--- license: llama2 --- # Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding **Paper or resources for more information:** [[Paper](https://huggingface.co/papers/2311.08046)] [[Code](https://github.com/PKU-YuanGroup/Chat-UniVi)] ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. ## 😮 Highlights ### 💡 Unified visual representation for image and video We employ **a set of dynamic visual tokens** to uniformly represent images and videos. This representation framework empowers the model to efficiently utilize **a limited number of visual tokens** to simultaneously capture **the spatial details necessary for images** and **the comprehensive temporal relationship required for videos**. ### 🔥 Joint training strategy, making LLMs understand both image and video Chat-UniVi is trained on a mixed dataset containing both images and videos, allowing direct application to tasks involving both mediums without requiring any modifications. ### 🤗 High performance, complementary learning with image and video Extensive experimental results demonstrate that Chat-UniVi, as a unified model, consistently outperforms even existing methods exclusively designed for either images or videos. ### Inference for Video Understanding ```python import torch import os from ChatUniVi.constants import * from ChatUniVi.conversation import conv_templates, SeparatorStyle from ChatUniVi.model.builder import load_pretrained_model from ChatUniVi.utils import disable_torch_init from ChatUniVi.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria from PIL import Image from decord import VideoReader, cpu import numpy as np def _get_rawvideo_dec(video_path, image_processor, max_frames=MAX_IMAGE_LENGTH, image_resolution=224, video_framerate=1, s=None, e=None): # speed up video decode via decord. if s is None: start_time, end_time = None, None else: start_time = int(s) end_time = int(e) start_time = start_time if start_time >= 0. else 0. end_time = end_time if end_time >= 0. else 0. if start_time > end_time: start_time, end_time = end_time, start_time elif start_time == end_time: end_time = start_time + 1 if os.path.exists(video_path): vreader = VideoReader(video_path, ctx=cpu(0)) else: print(video_path) raise FileNotFoundError fps = vreader.get_avg_fps() f_start = 0 if start_time is None else int(start_time * fps) f_end = int(min(1000000000 if end_time is None else end_time * fps, len(vreader) - 1)) num_frames = f_end - f_start + 1 if num_frames > 0: # T x 3 x H x W sample_fps = int(video_framerate) t_stride = int(round(float(fps) / sample_fps)) all_pos = list(range(f_start, f_end + 1, t_stride)) if len(all_pos) > max_frames: sample_pos = [all_pos[_] for _ in np.linspace(0, len(all_pos) - 1, num=max_frames, dtype=int)] else: sample_pos = all_pos patch_images = [Image.fromarray(f) for f in vreader.get_batch(sample_pos).asnumpy()] patch_images = torch.stack([image_processor.preprocess(img, return_tensors='pt')['pixel_values'][0] for img in patch_images]) slice_len = patch_images.shape[0] return patch_images, slice_len else: print("video path: {} error.".format(video_path)) if __name__ == '__main__': # Model Parameter model_path = "Chat-UniVi/Chat-UniVi" # or "Chat-UniVi/Chat-UniVi-13B" video_path = ${video_path} # The number of visual tokens varies with the length of the video. "max_frames" is the maximum number of frames. # When the video is long, we will uniformly downsample the video to meet the frames when equal to the "max_frames". max_frames = 100 # The number of frames retained per second in the video. video_framerate = 1 # Input Text qs = "Describe the video." # Sampling Parameter conv_mode = "simple" temperature = 0.2 top_p = None num_beams = 1 disable_torch_init() model_path = os.path.expanduser(model_path) model_name = "ChatUniVi" tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name) mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False) mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True) if mm_use_im_patch_token: tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) if mm_use_im_start_end: tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) model.resize_token_embeddings(len(tokenizer)) vision_tower = model.get_vision_tower() if not vision_tower.is_loaded: vision_tower.load_model() image_processor = vision_tower.image_processor if model.config.config["use_cluster"]: for n, m in model.named_modules(): m = m.to(dtype=torch.bfloat16) # Check if the video exists if video_path is not None: video_frames, slice_len = _get_rawvideo_dec(video_path, image_processor, max_frames=max_frames, video_framerate=video_framerate) cur_prompt = qs if model.config.mm_use_im_start_end: qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN * slice_len + DEFAULT_IM_END_TOKEN + '\n' + qs else: qs = DEFAULT_IMAGE_TOKEN * slice_len + '\n' + qs conv = conv_templates[conv_mode].copy() conv.append_message(conv.roles[0], qs) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze( 0).cuda() stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images=video_frames.half().cuda(), do_sample=True, temperature=temperature, top_p=top_p, num_beams=num_beams, output_scores=True, return_dict_in_generate=True, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria]) output_ids = output_ids.sequences input_token_len = input_ids.shape[1] n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() if n_diff_input_output > 0: print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] outputs = outputs.strip() if outputs.endswith(stop_str): outputs = outputs[:-len(stop_str)] outputs = outputs.strip() print(outputs) ``` ### Inference for Image Understanding ```python import torch import os from ChatUniVi.constants import * from ChatUniVi.conversation import conv_templates, SeparatorStyle from ChatUniVi.model.builder import load_pretrained_model from ChatUniVi.utils import disable_torch_init from ChatUniVi.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria from PIL import Image if __name__ == '__main__': # Model Parameter model_path = "Chat-UniVi/Chat-UniVi" # or "Chat-UniVi/Chat-UniVi-13B" image_path = ${image_path} # Input Text qs = "Describe the image." # Sampling Parameter conv_mode = "simple" temperature = 0.2 top_p = None num_beams = 1 disable_torch_init() model_path = os.path.expanduser(model_path) model_name = "ChatUniVi" tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name) mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False) mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True) if mm_use_im_patch_token: tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) if mm_use_im_start_end: tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) model.resize_token_embeddings(len(tokenizer)) vision_tower = model.get_vision_tower() if not vision_tower.is_loaded: vision_tower.load_model() image_processor = vision_tower.image_processor # Check if the video exists if image_path is not None: cur_prompt = qs if model.config.mm_use_im_start_end: qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs else: qs = DEFAULT_IMAGE_TOKEN + '\n' + qs conv = conv_templates[conv_mode].copy() conv.append_message(conv.roles[0], qs) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() image = Image.open(image_path) image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images=image_tensor.unsqueeze(0).half().cuda(), do_sample=True, temperature=temperature, top_p=top_p, num_beams=num_beams, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria]) input_token_len = input_ids.shape[1] n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() if n_diff_input_output > 0: print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] outputs = outputs.strip() if outputs.endswith(stop_str): outputs = outputs[:-len(stop_str)] outputs = outputs.strip() print(outputs) ```