(self, sigma, next_sigma, denoiser, x, cond, uc=None, s_noise=0.0)
| 132 | self.s_noise = s_noise |
| 133 | |
| 134 | def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, s_noise=0.0): |
| 135 | denoised = self.denoise(x, denoiser, sigma, cond, uc) |
| 136 | d = to_d(x, sigma, denoised) |
| 137 | dt = append_dims(next_sigma * (1 - s_noise**2) ** 0.5 - sigma, x.ndim) |
| 138 | |
| 139 | euler_step = x + dt * d + s_noise * append_dims(next_sigma, x.ndim) * torch.randn_like(x) |
| 140 | |
| 141 | x = self.possible_correction_step(euler_step, x, d, dt, next_sigma, denoiser, cond, uc) |
| 142 | return x |
| 143 | |
| 144 | def __call__(self, denoiser, x, cond, uc=None, num_steps=None): |
| 145 | x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) |
no test coverage detected