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Running
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Zero
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# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Sample new images from a pre-trained Latte.
"""
import os
import sys
try:
import utils
from diffusion import create_diffusion
from utils import find_model
except:
sys.path.append(os.path.split(sys.path[0])[0])
import utils
from diffusion import create_diffusion
from utils import find_model
import torch
import argparse
import torchvision
from einops import rearrange
from models import get_models
from torchvision.utils import save_image
from diffusers.models import AutoencoderKL
from models.clip import TextEmbedder
import imageio
from omegaconf import OmegaConf
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
def main(args):
# Setup PyTorch:
# torch.manual_seed(args.seed)
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
# device = "cpu"
if args.ckpt is None:
assert args.model == "Latte-XL/2", "Only Latte-XL/2 models are available for auto-download."
assert args.image_size in [256, 512]
assert args.num_classes == 1000
using_cfg = args.cfg_scale > 1.0
# Load model:
latent_size = args.image_size // 8
args.latent_size = latent_size
model = get_models(args).to(device)
if args.use_compile:
model = torch.compile(model)
# a pre-trained model or load a custom Latte checkpoint from train.py:
ckpt_path = args.ckpt
state_dict = find_model(ckpt_path)
model.load_state_dict(state_dict)
model.eval() # important!
diffusion = create_diffusion(str(args.num_sampling_steps))
# vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-{args.vae}").to(device)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae").to(device)
# text_encoder = TextEmbedder().to(device)
if args.use_fp16:
print('WARNING: using half percision for inferencing!')
vae.to(dtype=torch.float16)
model.to(dtype=torch.float16)
# text_encoder.to(dtype=torch.float16)
# Labels to condition the model with (feel free to change):
# Create sampling noise:
if args.use_fp16:
z = torch.randn(1, args.num_frames, 4, latent_size, latent_size, dtype=torch.float16, device=device) # b c f h w
else:
z = torch.randn(1, args.num_frames, 4, latent_size, latent_size, device=device)
# Setup classifier-free guidance:
# z = torch.cat([z, z], 0)
if using_cfg:
z = torch.cat([z, z], 0)
y = torch.randint(0, args.num_classes, (1,), device=device)
y_null = torch.tensor([101] * 1, device=device)
y = torch.cat([y, y_null], dim=0)
model_kwargs = dict(y=y, cfg_scale=args.cfg_scale, use_fp16=args.use_fp16)
sample_fn = model.forward_with_cfg
else:
sample_fn = model.forward
model_kwargs = dict(y=None, use_fp16=args.use_fp16)
# Sample images:
if args.sample_method == 'ddim':
samples = diffusion.ddim_sample_loop(
sample_fn, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device
)
elif args.sample_method == 'ddpm':
samples = diffusion.p_sample_loop(
sample_fn, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device
)
print(samples.shape)
if args.use_fp16:
samples = samples.to(dtype=torch.float16)
b, f, c, h, w = samples.shape
samples = rearrange(samples, 'b f c h w -> (b f) c h w')
samples = vae.decode(samples / 0.18215).sample
samples = rearrange(samples, '(b f) c h w -> b f c h w', b=b)
# Save and display images:
if not os.path.exists(args.save_video_path):
os.makedirs(args.save_video_path)
video_ = ((samples[0] * 0.5 + 0.5) * 255).add_(0.5).clamp_(0, 255).to(dtype=torch.uint8).cpu().permute(0, 2, 3, 1).contiguous()
video_save_path = os.path.join(args.save_video_path, 'sample' + '.mp4')
print(video_save_path)
imageio.mimwrite(video_save_path, video_, fps=8, quality=9)
print('save path {}'.format(args.save_video_path))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/ucf101/ucf101_sample.yaml")
parser.add_argument("--ckpt", type=str, default="")
parser.add_argument("--save_video_path", type=str, default="./sample_videos/")
args = parser.parse_args()
omega_conf = OmegaConf.load(args.config)
omega_conf.ckpt = args.ckpt
omega_conf.save_video_path = args.save_video_path
main(omega_conf)
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