Skip to content
/ InternVL Public

[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的开源多模态对话模型

internvl.readthedocs.io/en/latest/

License

MIT license
6.4k stars 496 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

OpenGVLab/InternVL

BranchesTags

Repository files navigation

News 🚀🚀🚀

  • 2024/12/17: 🚀 InternVL2/2.5 is supported in PaddleMIX by Paddle Team.
  • 2024/12/05: 🚀 We release the InternVL2.5, an advanced multimodal large language model (MLLM) series with parameter coverage ranging from 1B to 78B. InternVL2_5-78B is the first open-source MLLMs to achieve over 70% on the MMMU benchmark. matching the performance of leading closed-source commercial models like GPT-4o. These models are available at HF link.
  • 2024/11/14: We introduce MMPR, a high-quality, large-scale multimodal reasoning preference dataset, and MPO, an effective preference optimization algorithm. The resulting model, InternVL2-8B-MPO, achieves an accuracy of 67.0 on MathVista. Please refer to our paper, project page and document for more details.
  • 2024/10/21: We release the Mini-InternVL series. These models achieve impressive performance with minimal size: the 4B model achieves 90% of the performance with just 5% of the model size. For more details, please check our project page and document.
  • 2024/08/01: The Chartmimic team evaluated the InternVL2 series models on their benchmark. The InternVL2-26B and 76B models achieved the top two performances among open-source models, with the InternVL2 76B model surpassing GeminiProVision and exhibiting comparable results to Claude-3-opus.
  • 2024/08/01: InternVL2-Pro achieved the SOTA performance among open-source models on the CharXiv dataset, surpassing many closed-source models such as GPT-4V, Gemini 1.5 Flash, and Claude 3 Sonnet.
  • 2024/07/24: The MLVU team evaluated InternVL-1.5 on their benchmark. The average performance on the multiple-choice task was 50.4%, while the performance on the generative tasks was 4.02. The performance on the multiple-choice task ranked #1 among all open-source MLLMs.
  • 2024/07/18: 🔥🔥 InternVL2-40B achieved SOTA performance among open-source models on the 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 and InfoVQA benchmarks.
  • 2024/07/04: 🚀 We release the InternVL2 series. 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) / (申请表). Other models are available at HF link.
  • 2024/06/19: We propose Needle In A Multimodal Haystack (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, a large-scale dataset that we plan to open-source with 200K images, 10K videos, and 10K audios with detailed descriptions.
  • 2024/05/28: Thanks to the lmdeploy team for providing AWQ quantization support. The 4-bit model is available at OpenGVLab/InternVL-Chat-V1-5-AWQ.
  • 2024/05/13: InternVL 1.0 can now be used as the text encoder for diffusion models to support multilingual generation natively in over 110 languages worldwide. See MuLan for more details.
  • 2024/04/18: InternVL-Chat-V1-5 has been released at HF link, 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)! 🎉
  • 2024/02/21: InternVL-Chat-V1-2-Plus achieved SOTA performance on MathVista (59.9), MMBench (83.8), and MMVP (58.7). See our blog 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 and SFT data. The model is now available on HuggingFace, 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.
  • 2024/01/16: We release our customized mmcv/mmsegmentation/mmdetection code, integrated with DeepSpeed, which can be used for training large-scale detection and segmentation models.

TODO List

  • Support liger kernels to save GPU memory
  • Release the code, model, and data of MPO
  • Support multimodal packed dataset
  • Support vLLM and Ollama
  • Support video and PDF input in online demo
  • Release InternVL2 with VisionLLMv2 integration
  • Rebuild documents using readthedocs
  • Support fine-tuning different LLMs with LoRA
  • Release requirements.txt for InternVL2
  • Release training / evaluation code for InternVL2 series
  • Release Streamlit web UI for InternVL1.5 and InternVL2

Documents

Compared with SOTA VLLMs

waic_performance

Model Zoo

Multimodal Large Language Model (InternVL 2.5)

Model Name Vision Part Language Part HF Link
InternVL2_5-1B InternViT-300M-448px-V2_5 Qwen2.5-0.5B-Instruct 🤗 link
InternVL2_5-2B InternViT-300M-448px-V2_5 internlm2_5-1_8b-chat 🤗 link
InternVL2_5-4B InternViT-300M-448px-V2_5 Qwen2.5-3B-Instruct 🤗 link
InternVL2_5-8B InternViT-300M-448px-V2_5 internlm2_5-7b-chat 🤗 link
InternVL2_5-26B InternViT-6B-448px-V2_5 internlm2_5-20b-chat 🤗 link
InternVL2_5-38B InternViT-6B-448px-V2_5 Qwen2.5-32B-Instruct 🤗 link
InternVL2_5-78B InternViT-6B-448px-V2_5 Qwen2.5-72B-Instruct 🤗 link

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

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 1.0-2.5)

Model Date HF Link MS Link Note
InternViT-300M-448px-V2_5 2024.12.05 🤗 link 🚀🚀 A more powerful lightweight visual encoder. (🔥new)
InternViT-6B-448px-V2_5 2024.12.05 🤗 link 🚀🚀 A stronger visual encoder to extract visual features. (🔥new)
Mini‑InternVL‑Chat‑4B‑V1‑5 2024.05.28 🤗 link 🤖 link distilled small vision foundation model with 300M parameters
InternViT‑6B‑448px‑V1‑5 2024.04.20 🤗 link 🤖 link support dynamic resolution and super strong OCR feature extraction capability by incremental pre-training
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]

    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]

    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]

    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]

    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]

    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]

    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]

    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" section.

Quick Start with HuggingFace

using InternViT-6B for visual feature extraction (click to expand) 
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) 
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=<SoftmaxBackward0>)

# 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=<SoftmaxBackward0>)

# 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:

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 = '<image>\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 = '<image>\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 = '<image>\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: <image>\nImage-2: <image>\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 = ['<image>\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}: <image>\n' for i in range(len(num_patches_list))])
question = video_prefix + 'What is the red panda doing?'
# Frame1: <image>\nFrame2: <image>\n...\nFrame8: <image>\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. 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{chen2024expanding,
  title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
  author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
  journal={arXiv preprint arXiv:2412.05271},
  year={2024}
}
@article{wang2024mpo,
  title={Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization},
  author={Wang, Weiyun and Chen, Zhe and Wang, Wenhai and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Zhu, Jinguo and Zhu, Xizhou and Lu, Lewei and Qiao, Yu and Dai, Jifeng},
  journal={arXiv preprint arXiv:2411.10442},
  year={2024}
}
@article{gao2024mini,
  title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},
  author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},
  journal={arXiv preprint arXiv:2410.16261},
  year={2024}
}
@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}
}
@inproceedings{chen2024internvl,
  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 others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={24185--24198},
  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!

Scan the following QR Code, join our WeChat group.

image

About

[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的开源多模态对话模型

internvl.readthedocs.io/en/latest/

Topics

image-classification gpt multi-modal semantic-segmentation video-classification image-text-retrieval llm vision-language-model gpt-4v vit-6b vit-22b gpt-4o

Resources

Readme

License

MIT license
Activity
Custom properties

Stars

6.4k stars

Watchers

57 watching

Forks

496 forks
Report repository

Releases 6

InternVL-Chat-V1.5.0 Latest
May 8, 2024
+ 5 releases

Packages

No packages published

Contributors 15

Languages