Spaces:
Runtime error
Runtime error
File size: 5,394 Bytes
149cc2d |
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 |
from typing import List, Dict, Any, Union, Optional
import torch
from torch.utils.data import DataLoader, ConcatDataset
import datasets
from diffusers import DDPMScheduler
from functools import partial
import random
import numpy as np
@torch.no_grad()
def collate_fn(
batch: List[Dict[str, Any]],
noise_scheduler: DDPMScheduler,
num_frames: int,
hint_spacing: Optional[int] = None,
as_numpy: bool = True
) -> Dict[str, Union[torch.Tensor, np.ndarray]]:
if hint_spacing is None or hint_spacing < 1:
hint_spacing = num_frames
if as_numpy:
dtype = np.float32
else:
dtype = torch.float32
prompts = []
videos = []
for s in batch:
# prompt
prompts.append(torch.tensor(s['prompt']).to(dtype = torch.float32))
# frames
frames = torch.tensor(s['video']).to(dtype = torch.float32)
max_frames = len(frames)
assert max_frames >= num_frames
video_slice = random.randint(0, max_frames - num_frames)
frames = frames[video_slice:video_slice + num_frames]
frames = frames.permute(1, 0, 2, 3) # f, c, h, w -> c, f, h, w
videos.append(frames)
encoder_hidden_states = torch.cat(prompts) # b, 77, 768
latents = torch.stack(videos) # b, c, f, h, w
latents = latents * 0.18215
hint_latents = latents[:, :, ::hint_spacing, :, :]
hint_latents = hint_latents.repeat_interleave(hint_spacing, 2)
#hint_latents = hint_latents[:, :, :num_frames-1, :, :]
#input_latents = latents[:, :, 1:, :, :]
input_latents = latents
noise = torch.randn_like(input_latents)
bsz = input_latents.shape[0]
timesteps = torch.randint(
0,
noise_scheduler.config.num_train_timesteps,
(bsz,),
dtype = torch.int64
)
noisy_latents = noise_scheduler.add_noise(input_latents, noise, timesteps)
mask = torch.zeros([
noisy_latents.shape[0],
1,
noisy_latents.shape[2],
noisy_latents.shape[3],
noisy_latents.shape[4]
])
latent_model_input = torch.cat([noisy_latents, mask, hint_latents], dim = 1)
latent_model_input = latent_model_input.to(memory_format = torch.contiguous_format)
encoder_hidden_states = encoder_hidden_states.to(memory_format = torch.contiguous_format)
timesteps = timesteps.to(memory_format = torch.contiguous_format)
noise = noise.to(memory_format = torch.contiguous_format)
if as_numpy:
latent_model_input = latent_model_input.numpy().astype(dtype)
encoder_hidden_states = encoder_hidden_states.numpy().astype(dtype)
timesteps = timesteps.numpy().astype(np.int32)
noise = noise.numpy().astype(dtype)
else:
latent_model_input = latent_model_input.to(dtype = dtype)
encoder_hidden_states = encoder_hidden_states.to(dtype = dtype)
noise = noise.to(dtype = dtype)
return {
'latent_model_input': latent_model_input,
'encoder_hidden_states': encoder_hidden_states,
'timesteps': timesteps,
'noise': noise
}
def worker_init_fn(worker_id: int):
wseed = torch.initial_seed() % 4294967294 # max val for random 2**32 - 1
random.seed(wseed)
np.random.seed(wseed)
def load_dataset(
dataset_path: str,
model_path: str,
cache_dir: Optional[str] = None,
batch_size: int = 1,
num_frames: int = 24,
hint_spacing: Optional[int] = None,
num_workers: int = 0,
shuffle: bool = False,
as_numpy: bool = True,
pin_memory: bool = False,
pin_memory_device: str = ''
) -> DataLoader:
noise_scheduler: DDPMScheduler = DDPMScheduler.from_pretrained(
model_path,
subfolder = 'scheduler'
)
dataset = datasets.load_dataset(
dataset_path,
streaming = False,
cache_dir = cache_dir
)
merged_dataset = ConcatDataset([ dataset[s] for s in dataset ])
dataloader = DataLoader(
merged_dataset,
batch_size = batch_size,
num_workers = num_workers,
persistent_workers = num_workers > 0,
drop_last = True,
shuffle = shuffle,
worker_init_fn = worker_init_fn,
collate_fn = partial(collate_fn,
noise_scheduler = noise_scheduler,
num_frames = num_frames,
hint_spacing = hint_spacing,
as_numpy = as_numpy
),
pin_memory = pin_memory,
pin_memory_device = pin_memory_device
)
return dataloader
def validate_dataset(
dataset_path: str
) -> List[int]:
import os
import json
data_path = os.path.join(dataset_path, 'data')
meta = set(os.path.splitext(x)[0] for x in os.listdir(os.path.join(data_path, 'metadata')))
prompts = set(os.path.splitext(x)[0] for x in os.listdir(os.path.join(data_path, 'prompts')))
videos = set(os.path.splitext(x)[0] for x in os.listdir(os.path.join(data_path, 'videos')))
ok = meta.intersection(prompts).intersection(videos)
all_of_em = meta.union(prompts).union(videos)
not_ok = []
for a in all_of_em:
if a not in ok:
not_ok.append(a)
ok = list(ok)
ok.sort()
with open(os.path.join(data_path, 'id_list.json'), 'w') as f:
json.dump(ok, f)
|