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Model Type: multimodal large language model (MLLM)
Model Stats:
Training Strategy:
| Model | Vision Foundation Model | Release Date | Note |
|---|---|---|---|
| InternVL-Chat-V1.5(🤗 HF link) | InternViT-6B-448px-V1-5(🤗 HF link) | 2024.04.18 | 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(🤗 HF link ) | InternViT-6B-448px-V1-2(🤗 HF link) | 2024.02.21 | more SFT data and stronger |
| InternVL-Chat-V1.2(🤗 HF link ) | InternViT-6B-448px-V1-2(🤗 HF link) | 2024.02.11 | scaling up LLM to 34B |
| InternVL-Chat-V1.1(🤗 HF link) | InternViT-6B-448px-V1-0(🤗 HF link) | 2024.01.24 | support Chinese and stronger OCR |
We provide an example code to run InternVL-Chat-V1.5 using transformers.
You also can use our online demo for a quick experience of this model.
python
import jsonimport osfrom transformers import AutoTokenizer, AutoModelfrom tqdm import tqdmimport torchimport torchvision.transforms as Tfrom 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 conversationquestion = "请详细描述图片"response = model.chat(tokenizer, pixel_values, question, generation_config)print(question, response) # multi-round single-image conversationquestion = "请详细描述图片"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"response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)print(question, response) # multi-round multi-image conversationpixel_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"response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)print(question, response)# 第一张图片是一只红熊猫，它有着独特的橙红色皮毛，脸部、耳朵和四肢的末端有白色斑块。红熊猫的眼睛周围有深色的环，它的耳朵是圆形的，上面有白色的毛。它正坐在一个木制的结构上，看起来像是一个平台或休息的地方。背景中有树木和竹子，这表明红熊猫可能在一个模拟自然环境的动物园或保护区内。## 第二张图片是一只大熊猫，它是中国的国宝，以其黑白相间的皮毛而闻名。大熊猫的眼睛、耳朵和四肢的末端是黑色的，而它的脸部、耳朵内侧和身体其他部分是白色的。大熊猫正坐在地上，周围有竹子，这是它们的主要食物来源。背景中也有树木，这表明大熊猫可能在一个为它们提供自然栖息地模拟的动物园或保护区内。 question = "这两张图片的相同点和区别分别是什么"response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)print(question, response)# 这两张图片的相同点：# # 1. 都展示了熊猫，这是两种不同的熊猫物种。# 2. 熊猫都处于一个看起来像是模拟自然环境的场所，可能是动物园或保护区。# 3. 熊猫周围都有竹子，这是它们的主要食物来源。# # 这两张图片的区别：# # 1. 熊猫的种类不同：第一张图片是一只红熊猫，第二张图片是一只大熊猫。# 2. 熊猫的皮毛颜色和图案不同：红熊猫的皮毛是橙红色，脸部、耳朵和四肢的末端有白色斑块；而大熊猫的皮毛是黑白相间的，眼睛、耳朵和四肢的末端是黑色的，脸部、耳朵内侧和身体其他部分是白色的。# 3. 熊猫的姿态和位置不同：红熊猫坐在一个木制的结构上，而大熊猫坐在地上。# 4. 背景中的植被和环境细节略有不同，但都包含树木和竹子。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}}This project is released under the MIT license.
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!
import jsonimport osfrom transformers import AutoTokenizer, AutoModelfrom tqdm import tqdmimport torchimport torchvision.transforms as Tfrom 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 conversationquestion = "请详细描述图片"response = model.chat(tokenizer, pixel_values, question, generation_config)print(question, response) # multi-round single-image conversationquestion = "请详细描述图片"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"response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)print(question, response) # multi-round multi-image conversationpixel_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"response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)print(question, response)# 第一张图片是一只红熊猫，它有着独特的橙红色皮毛，脸部、耳朵和四肢的末端有白色斑块。红熊猫的眼睛周围有深色的环，它的耳朵是圆形的，上面有白色的毛。它正坐在一个木制的结构上，看起来像是一个平台或休息的地方。背景中有树木和竹子，这表明红熊猫可能在一个模拟自然环境的动物园或保护区内。## 第二张图片是一只大熊猫，它是中国的国宝，以其黑白相间的皮毛而闻名。大熊猫的眼睛、耳朵和四肢的末端是黑色的，而它的脸部、耳朵内侧和身体其他部分是白色的。大熊猫正坐在地上，周围有竹子，这是它们的主要食物来源。背景中也有树木，这表明大熊猫可能在一个为它们提供自然栖息地模拟的动物园或保护区内。 question = "这两张图片的相同点和区别分别是什么"response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)print(question, response)# 这两张图片的相同点：# # 1. 都展示了熊猫，这是两种不同的熊猫物种。# 2. 熊猫都处于一个看起来像是模拟自然环境的场所，可能是动物园或保护区。# 3. 熊猫周围都有竹子，这是它们的主要食物来源。# # 这两张图片的区别：# # 1. 熊猫的种类不同：第一张图片是一只红熊猫，第二张图片是一只大熊猫。# 2. 熊猫的皮毛颜色和图案不同：红熊猫的皮毛是橙红色，脸部、耳朵和四肢的末端有白色斑块；而大熊猫的皮毛是黑白相间的，眼睛、耳朵和四肢的末端是黑色的，脸部、耳朵内侧和身体其他部分是白色的。# 3. 熊猫的姿态和位置不同：红熊猫坐在一个木制的结构上，而大熊猫坐在地上。# 4. 背景中的植被和环境细节略有不同，但都包含树木和竹子。@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}}