--- license: mit pipeline_tag: image-text-to-text --- # InternVL-Chat-V1-5 [\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[📜 InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821) [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971) \[🌟 [魔搭社区](https://modelscope.cn/organization/OpenGVLab) | [教程](https://mp.weixin.qq.com/s/OUaVLkxlk1zhFb1cvMCFjg) \] ## Introduction

Image Description

> _Two interns holding hands, symbolizing the integration of InternViT and InternLM._ We introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple designs: 1. Strong Vision Encoder: we explored a continuous learning strategy for the large-scale vision foundation model---InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs. 2. Dynamic High-Resolution: we divide images into tiles ranging from 1 to 40 of 448 × 448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input. 3. High-Quality Bilingual Dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in OCR- and Chinese-related tasks. ## Model Details - **Model Type:** multimodal large language model (MLLM) - **Model Stats:** - Architecture: [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) + MLP + [InternLM2-Chat-20B](https://huggingface.co/internlm/internlm2-chat-20b) - Image size: dynamic resolution, max to 40 tiles of 448 x 448 (4K resolution). - Params: 25.5B - **Training Strategy:** - Learnable component in the pretraining stage: ViT + MLP - Learnable component in the finetuning stage: ViT + MLP + LLM - For more details on training hyperparameters, take a look at our code: [pretrain](https://github.com/OpenGVLab/InternVL/blob/main/internvl_chat/shell/internlm2_20b_dynamic/internvl_chat_v1_5_internlm2_20b_dynamic_res_pretrain.sh) | [finetune](https://github.com/OpenGVLab/InternVL/blob/main/internvl_chat/shell/internlm2_20b_dynamic/internvl_chat_v1_5_internlm2_20b_dynamic_res_finetune.sh) ## Architecture ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/YLvX3V-L0kwsyRn3Lhciw.png) ## Performance ### Image Benchmarks ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/4b85G7txoJ_LpT19SZJ4A.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/i2vp6zSHPS3UIr-1Q9cSe.png) - We simultaneously use InternVL and VLMEvalKit repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA, TextVQA, MME, AI2D, MMBench, CCBench, MMVet, and SEED-Image were tested using the InternVL repository. MMMU, OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit. - Please note that evaluating the same model using different testing toolkits like InternVL and VLMEvalKit can result in slight differences, which is normal. Updates to code versions and variations in environment and hardware can also cause minor discrepancies in results. - It is important to mention that the MMVet scores we report are evaluated using GPT-4-0613 as the judge model. Different versions of GPT-4 can lead to significant variations in the scores for this dataset. For instance, using GPT-4-Turbo would result in significantly lower scores. Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information. ## Examples ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/YVr-93mvVMR6UFpGezns7.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/ivhj4QqcO2NHUa28DTDkK.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/18GeOW10QVcSt5g--TgDY.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/tGM_TwdV297H1fCxQ0PZU.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/FwlSRBpKgURAVkXNOLoSp.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/to3nOaAnyv-fGLEoNPLzz.png) ## Quick Start We provide an example code to run InternVL-Chat-V1-5 using `transformers`. We also welcome you to experience the InternVL2 series models in our [online demo](https://internvl.opengvlab.com/). Currently, due to the limited GPU resources with public IP addresses, we can only deploy models up to a maximum of 26B. We will expand soon and deploy larger models to the online demo. > Please use transformers==4.37.2 to ensure the model works normally. ```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=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 def split_model(model_name): device_map = {} world_size = torch.cuda.device_count() num_layers = {'InternVL2-8B': 32, 'InternVL2-26B': 48, 'InternVL-Chat-V1-5': 48, 'InternVL2-40B': 60, 'InternVL2-Llama3-76B': 80,}[model_name] # Since the first GPU will be used for ViT, treat it as half a GPU. num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5)) num_layers_per_gpu = [num_layers_per_gpu] * world_size num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5) layer_cnt = 0 for i, num_layer in enumerate(num_layers_per_gpu): for j in range(num_layer): device_map[f'language_model.model.layers.{layer_cnt}'] = i layer_cnt += 1 device_map['vision_model'] = 0 device_map['mlp1'] = 0 device_map['language_model.model.tok_embeddings'] = 0 device_map['language_model.model.embed_tokens'] = 0 device_map['language_model.output'] = 0 device_map['language_model.model.norm'] = 0 device_map['language_model.lm_head'] = 0 device_map[f'language_model.model.layers.{num_layers - 1}'] = 0 return device_map 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 to use multiple GPUs for inference. # device_map = split_model('InternVL-Chat-V1-5') # print(device_map) # model = AutoModel.from_pretrained( # path, # torch_dtype=torch.bfloat16, # low_cpu_mem_usage=True, # trust_remote_code=True, # device_map=device_map).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=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}') print(f'Assistant: {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}') print(f'Assistant: {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}') print(f'Assistant: {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}') print(f'Assistant: {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}') print(f'Assistant: {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=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 = '\nDescribe the two images in detail.' response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) 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}') print(f'Assistant: {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=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) 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}') print(f'Assistant: {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}') print(f'Assistant: {response}') # batch inference, single image per sample (单图批处理) 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() 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}') print(f'Assistant: {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=32, max_num=1) 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...\nFrame31: \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}') print(f'Assistant: {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}') print(f'Assistant: {response}') ``` ## Finetune SWIFT from ModelScope community has supported the fine-tuning (Image/Video) of InternVL, please check [this link](https://github.com/modelscope/swift/blob/main/docs/source_en/Multi-Modal/internvl-best-practice.md) for more details. ## Deployment ### LMDeploy LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams. ```sh pip install lmdeploy ``` LMDeploy abstracts the complex inference process of multi-modal Vision-Language Models (VLM) into an easy-to-use pipeline, similar to the Large Language Model (LLM) inference pipeline. #### A 'Hello, world' example ```python from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig from lmdeploy.vl import load_image model = 'OpenGVLab/InternVL-Chat-V1-5' image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg') chat_template_config = ChatTemplateConfig('internvl-internlm2') pipe = pipeline(model, chat_template_config=chat_template_config, backend_config=TurbomindEngineConfig(session_len=8192)) response = pipe(('describe this image', image)) print(response.text) ``` If `ImportError` occurs while executing this case, please install the required dependency packages as prompted. #### Multi-images inference When dealing with multiple images, you can put them all in one list. Keep in mind that multiple images will lead to a higher number of input tokens, and as a result, the size of the context window typically needs to be increased. ```python from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig from lmdeploy.vl import load_image from lmdeploy.vl.constants import IMAGE_TOKEN model = 'OpenGVLab/InternVL-Chat-V1-5' chat_template_config = ChatTemplateConfig('internvl-internlm2') pipe = pipeline(model, chat_template_config=chat_template_config, backend_config=TurbomindEngineConfig(session_len=8192)) image_urls=[ 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg', 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/det.jpg' ] images = [load_image(img_url) for img_url in image_urls] # Numbering images improves multi-image conversations response = pipe((f'Image-1: {IMAGE_TOKEN}\nImage-2: {IMAGE_TOKEN}\ndescribe these two images', images)) print(response.text) ``` #### Batch prompts inference Conducting inference with batch prompts is quite straightforward; just place them within a list structure: ```python from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig from lmdeploy.vl import load_image model = 'OpenGVLab/InternVL-Chat-V1-5' chat_template_config = ChatTemplateConfig('internvl-internlm2') pipe = pipeline(model, chat_template_config=chat_template_config, backend_config=TurbomindEngineConfig(session_len=8192)) image_urls=[ "https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg", "https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/det.jpg" ] prompts = [('describe this image', load_image(img_url)) for img_url in image_urls] response = pipe(prompts) print(response) ``` #### Multi-turn conversation There are two ways to do the multi-turn conversations with the pipeline. One is to construct messages according to the format of OpenAI and use above introduced method, the other is to use the `pipeline.chat` interface. ```python from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig, GenerationConfig from lmdeploy.vl import load_image model = 'OpenGVLab/InternVL-Chat-V1-5' chat_template_config = ChatTemplateConfig('internvl-internlm2') pipe = pipeline(model, chat_template_config=chat_template_config, backend_config=TurbomindEngineConfig(session_len=8192)) image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg') gen_config = GenerationConfig(top_k=40, top_p=0.8, temperature=0.8) sess = pipe.chat(('describe this image', image), gen_config=gen_config) print(sess.response.text) sess = pipe.chat('What is the woman doing?', session=sess, gen_config=gen_config) print(sess.response.text) ``` #### Service LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup: ```shell lmdeploy serve api_server OpenGVLab/InternVL-Chat-V1-5 --model-name InternVL-Chat-V1-5 --backend turbomind --server-port 23333 ``` To use the OpenAI-style interface, you need to install OpenAI: ```shell pip install openai ``` Then, use the code below to make the API call: ```python from openai import OpenAI client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1') model_name = client.models.list().data[0].id response = client.chat.completions.create( model="InternVL-Chat-V1-5", messages=[{ 'role': 'user', 'content': [{ 'type': 'text', 'text': 'describe this image', }, { 'type': 'image_url', 'image_url': { 'url': 'https://modelscope.oss-cn-beijing.aliyuncs.com/resource/tiger.jpeg', }, }], }], temperature=0.8, top_p=0.8) print(response) ``` ### vLLM TODO ### Ollama TODO ## License This project is released under the MIT license, while InternLM is licensed under the Apache-2.0 license. ## Citation If you find this project useful in your research, please consider citing: ```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} } ```