# InternVL
**Repository Path**: luo_zhi_cheng/InternVL
## Basic Information
- **Project Name**: InternVL
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: llava_setting
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-08-01
- **Last Updated**: 2024-08-01
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
#
InternVL Family: Closing the Gap to Commercial Multimodal Models with Open-Source Suites —— A Pioneering Open-Source Alternative to GPT-4V
\[[Update Blog](./BLOG.md)\] \[[Paper](https://arxiv.org/abs/2312.14238)\] \[[InternVL 1.5 Technical Report](https://arxiv.org/abs/2404.16821)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] [\[HuggingFace Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) \[[Quick Start](#quick-start-with-huggingface)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)\]
## News🚀🚀🚀
- `2024/04/28`: We release the INT8 version of InternVL-Chat-V1-5, see [here](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5-Int8).
- `2024/04/28`: We achieve the SOTA performance (75.74) on the Infographics VQA benchmark, see [here](https://rrc.cvc.uab.es/?ch=17&com=evaluation&task=3).
- `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! 🎉
- `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) achieves SOTA performance on MathVista (59.9), MMBench (83.8), and MMVP (58.7). See our [blog](BLOG.md) 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](BLOG.md), [SFT data](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) or try our [demo](https://internvl.opengvlab.com/). 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.
- `2024/02/04`: [InternVL-Chat-V1.1](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1) achieves 44.67% on [MMVP](https://github.com/tsb0601/MMVP), higher than GPT-4V!
- `2024/01/27`: We release 448 resolution model, achieving 76.6 on MMBench dev, see [here](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#-evaluation-chinese-models).
- `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) or try our [demo](https://internvl.opengvlab.com/).
- `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 object detection and semantic segmentation models.
## Documents
- How to install InternVL? [\[link\]](./INSTALLATION.md)
- How to fine-tune InternVL? [\[link\]](./internvl_chat/README.md)
- How to evaluate InternVL-Chat-V1-5? [\[link\]](./document/how_to_evaluate_internvl_chat_1_5.md)
- How to evaluate InternVL-Chat-V1-5 using VLMEvalKit? (Recommend) [\[link\]](./document/how_to_evaluate_internvl_chat_1_5_using_vlmevalkit.md)
- How to deploy a local demo? [\[link\]](./document/how_to_deploy_a_local_demo.md)
## Compared with SOTA VLLMs

## 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−Chat−V1.5-Int8 | 2024.04.28 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5-Int8) | The INT8 version of InternVL-Chat-V1-5 |
| InternVL−Chat−V1.5 | 2024.04.18 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5) | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (🔥new) |
| InternVL−Chat−V1.2−Plus | 2024.02.21 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2-Plus) | more SFT data and stronger |
| InternVL−Chat−V1.2 | 2024.02.11 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) | scaling up LLM to 34B |
| InternVL−Chat−V1.1 | 2024.01.24 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1) | support Chinese and stronger OCR |
| InternVL−Chat−19B−448px | 2024.02.03 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B-448px) | 448 resolution |
| InternVL−Chat−19B | 2023.12.25 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B) | English multimodal dialogue |
| InternVL−Chat−13B | 2023.12.25 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B) | English multimodal dialogue |
**Vision-Language Foundation Model**
| Model | Date | Download | Note |
| ----------------------- | ---------- | ---------------------------------------------------------------------- | ---------------------------------------------------- |
| InternViT−6B−448px−V1.5 | 2024.04.20 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) | support dynamic resolution, super strong OCR (🔥new) |
| InternViT−6B−448px−V1.2 | 2024.02.11 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2) | 448 resolution |
| InternViT−6B−448px−V1.0 | 2024.01.30 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0) | 448 resolution |
| InternViT−6B−224px | 2023.12.22 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-224px) | vision foundation model |
| InternVL−14B−224px | 2023.12.22 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-14B-224px) | vision-language foundation model |
## 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 + '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 (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 = "请详细描述图片" # Please describe the picture in detail
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(question, response)
# multi-round single-image conversation
question = "请详细描述图片" # 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 = "请根据图片写一首诗" # 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 = "详细描述这两张图片" # 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 = "这两张图片的相同点和区别分别是什么" # 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)
```
## 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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