| import torch |
|
|
| from src.diffusion.base.scheduling import * |
| from src.diffusion.base.sampling import * |
| from typing import Callable |
|
|
| def ode_step_fn(x, eps, beta, sigma, dt): |
| return x + (-0.5*beta*x + 0.5*eps*beta/sigma)*dt |
|
|
| def sde_step_fn(x, eps, beta, sigma, dt): |
| return x + (-0.5*beta*x + eps*beta/sigma)*dt + torch.sqrt(dt.abs()*beta)*torch.randn_like(x) |
|
|
| import logging |
| logger = logging.getLogger(__name__) |
|
|
| class VPEulerSampler(BaseSampler): |
| def __init__( |
| self, |
| train_max_t=1000, |
| guidance_fn: Callable = None, |
| step_fn: Callable = ode_step_fn, |
| last_step=None, |
| last_step_fn: Callable = ode_step_fn, |
| *args, |
| **kwargs |
| ): |
| super().__init__(*args, **kwargs) |
| self.guidance_fn = guidance_fn |
| self.step_fn = step_fn |
| self.last_step = last_step |
| self.last_step_fn = last_step_fn |
| self.train_max_t = train_max_t |
|
|
| if self.last_step is None or self.num_steps == 1: |
| self.last_step = 1.0 / self.num_steps |
| assert self.last_step > 0.0 |
| assert self.scheduler is not None |
|
|
| def _impl_sampling(self, net, noise, condition, uncondition): |
| batch_size = noise.shape[0] |
| steps = torch.linspace(1.0, self.last_step, self.num_steps, device=noise.device) |
| steps = torch.cat([steps, torch.tensor([0.0], device=noise.device)], dim=0) |
| cfg_condition = torch.cat([uncondition, condition], dim=0) |
| x = noise |
| x_trajs = [noise, ] |
| eps_trajs = [] |
| for i, (t_cur, t_next) in enumerate(zip(steps[:-1], steps[1:])): |
| dt = t_next - t_cur |
| t_cur = t_cur.repeat(batch_size) |
| sigma = self.scheduler.sigma(t_cur) |
| beta = self.scheduler.beta(t_cur) |
| cfg_x = torch.cat([x, x], dim=0) |
| cfg_t = t_cur.repeat(2) |
| out = net(cfg_x, cfg_t*self.train_max_t, cfg_condition) |
| eps = self.guidance_fn(out, self.guidance) |
| if i < self.num_steps -1 : |
| x0 = self.last_step_fn(x, eps, beta, sigma, -t_cur[0]) |
| x = self.step_fn(x, eps, beta, sigma, dt) |
| else: |
| x = x0 = self.last_step_fn(x, eps, beta, sigma, -self.last_step) |
| x_trajs.append(x) |
| eps_trajs.append(eps) |
| eps_trajs.append(torch.zeros_like(x)) |
| return x_trajs, eps_trajs |
