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OpenGVLab / InternVL

[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4V. ๆŽฅ่ฟ‘GPT-4V่กจ็Žฐ็š„ๅฏๅ•†็”จๅผ€ๆบๅคšๆจกๆ€ๅฏน่ฏๆจกๅž‹
https://internvl.github.io/
MIT License
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gpt gpt-4o gpt-4v image-classification image-text-retrieval llm mme multi-modal semantic-segmentation video-classification vision-language-model vit-22b vit-6b

readme

image InternVL Family: Closing the Gap to Commercial Multimodal Models with Open-Source Suites โ€”โ€” A Pioneering Open-Source Alternative to GPT-4o

[๐Ÿ“–ไธญๆ–‡็‰ˆๆœฌ] [๐Ÿ†• Blog] [๐Ÿ“œ InternVL 1.0 Paper] [๐Ÿ“œ InternVL 1.5 Report] [๐Ÿ“œ InternVL 2.0 Blog] [๐Ÿ—จ๏ธ Chat Demo] [๐Ÿค— HuggingFace Demo]

[๐Ÿš€ Quick Start] [๐ŸŒ Community-hosted API] [๐Ÿ“– ไธญๆ–‡่งฃ่ฏป]

OpenGVLab%2FInternVL | Trendshift  

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News๐Ÿš€๐Ÿš€๐Ÿš€

Documents

Compared with SOTA VLLMs

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What is InternVL?

InternVL scales up the ViT to 6B parameters and aligns it with LLM.

Model Zoo

Vision Large Language Model

Model Date Download Note
InternVL-2 2024.07.04 ๐Ÿค— HF link achieving performance close to GPT-4o
Mini‑InternVL‑Chat‑4B‑V1‑5 2024.05.28 ๐Ÿค— HF link ๐Ÿš€๐Ÿš€ 16% of the model size, 90% of the performance
Mini-InternVL-Chat-2B-V1-5 2024.05.19 ๐Ÿค— HF link ๐Ÿš€ 8% of the model size, 80% of the performance
InternVL-Chat-V1-5-AWQ 2024.05.28 ๐Ÿค— HF link The 4-bit version of InternVL-Chat-V1-5
InternVL-Chat-V1-5-Int8 2024.04.28 ๐Ÿค— HF link The 8-bit version of InternVL-Chat-V1-5
InternVL-Chat-V1-5 2024.04.18 ๐Ÿค— HF 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. (๐Ÿ”ฅnew)
InternVL-Chat-V1-2-Plus 2024.02.21 ๐Ÿค— HF link more SFT data and stronger
InternVL-Chat-V1-2 2024.02.11 ๐Ÿค— HF link scaling up LLM to 34B
InternVL-Chat-V1-1 2024.01.24 ๐Ÿค— HF link support Chinese and stronger OCR
InternVL-Chat-19B-448px 2024.02.03 ๐Ÿค— HF link 448 resolution
InternVL-Chat-19B 2023.12.25 ๐Ÿค— HF link English multimodal dialogue
InternVL-Chat-13B 2023.12.25 ๐Ÿค— HF link English multimodal dialogue

Vision-Language Foundation Model

Model Date Download Note
InternViT-300M-448px 2024.05.25 ๐Ÿค— HF link distilled small vision foundation model with 300M parameters (๐Ÿ”ฅnew)
InternViT-6B-448px-V1-5 2024.04.20 ๐Ÿค— HF link support dynamic resolution, super strong OCR (๐Ÿ”ฅnew)
InternViT-6B-448px-V1-2 2024.02.11 ๐Ÿค— HF link 448 resolution
InternViT‑6B‑448px‑V1‑0 2024.01.30 ๐Ÿค— HF link 448 resolution
InternViT-6B-224px 2023.12.22 ๐Ÿค— HF link vision foundation model
InternVL-14B-224px 2023.12.22 ๐Ÿค— HF link vision-language foundation model, InternViT-6B + QLLaMA, can be used for image-text retrival like CLIP

InternVL-2 API
We encourage everyone to use our API for research. For better management, please submit (English application form)/(Chinese application form) to obtain API access.

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 \[see details\] | 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")

Quick Start with Huggingface

using InternViT-6B (click to expand) ```python import torch from PIL import Image from transformers import AutoModel, CLIPImageProcessor model = AutoModel.from_pretrained( 'OpenGVLab/InternViT-6B-224px', 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-224px') 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) (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 + 'ไธ€ๅผ ็†Š็Œซ็š„็…ง็‰‡', # Chinese prefix + 'ไบŒๅŒนใฎ็Œซใฎๅ†™็œŸ' # 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 (click to expand) ```python from transformers import AutoTokenizer, AutoModel import torch import torchvision.transforms as T from PIL import Image from torchvision.transforms.functional import InterpolationMode 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=6, 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=6): 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 path = "OpenGVLab/InternVL-Chat-V1-5" # If you have an 80G A100 GPU, you can put the entire model on a single GPU. model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True).eval().cuda() # Otherwise, you need to set device_map='auto' to use multiple GPUs for inference. # model = AutoModel.from_pretrained( # path, # torch_dtype=torch.bfloat16, # low_cpu_mem_usage=True, # trust_remote_code=True, # device_map='auto').eval() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) # set the max number of tiles in `max_num` pixel_values = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() generation_config = dict( num_beams=1, max_new_tokens=512, do_sample=False, ) # single-round single-image conversation question = "่ฏท่ฏฆ็ป†ๆ่ฟฐๅ›พ็‰‡" # Please describe the picture in detail response = model.chat(tokenizer, pixel_values, question, generation_config) print(question, response) # multi-round single-image conversation question = "่ฏท่ฏฆ็ป†ๆ่ฟฐๅ›พ็‰‡" # Please describe the picture in detail response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) print(question, response) question = "่ฏทๆ นๆฎๅ›พ็‰‡ๅ†™ไธ€้ฆ–่ฏ—" # Please write a poem according to the picture response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) print(question, response) # multi-round multi-image conversation pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda() pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) question = "่ฏฆ็ป†ๆ่ฟฐ่ฟ™ไธคๅผ ๅ›พ็‰‡" # Describe the two pictures in detail response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) print(question, response) question = "่ฟ™ไธคๅผ ๅ›พ็‰‡็š„็›ธๅŒ็‚นๅ’ŒๅŒบๅˆซๅˆ†ๅˆซๆ˜ฏไป€ไนˆ" # What are the similarities and differences between these two pictures response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) print(question, response) # batch inference (single image per sample) pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda() image_counts = [pixel_values1.size(0), pixel_values2.size(0)] pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) questions = ["Describe the image in detail."] * len(image_counts) responses = model.batch_chat(tokenizer, pixel_values, image_counts=image_counts, questions=questions, generation_config=generation_config) for question, response in zip(questions, responses): print(question) print(response) ```

Inference Acceleration by LMDeploy

We recommend using LMDeploy, if InternVL-Chat model inference optimization is required.

In the following subsections, we will introduce the usage of LMDeploy with the InternVL-Chat-V1-5 model as an example.

First of all, please setup the inference environment as follows:

conda create -n internvl python=3.10 -y
conda activate internvl

pip install timm torchvision==0.17.2
pip install lmdeploy

LMDeploy pypi package depends on CUDA 12.x by default. For a CUDA 11.x environment, please refer to the installation guide.

Offline Inference Pipeline

from lmdeploy import pipeline
from lmdeploy.vl import load_image
pipe = pipeline('OpenGVLab/InternVL-Chat-V1-5')
image = load_image('examples/image2.jpg')
response = pipe(('describe this image', image))
print(response)

For more on using the VLM pipeline, including multi-image inference or multi-turn chat, please overview this guide.

Online Inference Service

LMDeploy supports one-click packaging of the VLM model into an OpenAI service, providing seamless integration with the OpenAI API.

The service can be launched by one command as below:

lmdeploy serve api_server OpenGVLab/InternVL-Chat-V1-5

The arguments of api_server can be viewed through the command lmdeploy serve api_server -h, for instance, --tp to set tensor parallelism, --session-len to specify the max length of the context window, --cache-max-entry-count to adjust the GPU mem ratio for k/v cache etc.

For more details, including service startup with docker, RESTful API information, and openai integration methods, please refer to this guide.

License

This project is released under the MIT 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:

@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, Open CLIP, CLIP Benchmark, EVA, InternImage, ViT-Adapter, MMSegmentation, Transformers, DINOv2, BLIP-2, Qwen-VL, and LLaVA-1.5. Thanks for their awesome work!

If you want to join our WeChat group, please scan the following QR Code to add our assistant as a Wechat friend:

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