79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
from typing import overload, Optional
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import torch
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from torch.nn import functional as F
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class Guidance:
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def __init__(
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self,
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scale: float,
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t_start: int,
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t_stop: int,
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space: str,
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repeat: int
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) -> "Guidance":
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"""
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Initialize latent image guidance.
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Args:
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scale (float): Gradient scale (denoted as `s` in our paper). The larger the gradient scale,
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the closer the final result will be to the output of the first stage model.
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t_start (int), t_stop (int): The timestep to start or stop guidance. Note that the sampling
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process starts from t=1000 to t=0, the `t_start` should be larger than `t_stop`.
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space (str): The data space for computing loss function (rgb or latent).
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repeat (int): Repeat gradient descent for `repeat` times.
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Our latent image guidance is based on [GDP](https://github.com/Fayeben/GenerativeDiffusionPrior).
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Thanks for their work!
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"""
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self.scale = scale
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self.t_start = t_start
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self.t_stop = t_stop
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self.target = None
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self.space = space
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self.repeat = repeat
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def load_target(self, target: torch.Tensor) -> torch.Tensor:
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self.target = target
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def __call__(self, target_x0: torch.Tensor, pred_x0: torch.Tensor, t: int) -> Optional[torch.Tensor]:
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if self.t_stop < t and t < self.t_start:
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# print("sampling with classifier guidance")
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# avoid propagating gradient out of this scope
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pred_x0 = pred_x0.detach().clone()
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target_x0 = target_x0.detach().clone()
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return self.scale * self._forward(target_x0, pred_x0)
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else:
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return None
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@overload
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def _forward(self, target_x0: torch.Tensor, pred_x0: torch.Tensor) -> torch.Tensor:
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...
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class MSEGuidance(Guidance):
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def __init__(
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self,
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scale: float,
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t_start: int,
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t_stop: int,
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space: str,
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repeat: int
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) -> "MSEGuidance":
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super().__init__(
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scale, t_start, t_stop, space, repeat
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)
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@torch.enable_grad()
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def _forward(self, target_x0: torch.Tensor, pred_x0: torch.Tensor) -> torch.Tensor:
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# inputs: [-1, 1], nchw, rgb
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pred_x0.requires_grad_(True)
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# This is what we actually use.
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loss = (pred_x0 - target_x0).pow(2).mean((1, 2, 3)).sum()
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print(f"loss = {loss.item()}")
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return -torch.autograd.grad(loss, pred_x0)[0]
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