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# 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 SiT.
"""
import os
import sys
from opensora.dataset import ae_denorm
from opensora.models.ae import ae_channel_config, getae, ae_stride_config
from opensora.models.diffusion import Diffusion_models
from opensora.models.diffusion.transport import create_transport, Sampler
from opensora.utils.utils import find_model
import torch
import argparse
from einops import rearrange
import imageio
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
def main(mode, args):
# Setup PyTorch:
# torch.manual_seed(args.seed)
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
using_cfg = args.cfg_scale > 1.0
# Load model:
latent_size = args.image_size // ae_stride_config[args.ae][1]
args.latent_size = latent_size
model = Diffusion_models[args.model](
input_size=latent_size,
num_classes=args.num_classes,
in_channels=ae_channel_config[args.ae],
extras=args.extras
).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!
transport = create_transport(
args.path_type,
args.prediction,
args.loss_weight,
args.train_eps,
args.sample_eps
)
sampler = Sampler(transport)
if mode == "ODE":
if args.likelihood:
assert args.cfg_scale == 1, "Likelihood is incompatible with guidance"
sample_fn = sampler.sample_ode_likelihood(
sampling_method=args.sampling_method,
num_steps=args.num_sampling_steps,
atol=args.atol,
rtol=args.rtol,
)
else:
sample_fn = sampler.sample_ode(
sampling_method=args.sampling_method,
num_steps=args.num_sampling_steps,
atol=args.atol,
rtol=args.rtol,
reverse=args.reverse
)
elif mode == "SDE":
sample_fn = sampler.sample_sde(
sampling_method=args.sampling_method,
diffusion_form=args.diffusion_form,
diffusion_norm=args.diffusion_norm,
last_step=args.last_step,
last_step_size=args.last_step_size,
num_steps=args.num_sampling_steps,
)
ae = getae(args).to(device)
if args.use_fp16:
print('WARNING: using half percision for inferencing!')
ae.to(dtype=torch.float16)
model.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 // ae_stride_config[args.ae][0], model.in_channels, latent_size, latent_size, dtype=torch.float16, device=device) # b c f h w
else:
z = torch.randn(1, args.num_frames // ae_stride_config[args.ae][0], model.in_channels, latent_size, latent_size, device=device)
# Setup classifier-free guidance:
if using_cfg:
z = torch.cat([z, z], 0)
y = torch.randint(0, args.num_classes, (1,), device=device)
y_null = torch.tensor([args.num_classes] * 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)
forward_fn = model.forward_with_cfg
else:
forward_fn = model.forward
model_kwargs = dict(y=None, use_fp16=args.use_fp16)
# Sample images:
samples = sample_fn(z, forward_fn, **model_kwargs)[-1]
if args.use_fp16:
samples = samples.to(dtype=torch.float16)
samples = ae.decode(samples)
# Save and display images:
if not os.path.exists(args.save_video_path):
os.makedirs(args.save_video_path)
video_ = (ae_denorm[args.ae](samples[0]) * 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=args.fps, quality=9)
print('save path {}'.format(args.save_video_path))
def none_or_str(value):
if value == 'None':
return None
return value
def parse_transport_args(parser):
group = parser.add_argument_group("Transport arguments")
group.add_argument("--path-type", type=str, default="Linear", choices=["Linear", "GVP", "VP"])
group.add_argument("--prediction", type=str, default="velocity", choices=["velocity", "score", "noise"])
group.add_argument("--loss-weight", type=none_or_str, default=None, choices=[None, "velocity", "likelihood"])
group.add_argument("--sample-eps", type=float)
group.add_argument("--train-eps", type=float)
def parse_ode_args(parser):
group = parser.add_argument_group("ODE arguments")
group.add_argument("--sampling-method", type=str, default="dopri5", help="blackbox ODE solver methods; for full list check https://github.com/rtqichen/torchdiffeq")
group.add_argument("--atol", type=float, default=1e-6, help="Absolute tolerance")
group.add_argument("--rtol", type=float, default=1e-3, help="Relative tolerance")
group.add_argument("--reverse", action="store_true")
group.add_argument("--likelihood", action="store_true")
def parse_sde_args(parser):
group = parser.add_argument_group("SDE arguments")
group.add_argument("--sampling-method", type=str, default="Euler", choices=["Euler", "Heun"])
group.add_argument("--diffusion-form", type=str, default="sigma", \
choices=["constant", "SBDM", "sigma", "linear", "decreasing", "increasing-decreasing"],\
help="form of diffusion coefficient in the SDE")
group.add_argument("--diffusion-norm", type=float, default=1.0)
group.add_argument("--last-step", type=none_or_str, default="Mean", choices=[None, "Mean", "Tweedie", "Euler"],\
help="form of last step taken in the SDE")
group.add_argument("--last-step-size", type=float, default=0.04, \
help="size of the last step taken")
if __name__ == "__main__":
if len(sys.argv) < 2:
print("Usage: program.py <mode> [options]")
sys.exit(1)
mode = sys.argv[1]
assert mode[:2] != "--", "Usage: program.py <mode> [options]"
assert mode in ["ODE", "SDE"], "Invalid mode. Please choose 'ODE' or 'SDE'"
parser = argparse.ArgumentParser()
parser.add_argument("--ckpt", type=str, default="")
parser.add_argument("--model", type=str, default='Latte-XL/122')
parser.add_argument("--ae", type=str, default='stabilityai/sd-vae-ft-mse')
parser.add_argument("--save-video-path", type=str, default="./sample_videos/")
parser.add_argument("--fps", type=int, default=10)
parser.add_argument("--num-classes", type=int, default=101)
parser.add_argument("--num-frames", type=int, default=16)
parser.add_argument("--image-size", type=int, default=256, choices=[256, 512])
parser.add_argument("--extras", type=int, default=1)
parser.add_argument("--num-sampling-steps", type=int, default=250)
parser.add_argument("--cfg-scale", type=float, default=1.0)
parser.add_argument("--use-fp16", action="store_true")
parser.add_argument("--use-compile", action="store_true")
parser.add_argument("--sample-method", type=str, default='ddpm')
parse_transport_args(parser)
if mode == "ODE":
parse_ode_args(parser)
# Further processing for ODE
elif mode == "SDE":
parse_sde_args(parser)
# Further processing for SDE
args = parser.parse_known_args()[0]
main(mode, args)
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