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