--- license: mit base_model: - OpenGVLab/InternViT-300M-448px - internlm/internlm2_5-7b-chat base_model_relation: merge language: - multilingual pipeline_tag: image-text-to-text library_name: transformers tags: - internvl - video - token compression --- # PVC-InternVL2-8B [\[📜 Paper\]](https://arxiv.org/abs/2412.09613) [\[📂 GitHub\]](https://github.com/OpenGVLab/PVC) [\[🚀 Quick Start\]](#quick-start) ## Introduction We introduce the **Progressive Visual Token Compression (PVC)** in large vision-language models (VLMs), which unifies the visual inputs as videos and progressively compresses vision tokens across video frames. Our PVC achieves: * Preserve spatial details and temporal dynamics for both images and videos. * Effectively reduce the tokens used for each video frame and image tile. * SoTA performance on various video benchmarks, including long and fine-grained short video tasks. * No performance loss on image benchmarks, especially on detail-sensitive tasks.
## Results Our implementation is based on the [InternVL2](https://github.com/OpenGVLab/InternVL) model, referred to as **PVCInternVL2** ### Video Understanding Benckmarks | Model | LLaVA-OneVision-7B | Qwen2-VL-7B | InternVL2-8B | PVCInternVL2-8B | | :--------------: | :--: | :--: | :--: | :--: | | \# token/frame | 196 | - | 256 | 64 | | | | | | | | MVbench | 56.7 | 67.0 | 66.4 | 73.8 | | VideoMME w/o-sub | 58.2 | 63.3 | 54.0 | 64.1 | | VideoMME w-sub | 61.5 | 69.0 | 56.9 | 69.7 | | MLVU | 64.7 | - | 52.0 | 72.4 | | LongVideoBench | 56.5 | - | - | 59.2 | | NextQA | 79.4 | - | - | 82.0 | | Egoschema | 60.1 | 66.7 | 55.0 | 59.6 | | PercepTest | 57.1 | 62.3 | 52.0 | 68.4 | | AcNet-QA | 56.6 | - | - | 57.1 | ### Image Understanding Benckmarks | Model | LLaVA-OneVision-7B | Qwen2-VL-7B | InternVL2-8B | PVCInternVL2-8B | | :--------------------: | :--: | :--: | :--: | :--: | | \# token/image tile | 729 | - | 256 | 64 | | | | | | | | AI2Dtest | 81.4 | 83.0 | 83.8 | 83.8 | | ChartQAtest | 80.0 | 83.0 | 83.3 | 84.1 | | DocVQAtest | 87.5 | 94.5 | 91.6 | 92.5 | | InfoVQAtest | 68.8 | 76.5 | 74.8 | 75.0 | | SQAtest | 96.0 | - | 97.1 | 97.7 | | TextVQAval | - | 84.3 | 77.4 | 80.0 | | MMBen-test | - | 83.0 | 81.7 | 83.9 | | MMEsum | 1998 | 2327 | 2210 | 2282 | | MMMUval | 48.8 | 54.1 | 49.3 | 50.9 | | SEEDI | 75.4 | - | 76.2 | 77.2 | | OCRBench | - | 866 | 794 | 807 | ## Quick Start ```python import numpy as np import torch import torchvision.transforms as T from decord import VideoReader, cpu from PIL import Image from torchvision.transforms.functional import InterpolationMode from transformers import AutoModel, AutoTokenizer IMAGENET_MEAN = (0.485, 0.456, 0.406) IMAGENET_STD = (0.229, 0.224, 0.225) def build_transform(input_size): MEAN, STD = IMAGENET_MEAN, IMAGENET_STD transform = T.Compose([ T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=MEAN, std=STD) ]) return transform def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): best_ratio_diff = float('inf') best_ratio = (1, 1) area = width * height for ratio in target_ratios: target_aspect_ratio = ratio[0] / ratio[1] ratio_diff = abs(aspect_ratio - target_aspect_ratio) if ratio_diff < best_ratio_diff: best_ratio_diff = ratio_diff best_ratio = ratio elif ratio_diff == best_ratio_diff: if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: best_ratio = ratio return best_ratio def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False): orig_width, orig_height = image.size aspect_ratio = orig_width / orig_height # calculate the existing image aspect ratio target_ratios = set( (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if i * j <= max_num and i * j >= min_num) target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) # find the closest aspect ratio to the target target_aspect_ratio = find_closest_aspect_ratio( aspect_ratio, target_ratios, orig_width, orig_height, image_size) # calculate the target width and height target_width = image_size * target_aspect_ratio[0] target_height = image_size * target_aspect_ratio[1] blocks = target_aspect_ratio[0] * target_aspect_ratio[1] # resize the image resized_img = image.resize((target_width, target_height)) processed_images = [] for i in range(blocks): box = ( (i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size ) # split the image split_img = resized_img.crop(box) processed_images.append(split_img) assert len(processed_images) == blocks if use_thumbnail and len(processed_images) != 1: thumbnail_img = image.resize((image_size, image_size)) processed_images.append(thumbnail_img) return processed_images def load_image(image_file, input_size=448, max_num=12): image = Image.open(image_file).convert('RGB') transform = build_transform(input_size=input_size) images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) pixel_values = [transform(image) for image in images] pixel_values = torch.stack(pixel_values) return pixel_values 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 path = 'OpenGVLab/PVC-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) generation_config = dict(max_new_tokens=1024, do_sample=True) # single-image conversation pixel_values = load_image('./assets/example_image1.jpg', max_num=12).to(torch.bfloat16).cuda() data_flag = torch.tensor([1], dtype=torch.long).cuda() question = '\nWhat is in the image?' response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag) print(f'User: {question}\nAssistant: {response}') # multi-image conversation pixel_values1 = load_image('./assets/example_image1.jpg', max_num=12).to(torch.bfloat16).cuda() pixel_values2 = load_image('./assets/example_image2.jpg', max_num=12).to(torch.bfloat16).cuda() pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) data_flag = torch.tensor([2], dtype=torch.long).cuda() num_patches_list = [pixel_values1.shape[0], pixel_values2.shape[0]] question = 'Image-1: \nImage-2: \nWhat are the similarities and differences between these two images.' response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag, num_patches_list=num_patches_list) print(f'User: {question}\nAssistant: {response}') # video conversation pixel_values, num_patches_list = load_video('./assets/example_video.mp4', num_segments=64, 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))]) # Frame1: \nFrame2: \n...\nFrameN: \n{question} data_flag = torch.tensor([3], dtype=torch.long).cuda() question = video_prefix + 'Describe this video in detail.' response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag, num_patches_list=num_patches_list) print(f'User: {question}\nAssistant: {response}') ``` ## Evaluation Please refer to our [Github Repo](https://github.com/OpenGVLab/PVC?tab=readme-ov-file#-evaluation). ## Citation If you find this work helpful in your research, please consider citing: ```bibtex @article{yang2024pvc, title={PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models}, author={Yang, Chenyu and Dong, Xuan and Zhu, Xizhou and Su, Weijie and Wang, Jiahao and Tian, Hao and Chen, Zhe and Wang, Wenhai and Lu, Lewei and and Dai, Jifeng}, journal={arXiv preprint arXiv:2412.09613}, year={2024} } ``` ## 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.