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from pathlib import Path
import torch
from ml_collections.config_flags import config_flags
from sde.config import get_config
from sde import ddpm, ncsnv2, ncsnpp # need to import to trigger its registry
from sde import utils as mutils
from sde.ema import ExponentialMovingAverage
from adapt import ScoreAdapter
device = torch.device("cuda")
def restore_checkpoint(ckpt_dir, state, device):
loaded_state = torch.load(ckpt_dir, map_location=device)
# state['optimizer'].load_state_dict(loaded_state['optimizer'])
state['model'].load_state_dict(loaded_state['model'], strict=False)
state['ema'].load_state_dict(loaded_state['ema'])
state['step'] = loaded_state['step']
return state
def save_checkpoint(ckpt_dir, state):
saved_state = {
'optimizer': state['optimizer'].state_dict(),
'model': state['model'].state_dict(),
'ema': state['ema'].state_dict(),
'step': state['step']
}
torch.save(saved_state, ckpt_dir)
class VESDE(ScoreAdapter):
def __init__(self):
config = get_config()
config.device = device
ckpt_fname = self.checkpoint_root() / "sde" / 'checkpoint_127.pth'
score_model = mutils.create_model(config)
ema = ExponentialMovingAverage(
score_model.parameters(), decay=config.model.ema_rate
)
state = dict(model=score_model, ema=ema, step=0)
self._data_shape = (
config.data.num_channels, config.data.image_size, config.data.image_size
)
self._σ_min = float(config.model.sigma_min * 2)
state = restore_checkpoint(ckpt_fname, state, device=config.device)
ema.copy_to(score_model.parameters())
score_model.eval()
score_model = score_model.module # remove DataParallel
self.model = score_model
self._device = device
def data_shape(self):
return self._data_shape
@property
def σ_min(self):
return self._σ_min
@torch.no_grad()
def denoise(self, xs, σ):
N = xs.shape[0]
# see Karras eqn. 212-215 for the 1/2 σ correction
cond_t = (0.5 * σ) * torch.ones(N, device=self.device)
# note that the forward function the model has been modified; see comments
n_hat = self.model(xs, cond_t)
Ds = xs + σ * n_hat
return Ds
def unet_is_cond(self):
return False
def use_cls_guidance(self):
return False
def snap_t_to_nearest_tick(self, t):
return super().snap_t_to_nearest_tick(t)
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