289 lines
10 KiB
Python
289 lines
10 KiB
Python
|
#!/usr/bin/env python3
|
||
|
|
import numpy as np
|
||
|
|
import PIL
|
||
|
|
import functools
|
||
|
|
|
||
|
|
import models
|
||
|
|
from models import utils as mutils
|
||
|
|
from models import ncsnv2
|
||
|
|
from models import ncsnpp
|
||
|
|
from models import ddpm as ddpm_model
|
||
|
|
from models import layerspp
|
||
|
|
from models import layers
|
||
|
|
from models import normalization
|
||
|
|
|
||
|
|
from utils import restore_checkpoint
|
||
|
|
|
||
|
|
import sampling
|
||
|
|
from sde_lib import VESDE, VPSDE, subVPSDE
|
||
|
|
from sampling import (NoneCorrector,
|
||
|
|
ReverseDiffusionPredictor,
|
||
|
|
LangevinCorrector,
|
||
|
|
EulerMaruyamaPredictor,
|
||
|
|
AncestralSamplingPredictor,
|
||
|
|
NonePredictor,
|
||
|
|
AnnealedLangevinDynamics)
|
||
|
|
import datasets
|
||
|
|
import torch
|
||
|
|
|
||
|
|
|
||
|
|
torch.manual_seed(0)
|
||
|
|
|
||
|
|
|
||
|
|
#class NewVESDE(SDE):
|
||
|
|
# def __init__(self, sigma_min=0.01, sigma_max=50, N=1000):
|
||
|
|
# """Construct a Variance Exploding SDE.
|
||
|
|
#
|
||
|
|
# Args:
|
||
|
|
# sigma_min: smallest sigma.
|
||
|
|
# sigma_max: largest sigma.
|
||
|
|
# N: number of discretization steps
|
||
|
|
# """
|
||
|
|
# super().__init__(N)
|
||
|
|
# self.sigma_min = sigma_min
|
||
|
|
# self.sigma_max = sigma_max
|
||
|
|
# self.discrete_sigmas = torch.exp(torch.linspace(np.log(self.sigma_min), np.log(self.sigma_max), N))
|
||
|
|
# self.N = N
|
||
|
|
#
|
||
|
|
# @property
|
||
|
|
# def T(self):
|
||
|
|
# return 1
|
||
|
|
#
|
||
|
|
# def sde(self, x, t):
|
||
|
|
# sigma = self.sigma_min * (self.sigma_max / self.sigma_min) ** t
|
||
|
|
# drift = torch.zeros_like(x)
|
||
|
|
# diffusion = sigma * torch.sqrt(torch.tensor(2 * (np.log(self.sigma_max) - np.log(self.sigma_min)),
|
||
|
|
# device=t.device))
|
||
|
|
# return drift, diffusion
|
||
|
|
#
|
||
|
|
# def marginal_prob(self, x, t):
|
||
|
|
# std = self.sigma_min * (self.sigma_max / self.sigma_min) ** t
|
||
|
|
# mean = x
|
||
|
|
# return mean, std
|
||
|
|
#
|
||
|
|
# def prior_sampling(self, shape):
|
||
|
|
# return torch.randn(*shape) * self.sigma_max
|
||
|
|
#
|
||
|
|
# def prior_logp(self, z):
|
||
|
|
# shape = z.shape
|
||
|
|
# N = np.prod(shape[1:])
|
||
|
|
# return -N / 2. * np.log(2 * np.pi * self.sigma_max ** 2) - torch.sum(z ** 2, dim=(1, 2, 3)) / (2 * self.sigma_max ** 2)
|
||
|
|
#
|
||
|
|
# def discretize(self, x, t):
|
||
|
|
# """SMLD(NCSN) discretization."""
|
||
|
|
# timestep = (t * (self.N - 1) / self.T).long()
|
||
|
|
# sigma = self.discrete_sigmas.to(t.device)[timestep]
|
||
|
|
# adjacent_sigma = torch.where(timestep == 0, torch.zeros_like(t),
|
||
|
|
# self.discrete_sigmas[timestep - 1].to(t.device))
|
||
|
|
# f = torch.zeros_like(x)
|
||
|
|
# G = torch.sqrt(sigma ** 2 - adjacent_sigma ** 2)
|
||
|
|
# return f, G
|
||
|
|
|
||
|
|
|
||
|
|
class NewReverseDiffusionPredictor:
|
||
|
|
|
||
|
|
def __init__(self, sde, score_fn, probability_flow=False):
|
||
|
|
super().__init__()
|
||
|
|
self.sde = sde
|
||
|
|
self.probability_flow = probability_flow
|
||
|
|
self.score_fn = score_fn
|
||
|
|
|
||
|
|
def discretize(self, x, t):
|
||
|
|
timestep = (t * (self.sde.N - 1) / self.sde.T).long()
|
||
|
|
sigma = self.sde.discrete_sigmas.to(t.device)[timestep]
|
||
|
|
adjacent_sigma = torch.where(timestep == 0, torch.zeros_like(t),
|
||
|
|
self.sde.discrete_sigmas[timestep - 1].to(t.device))
|
||
|
|
f = torch.zeros_like(x)
|
||
|
|
G = torch.sqrt(sigma ** 2 - adjacent_sigma ** 2)
|
||
|
|
|
||
|
|
labels = self.sde.marginal_prob(torch.zeros_like(x), t)[1]
|
||
|
|
result = self.score_fn(x, labels)
|
||
|
|
|
||
|
|
rev_f = f - G[:, None, None, None] ** 2 * result * (0.5 if self.probability_flow else 1.)
|
||
|
|
rev_G = torch.zeros_like(G) if self.probability_flow else G
|
||
|
|
return rev_f, rev_G
|
||
|
|
|
||
|
|
def update_fn(self, x, t):
|
||
|
|
f, G = self.discretize(x, t)
|
||
|
|
z = torch.randn_like(x)
|
||
|
|
x_mean = x - f
|
||
|
|
x = x_mean + G[:, None, None, None] * z
|
||
|
|
return x, x_mean
|
||
|
|
|
||
|
|
|
||
|
|
class NewLangevinCorrector:
|
||
|
|
|
||
|
|
def __init__(self, sde, score_fn, snr, n_steps):
|
||
|
|
super().__init__()
|
||
|
|
self.sde = sde
|
||
|
|
self.score_fn = score_fn
|
||
|
|
self.snr = snr
|
||
|
|
self.n_steps = n_steps
|
||
|
|
|
||
|
|
def update_fn(self, x, t):
|
||
|
|
sde = self.sde
|
||
|
|
score_fn = self.score_fn
|
||
|
|
n_steps = self.n_steps
|
||
|
|
target_snr = self.snr
|
||
|
|
if isinstance(sde, VPSDE) or isinstance(sde, subVPSDE):
|
||
|
|
timestep = (t * (sde.N - 1) / sde.T).long()
|
||
|
|
alpha = sde.alphas.to(t.device)[timestep]
|
||
|
|
else:
|
||
|
|
alpha = torch.ones_like(t)
|
||
|
|
|
||
|
|
for i in range(n_steps):
|
||
|
|
labels = sde.marginal_prob(torch.zeros_like(x), t)[1]
|
||
|
|
grad = score_fn(x, labels)
|
||
|
|
noise = torch.randn_like(x)
|
||
|
|
grad_norm = torch.norm(grad.reshape(grad.shape[0], -1), dim=-1).mean()
|
||
|
|
noise_norm = torch.norm(noise.reshape(noise.shape[0], -1), dim=-1).mean()
|
||
|
|
step_size = (target_snr * noise_norm / grad_norm) ** 2 * 2 * alpha
|
||
|
|
x_mean = x + step_size[:, None, None, None] * grad
|
||
|
|
x = x_mean + torch.sqrt(step_size * 2)[:, None, None, None] * noise
|
||
|
|
|
||
|
|
return x, x_mean
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def save_image(x):
|
||
|
|
# image_processed = x.cpu().permute(0, 2, 3, 1)
|
||
|
|
# image_processed = (image_processed + 1.0) * 127.5
|
||
|
|
# image_processed = image_processed.numpy().astype(np.uint8)
|
||
|
|
image_processed = np.clip(x.permute(0, 2, 3, 1).cpu().numpy() * 255, 0, 255).astype(np.uint8)
|
||
|
|
image_pil = PIL.Image.fromarray(image_processed[0])
|
||
|
|
|
||
|
|
# 6. save image
|
||
|
|
image_pil.save("../images/hey.png")
|
||
|
|
|
||
|
|
|
||
|
|
#x = np.load("cifar10.npy")
|
||
|
|
#
|
||
|
|
#save_image(x)
|
||
|
|
# @title Load the score-based model
|
||
|
|
sde = 'VESDE' #@param ['VESDE', 'VPSDE', 'subVPSDE'] {"type": "string"}
|
||
|
|
if sde.lower() == 'vesde':
|
||
|
|
from configs.ve import cifar10_ncsnpp_continuous as configs
|
||
|
|
ckpt_filename = "exp/ve/cifar10_ncsnpp_continuous/checkpoint_24.pth"
|
||
|
|
# from configs.ve import ffhq_ncsnpp_continuous as configs
|
||
|
|
# ckpt_filename = "exp/ve/ffhq_1024_ncsnpp_continuous/checkpoint_60.pth"
|
||
|
|
config = configs.get_config()
|
||
|
|
config.model.num_scales = 1000
|
||
|
|
sde = VESDE(sigma_min=config.model.sigma_min, sigma_max=config.model.sigma_max, N=config.model.num_scales)
|
||
|
|
sampling_eps = 1e-5
|
||
|
|
elif sde.lower() == 'vpsde':
|
||
|
|
from configs.vp import cifar10_ddpmpp_continuous as configs
|
||
|
|
ckpt_filename = "exp/vp/cifar10_ddpmpp_continuous/checkpoint_8.pth"
|
||
|
|
config = configs.get_config()
|
||
|
|
sde = VPSDE(beta_min=config.model.beta_min, beta_max=config.model.beta_max, N=config.model.num_scales)
|
||
|
|
sampling_eps = 1e-3
|
||
|
|
elif sde.lower() == 'subvpsde':
|
||
|
|
from configs.subvp import cifar10_ddpmpp_continuous as configs
|
||
|
|
ckpt_filename = "exp/subvp/cifar10_ddpmpp_continuous/checkpoint_26.pth"
|
||
|
|
config = configs.get_config()
|
||
|
|
sde = subVPSDE(beta_min=config.model.beta_min, beta_max=config.model.beta_max, N=config.model.num_scales)
|
||
|
|
sampling_eps = 1e-3
|
||
|
|
|
||
|
|
batch_size = 1 #@param {"type":"integer"}
|
||
|
|
config.training.batch_size = batch_size
|
||
|
|
config.eval.batch_size = batch_size
|
||
|
|
|
||
|
|
random_seed = 0 #@param {"type": "integer"}
|
||
|
|
|
||
|
|
score_model = mutils.create_model(config)
|
||
|
|
|
||
|
|
loaded_state = torch.load(ckpt_filename)
|
||
|
|
score_model.load_state_dict(loaded_state["model"], strict=False)
|
||
|
|
|
||
|
|
inverse_scaler = datasets.get_data_inverse_scaler(config)
|
||
|
|
predictor = ReverseDiffusionPredictor #@param ["EulerMaruyamaPredictor", "AncestralSamplingPredictor", "ReverseDiffusionPredictor", "None"] {"type": "raw"}
|
||
|
|
corrector = LangevinCorrector #@param ["LangevinCorrector", "AnnealedLangevinDynamics", "None"] {"type": "raw"}
|
||
|
|
|
||
|
|
def image_grid(x):
|
||
|
|
size = config.data.image_size
|
||
|
|
channels = config.data.num_channels
|
||
|
|
img = x.reshape(-1, size, size, channels)
|
||
|
|
w = int(np.sqrt(img.shape[0]))
|
||
|
|
img = img.reshape((w, w, size, size, channels)).transpose((0, 2, 1, 3, 4)).reshape((w * size, w * size, channels))
|
||
|
|
return img
|
||
|
|
|
||
|
|
#@title PC sampling
|
||
|
|
img_size = config.data.image_size
|
||
|
|
channels = config.data.num_channels
|
||
|
|
shape = (batch_size, channels, img_size, img_size)
|
||
|
|
probability_flow = False
|
||
|
|
snr = 0.16 #@param {"type": "number"}
|
||
|
|
n_steps = 1#@param {"type": "integer"}
|
||
|
|
|
||
|
|
|
||
|
|
def shared_predictor_update_fn(x, t, sde, model, predictor, probability_flow, continuous):
|
||
|
|
"""A wrapper that configures and returns the update function of predictors."""
|
||
|
|
score_fn = mutils.get_score_fn(sde, model, train=False, continuous=continuous)
|
||
|
|
if predictor is None:
|
||
|
|
# Corrector-only sampler
|
||
|
|
predictor_obj = NonePredictor(sde, score_fn, probability_flow)
|
||
|
|
else:
|
||
|
|
predictor_obj = predictor(sde, score_fn, probability_flow)
|
||
|
|
return predictor_obj.update_fn(x, t)
|
||
|
|
|
||
|
|
|
||
|
|
def shared_corrector_update_fn(x, t, sde, model, corrector, continuous, snr, n_steps):
|
||
|
|
"""A wrapper tha configures and returns the update function of correctors."""
|
||
|
|
score_fn = mutils.get_score_fn(sde, model, train=False, continuous=continuous)
|
||
|
|
if corrector is None:
|
||
|
|
# Predictor-only sampler
|
||
|
|
corrector_obj = NoneCorrector(sde, score_fn, snr, n_steps)
|
||
|
|
else:
|
||
|
|
corrector_obj = corrector(sde, score_fn, snr, n_steps)
|
||
|
|
return corrector_obj.update_fn(x, t)
|
||
|
|
|
||
|
|
|
||
|
|
continuous = config.training.continuous
|
||
|
|
|
||
|
|
|
||
|
|
predictor_update_fn = functools.partial(shared_predictor_update_fn,
|
||
|
|
sde=sde,
|
||
|
|
predictor=predictor,
|
||
|
|
probability_flow=probability_flow,
|
||
|
|
continuous=continuous)
|
||
|
|
|
||
|
|
corrector_update_fn = functools.partial(shared_corrector_update_fn,
|
||
|
|
sde=sde,
|
||
|
|
corrector=corrector,
|
||
|
|
continuous=continuous,
|
||
|
|
snr=snr,
|
||
|
|
n_steps=n_steps)
|
||
|
|
|
||
|
|
device = "cuda"
|
||
|
|
model = score_model.to(device)
|
||
|
|
denoise = False
|
||
|
|
|
||
|
|
new_corrector = NewLangevinCorrector(sde=sde, score_fn=model, snr=snr, n_steps=n_steps)
|
||
|
|
new_predictor = NewReverseDiffusionPredictor(sde=sde, score_fn=model)
|
||
|
|
|
||
|
|
|
||
|
|
with torch.no_grad():
|
||
|
|
# Initial sample
|
||
|
|
x = sde.prior_sampling(shape).to(device)
|
||
|
|
timesteps = torch.linspace(sde.T, sampling_eps, sde.N, device=device)
|
||
|
|
|
||
|
|
for i in range(sde.N):
|
||
|
|
t = timesteps[i]
|
||
|
|
vec_t = torch.ones(shape[0], device=t.device) * t
|
||
|
|
# x, x_mean = corrector_update_fn(x, vec_t, model=model)
|
||
|
|
# x, x_mean = predictor_update_fn(x, vec_t, model=model)
|
||
|
|
x, x_mean = new_corrector.update_fn(x, vec_t)
|
||
|
|
x, x_mean = new_predictor.update_fn(x, vec_t)
|
||
|
|
|
||
|
|
x, n = inverse_scaler(x_mean if denoise else x), sde.N * (n_steps + 1)
|
||
|
|
|
||
|
|
|
||
|
|
# for 5
|
||
|
|
#assert x.abs().sum().cpu().item() - 106114.90625 < 1e-2, "sum wrong"
|
||
|
|
#assert x.abs().mean().cpu().item() - 34.5426139831543 < 1e-4, "mean wrong"
|
||
|
|
|
||
|
|
# for 1000
|
||
|
|
assert x.abs().sum().cpu().item() - 436.5811 < 1e-2, "sum wrong"
|
||
|
|
assert x.abs().mean().cpu().item() - 0.1421 < 1e-4, "mean wrong"
|
||
|
|
|
||
|
|
save_image(x)
|