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from inspect import isfunction | |
import numpy as np | |
import torch | |
import torch.nn.functional as F | |
from tqdm import tqdm | |
def exists(x): | |
return x is not None | |
def default(val, d): | |
if exists(val): | |
return val | |
return d() if isfunction(d) else d | |
def noise_like(shape, device, repeat=False): | |
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) | |
noise = lambda: torch.randn(shape, device=device) | |
return repeat_noise() if repeat else noise() | |
def extract(a, t, x_shape): | |
b, *_ = t.shape | |
out = a.gather(-1, t) | |
return out.reshape(b, *((1,) * (len(x_shape) - 1))) | |
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): | |
# select alphas for computing the variance schedule | |
alphas = alphacums[ddim_timesteps] | |
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) | |
# according the the formula provided in https://arxiv.org/abs/2010.02502 | |
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) | |
if verbose: | |
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') | |
print(f'For the chosen value of eta, which is {eta}, ' | |
f'this results in the following sigma_t schedule for ddim sampler {sigmas}') | |
return sigmas, alphas, alphas_prev | |
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): | |
if ddim_discr_method == 'uniform': | |
c = num_ddpm_timesteps // num_ddim_timesteps | |
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) | |
elif ddim_discr_method == 'quad': | |
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) | |
else: | |
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') | |
# assert ddim_timesteps.shape[0] == num_ddim_timesteps | |
# add one to get the final alpha values right (the ones from first scale to data during sampling) | |
steps_out = ddim_timesteps + 1 | |
if verbose: | |
print(f'Selected timesteps for ddim sampler: {steps_out}') | |
return steps_out | |
class DDIMSampler(object): | |
def __init__(self, model, schedule="linear", **kwargs): | |
super().__init__() | |
self.model = model | |
self.ddpm_num_timesteps = model.num_timesteps | |
self.schedule = schedule | |
def register_buffer(self, name, attr): | |
if type(attr) == torch.Tensor: | |
if attr.device != torch.device("cuda"): | |
attr = attr.to(torch.device("cuda")) | |
setattr(self, name, attr) | |
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): | |
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, | |
num_ddpm_timesteps=self.ddpm_num_timesteps, verbose=verbose) | |
alphas_cumprod = self.model.alphas_cumprod | |
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' | |
to_torch = lambda x: x.clone().detach().to(torch.float32).to(torch.device("cuda")) | |
self.register_buffer('betas', to_torch(self.model.betas)) | |
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) | |
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) | |
# calculations for diffusion q(x_t | x_{t-1}) and others | |
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) | |
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) | |
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) | |
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) | |
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) | |
# ddim sampling parameters | |
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), | |
ddim_timesteps=self.ddim_timesteps, | |
eta=ddim_eta, verbose=verbose) | |
self.register_buffer('ddim_sigmas', ddim_sigmas) | |
self.register_buffer('ddim_alphas', ddim_alphas) | |
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) | |
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) | |
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( | |
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( | |
1 - self.alphas_cumprod / self.alphas_cumprod_prev)) | |
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) | |
def sample(self, | |
S, | |
batch_size, | |
shape, | |
conditioning=None, | |
callback=None, | |
img_callback=None, | |
quantize_x0=False, | |
eta=0., | |
mask=None, | |
x0=None, | |
temperature=1., | |
noise_dropout=0., | |
score_corrector=None, | |
corrector_kwargs=None, | |
verbose=True, | |
x_T=None, | |
log_every_t=100, | |
**kwargs | |
): | |
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) | |
# sampling | |
C, H, W, D = shape | |
size = (batch_size, C, H, W, D) | |
print(f'Data shape for DDIM sampling is {size}, eta {eta}') | |
samples, intermediates = self.ddim_sampling( | |
conditioning, size, | |
callback=callback, | |
img_callback=img_callback, | |
quantize_denoised=quantize_x0, | |
mask=mask, x0=x0, | |
ddim_use_original_steps=False, | |
noise_dropout=noise_dropout, | |
temperature=temperature, | |
score_corrector=score_corrector, | |
corrector_kwargs=corrector_kwargs, | |
x_T=x_T, | |
log_every_t=log_every_t | |
) | |
return samples, intermediates | |
def ddim_sampling(self, cond, shape, | |
x_T=None, ddim_use_original_steps=False, | |
callback=None, timesteps=None, quantize_denoised=False, | |
mask=None, x0=None, img_callback=None, log_every_t=100, | |
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): | |
device = self.model.betas.device | |
b = shape[0] | |
if x_T is None: | |
img = torch.randn(shape, device=device) | |
else: | |
img = x_T | |
if timesteps is None: | |
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps | |
elif timesteps is not None and not ddim_use_original_steps: | |
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 | |
timesteps = self.ddim_timesteps[:subset_end] | |
intermediates = {'x_inter': [img], 'pred_x0': [img]} | |
time_range = reversed(range(0, timesteps)) if ddim_use_original_steps else np.flip(timesteps) | |
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] | |
print(f"Running DDIM Sampling with {total_steps} timesteps") | |
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) | |
for i, step in enumerate(iterator): | |
index = total_steps - i - 1 | |
ts = torch.full((b,), step, device=device, dtype=torch.long) | |
if mask is not None: | |
assert x0 is not None | |
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? | |
img = img_orig * mask + (1. - mask) * img | |
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, | |
quantize_denoised=quantize_denoised, temperature=temperature, | |
noise_dropout=noise_dropout, score_corrector=score_corrector, | |
corrector_kwargs=corrector_kwargs) | |
img, pred_x0 = outs | |
if callback: callback(i) | |
if img_callback: img_callback(pred_x0, i) | |
if index % log_every_t == 0 or index == total_steps - 1: | |
intermediates['x_inter'].append(img) | |
intermediates['pred_x0'].append(pred_x0) | |
return img, intermediates | |
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, | |
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): | |
b, *_, device = *x.shape, x.device | |
e_t = self.model.apply_model(x, t, c) | |
if score_corrector is not None: | |
assert self.model.parameterization == "eps" | |
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) | |
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas | |
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev | |
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas | |
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas | |
# select parameters corresponding to the currently considered timestep | |
a_t = torch.full((b, 1, 1, 1, 1), alphas[index], device=device) | |
a_prev = torch.full((b, 1, 1, 1, 1), alphas_prev[index], device=device) | |
sigma_t = torch.full((b, 1, 1, 1, 1), sigmas[index], device=device) | |
sqrt_one_minus_at = torch.full((b, 1, 1, 1, 1), sqrt_one_minus_alphas[index], device=device) | |
# current prediction for x_0 | |
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() | |
if quantize_denoised: | |
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) | |
# direction pointing to x_t | |
dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t | |
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature | |
if noise_dropout > 0.: | |
noise = torch.nn.functional.dropout(noise, p=noise_dropout) | |
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise | |
return x_prev, pred_x0 | |