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Running
on
Zero
File size: 6,330 Bytes
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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))
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