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import torch
from sgm.modules.diffusionmodules.discretizer import Discretization
class Img2ImgDiscretizationWrapper:
"""
wraps a discretizer, and prunes the sigmas
params:
strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
"""
def __init__(self, discretization: Discretization, strength: float = 1.0):
self.discretization = discretization
self.strength = strength
assert 0.0 <= self.strength <= 1.0
def __call__(self, *args, **kwargs):
# sigmas start large first, and decrease then
sigmas = self.discretization(*args, **kwargs)
print(f"sigmas after discretization, before pruning img2img: ", sigmas)
sigmas = torch.flip(sigmas, (0,))
sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
print("prune index:", max(int(self.strength * len(sigmas)), 1))
sigmas = torch.flip(sigmas, (0,))
print(f"sigmas after pruning: ", sigmas)
return sigmas
class Txt2NoisyDiscretizationWrapper:
"""
wraps a discretizer, and prunes the sigmas
params:
strength: float between 0.0 and 1.0. 0.0 means full sampling (all sigmas are returned)
"""
def __init__(
self, discretization: Discretization, strength: float = 0.0, original_steps=None
):
self.discretization = discretization
self.strength = strength
self.original_steps = original_steps
assert 0.0 <= self.strength <= 1.0
def __call__(self, *args, **kwargs):
# sigmas start large first, and decrease then
sigmas = self.discretization(*args, **kwargs)
print(f"sigmas after discretization, before pruning img2img: ", sigmas)
sigmas = torch.flip(sigmas, (0,))
if self.original_steps is None:
steps = len(sigmas)
else:
steps = self.original_steps + 1
prune_index = max(min(int(self.strength * steps) - 1, steps - 1), 0)
sigmas = sigmas[prune_index:]
print("prune index:", prune_index)
sigmas = torch.flip(sigmas, (0,))
print(f"sigmas after pruning: ", sigmas)
return sigmas
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