import os import torch import lightning as pl from PIL import Image from diffsynth import WanVideoReCamMasterPipeline, ModelManager import json import imageio from torchvision.transforms import v2 from einops import rearrange import argparse import numpy as np import pdb from tqdm import tqdm os.environ["TOKENIZERS_PARALLELISM"] = "false" class VideoEncoder(pl.LightningModule): def __init__(self, text_encoder_path, vae_path, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): super().__init__() model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu") model_manager.load_models([text_encoder_path, vae_path]) self.pipe = WanVideoReCamMasterPipeline.from_model_manager(model_manager) self.tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} self.frame_process = v2.Compose([ # v2.CenterCrop(size=(900, 1600)), # v2.Resize(size=(900, 1600), antialias=True), v2.ToTensor(), v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ]) def crop_and_resize(self, image): width, height = image.size # print(width,height) width_ori, height_ori_ = 832 , 480 image = v2.functional.resize( image, (round(height_ori_), round(width_ori)), interpolation=v2.InterpolationMode.BILINEAR ) return image def load_video_frames(self, video_path): """加载完整视频""" reader = imageio.get_reader(video_path) frames = [] for frame_data in reader: frame = Image.fromarray(frame_data) frame = self.crop_and_resize(frame) frame = self.frame_process(frame) frames.append(frame) reader.close() if len(frames) == 0: return None frames = torch.stack(frames, dim=0) frames = rearrange(frames, "T C H W -> C T H W") return frames def encode_scenes(scenes_path, text_encoder_path, vae_path,output_dir): """编码所有场景的视频""" encoder = VideoEncoder(text_encoder_path, vae_path) encoder = encoder.cuda() encoder.pipe.device = "cuda" processed_count = 0 processed_chunk_count = 0 prompt_emb = 0 os.makedirs(output_dir,exist_ok=True) chunk_size = 300 for i, scene_name in tqdm(enumerate(os.listdir(scenes_path)),total=len(os.listdir(scenes_path))): # print('index-----:',type(i)) # if i < 3000 :#or i >=2000: # # print('index-----:',i) # continue # print('index:',i) print('index:',i) scene_dir = os.path.join(scenes_path, scene_name) # save_dir = os.path.join(output_dir,scene_name.split('.')[0]) # print('in:',scene_dir) # print('out:',save_dir) if not scene_dir.endswith(".mp4"):# or os.path.isdir(output_dir): continue scene_cam_path = scene_dir.replace(".mp4", ".npz") if not os.path.exists(scene_cam_path): continue with np.load(scene_cam_path) as data: cam_data = data.files cam_emb = {k: data[k].cpu() if isinstance(data[k], torch.Tensor) else data[k] for k in cam_data} # with open(scene_cam_path, 'rb') as f: # cam_data = np.load(f) # 此时cam_data仅包含数据，无文件句柄引用 video_name = scene_name[:-4].split('_')[0] start_frame = int(scene_name[:-4].split('_')[1]) end_frame = int(scene_name[:-4].split('_')[2]) sampled_range = range(start_frame, end_frame , chunk_size) sampled_frames = list(sampled_range) sampled_chunk_end = sampled_frames[0] + 300 start_str = f"{sampled_frames[0]:07d}" end_str = f"{sampled_chunk_end:07d}" chunk_name = f"{video_name}_{start_str}_{end_str}" save_chunk_path = os.path.join(output_dir,chunk_name,"encoded_video.pth") if os.path.exists(save_chunk_path): print(f"Video {video_name} already encoded, skipping...") continue # 加载视频 video_path = scene_dir if not os.path.exists(video_path): print(f"Video not found: {video_path}") continue video_frames = encoder.load_video_frames(video_path) if video_frames is None: print(f"Failed to load video: {video_path}") continue video_frames = video_frames.unsqueeze(0).to("cuda", dtype=torch.bfloat16) print('video shape:',video_frames.shape) # print(sampled_frames) print(f"Encoding scene {scene_name}...") for sampled_chunk_start in sampled_frames: sampled_chunk_end = sampled_chunk_start + 300 start_str = f"{sampled_chunk_start:07d}" end_str = f"{sampled_chunk_end:07d}" # 生成保存目录名（假设video_name已定义） chunk_name = f"{video_name}_{start_str}_{end_str}" save_chunk_dir = os.path.join(output_dir,chunk_name) os.makedirs(save_chunk_dir,exist_ok=True) print(f"Encoding chunk {chunk_name}...") encoded_path = os.path.join(save_chunk_dir, "encoded_video.pth") if os.path.exists(encoded_path): print(f"Chunk {chunk_name} already encoded, skipping...") continue chunk_frames = video_frames[:,:, sampled_chunk_start - start_frame : sampled_chunk_end - start_frame,...] # print('extrinsic:',cam_emb['extrinsic'].shape) chunk_cam_emb ={'extrinsic':cam_emb['extrinsic'][sampled_chunk_start - start_frame : sampled_chunk_end - start_frame], 'intrinsic':cam_emb['intrinsic']} # print('chunk shape:',chunk_frames.shape) with torch.no_grad(): latents = encoder.pipe.encode_video(chunk_frames, **encoder.tiler_kwargs)[0] # 编码文本 # if processed_count == 0: # print('encode prompt!!!') # prompt_emb = encoder.pipe.encode_prompt("A video of a scene shot using a pedestrian's front camera while walking") # del encoder.pipe.prompter # pdb.set_trace() # 保存编码结果 encoded_data = { "latents": latents.cpu(), # "prompt_emb": {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in prompt_emb.items()}, "cam_emb": chunk_cam_emb } # pdb.set_trace() torch.save(encoded_data, encoded_path) print(f"Saved encoded data: {encoded_path}") processed_chunk_count += 1 processed_count += 1 print("Encoded scene numebr:",processed_count) print("Encoded chunk numebr:",processed_chunk_count) # os.makedirs(save_dir,exist_ok=True) # # 检查是否已编码 # encoded_path = os.path.join(save_dir, "encoded_video.pth") # if os.path.exists(encoded_path): # print(f"Scene {scene_name} already encoded, skipping...") # continue # 加载场景信息 # try: # print(f"Encoding scene {scene_name}...") # 加载和编码视频 # 编码视频 # with torch.no_grad(): # latents = encoder.pipe.encode_video(video_frames, **encoder.tiler_kwargs)[0] # # 编码文本 # if processed_count == 0: # print('encode prompt!!!') # prompt_emb = encoder.pipe.encode_prompt("A video of a scene shot using a pedestrian's front camera while walking") # del encoder.pipe.prompter # # pdb.set_trace() # # 保存编码结果 # encoded_data = { # "latents": latents.cpu(), # #"prompt_emb": {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in prompt_emb.items()}, # "cam_emb": cam_emb # } # # pdb.set_trace() # torch.save(encoded_data, encoded_path) # print(f"Saved encoded data: {encoded_path}") # processed_count += 1 # except Exception as e: # print(f"Error encoding scene {scene_name}: {e}") # continue print(f"Encoding completed! Processed {processed_count} scenes.") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--scenes_path", type=str, default="/share_zhuyixuan05/public_datasets/sekai/Sekai-Project/sekai-game-walking") parser.add_argument("--text_encoder_path", type=str, default="models/Wan-AI/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth") parser.add_argument("--vae_path", type=str, default="models/Wan-AI/Wan2.1-T2V-1.3B/Wan2.1_VAE.pth") parser.add_argument("--output_dir",type=str, default="/share_zhuyixuan05/zhuyixuan05/sekai-game-walking") args = parser.parse_args() encode_scenes(args.scenes_path, args.text_encoder_path, args.vae_path,args.output_dir)