import random import argparse import cv2 from tqdm import tqdm import numpy as np import numpy.typing as npt import torch import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.data import DataLoader, DistributedSampler, Subset from decord import VideoReader, cpu from torch.nn import functional as F from pytorchvideo.transforms import ShortSideScale from torchvision.transforms import Lambda, Compose from torchvision.transforms._transforms_video import CenterCropVideo import sys from torch.utils.data import Dataset, DataLoader, Subset import os import glob sys.path.append(".") from causalvideovae.model import CausalVAEModel from diffusers.models import AutoencoderKL from diffusers.models import AutoencoderKLTemporalDecoder from CV_VAE.models.modeling_vae import CVVAEModel from opensora.registry import MODELS, build_module from opensora.utils.config_utils import parse_configs from opensora.registry import MODELS, build_module from opensora.utils.config_utils import parse_configs import gradio as gr from functools import partial from einops import rearrange import torchvision.transforms as transforms from PIL import Image import time import imageio # 创建一个transform，用于中心裁剪图像到指定的大小 transform = transforms.Compose([ transforms.CenterCrop(512), ]) def array_to_video( image_array: npt.NDArray, fps: float = 30.0, output_file: str = "output_video.mp4" ) -> None: height, width, channels = image_array[0].shape imageio.mimwrite(output_file, image_array, fps=fps, quality=6,) """ fourcc = cv2.VideoWriter_fourcc(*"mp4v") video_writer = cv2.VideoWriter(output_file, fourcc, float(fps), (width, height)) for image in image_array: image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) video_writer.write(image_rgb) video_writer.release() """ def custom_to_video( x: torch.Tensor, fps: float = 2.0, output_file: str = "output_video.mp4" ) -> None: x = x.detach().cpu() x = torch.clamp(x, -1, 1) x = (x + 1) / 2 x = x.permute(1, 2, 3, 0).float().numpy() x = (255 * x).astype(np.uint8) array_to_video(x, fps=fps, output_file=output_file) return def _format_video_shape(video, time_compress=4, spatial_compress=8): time = video.shape[1] height = video.shape[2] width = video.shape[3] new_time = ( (time - (time - 1) % time_compress) if (time - 1) % time_compress != 0 else time ) new_height = ( (height - (height) % spatial_compress) if height % spatial_compress != 0 else height ) new_width = ( (width - (width) % spatial_compress) if width % spatial_compress != 0 else width ) return video[:, :new_time, :new_height, :new_width] @torch.no_grad() def rec_nusvae(input_file): nus_vae_path = '/storage/clh/Causal-Video-VAE/gradio/nus_vae_temp/video.mp4' if input_file.endswith(('.jpg', '.jpeg', '.png', '.gif', '.bmp')): #处理图像 image = cv2.imread(input_file, cv2.IMREAD_COLOR) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) fps=10 total_frames = 1 video_data = torch.from_numpy(image) video_data = video_data.unsqueeze(0) video_data = video_data.permute(3, 0, 1, 2) video_data = (video_data / 255.0) * 2 - 1 video_data = _format_video_shape(video_data) video_data = video_data.unsqueeze(0) video_data = video_data.to(dtype=data_type) # b c t h w elif input_file.endswith(('.mp4', '.avi', '.mov', '.wmv')): # 处理视频 decord_vr = VideoReader(input_file, ctx=cpu(0)) total_frames = len(decord_vr) video = cv2.VideoCapture(input_file) fps = video.get(cv2.CAP_PROP_FPS) frame_id_list = np.linspace(0, total_frames-1, total_frames, dtype=int) video_data = decord_vr.get_batch(frame_id_list).asnumpy() video_data = torch.from_numpy(video_data) video_data = video_data.permute(3, 0, 1, 2) video_data = (video_data / 255.0) * 2 - 1 video_data = _format_video_shape(video_data) video_data = video_data.unsqueeze(0) video_data = video_data.to(dtype=data_type) # b c t h w video_data = video_data.to(device4) latents, posterior, x_z = nus_vae.encode(video_data) video_recon, x_z_rec = nus_vae.decode(latents, num_frames=video_data.size(2)) custom_to_video(video_recon[0], fps=fps, output_file=nus_vae_path) time.sleep(15) return nus_vae_path @torch.no_grad() def rec_cvvae(input_file): cv_vae_path = '/storage/clh/Causal-Video-VAE/gradio/cv_vae_temp/video.mp4' if input_file.endswith(('.jpg', '.jpeg', '.png', '.gif', '.bmp')): #处理图像 image = cv2.imread(input_file, cv2.IMREAD_COLOR) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) fps=10 total_frames = 1 video_data = torch.from_numpy(image) video_data = video_data.unsqueeze(0) video_data = video_data.permute(3, 0, 1, 2) video_data = (video_data / 255.0) * 2 - 1 video_data = _format_video_shape(video_data) video_data = video_data.unsqueeze(0) video_data = video_data.to(dtype=data_type) # b c t h w elif input_file.endswith(('.mp4', '.avi', '.mov', '.wmv')): # 处理视频 decord_vr = VideoReader(input_file, ctx=cpu(0)) total_frames = len(decord_vr) video = cv2.VideoCapture(input_file) fps = video.get(cv2.CAP_PROP_FPS) frame_id_list = np.linspace(0, total_frames-1, total_frames, dtype=int) video_data = decord_vr.get_batch(frame_id_list).asnumpy() video_data = torch.from_numpy(video_data) video_data = video_data.permute(3, 0, 1, 2) video_data = (video_data / 255.0) * 2 - 1 video_data = _format_video_shape(video_data) video_data = video_data.unsqueeze(0) video_data = video_data.to(dtype=data_type) # b c t h w video_data = video_data.to(device3) latent = cvvae.encode(video_data).latent_dist.sample() video_recon = cvvae.decode(latent).sample custom_to_video(video_recon[0], fps=fps, output_file=cv_vae_path) time.sleep(10) return cv_vae_path @torch.no_grad() def rec_our12(input_file): our_vae_path = '/storage/clh/Causal-Video-VAE/gradio/our_temp/video.mp4' if input_file.endswith(('.jpg', '.jpeg', '.png', '.gif', '.bmp')): #处理图像 image = cv2.imread(input_file, cv2.IMREAD_COLOR) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) fps=10 total_frames = 1 video_data = torch.from_numpy(image) video_data = video_data.unsqueeze(0) video_data = video_data.permute(3, 0, 1, 2) video_data = (video_data / 255.0) * 2 - 1 video_data = _format_video_shape(video_data) video_data = video_data.unsqueeze(0) video_data = video_data.to(dtype=data_type) # b c t h w elif input_file.endswith(('.mp4', '.avi', '.mov', '.wmv')): # 处理视频 decord_vr = VideoReader(input_file, ctx=cpu(0)) total_frames = len(decord_vr) video = cv2.VideoCapture(input_file) fps = video.get(cv2.CAP_PROP_FPS) frame_id_list = np.linspace(0, total_frames-1, total_frames, dtype=int) video_data = decord_vr.get_batch(frame_id_list).asnumpy() video_data = torch.from_numpy(video_data) video_data = video_data.permute(3, 0, 1, 2) video_data = (video_data / 255.0) * 2 - 1 video_data = _format_video_shape(video_data) video_data = video_data.unsqueeze(0) video_data = video_data.to(dtype=data_type) # b c t h w ##我们的VAE的输出 input_data = video_data.clone() input_data = input_data.to(device0) latents = vqvae.encode(input_data).sample().to(data_type) video_recon = vqvae.decode(latents) custom_to_video(video_recon[0], fps=fps, output_file=our_vae_path) return our_vae_path @torch.no_grad() def rec_new(input_file): our_vae_path = '/storage/clh/Causal-Video-VAE/gradio/new_temp/video.mp4' if input_file.endswith(('.jpg', '.jpeg', '.png', '.gif', '.bmp')): #处理图像 image = cv2.imread(input_file, cv2.IMREAD_COLOR) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) fps=10 total_frames = 1 video_data = torch.from_numpy(image) video_data = video_data.unsqueeze(0) video_data = video_data.permute(3, 0, 1, 2) video_data = (video_data / 255.0) * 2 - 1 video_data = _format_video_shape(video_data) video_data = video_data.unsqueeze(0) video_data = video_data.to(dtype=data_type) # b c t h w elif input_file.endswith(('.mp4', '.avi', '.mov', '.wmv')): # 处理视频 decord_vr = VideoReader(input_file, ctx=cpu(0)) total_frames = len(decord_vr) video = cv2.VideoCapture(input_file) fps = video.get(cv2.CAP_PROP_FPS) frame_id_list = np.linspace(0, total_frames-1, total_frames, dtype=int) video_data = decord_vr.get_batch(frame_id_list).asnumpy() video_data = torch.from_numpy(video_data) video_data = video_data.permute(3, 0, 1, 2) video_data = (video_data / 255.0) * 2 - 1 video_data = _format_video_shape(video_data) video_data = video_data.unsqueeze(0) video_data = video_data.to(dtype=data_type) # b c t h w ##我们的VAE的输出 input_data = video_data.clone() input_data = input_data.to(device0) latents = newvae.encode(input_data).sample().to(data_type) video_recon = newvae.decode(latents) custom_to_video(video_recon[0], fps=fps, output_file=our_vae_path) return our_vae_path @torch.no_grad() def show_origin(input_file): return input_file @torch.no_grad() def main(args: argparse.Namespace): # 创建输出界面 with gr.Blocks() as demo: with gr.Row(): input_interface = gr.components.File(label="上传文件（图片或视频）") with gr.Row(): output_video1 = gr.Video(label="原始视频或图片") output_video2 = gr.Video(label="我们的3D VAE输出视频或图片") with gr.Row(): show_origin_button = gr.components.Button("展示原始视频或图片") show_origin_button.click(fn=show_origin, inputs=input_interface, outputs=output_video1) our12_button = gr.components.Button("用我们的3D VAE重建视频或图片") our12_button.click(fn=rec_our12, inputs=input_interface, outputs=output_video2) with gr.Row(): output_video3 = gr.Video(label="CV-VAE VAE输出视频或图片") output_video4 = gr.Video(label="Open-Sora VAE输出视频或图片") with gr.Row(): cvvae_button = gr.components.Button("用CV VAE重建视频或图片") cvvae_button.click(fn=rec_cvvae, inputs=input_interface, outputs=output_video3) nusvae_button = gr.components.Button("用Open-Sora VAE重建视频或图片") nusvae_button.click(fn=rec_nusvae, inputs=input_interface, outputs=output_video4) """ with gr.Row(): output_video5 = gr.Video(label="我们最新内部版本VAE") with gr.Row(): new_button = gr.components.Button("用新VAE重建视频或图片") new_button.click(fn=rec_new, inputs=input_interface, outputs=output_video5) """ demo.launch(server_name="0.0.0.0", server_port=11904) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--ckpt", type=str, default="") parser.add_argument("--sample_fps", type=int, default=30) parser.add_argument("--tile_overlap_factor", type=float, default=0.125) parser.add_argument('--enable_tiling', action='store_true') parser.add_argument("--device", type=str, default="cuda") parser.add_argument("--config", type=str, default="cuda") args = parser.parse_args() device = args.device data_type = torch.bfloat16 device0 = torch.device('cuda:2') ckpt = '/storage/clh/models/488dim8_layernorm_nearst' vqvae = CausalVAEModel.from_pretrained(ckpt) if args.enable_tiling: vqvae.enable_tiling() vqvae.tile_overlap_factor = args.tile_overlap_factor vqvae = vqvae.to(data_type).to(device0) vqvae.eval() device3 = torch.device('cuda:3') ckpt = '/storage/clh/CV-VAE/vae3d' cvvae = CVVAEModel.from_pretrained(ckpt) cvvae = cvvae.to(device3).to(data_type) cvvae.eval() device4 = torch.device('cuda:4') cfg = parse_configs(args, training=False) nus_vae = build_module(cfg.model, MODELS) nus_vae = nus_vae.to(device4).to(data_type) nus_vae.eval() """ device5 = torch.device('cuda:5') ckpt = '/storage/clh/models/488dim8' newvae = CausalVAEModel.from_pretrained(ckpt) if args.enable_tiling: newvae.enable_tiling() newvae.tile_overlap_factor = args.tile_overlap_factor newvae = vqvae.to(data_type).to(device0) newvae.eval() """ main(args)