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Running
on
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Running
on
Zero
File size: 1,834 Bytes
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"""SAMPLING ONLY."""
import torch
from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC
class UniPCSampler(object):
def __init__(self, model, **kwargs):
super().__init__()
self.model = model
def to_torch(x):
return x.clone().detach().to(torch.float32).to(model.device)
self.register_buffer("alphas_cumprod", to_torch(model.alphas_cumprod))
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
@torch.no_grad()
def sample(
self,
S,
batch_size,
shape,
conditioning=None,
x_T=None,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
):
# sampling
T, C, H, W = shape
size = (batch_size, T, C, H, W)
device = self.model.betas.device
if x_T is None:
img = torch.randn(size, device=device)
else:
img = x_T
ns = NoiseScheduleVP("discrete", alphas_cumprod=self.alphas_cumprod)
model_fn = model_wrapper(
lambda x, t, c: self.model.apply_model(x, t, c),
ns,
model_type="v",
guidance_type="classifier-free",
condition=conditioning,
unconditional_condition=unconditional_conditioning,
guidance_scale=unconditional_guidance_scale,
)
uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False)
x = uni_pc.sample(
img,
steps=S,
skip_type="time_uniform",
method="multistep",
order=2,
lower_order_final=True,
)
return x.to(device), None
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