text-to-video

from llava.model.builder import load_pretrained_model from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX from llava.conversation import conv_templates, SeparatorStyle from PIL import Image import requests import copy import torch import sys import warnings from decord import VideoReader, cpu import numpy as np warnings.filterwarnings("ignore") def load_video(video_path, max_frames_num,fps=1,force_sample=False): if max_frames_num == 0: return np.zeros((1, 336, 336, 3)) vr = VideoReader(video_path, ctx=cpu(0),num_threads=1) total_frame_num = len(vr) video_time = total_frame_num / vr.get_avg_fps() fps = round(vr.get_avg_fps()/fps) frame_idx = [i for i in range(0, len(vr), fps)] frame_time = [i/fps for i in frame_idx] if len(frame_idx) > max_frames_num or force_sample: sample_fps = max_frames_num uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int) frame_idx = uniform_sampled_frames.tolist() frame_time = [i/vr.get_avg_fps() for i in frame_idx] frame_time = ",".join([f"{i:.2f}s" for i in frame_time]) spare_frames = vr.get_batch(frame_idx).asnumpy() # import pdb;pdb.set_trace() return spare_frames,frame_time,video_time pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2" model_name = "llava_qwen" device = "cuda" device_map = "auto" tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map) # Add any other thing you want to pass in llava_model_args model.eval() video_path = "XXXX" max_frames_num = 64 video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sample=True) video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().half() video = [video] conv_template = "qwen_1_5" # Make sure you use correct chat template for different models time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}.Please answer the following questions related to this video." question = DEFAULT_IMAGE_TOKEN + f"{time_instruciton}\nPlease describe this video in detail." conv = copy.deepcopy(conv_templates[conv_template]) conv.append_message(conv.roles[0], question) conv.append_message(conv.roles[1], None) prompt_question = conv.get_prompt() input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device) cont = model.generate( input_ids, images=video, modalities= ["video"], do_sample=False, temperature=0, max_new_tokens=4096, ) text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)[0].strip() print(text_outputs