204 lines
7.8 KiB
Python
204 lines
7.8 KiB
Python
# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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Sample new images from a pre-trained SiT.
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"""
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import os
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import sys
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from opensora.dataset import ae_denorm
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from opensora.models.ae import ae_channel_config, getae, ae_stride_config
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from opensora.models.diffusion import Diffusion_models
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from opensora.models.diffusion.transport import create_transport, Sampler
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from opensora.utils.utils import find_model
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import torch
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import argparse
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from einops import rearrange
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import imageio
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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def main(mode, args):
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# Setup PyTorch:
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# torch.manual_seed(args.seed)
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torch.set_grad_enabled(False)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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using_cfg = args.cfg_scale > 1.0
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# Load model:
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latent_size = args.image_size // ae_stride_config[args.ae][1]
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args.latent_size = latent_size
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model = Diffusion_models[args.model](
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input_size=latent_size,
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num_classes=args.num_classes,
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in_channels=ae_channel_config[args.ae],
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extras=args.extras
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).to(device)
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if args.use_compile:
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model = torch.compile(model)
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# a pre-trained model or load a custom Latte checkpoint from train.py:
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ckpt_path = args.ckpt
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state_dict = find_model(ckpt_path)
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model.load_state_dict(state_dict)
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model.eval() # important!
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transport = create_transport(
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args.path_type,
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args.prediction,
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args.loss_weight,
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args.train_eps,
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args.sample_eps
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)
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sampler = Sampler(transport)
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if mode == "ODE":
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if args.likelihood:
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assert args.cfg_scale == 1, "Likelihood is incompatible with guidance"
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sample_fn = sampler.sample_ode_likelihood(
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sampling_method=args.sampling_method,
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num_steps=args.num_sampling_steps,
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atol=args.atol,
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rtol=args.rtol,
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)
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else:
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sample_fn = sampler.sample_ode(
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sampling_method=args.sampling_method,
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num_steps=args.num_sampling_steps,
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atol=args.atol,
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rtol=args.rtol,
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reverse=args.reverse
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)
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elif mode == "SDE":
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sample_fn = sampler.sample_sde(
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sampling_method=args.sampling_method,
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diffusion_form=args.diffusion_form,
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diffusion_norm=args.diffusion_norm,
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last_step=args.last_step,
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last_step_size=args.last_step_size,
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num_steps=args.num_sampling_steps,
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)
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ae = getae(args).to(device)
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if args.use_fp16:
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print('WARNING: using half percision for inferencing!')
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ae.to(dtype=torch.float16)
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model.to(dtype=torch.float16)
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# Labels to condition the model with (feel free to change):
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# Create sampling noise:
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if args.use_fp16:
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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
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else:
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z = torch.randn(1, args.num_frames // ae_stride_config[args.ae][0], model.in_channels, latent_size, latent_size, device=device)
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# Setup classifier-free guidance:
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if using_cfg:
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z = torch.cat([z, z], 0)
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y = torch.randint(0, args.num_classes, (1,), device=device)
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y_null = torch.tensor([args.num_classes] * 1, device=device)
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y = torch.cat([y, y_null], dim=0)
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model_kwargs = dict(y=y, cfg_scale=args.cfg_scale, use_fp16=args.use_fp16)
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forward_fn = model.forward_with_cfg
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else:
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forward_fn = model.forward
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model_kwargs = dict(y=None, use_fp16=args.use_fp16)
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# Sample images:
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samples = sample_fn(z, forward_fn, **model_kwargs)[-1]
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if args.use_fp16:
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samples = samples.to(dtype=torch.float16)
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samples = ae.decode(samples)
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# Save and display images:
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if not os.path.exists(args.save_video_path):
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os.makedirs(args.save_video_path)
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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()
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video_save_path = os.path.join(args.save_video_path, 'sample' + '.mp4')
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print(video_save_path)
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imageio.mimwrite(video_save_path, video_, fps=args.fps, quality=9)
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print('save path {}'.format(args.save_video_path))
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def none_or_str(value):
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if value == 'None':
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return None
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return value
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def parse_transport_args(parser):
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group = parser.add_argument_group("Transport arguments")
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group.add_argument("--path-type", type=str, default="Linear", choices=["Linear", "GVP", "VP"])
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group.add_argument("--prediction", type=str, default="velocity", choices=["velocity", "score", "noise"])
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group.add_argument("--loss-weight", type=none_or_str, default=None, choices=[None, "velocity", "likelihood"])
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group.add_argument("--sample-eps", type=float)
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group.add_argument("--train-eps", type=float)
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def parse_ode_args(parser):
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group = parser.add_argument_group("ODE arguments")
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group.add_argument("--sampling-method", type=str, default="dopri5", help="blackbox ODE solver methods; for full list check https://github.com/rtqichen/torchdiffeq")
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group.add_argument("--atol", type=float, default=1e-6, help="Absolute tolerance")
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group.add_argument("--rtol", type=float, default=1e-3, help="Relative tolerance")
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group.add_argument("--reverse", action="store_true")
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group.add_argument("--likelihood", action="store_true")
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def parse_sde_args(parser):
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group = parser.add_argument_group("SDE arguments")
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group.add_argument("--sampling-method", type=str, default="Euler", choices=["Euler", "Heun"])
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group.add_argument("--diffusion-form", type=str, default="sigma", \
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choices=["constant", "SBDM", "sigma", "linear", "decreasing", "increasing-decreasing"],\
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help="form of diffusion coefficient in the SDE")
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group.add_argument("--diffusion-norm", type=float, default=1.0)
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group.add_argument("--last-step", type=none_or_str, default="Mean", choices=[None, "Mean", "Tweedie", "Euler"],\
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help="form of last step taken in the SDE")
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group.add_argument("--last-step-size", type=float, default=0.04, \
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help="size of the last step taken")
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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print("Usage: program.py <mode> [options]")
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sys.exit(1)
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mode = sys.argv[1]
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assert mode[:2] != "--", "Usage: program.py <mode> [options]"
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assert mode in ["ODE", "SDE"], "Invalid mode. Please choose 'ODE' or 'SDE'"
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parser = argparse.ArgumentParser()
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parser.add_argument("--ckpt", type=str, default="")
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parser.add_argument("--model", type=str, default='Latte-XL/122')
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parser.add_argument("--ae", type=str, default='stabilityai/sd-vae-ft-mse')
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parser.add_argument("--save-video-path", type=str, default="./sample_videos/")
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parser.add_argument("--fps", type=int, default=10)
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parser.add_argument("--num-classes", type=int, default=101)
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parser.add_argument("--num-frames", type=int, default=16)
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parser.add_argument("--image-size", type=int, default=256, choices=[256, 512])
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parser.add_argument("--extras", type=int, default=1)
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parser.add_argument("--num-sampling-steps", type=int, default=250)
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parser.add_argument("--cfg-scale", type=float, default=1.0)
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parser.add_argument("--use-fp16", action="store_true")
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parser.add_argument("--use-compile", action="store_true")
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parser.add_argument("--sample-method", type=str, default='ddpm')
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parse_transport_args(parser)
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if mode == "ODE":
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parse_ode_args(parser)
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# Further processing for ODE
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elif mode == "SDE":
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parse_sde_args(parser)
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# Further processing for SDE
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args = parser.parse_known_args()[0]
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main(mode, args)
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