gdf/samplers.py

import torch

class SimpleSampler():
    def __init__(self, gdf):
        self.gdf = gdf
        self.current_step = -1

    def __call__(self, *args, **kwargs):
        self.current_step += 1
        return self.step(*args, **kwargs)

    def init_x(self, shape):
        return torch.randn(*shape)

    def step(self, x, x0, epsilon, logSNR, logSNR_prev):
        raise NotImplementedError("You should override the 'apply' function.")

class DDIMSampler(SimpleSampler):
    def step(self, x, x0, epsilon, logSNR, logSNR_prev, eta=0):
        a, b = self.gdf.input_scaler(logSNR)
        if len(a.shape) == 1:
            a, b = a.view(-1, *[1]*(len(x0.shape)-1)), b.view(-1, *[1]*(len(x0.shape)-1))

        a_prev, b_prev = self.gdf.input_scaler(logSNR_prev)
        if len(a_prev.shape) == 1:
            a_prev, b_prev = a_prev.view(-1, *[1]*(len(x0.shape)-1)), b_prev.view(-1, *[1]*(len(x0.shape)-1))

        sigma_tau = eta * (b_prev**2 / b**2).sqrt() * (1 - a**2 / a_prev**2).sqrt() if eta > 0 else 0
        # x = a_prev * x0 + (1 - a_prev**2 - sigma_tau ** 2).sqrt() * epsilon + sigma_tau * torch.randn_like(x0)
        x = a_prev * x0 + (b_prev**2 - sigma_tau**2).sqrt() * epsilon + sigma_tau * torch.randn_like(x0)
        return x

class DDPMSampler(DDIMSampler):
    def step(self, x, x0, epsilon, logSNR, logSNR_prev, eta=1):
        return super().step(x, x0, epsilon, logSNR, logSNR_prev, eta)

class LCMSampler(SimpleSampler):
    def step(self, x, x0, epsilon, logSNR, logSNR_prev):        
        a_prev, b_prev = self.gdf.input_scaler(logSNR_prev)
        if len(a_prev.shape) == 1:
            a_prev, b_prev = a_prev.view(-1, *[1]*(len(x0.shape)-1)), b_prev.view(-1, *[1]*(len(x0.shape)-1))
        return x0 * a_prev + torch.randn_like(epsilon) * b_prev