
🤖 link
support Chinese and stronger OCR
InternVL‑Chat‑19B
2023.12.25
🤗 link
🤖 link
English multimodal dialogue
InternVL‑Chat‑13B
2023.12.25
🤗 link
🤖 link
English multimodal dialogue
#### Vision Foundation Model (InternVL 1.0-1.5)
Model
Date
HF Link
MS Link
Note
InternViT‑300M‑448px
2024.05.25
🤗 link
🤖 link
distilled small vision foundation model with 300M parameters (🔥new)
InternViT‑6B‑448px‑V1‑5
2024.04.20
🤗 link
🤖 link
support dynamic resolution and super strong OCR feature extraction capability by incremental pre-training (🔥new)
InternViT‑6B‑448px‑V1‑2
2024.02.11
🤗 link
🤖 link
support 448 resolution by incremental pre-training
InternViT‑6B‑448px‑V1‑0
2024.01.30
🤗 link
🤖 link
support 448 resolution by incremental pre-training
InternViT‑6B‑224px
2023.12.22
🤗 link
🤖 link
the first version of InternViT-6B, extracted from InternVL‑14B‑224px
#### Vision-Language Foundation Model (InternVL 1.0)
Model
Date
HF Link
MS Link
Note
InternVL‑14B‑224px
2023.12.22
🤗 link
🤖 link
vision-language foundation model, InternViT-6B + QLLaMA, can be used for image-text retrieval like CLIP
## What can InternVL do?
Visual Perception (click to expand)
- Linear-Probe Image Classification [\[see details\]](./classification#-evaluation)
ViT-22B uses the private JFT-3B dataset.
| method | #param | IN-1K | IN-ReaL | IN-V2 | IN-A | IN-R | IN-Sketch |
| ------------------- | :----: | :---: | :-----: | :---: | :--: | :--: | :-------: |
| OpenCLIP-G | 1.8B | 86.2 | 89.4 | 77.2 | 63.8 | 87.8 | 66.4 |
| DINOv2-g | 1.1B | 86.5 | 89.6 | 78.4 | 75.9 | 78.8 | 62.5 |
| EVA-01-CLIP-g | 1.1B | 86.5 | 89.3 | 77.4 | 70.5 | 87.7 | 63.1 |
| MAWS-ViT-6.5B | 6.5B | 87.8 | - | - | - | - | - |
| ViT-22B\* | 21.7B | 89.5 | 90.9 | 83.2 | 83.8 | 87.4 | - |
| InternViT-6B (ours) | 5.9B | 88.2 | 90.4 | 79.9 | 77.5 | 89.8 | 69.1 |
- Semantic Segmentation [\[see details\]](./segmentation#-evaluation)
| method | decoder | #param (train/total) | crop size | mIoU |
| --------------------- | :-----: | :------------------: | :-------: | ------------ |
| OpenCLIP-G (frozen) | Linear | 0.3M / 1.8B | 512 | 39.3 |
| ViT-22B (frozen) | Linear | 0.9M / 21.7B | 504 | 34.6 |
| InternViT-6B (frozen) | Linear | 0.5M / 5.9B | 504 | 47.2 (+12.6) |
| ViT-22B (frozen) | UperNet | 0.8B / 22.5B | 504 | 52.7 |
| InternViT-6B (frozen) | UperNet | 0.4B / 6.3B | 504 | 54.9 (+2.2) |
| ViT-22B | UperNet | 22.5B / 22.5B | 504 | 55.3 |
| InternViT-6B | UperNet | 6.3B / 6.3B | 504 | 58.9 (+3.6) |
- Zero-Shot Image Classification [\[see details\]](./clip_benchmark#imagenet-variants-and-objectnet)
| method | IN-1K | IN-A | IN-R | IN-V2 | IN-Sketch | ObjectNet |
| ----------------- | :---: | :--: | :--: | :---: | :-------: | :-------: |
| OpenCLIP-G | 80.1 | 69.3 | 92.1 | 73.6 | 68.9 | 73.0 |
| EVA-02-CLIP-E+ | 82.0 | 82.1 | 94.5 | 75.7 | 71.6 | 79.6 |
| ViT-22B\* | 85.9 | 90.1 | 96.0 | 80.9 | - | 87.6 |
| InternVL-C (ours) | 83.2 | 83.8 | 95.5 | 77.3 | 73.9 | 80.6 |
- Multilingual Zero-Shot Image Classification [\[see details\]](./clip_benchmark#multilingual-imagenet-1k)
EN: English, ZH: Chinese, JP: Japanese, Ar: Arabic, IT: Italian
| method | IN-1K (EN) | IN-1K (ZH) | IN-1K (JP) | IN-1K (AR) | IN-1K (IT) |
| ----------------- | :--------: | :--------: | :--------: | :--------: | :--------: |
| Taiyi-CLIP-ViT-H | - | 54.4 | - | - | - |
| WuKong-ViT-L-G | - | 57.5 | - | - | - |
| CN-CLIP-ViT-H | - | 59.6 | - | - | - |
| AltCLIP-ViT-L | 74.5 | 59.6 | - | - | - |
| EVA-02-CLIP-E+ | 82.0 | - | - | - | 41.2 |
| OpenCLIP-XLM-R-H | 77.0 | 55.7 | 53.1 | 37.0 | 56.8 |
| InternVL-C (ours) | 83.2 | 64.5 | 61.5 | 44.9 | 65.7 |
- Zero-Shot Video Classification
| method | #frame | K400 | K600 | K700 |
| ----------------- | :----: | :--: | :--: | :--: |
| OpenCLIP-G | 1 | 65.9 | 66.1 | 59.2 |
| EVA-02-CLIP-E+ | 1 | 69.8 | 69.3 | 63.4 |
| InternVL-C (ours) | 1 | 71.0 | 71.3 | 65.7 |
| ViCLIP | 8 | 75.7 | 73.5 | 66.4 |
| InternVL-C (ours) | 8 | 79.4 | 78.8 | 71.5 |
Cross-Modal Retrieval (click to expand)
- English Zero-Shot Image-Text Retrieval [\[see details\]](./clip_benchmark#flickr30k--coco)
model
Flickr30K
COCO
avg
image-to-text
text-to-image
image-to-text
text-to-image
R@1
R@5
R@10
R@1
R@5
R@10
R@1
R@5
R@10
R@1
R@5
R@10
OpenCLIP-G
92.9
99.3
99.8
79.5
95.0
97.1
67.3
86.9
92.6
51.4
74.9
83.0
85.0
EVA-02-CLIP-E+
93.9
99.4
99.8
78.8
94.2
96.8
68.8
87.8
92.8
51.1
75.0
82.7
85.1
EVA-CLIP-8B
95.6
99.6
99.9
80.8
95.5
97.6
70.3
89.3
93.9
53.0
76.0
83.4
86.2
InternVL-C (ours)
94.7
99.6
99.9
81.7
96.0
98.2
70.6
89.0
93.5
54.1
77.3
84.6
86.6
InternVL-G (ours)
95.7
99.7
99.9
85.0
97.0
98.6
74.9
91.3
95.2
58.6
81.3
88.0
88.8
- Chinese Zero-Shot Image-Text Retrieval [\[see details\]](./clip_benchmark#flickr30k-cn--coco-cn)
model
Flickr30K-CN
COCO-CN
avg
image-to-text
text-to-image
image-to-text
text-to-image
R@1
R@5
R@10
R@1
R@5
R@10
R@1
R@5
R@10
R@1
R@5
R@10
CN-CLIP-ViT-H
81.6
97.5
98.8
71.2
91.4
95.5
63.0
86.6
92.9
69.2
89.9
96.1
86.1
OpenCLIP-XLM-R-H
86.1
97.5
99.2
71.0
90.5
94.9
70.0
91.5
97.0
66.1
90.8
96.0
87.6
InternVL-C (ours)
90.3
98.8
99.7
75.1
92.9
96.4
68.8
92.0
96.7
68.9
91.9
96.5
89.0
InternVL-G (ours)
92.9
99.4
99.8
77.7
94.8
97.3
71.4
93.9
97.7
73.8
94.4
98.1
90.9
- Multilingual Zero-Shot Image-Text Retrieval on XTD [\[see details\]](./clip_benchmark#xtd)
| method | EN | ES | FR | ZH | IT | KO | RU | JP | average |
| ----------------- | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :-----: |
| AltCLIP | 95.4 | 94.1 | 92.9 | 95.1 | 94.2 | 94.4 | 91.8 | 91.7 | 93.7 |
| OpenCLIP-XLM-R-H | 97.3 | 96.1 | 94.5 | 94.7 | 96.0 | 90.2 | 93.9 | 94.0 | 94.6 |
| InternVL-C (ours) | 97.3 | 95.7 | 95.1 | 95.6 | 96.0 | 92.2 | 93.3 | 95.5 | 95.1 |
| InternVL-G (ours) | 98.6 | 97.7 | 96.5 | 96.7 | 96.9 | 95.1 | 94.8 | 96.1 | 96.6 |
Multimodal Dialogue
See ["Compared with SOTA VLLMs"](#compared-with-sota-vllms) section.
## Quick Start with HuggingFace
using InternViT-6B for visual feature extraction (click to expand)
```python
import torch
from PIL import Image
from transformers import AutoModel, CLIPImageProcessor
model = AutoModel.from_pretrained(
'OpenGVLab/InternViT-6B-448px-V1-5',
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True).cuda().eval()
image = Image.open('./examples/image1.jpg').convert('RGB')
image_processor = CLIPImageProcessor.from_pretrained('OpenGVLab/InternViT-6B-448px-V1-5')
pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
pixel_values = pixel_values.to(torch.bfloat16).cuda()
outputs = model(pixel_values)
```
using InternVL-C(ontrastive) and InternVL-G(enerative) for cross-modal retrieval (click to expand)
```python
import torch
from PIL import Image
from transformers import AutoModel, CLIPImageProcessor
from transformers import AutoTokenizer
model = AutoModel.from_pretrained(
'OpenGVLab/InternVL-14B-224px',
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True).cuda().eval()
image_processor = CLIPImageProcessor.from_pretrained('OpenGVLab/InternVL-14B-224px')
tokenizer = AutoTokenizer.from_pretrained(
'OpenGVLab/InternVL-14B-224px', use_fast=False, add_eos_token=True)
tokenizer.pad_token_id = 0 # set pad_token_id to 0
images = [
Image.open('./examples/image1.jpg').convert('RGB'),
Image.open('./examples/image2.jpg').convert('RGB'),
Image.open('./examples/image3.jpg').convert('RGB')
]
prefix = 'summarize:'
texts = [
prefix + 'a photo of a red panda', # English
prefix + '一张熊猫的照片', # Chinese
prefix + '二匹の猫の写真' # Japanese
]
pixel_values = image_processor(images=images, return_tensors='pt').pixel_values
pixel_values = pixel_values.to(torch.bfloat16).cuda()
input_ids = tokenizer(texts, return_tensors='pt', max_length=80,
truncation=True, padding='max_length').input_ids.cuda()
# InternVL-C
logits_per_image, logits_per_text = model(
image=pixel_values, text=input_ids, mode='InternVL-C')
probs = logits_per_image.softmax(dim=-1)
# tensor([[9.9609e-01, 5.2185e-03, 6.0070e-08],
# [2.2949e-02, 9.7656e-01, 5.9903e-06],
# [3.2932e-06, 7.4863e-05, 1.0000e+00]], device='cuda:0',
# dtype=torch.bfloat16, grad_fn=)
# InternVL-G
logits_per_image, logits_per_text = model(
image=pixel_values, text=input_ids, mode='InternVL-G')
probs = logits_per_image.softmax(dim=-1)
# tensor([[9.9609e-01, 3.1738e-03, 3.6322e-08],
# [8.6060e-03, 9.9219e-01, 2.8759e-06],
# [1.7583e-06, 3.1233e-05, 1.0000e+00]], device='cuda:0',
# dtype=torch.bfloat16, grad_fn=)
# please set add_eos_token to False for generation
tokenizer.add_eos_token = False
image = Image.open('./examples/image1.jpg').convert('RGB')
pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
pixel_values = pixel_values.to(torch.bfloat16).cuda()
tokenized = tokenizer("English caption:", return_tensors='pt')
pred = model.generate(
pixel_values=pixel_values,
input_ids=tokenized.input_ids.cuda(),
attention_mask=tokenized.attention_mask.cuda(),
num_beams=5,
min_new_tokens=8,
)
caption = tokenizer.decode(pred[0].cpu(), skip_special_tokens=True).strip()
# English caption: a red panda sitting on top of a wooden platform
```
using InternVL-Chat for multimodal chat (click to expand)
Here, we take the smaller `OpenGVLab/InternVL2-8B` as an example:
```python
import numpy as np
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(input_size):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def load_image(image_file, input_size=448, max_num=12):
image = Image.open(image_file).convert('RGB')
transform = build_transform(input_size=input_size)
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
# If you have an 80G A100 GPU, you can put the entire model on a single GPU.
# Otherwise, you need to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
path = 'OpenGVLab/InternVL2-8B'
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
# set the max number of tiles in `max_num`
pixel_values = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
generation_config = dict(max_new_tokens=1024, do_sample=False)
# pure-text conversation (纯文本对话)
question = 'Hello, who are you?'
response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'Can you tell me a story?'
response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# single-image single-round conversation (单图单轮对话)
question = '\nPlease describe the image shortly.'
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(f'User: {question}\nAssistant: {response}')
# single-image multi-round conversation (单图多轮对话)
question = '\nPlease describe the image in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'Please write a poem according to the image.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
question = '\nDescribe the two images in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'What are the similarities and differences between these two images.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
question = 'Image-1: \nImage-2: \nDescribe the two images in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
num_patches_list=num_patches_list,
history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'What are the similarities and differences between these two images.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
num_patches_list=num_patches_list,
history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# batch inference, single image per sample (单图批处理)
pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
questions = ['\nDescribe the image in detail.'] * len(num_patches_list)
responses = model.batch_chat(tokenizer, pixel_values,
num_patches_list=num_patches_list,
questions=questions,
generation_config=generation_config)
for question, response in zip(questions, responses):
print(f'User: {question}\nAssistant: {response}')
# video multi-round conversation (视频多轮对话)
def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
if bound:
start, end = bound[0], bound[1]
else:
start, end = -100000, 100000
start_idx = max(first_idx, round(start * fps))
end_idx = min(round(end * fps), max_frame)
seg_size = float(end_idx - start_idx) / num_segments
frame_indices = np.array([
int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
for idx in range(num_segments)
])
return frame_indices
def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
max_frame = len(vr) - 1
fps = float(vr.get_avg_fps())
pixel_values_list, num_patches_list = [], []
transform = build_transform(input_size=input_size)
frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
for frame_index in frame_indices:
img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(tile) for tile in img]
pixel_values = torch.stack(pixel_values)
num_patches_list.append(pixel_values.shape[0])
pixel_values_list.append(pixel_values)
pixel_values = torch.cat(pixel_values_list)
return pixel_values, num_patches_list
video_path = './examples/red-panda.mp4'
pixel_values, num_patches_list = load_video(video_path, num_segments=8, max_num=1)
pixel_values = pixel_values.to(torch.bfloat16).cuda()
video_prefix = ''.join([f'Frame{i+1}: \n' for i in range(len(num_patches_list))])
question = video_prefix + 'What is the red panda doing?'
# Frame1: \nFrame2: \n...\nFrame8: \n{question}
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
num_patches_list=num_patches_list, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'Describe this video in detail. Don\'t repeat.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
num_patches_list=num_patches_list, history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
```
## License
This project is released under the [MIT license](LICENSE). Parts of this project contain code and models from other sources, which are subject to their respective licenses.
## Citation
If you find this project useful in your research, please consider cite:
```BibTeX
@article{chen2023internvl,
title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
journal={arXiv preprint arXiv:2312.14238},
year={2023}
}
@article{chen2024far,
title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
journal={arXiv preprint arXiv:2404.16821},
year={2024}
}
```
## Acknowledgement
InternVL is built with reference to the code of the following projects: [OpenAI CLIP](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip), [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark), [EVA](https://github.com/baaivision/EVA/tree/master), [InternImage](https://github.com/OpenGVLab/InternImage), [ViT-Adapter](https://github.com/czczup/ViT-Adapter), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation), [Transformers](https://github.com/huggingface/transformers), [DINOv2](https://github.com/facebookresearch/dinov2), [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [Qwen-VL](https://github.com/QwenLM/Qwen-VL/tree/master/eval_mm), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work!
______________________________________________________________________
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