-
Notifications
You must be signed in to change notification settings - Fork 21
/
Copy pathanimation.py
137 lines (112 loc) · 6.18 KB
/
animation.py
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
import os
import torch
import argparse
import torchvision
from pipeline_videogen import VideoGenPipeline
from diffusers.schedulers import DDIMScheduler
from diffusers.models import AutoencoderKL
from diffusers.models import AutoencoderKLTemporalDecoder
from transformers import CLIPTokenizer, CLIPTextModel
from omegaconf import OmegaConf
import os, sys
sys.path.append(os.path.split(sys.path[0])[0])
from models import get_models
import imageio
from PIL import Image
import numpy as np
from datasets import video_transforms
from torchvision import transforms
from einops import rearrange, repeat
from utils import dct_low_pass_filter, exchanged_mixed_dct_freq
from copy import deepcopy
def prepare_image(path, vae, transform_video, device, dtype=torch.float16):
with open(path, 'rb') as f:
image = Image.open(f).convert('RGB')
image = torch.as_tensor(np.array(image, dtype=np.uint8, copy=True)).unsqueeze(0).permute(0, 3, 1, 2)
image, ori_h, ori_w, crops_coords_top, crops_coords_left = transform_video(image)
image = vae.encode(image.to(dtype=dtype, device=device)).latent_dist.sample().mul_(vae.config.scaling_factor)
image = image.unsqueeze(2)
return image
def main(args):
if args.seed:
torch.manual_seed(args.seed)
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 # torch.float16
unet = get_models(args).to(device, dtype=dtype)
if args.enable_vae_temporal_decoder:
if args.use_dct:
vae_for_base_content = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float64).to(device)
else:
vae_for_base_content = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float16).to(device)
vae = deepcopy(vae_for_base_content).to(dtype=dtype)
else:
vae_for_base_content = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae",).to(device, dtype=torch.float64)
vae = deepcopy(vae_for_base_content).to(dtype=dtype)
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder", torch_dtype=dtype).to(device) # huge
# set eval mode
unet.eval()
vae.eval()
text_encoder.eval()
scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule)
videogen_pipeline = VideoGenPipeline(vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
unet=unet).to(device)
# videogen_pipeline.enable_xformers_memory_efficient_attention()
# videogen_pipeline.enable_vae_slicing()
if not os.path.exists(args.save_img_path):
os.makedirs(args.save_img_path)
transform_video = video_transforms.Compose([
video_transforms.ToTensorVideo(),
video_transforms.SDXLCenterCrop((args.image_size[0], args.image_size[1])), # center crop using shor edge, then resize
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
])
for i, (image, prompt) in enumerate(args.image_prompts):
if args.use_dct:
base_content = prepare_image("./animated_images/" + image, vae_for_base_content, transform_video, device, dtype=torch.float64).to(device)
else:
base_content = prepare_image("./animated_images/" + image, vae_for_base_content, transform_video, device, dtype=torch.float16).to(device)
if args.use_dct:
# filter params
print("Using DCT!")
base_content_repeat = repeat(base_content, 'b c f h w -> b c (f r) h w', r=15).contiguous()
# define filter
freq_filter = dct_low_pass_filter(dct_coefficients=base_content,
percentage=0.23)
noise = torch.randn(1, 4, 15, 40, 64).to(device)
# add noise to base_content
diffuse_timesteps = torch.full((1,),int(975))
diffuse_timesteps = diffuse_timesteps.long()
# 3d content
base_content_noise = scheduler.add_noise(
original_samples=base_content_repeat.to(device),
noise=noise,
timesteps=diffuse_timesteps.to(device))
# 3d content
latents = exchanged_mixed_dct_freq(noise=noise,
base_content=base_content_noise,
LPF_3d=freq_filter).to(dtype=torch.float16)
base_content = base_content.to(dtype=torch.float16)
videos = videogen_pipeline(prompt,
latents=latents if args.use_dct else None,
base_content=base_content,
video_length=args.video_length,
height=args.image_size[0],
width=args.image_size[1],
num_inference_steps=args.num_sampling_steps,
guidance_scale=args.guidance_scale,
motion_bucket_id=args.motion_bucket_id,
enable_vae_temporal_decoder=args.enable_vae_temporal_decoder).video
imageio.mimwrite(args.save_img_path + prompt.replace(' ', '_') + '_%04d' % i + '_%04d' % args.run_time + '-imageio.mp4', videos[0], fps=8, quality=8) # highest quality is 10, lowest is 0
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/sample.yaml")
args = parser.parse_args()
main(OmegaConf.load(args.config))