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from enum import Enum
import math
import torch
from k_diffusion.sampling import to_d
def clamp(x: int | float, lower: int | float, upper: int | float) -> int | float:
return max(lower, min(x, upper))
# From ComfyUI
def default_noise_sampler(x, seed=None):
"""
Default noise sampler for the extended reverse SDE solver.
Generates Gaussian noise based on the input tensor's shape and device.
If a seed is provided, it uses that seed for reproducibility.
"""
if seed is not None:
generator = torch.Generator(device=x.device)
generator.manual_seed(seed)
else:
generator = None
return lambda sigma, sigma_next: torch.randn(
x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator
)
class _Rescaler:
def __init__(self, model, x, mode, **extra_args):
self.model = model
self.x = x
self.mode = mode
self.extra_args = extra_args
self.init_latent, self.mask, self.nmask = model.init_latent, model.mask, model.nmask
def __enter__(self):
if self.init_latent is not None:
self.model.init_latent = torch.nn.functional.interpolate(
input=self.init_latent, size=self.x.shape[2:4], mode=self.mode
)
if self.mask is not None:
self.model.mask = torch.nn.functional.interpolate(
input=self.mask.unsqueeze(0), size=self.x.shape[2:4], mode=self.mode
).squeeze(0)
if self.nmask is not None:
self.model.nmask = torch.nn.functional.interpolate(
input=self.nmask.unsqueeze(0), size=self.x.shape[2:4], mode=self.mode
).squeeze(0)
return self
def __exit__(self, type, value, traceback):
del self.model.init_latent, self.model.mask, self.model.nmask
self.model.init_latent, self.model.mask, self.model.nmask = self.init_latent, self.mask, self.nmask
@torch.no_grad()
def overall_sampling_step(x, model, dt, sigma_hat, **extra_args):
original_shape = x.shape
batch_size, channels, m, n = original_shape[0], original_shape[1], original_shape[2] // 2, original_shape[3] // 2
extra_row = x.shape[2] % 2 == 1
extra_col = x.shape[3] % 2 == 1
if extra_row:
extra_row_content = x[:, :, -1:, :]
x = x[:, :, :-1, :]
if extra_col:
extra_col_content = x[:, :, :, -1:]
x = x[:, :, :, :-1]
a_list = x.unfold(2, 2, 2).unfold(3, 2, 2).contiguous().view(batch_size, channels, m * n, 2, 2)
c = a_list[:, :, :, 1, 1].view(batch_size, channels, m, n)
denoised = model(c, sigma_hat * c.new_ones([c.shape[0]]), **extra_args)
d = to_d(c, sigma_hat, denoised)
c = c + d * dt
d_list = denoised.view(batch_size, channels, m * n, 1, 1)
a_list[:, :, :, 1, 1] = d_list[:, :, :, 0, 0]
x = (
a_list.view(batch_size, channels, m, n, 2, 2)
.permute(0, 1, 2, 4, 3, 5)
.reshape(batch_size, channels, 2 * m, 2 * n)
)
if extra_row or extra_col:
x_expanded = torch.zeros(original_shape, dtype=x.dtype, device=x.device)
x_expanded[:, :, : 2 * m, : 2 * n] = x
if extra_row:
x_expanded[:, :, -1:, : 2 * n + 1] = extra_row_content
if extra_col:
x_expanded[:, :, : 2 * m, -1:] = extra_col_content
if extra_row and extra_col:
x_expanded[:, :, -1:, -1:] = extra_col_content[:, :, -1:, :]
x = x_expanded
return x
@torch.no_grad()
def smea_sampling_step(x, model, dt, sigma_hat, **extra_args):
m, n = x.shape[2], x.shape[3]
x = torch.nn.functional.interpolate(input=x, scale_factor=(1.25, 1.25), mode="nearest-exact")
with _Rescaler(model, x, "nearest-exact", **extra_args) as rescaler:
denoised = model(x, sigma_hat * x.new_ones([x.shape[0]]), **rescaler.extra_args)
d = to_d(x, sigma_hat, denoised)
x = x + d * dt
x = torch.nn.functional.interpolate(input=x, size=(m, n), mode="nearest-exact")
return x
@torch.no_grad()
def dy_sampling_step(x, model, dt, sigma_hat, **extra_args):
original_shape = x.shape
batch_size, channels, m, n = original_shape[0], original_shape[1], original_shape[2] // 2, original_shape[3] // 2
extra_row = x.shape[2] % 2 == 1
extra_col = x.shape[3] % 2 == 1
if extra_row:
extra_row_content = x[:, :, -1:, :]
x = x[:, :, :-1, :]
if extra_col:
extra_col_content = x[:, :, :, -1:]
x = x[:, :, :, :-1]
a_list = x.unfold(2, 2, 2).unfold(3, 2, 2).contiguous().view(batch_size, channels, m * n, 2, 2)
c = a_list[:, :, :, 1, 1].view(batch_size, channels, m, n)
with _Rescaler(model, c, "nearest-exact", **extra_args) as rescaler:
denoised = model(c, sigma_hat * c.new_ones([c.shape[0]]), **rescaler.extra_args)
d = to_d(c, sigma_hat, denoised)
c = c + d * dt
d_list = c.view(batch_size, channels, m * n, 1, 1)
a_list[:, :, :, 1, 1] = d_list[:, :, :, 0, 0]
x = (
a_list.view(batch_size, channels, m, n, 2, 2)
.permute(0, 1, 2, 4, 3, 5)
.reshape(batch_size, channels, 2 * m, 2 * n)
)
if extra_row or extra_col:
x_expanded = torch.zeros(original_shape, dtype=x.dtype, device=x.device)
x_expanded[:, :, : 2 * m, : 2 * n] = x
if extra_row:
x_expanded[:, :, -1:, : 2 * n + 1] = extra_row_content
if extra_col:
x_expanded[:, :, : 2 * m, -1:] = extra_col_content
if extra_row and extra_col:
x_expanded[:, :, -1:, -1:] = extra_col_content[:, :, -1:, :]
x = x_expanded
return x
def sampler_metadata(name: str, extra_params: dict = {}, sampler_aliases: list[str] = []):
def decorator(func):
func.sampler_extra_params = extra_params
func.sampler_name = name
func.sampler_k_names = [name.replace(" ", "_").lower(), *sampler_aliases]
return func
return decorator
def scheduler_metadata(name: str, alias: str, need_inner_model: bool = False):
def decorator(func):
func.name = name
func.alias = alias
func.need_inner_model = need_inner_model
return func
return decorator
class Interpolator(Enum):
LINEAR = (lambda x: x,) # noqa: E731
COSINE = (lambda x: torch.sin(x * math.pi / 2),) # noqa: E731
SINE = (lambda x: 1 - torch.cos(x * math.pi / 2),) # noqa: E731
# Original Implementation `ExtendIntermediateSigmas` by catboxanon: https://www.github.com/catboxanon/
# Original class impl: https://github.com/comfyanonymous/ComfyUI/blob/065d855f14968406051a1340e3f2f26461a00e5d/comfy_extras/nodes_custom_sampler.py#L253
def extend_sigmas(
sigmas: torch.Tensor,
steps: int,
start_at_sigma: float,
end_at_sigma: float,
interpolator: Interpolator = Interpolator.LINEAR,
) -> torch.FloatTensor:
if start_at_sigma < 0:
start_at_sigma = float("inf")
# linear space for our interpolation function
x = torch.linspace(0, 1, steps + 1, device=sigmas.device)[1:-1]
computed_spacing: torch.Tensor = interpolator.value[0](x)
extended_sigmas: list[torch.Tensor] = []
for i in range(len(sigmas) - 1):
sigma_current = sigmas[i]
sigma_next = sigmas[i + 1]
extended_sigmas.append(sigma_current)
if end_at_sigma <= sigma_current <= start_at_sigma:
interpolated_steps: torch.Tensor = computed_spacing * (sigma_next - sigma_current) + sigma_current
extended_sigmas.extend(interpolated_steps.tolist())
# Add the last sigma value
if len(sigmas) > 0:
extended_sigmas.append(sigmas[-1])
return torch.FloatTensor(extended_sigmas)
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