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