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Zero
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import os
import torch
import argparse
import torchvision
from diffusers.schedulers import (DDIMScheduler, DDPMScheduler, PNDMScheduler,
EulerDiscreteScheduler, DPMSolverMultistepScheduler,
HeunDiscreteScheduler, EulerAncestralDiscreteScheduler,
DEISMultistepScheduler, KDPM2AncestralDiscreteScheduler)
from diffusers.schedulers.scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from diffusers.models import AutoencoderKL, AutoencoderKLTemporalDecoder
from omegaconf import OmegaConf
from transformers import T5EncoderModel, T5Tokenizer
import os, sys
sys.path.append(os.path.split(sys.path[0])[0])
from pipeline_latte import LattePipeline
from models import get_models
from utils import save_video_grid
import imageio
from torchvision.utils import save_image
def main(args):
# torch.manual_seed(args.seed)
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
transformer_model = get_models(args).to(device, dtype=torch.float16)
if args.enable_vae_temporal_decoder:
vae = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float16).to(device)
else:
vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae", torch_dtype=torch.float16).to(device)
tokenizer = T5Tokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
text_encoder = T5EncoderModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder", torch_dtype=torch.float16).to(device)
# set eval mode
transformer_model.eval()
vae.eval()
text_encoder.eval()
if args.sample_method == 'DDIM':
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,
variance_type=args.variance_type,
clip_sample=False)
elif args.sample_method == 'EulerDiscrete':
scheduler = EulerDiscreteScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif args.sample_method == 'DDPM':
scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
clip_sample=False)
elif args.sample_method == 'DPMSolverMultistep':
scheduler = DPMSolverMultistepScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif args.sample_method == 'DPMSolverSinglestep':
scheduler = DPMSolverSinglestepScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif args.sample_method == 'PNDM':
scheduler = PNDMScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif args.sample_method == 'HeunDiscrete':
scheduler = HeunDiscreteScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif args.sample_method == 'EulerAncestralDiscrete':
scheduler = EulerAncestralDiscreteScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif args.sample_method == 'DEISMultistep':
scheduler = DEISMultistepScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif args.sample_method == 'KDPM2AncestralDiscrete':
scheduler = KDPM2AncestralDiscreteScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
videogen_pipeline = LattePipeline(vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
transformer=transformer_model).to(device)
# videogen_pipeline.enable_xformers_memory_efficient_attention()
if not os.path.exists(args.save_img_path):
os.makedirs(args.save_img_path)
# video_grids = []
for num_prompt, prompt in enumerate(args.text_prompt):
print('Processing the ({}) prompt'.format(prompt))
videos = videogen_pipeline(prompt,
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,
enable_temporal_attentions=args.enable_temporal_attentions,
num_images_per_prompt=1,
mask_feature=True,
enable_vae_temporal_decoder=args.enable_vae_temporal_decoder
).video
if videos.shape[1] == 1:
try:
save_image(videos[0][0], args.save_img_path + prompt.replace(' ', '_') + '.png')
except:
save_image(videos[0][0], args.save_img_path + str(num_prompt)+ '.png')
print('Error when saving {}'.format(prompt))
else:
try:
imageio.mimwrite(args.save_img_path + prompt.replace(' ', '_') + '_%04d' % args.run_time + '.mp4', videos[0], fps=8, quality=9) # highest quality is 10, lowest is 0
except:
print('Error when saving {}'.format(prompt))
# save video grid
# video_grids.append(videos)
# video_grids = torch.cat(video_grids, dim=0)
# video_grids = save_video_grid(video_grids)
# # torchvision.io.write_video(args.save_img_path + '_%04d' % args.run_time + '-.mp4', video_grids, fps=6)
# imageio.mimwrite(args.save_img_path + '_%04d' % args.run_time + '-.mp4', video_grids, fps=8, quality=6)
# print('save path {}'.format(args.save_img_path))
# save_videos_grid(video, f"./{prompt}.gif")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/wbv10m_train.yaml")
args = parser.parse_args()
main(OmegaConf.load(args.config))
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