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from pathlib import Path
import json
from math import sqrt
import numpy as np
import torch
from abc import ABCMeta, abstractmethod
class ScoreAdapter(metaclass=ABCMeta):
@abstractmethod
def denoise(self, xs, σ, **kwargs):
pass
def score(self, xs, σ, **kwargs):
Ds = self.denoise(xs, σ, **kwargs)
grad_log_p_t = (Ds - xs) / (σ ** 2)
return grad_log_p_t
@abstractmethod
def data_shape(self):
return (3, 256, 256) # for example
def samps_centered(self):
# if centered, samples expected to be in range [-1, 1], else [0, 1]
return True
@property
@abstractmethod
def σ_max(self):
pass
@property
@abstractmethod
def σ_min(self):
pass
def cond_info(self, batch_size):
return {}
@abstractmethod
def unet_is_cond(self):
return False
@abstractmethod
def use_cls_guidance(self):
return False # most models do not use cls guidance
def classifier_grad(self, xs, σ, ys):
raise NotImplementedError()
@abstractmethod
def snap_t_to_nearest_tick(self, t):
# need to confirm for each model; continuous time model doesn't need this
return t, None
@property
def device(self):
return self._device
def checkpoint_root(self):
"""the path at which the pretrained checkpoints are stored"""
with Path(__file__).resolve().with_name("env.json").open("r") as f:
root = json.load(f)
return root
def karras_t_schedule(ρ=7, N=10, σ_max=80, σ_min=0.002):
ts = []
for i in range(N):
t = (
σ_max ** (1 / ρ) + (i / (N - 1)) * (σ_min ** (1 / ρ) - σ_max ** (1 / ρ))
) ** ρ
ts.append(t)
return ts
def power_schedule(σ_max, σ_min, num_stages):
σs = np.exp(np.linspace(np.log(σ_max), np.log(σ_min), num_stages))
return σs
class Karras():
@classmethod
@torch.no_grad()
def inference(
cls, model, batch_size, num_t, *,
σ_max=80, cls_scaling=1,
init_xs=None, heun=True,
langevin=False,
S_churn=80, S_min=0.05, S_max=50, S_noise=1.003,
):
σ_max = min(σ_max, model.σ_max)
σ_min = model.σ_min
ts = karras_t_schedule(ρ=7, N=num_t, σ_max=σ_max, σ_min=σ_min)
assert len(ts) == num_t
ts = [model.snap_t_to_nearest_tick(t)[0] for t in ts]
ts.append(0) # 0 is the destination
σ_max = ts[0]
cond_inputs = model.cond_info(batch_size)
def compute_step(xs, σ):
grad_log_p_t = model.score(
xs, σ, **(cond_inputs if model.unet_is_cond() else {})
)
if model.use_cls_guidance():
grad_cls = model.classifier_grad(xs, σ, cond_inputs["y"])
grad_cls = grad_cls * cls_scaling
grad_log_p_t += grad_cls
d_i = -1 * σ * grad_log_p_t
return d_i
if init_xs is not None:
xs = init_xs.to(model.device)
else:
xs = σ_max * torch.randn(
batch_size, *model.data_shape(), device=model.device
)
yield xs
for i in range(num_t):
t_i = ts[i]
if langevin and (S_min < t_i and t_i < S_max):
xs, t_i = cls.noise_backward_in_time(
model, xs, t_i, S_noise, S_churn / num_t
)
Δt = ts[i+1] - t_i
d_1 = compute_step(xs, σ=t_i)
xs_1 = xs + Δt * d_1
# Heun's 2nd order method; don't apply on the last step
if (not heun) or (ts[i+1] == 0):
xs = xs_1
else:
d_2 = compute_step(xs_1, σ=ts[i+1])
xs = xs + Δt * (d_1 + d_2) / 2
yield xs
@staticmethod
def noise_backward_in_time(model, xs, t_i, S_noise, S_churn_i):
n = S_noise * torch.randn_like(xs)
γ_i = min(sqrt(2)-1, S_churn_i)
t_i_hat = t_i * (1 + γ_i)
t_i_hat = model.snap_t_to_nearest_tick(t_i_hat)[0]
xs = xs + n * sqrt(t_i_hat ** 2 - t_i ** 2)
return xs, t_i_hat
def test():
pass
if __name__ == "__main__":
test()
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