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e14f899 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 | import os
import sys
import logging
import torch
import numpy as np
import argparse
import math
import random
import traceback
import json
import glob
import io
import urllib
import requests
import cv2
import time
from decord import VideoReader, cpu
from easydict import EasyDict
from einops import rearrange
from tqdm import tqdm
from torchvision import transforms
from transformers import AutoProcessor
from diffusers_lite.arguments import args_init
from diffusers_lite.constants import PRECISION_TO_TYPE
from diffusers_lite.wan.modules.vae import WanVAE
from diffusers_lite.wan.modules.t5 import T5EncoderModel
from diffusers_lite.wan.modules.clip import CLIPModel
from diffusers_lite.utils.data_utils import split_list, align_ceil_to, align_floor_to
from diffusers_lite.utils.diffusion_utils import (
vae_encode,
image_encode,
prompt2states,
)
from omegaconf import OmegaConf
DEVICE = "cuda"
DTYPE = torch.float16
def read_json(json_path):
with open(json_path, 'r', encoding='utf-8') as file:
data = json.load(file)
return data
def write_json(json_data,json_file, encoding='utf-8'):
with open(json_file, 'w') as file:
json.dump(json_data,file,indent=4)
def seed_everything(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
logging.basicConfig(stream=sys.stdout,
filemode='a',
level=logging.INFO,
datefmt='%Y-%m-%d %H:%M:%S',
format='%(asctime)s.%(msecs)03d %(filename)s[line:%(lineno)d] %(levelname)s %(message)s')
logger = logging.getLogger('default')
logFormater = logging.Formatter("%(asctime)s.%(msecs)03d %(filename)s[line:%(lineno)d] %(levelname)s %(message)s",
datefmt='%Y-%m-%d %H:%M:%S')
def load_and_analyze_video(video_path, args):
if video_path.startswith('http'):
req = urllib.request.Request(video_path)
with urllib.request.urlopen(req, timeout=20) as resp:
video_reader = VideoReader(io.BytesIO(resp.read()), ctx=cpu(0))
else:
video_reader = VideoReader(video_path)
video_fps = video_reader.get_avg_fps()
total_frames = len(video_reader)
frame_interval = video_fps / args.extract_fps
extract_frames = min(
int(math.ceil((total_frames * args.extract_fps) / video_fps)),
args.num_frames
)
return video_reader, video_fps, total_frames, frame_interval, extract_frames
def get_common_video_params(win_video_path, lose_video_path, args):
win_reader, win_fps, win_total, win_interval, win_frames = load_and_analyze_video(win_video_path, args)
lose_reader, lose_fps, lose_total, lose_interval, lose_frames = load_and_analyze_video(lose_video_path, args)
common_frames = min(win_frames, lose_frames)
common_frames = align_floor_to(common_frames-1, alignment=4) + 1
print(f"Win video - fps:{win_fps}, total_frames:{win_total}, extract_frames:{win_frames}")
print(f"Lose video - fps:{lose_fps}, total_frames:{lose_total}, extract_frames:{lose_frames}")
print(f"Common extract_frames: {common_frames}")
return win_reader, lose_reader, common_frames
def extract_video_frames(video_reader, common_frames, args, video_path):
total_frames = len(video_reader)
video_fps = video_reader.get_avg_fps()
frame_interval = video_fps / args.extract_fps
frame_indices = []
current_position = args.start_idx
while len(frame_indices) < common_frames and current_position < total_frames:
frame_indices.append(int(current_position))
current_position += frame_interval
frame_indices = np.array(frame_indices[:common_frames])
print(f"Frame indices: {frame_indices}, count: {len(frame_indices)}")
frames = video_reader.get_batch(frame_indices).asnumpy()
return frames
def height_width_scale(frames, args):
height, width = frames.shape[1], frames.shape[2]
scale = args.resolution[0] / min(height, width)
resize_height_scale = align_ceil_to(int(height * scale), 32)
resize_width_scale = align_ceil_to(int(width * scale), 32)
max_resolution = args.resolution[0] * args.aspect_ratio
max_resolution = align_ceil_to(max_resolution, 32)
height_scale = resize_height_scale
width_scale = resize_width_scale
if resize_height_scale > max_resolution:
height_scale = max_resolution
if resize_width_scale > max_resolution:
width_scale = max_resolution
if int(width * scale) < width_scale:
scale_new = width_scale / width
else:
scale_new = scale
if int(height * scale_new) < height_scale:
scale_new = height_scale/height
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((int(height * scale_new), int(width * scale_new))),
transforms.CenterCrop((height_scale, width_scale)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
return height_scale, width_scale, transform
def process_video_frames(frames, args, save_first_frame_path, height_scale, width_scale,transform):
processed_frames = []
for i, frame in enumerate(frames):
processed_frame = transform(frame)
processed_frames.append(processed_frame)
if i == 0 and save_first_frame_path:
denormalized_frame = processed_frame * 0.5 + 0.5
denormalized_frame = denormalized_frame.clamp(0, 1)
first_frame = transforms.ToPILImage()(denormalized_frame)
first_frame.save(save_first_frame_path)
print(f"Processed video scale height {height_scale} width {width_scale}")
return torch.stack(processed_frames)
def encode_single_video(video_tensor, basic_kwargs, model_kwargs):
vae = model_kwargs.vae
image_encoder = model_kwargs.image_encoder
video = video_tensor.unsqueeze(0).to(basic_kwargs.device) # (b, t, c, h, w)
video = rearrange(video, "b t c h w -> b c t h w")
batch_size, _, num_frames, height, width = video.shape
image = video[:, :, 0:1, :, :]
video_condition = torch.cat([
image,
image.new_zeros(image.shape[0], image.shape[1], num_frames - 1, height, width)
], dim=2).to(basic_kwargs.device)
with torch.autocast(device_type="cuda", dtype=basic_kwargs.dtype):
latents = vae_encode(vae, video, vae_type="wanx")
latents_condition = vae_encode(vae, video_condition, vae_type="wanx")
image_embeds = image_encode(image_encoder, image, image_encoder_type="wanx")
return {
"latents": latents,
"image_embeds": image_embeds,
"latents_condition": latents_condition
}
def encode_video(args, video_path, basic_kwargs, model_kwargs, save_first_frame_path):
video_reader, video_fps, total_frames, frame_interval, extract_frames = load_and_analyze_video(video_path, args)
extract_frames = align_floor_to(extract_frames-1, alignment=4) + 1
frames = extract_video_frames(video_reader, extract_frames, args, video_path)
height_scale, width_scale,transform = height_width_scale(frames, args)
video_tensor = process_video_frames(frames, args, save_first_frame_path, height_scale, width_scale,transform)
encode_kwargs = encode_single_video(video_tensor, basic_kwargs, model_kwargs)
print(f"Encoded shapes -latents: {encode_kwargs['latents'].shape}, "
f"Lose latents: {encode_kwargs['latents'].shape}")
return encode_kwargs
def basic_init(args):
device = torch.device("cuda", 0)
dtype = PRECISION_TO_TYPE[args.precision]
basic_kwargs = EasyDict({
"device": device,
"dtype": dtype,
})
return basic_kwargs
def model_init(args, basic_kwargs):
vae = WanVAE(
vae_pth=args.vae_path,
device=basic_kwargs.device,
)
image_encoder = CLIPModel(
checkpoint_path=args.image_encoder_path,
tokenizer_path=args.image_processor_path,
dtype=basic_kwargs.dtype,
device=basic_kwargs.device,
)
text_encoder = T5EncoderModel(
checkpoint_path=args.text_encoder_path,
tokenizer_path=args.tokenizer_path,
text_len=args.max_sequence_length,
dtype=basic_kwargs.dtype,
device=basic_kwargs.device,
shard_fn=None,
)
model_kwargs = EasyDict({
"vae": vae,
"image_encoder": image_encoder,
"text_encoder": text_encoder,
})
return model_kwargs
def encode_caption(args, caption, basic_kwargs, model_kwargs):
text_encoder = model_kwargs.text_encoder
text_states = prompt2states(
caption, text_encoder, device=basic_kwargs.device, text_encoder_type=args.model_type,
)
return text_states
@torch.no_grad()
def main_wan(config):
seed_everything(config.seed)
start = time.time()
basic_kwargs = basic_init(config)
model_kwargs = model_init(config, basic_kwargs)
print(f"Load VAE: {time.time() - start:.2f}s")
output_base_dir = config.save_dir
save_latents_dir = os.path.join(output_base_dir, 'latents')
save_first_frame_dir = os.path.join(output_base_dir, 'first_frame')
save_clip_dir = os.path.join(output_base_dir, 'meta_v1')
for dir_path in [save_latents_dir, save_clip_dir, save_first_frame_dir]:
os.makedirs(dir_path, exist_ok=True)
data = read_json(config.json_path)
for clip_data in data:
caption_short = clip_data['short_caption']
caption_long = clip_data['long_caption']
if "video_path" in clip_data and clip_data['video_path']:
video_path = clip_data["video_path"]
base_name = clip_data["source_id"]
refl_metafile_path = os.path.join(save_clip_dir, base_name + '_meta_v1.json')
if not os.path.isfile(refl_metafile_path):
vae_latent_path = os.path.join(save_latents_dir, base_name + '.npy')
f1_black_path = os.path.join(save_latents_dir, base_name + '_f1_black.npy')
imgclip_path = os.path.join(save_latents_dir, base_name + '_img_clip.npy')
first_frame_path = os.path.join(save_first_frame_dir, base_name + '.jpg')
textshort_path = os.path.join(save_latents_dir, base_name + '_textshort.npy')
textlong_path = os.path.join(save_latents_dir, base_name + '_textlong.npy')
try:
encode_kwargs = encode_video(
config,video_path, basic_kwargs, model_kwargs, first_frame_path
)
text_states_short = encode_caption(config, caption_short, basic_kwargs, model_kwargs)
text_states_long = encode_caption(config, caption_long, basic_kwargs, model_kwargs)
np.save(vae_latent_path, encode_kwargs["latents"].to(torch.float32).cpu().numpy())
np.save(f1_black_path, encode_kwargs["latents_condition"].to(torch.float32).cpu().numpy())
np.save(imgclip_path, encode_kwargs["image_embeds"].to(torch.float32).cpu().numpy())
np.save(textshort_path, text_states_short.to(torch.float32).cpu().numpy())
np.save(textlong_path, text_states_long.to(torch.float32).cpu().numpy())
dpo_meta_data = clip_data.copy()
dpo_meta_data.update({
'vae_latent_path': vae_latent_path,
'f1_black_path': f1_black_path,
'imgclip_path': imgclip_path,
'latent_shape': encode_kwargs["latents"].shape,
'textshort_path': textshort_path,
'text_states_short_shape': text_states_short.shape,
'textlong_path': textlong_path,
'text_states_long_shape': text_states_long.shape,
})
with open(refl_metafile_path, 'w') as file:
json.dump(dpo_meta_data, file, indent=4, ensure_ascii=False)
print(f'Data processed successfully: {refl_metafile_path}')
except Exception as e:
print(f'Error processing DPO pair: {e}')
traceback.print_exc()
continue
else:
print(f'Data already processed: {refl_metafile_path}')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", default='', type=str)
args = parser.parse_args()
config = OmegaConf.load(args.config)
main_wan(config) |