| 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 = 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 = 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 |
|
|
