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"""SAMPLING ONLY."""
import torch
from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC
from modules import shared, devices
class UniPCSampler(object):
def __init__(self, model, **kwargs):
super().__init__()
self.model = model
to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
self.before_sample = None
self.after_sample = None
self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != devices.device:
attr = attr.to(devices.device)
setattr(self, name, attr)
def set_hooks(self, before_sample, after_sample, after_update):
self.before_sample = before_sample
self.after_sample = after_sample
self.after_update = after_update
@torch.no_grad()
def sample(self,
S,
batch_size,
shape,
conditioning=None,
callback=None,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):
ctmp = conditioning[list(conditioning.keys())[0]]
while isinstance(ctmp, list): ctmp = ctmp[0]
cbs = ctmp.shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
elif isinstance(conditioning, list):
for ctmp in conditioning:
if ctmp.shape[0] != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
print(f'Data shape for UniPC sampling is {size}')
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)
# SD 1.X is "noise", SD 2.X is "v"
model_type = "v" if self.model.parameterization == "v" else "noise"
model_fn = model_wrapper(
lambda x, t, c: self.model.apply_model(x, t, c),
ns,
model_type=model_type,
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, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
return x.to(device), None
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