exp_code/1_benchmark/CausVid/causvid/scheduler.py

from abc import abstractmethod, ABC
import torch


class SchedulerInterface(ABC):
    """
    Base class for diffusion noise schedule.
    """
    alphas_cumprod: torch.Tensor  # [T], alphas for defining the noise schedule

    @abstractmethod
    def add_noise(
        self, clean_latent: torch.Tensor,
        noise: torch.Tensor, timestep: torch.Tensor
    ):
        """
        Diffusion forward corruption process.
        Input:
            - clean_latent: the clean latent with shape [B, C, H, W]
            - noise: the noise with shape [B, C, H, W]
            - timestep: the timestep with shape [B]
        Output: the corrupted latent with shape [B, C, H, W]
        """
        pass

    def convert_x0_to_noise(
        self, x0: torch.Tensor, xt: torch.Tensor,
        timestep: torch.Tensor
    ) -> torch.Tensor:
        """
        Convert the diffusion network's x0 prediction to noise predidction.
        x0: the predicted clean data with shape [B, C, H, W]
        xt: the input noisy data with shape [B, C, H, W]
        timestep: the timestep with shape [B]

        noise = (xt-sqrt(alpha_t)*x0) / sqrt(beta_t) (eq 11 in https://arxiv.org/abs/2311.18828)
        """
        # use higher precision for calculations
        original_dtype = x0.dtype
        x0, xt, alphas_cumprod = map(
            lambda x: x.double().to(x0.device), [x0, xt,
                                                 self.alphas_cumprod]
        )

        alpha_prod_t = alphas_cumprod[timestep].reshape(-1, 1, 1, 1)
        beta_prod_t = 1 - alpha_prod_t

        noise_pred = (xt - alpha_prod_t **
                      (0.5) * x0) / beta_prod_t ** (0.5)
        return noise_pred.to(original_dtype)

    def convert_noise_to_x0(
        self, noise: torch.Tensor, xt: torch.Tensor,
        timestep: torch.Tensor
    ) -> torch.Tensor:
        """
        Convert the diffusion network's noise prediction to x0 predidction.
        noise: the predicted noise with shape [B, C, H, W]
        xt: the input noisy data with shape [B, C, H, W]
        timestep: the timestep with shape [B]

        x0 = (x_t - sqrt(beta_t) * noise) / sqrt(alpha_t) (eq 11 in https://arxiv.org/abs/2311.18828)
        """
        # use higher precision for calculations
        original_dtype = noise.dtype
        noise, xt, alphas_cumprod = map(
            lambda x: x.double().to(noise.device), [noise, xt,
                                                    self.alphas_cumprod]
        )
        alpha_prod_t = alphas_cumprod[timestep].reshape(-1, 1, 1, 1)
        beta_prod_t = 1 - alpha_prod_t

        x0_pred = (xt - beta_prod_t **
                   (0.5) * noise) / alpha_prod_t ** (0.5)
        return x0_pred.to(original_dtype)

    def convert_velocity_to_x0(
        self, velocity: torch.Tensor, xt: torch.Tensor,
        timestep: torch.Tensor
    ) -> torch.Tensor:
        """
        Convert the diffusion network's velocity prediction to x0 predidction.
        velocity: the predicted noise with shape [B, C, H, W]
        xt: the input noisy data with shape [B, C, H, W]
        timestep: the timestep with shape [B]

        v = sqrt(alpha_t) * noise - sqrt(beta_t) x0
        noise = (xt-sqrt(alpha_t)*x0) / sqrt(beta_t)
        given v, x_t, we have
        x0 = sqrt(alpha_t) * x_t - sqrt(beta_t) * v
        see derivations https://chatgpt.com/share/679fb6c8-3a30-8008-9b0e-d1ae892dac56
        """
        # use higher precision for calculations
        original_dtype = velocity.dtype
        velocity, xt, alphas_cumprod = map(
            lambda x: x.double().to(velocity.device), [velocity, xt,
                                                       self.alphas_cumprod]
        )
        alpha_prod_t = alphas_cumprod[timestep].reshape(-1, 1, 1, 1)
        beta_prod_t = 1 - alpha_prod_t

        x0_pred = (alpha_prod_t ** 0.5) * xt - (beta_prod_t ** 0.5) * velocity
        return x0_pred.to(original_dtype)