File size: 5,445 Bytes
08bf07d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
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
from tqdm import tqdm
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
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):
"""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
for idx, scene_name in enumerate(tqdm(os.listdir(scenes_path))):
if idx < 450:
continue
scene_dir = os.path.join(scenes_path, scene_name)
if not os.path.isdir(scene_dir):
continue
# 检查是否已编码
encoded_path = os.path.join(scene_dir, "encoded_video-480p-1.pth")
if os.path.exists(encoded_path):
print(f"Scene {scene_name} already encoded, skipping...")
continue
# 加载场景信息
scene_info_path = os.path.join(scene_dir, "scene_info.json")
if not os.path.exists(scene_info_path):
continue
with open(scene_info_path, 'r') as f:
scene_info = json.load(f)
# 加载视频
video_path = os.path.join(scene_dir, scene_info['video_path'])
if not os.path.exists(video_path):
print(f"Video not found: {video_path}")
continue
try:
print(f"Encoding scene {scene_name}...")
# 加载和编码视频
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)
# 编码视频
with torch.no_grad():
latents = encoder.pipe.encode_video(video_frames, **encoder.tiler_kwargs)[0]
# print(latents.shape)
# assert False
# 编码文本
# prompt_emb = encoder.pipe.encode_prompt("A car driving scene captured by front camera")
if processed_count == 0:
print('encode prompt!!!')
prompt_emb = encoder.pipe.encode_prompt("A car driving scene captured by front camera")
del encoder.pipe.prompter
# 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()},
"image_emb": {}
}
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/zhuyixuan05/nuscenes_video_generation_dynamic/scenes")
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")
args = parser.parse_args()
encode_scenes(args.scenes_path, args.text_encoder_path, args.vae_path)
|