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import math
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 torchvision.utils import save_image
from transformers import T5EncoderModel, T5Tokenizer, AutoTokenizer
import os, sys
from opensora.models.ae import ae_stride_config, getae, getae_wrapper
from opensora.models.ae.videobase import CausalVQVAEModelWrapper, CausalVAEModelWrapper
from opensora.models.diffusion.latte.modeling_latte import LatteT2V
from opensora.models.text_encoder import get_text_enc
from opensora.utils.utils import save_video_grid
sys.path.append(os.path.split(sys.path[0])[0])
from pipeline_videogen import VideoGenPipeline
import imageio
def main(args):
# torch.manual_seed(args.seed)
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
vae = getae_wrapper(args.ae)(args.model_path, subfolder="vae", cache_dir='cache_dir').to(device, dtype=torch.float16)
if args.enable_tiling:
vae.vae.enable_tiling()
vae.vae.tile_overlap_factor = args.tile_overlap_factor
# Load model:
transformer_model = LatteT2V.from_pretrained(args.model_path, subfolder=args.version, cache_dir="cache_dir", torch_dtype=torch.float16).to(device)
transformer_model.force_images = args.force_images
tokenizer = T5Tokenizer.from_pretrained(args.text_encoder_name, cache_dir="cache_dir")
text_encoder = T5EncoderModel.from_pretrained(args.text_encoder_name, cache_dir="cache_dir", torch_dtype=torch.float16).to(device)
video_length, image_size = transformer_model.config.video_length, int(args.version.split('x')[1])
latent_size = (image_size // ae_stride_config[args.ae][1], image_size // ae_stride_config[args.ae][2])
vae.latent_size = latent_size
if args.force_images:
video_length = 1
ext = 'jpg'
else:
ext = 'mp4'
# set eval mode
transformer_model.eval()
vae.eval()
text_encoder.eval()
if args.sample_method == 'DDIM': #########
scheduler = DDIMScheduler()
elif args.sample_method == 'EulerDiscrete':
scheduler = EulerDiscreteScheduler()
elif args.sample_method == 'DDPM': #############
scheduler = DDPMScheduler()
elif args.sample_method == 'DPMSolverMultistep':
scheduler = DPMSolverMultistepScheduler()
elif args.sample_method == 'DPMSolverSinglestep':
scheduler = DPMSolverSinglestepScheduler()
elif args.sample_method == 'PNDM':
scheduler = PNDMScheduler()
elif args.sample_method == 'HeunDiscrete': ########
scheduler = HeunDiscreteScheduler()
elif args.sample_method == 'EulerAncestralDiscrete':
scheduler = EulerAncestralDiscreteScheduler()
elif args.sample_method == 'DEISMultistep':
scheduler = DEISMultistepScheduler()
elif args.sample_method == 'KDPM2AncestralDiscrete': #########
scheduler = KDPM2AncestralDiscreteScheduler()
print('videogen_pipeline', device)
videogen_pipeline = VideoGenPipeline(vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
transformer=transformer_model).to(device=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 = []
if not isinstance(args.text_prompt, list):
args.text_prompt = [args.text_prompt]
if len(args.text_prompt) == 1 and args.text_prompt[0].endswith('txt'):
text_prompt = open(args.text_prompt[0], 'r').readlines()
args.text_prompt = [i.strip() for i in text_prompt]
for prompt in args.text_prompt:
print('Processing the ({}) prompt'.format(prompt))
videos = videogen_pipeline(prompt,
video_length=video_length,
height=image_size,
width=image_size,
num_inference_steps=args.num_sampling_steps,
guidance_scale=args.guidance_scale,
enable_temporal_attentions=not args.force_images,
num_images_per_prompt=1,
mask_feature=True,
).video
try:
if args.force_images:
videos = videos[:, 0].permute(0, 3, 1, 2) # b t h w c -> b c h w
save_image(videos / 255.0, os.path.join(args.save_img_path,
prompt.replace(' ', '_')[:100] + f'{args.sample_method}_gs{args.guidance_scale}_s{args.num_sampling_steps}.{ext}'),
nrow=1, normalize=True, value_range=(0, 1)) # t c h w
else:
imageio.mimwrite(
os.path.join(
args.save_img_path,
prompt.replace(' ', '_')[:100] + f'{args.sample_method}_gs{args.guidance_scale}_s{args.num_sampling_steps}.{ext}'
), videos[0],
fps=args.fps, quality=9) # highest quality is 10, lowest is 0
except:
print('Error when saving {}'.format(prompt))
video_grids.append(videos)
video_grids = torch.cat(video_grids, dim=0)
# torchvision.io.write_video(args.save_img_path + '_%04d' % args.run_time + '-.mp4', video_grids, fps=6)
if args.force_images:
save_image(video_grids / 255.0, os.path.join(args.save_img_path, f'{args.sample_method}_gs{args.guidance_scale}_s{args.num_sampling_steps}.{ext}'),
nrow=math.ceil(math.sqrt(len(video_grids))), normalize=True, value_range=(0, 1))
else:
video_grids = save_video_grid(video_grids)
imageio.mimwrite(os.path.join(args.save_img_path, f'{args.sample_method}_gs{args.guidance_scale}_s{args.num_sampling_steps}.{ext}'), video_grids, fps=args.fps, quality=9)
print('save path {}'.format(args.save_img_path))
# save_videos_grid(video, f"./{prompt}.gif")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default='LanguageBind/Open-Sora-Plan-v1.0.0')
parser.add_argument("--version", type=str, default='65x512x512', choices=['65x512x512', '65x256x256', '17x256x256'])
parser.add_argument("--ae", type=str, default='CausalVAEModel_4x8x8')
parser.add_argument("--text_encoder_name", type=str, default='DeepFloyd/t5-v1_1-xxl')
parser.add_argument("--save_img_path", type=str, default="./sample_videos/t2v")
parser.add_argument("--guidance_scale", type=float, default=7.5)
parser.add_argument("--sample_method", type=str, default="PNDM")
parser.add_argument("--num_sampling_steps", type=int, default=50)
parser.add_argument("--fps", type=int, default=24)
parser.add_argument("--run_time", type=int, default=0)
parser.add_argument("--text_prompt", nargs='+')
parser.add_argument('--force_images', action='store_true')
parser.add_argument('--tile_overlap_factor', type=float, default=0.25)
parser.add_argument('--enable_tiling', action='store_true')
args = parser.parse_args()
main(args) |